diff --git "a/sd-turbo/compiled/quantize-2-bit/ORIGINAL/Unet.mlmodelc/model.mil" "b/sd-turbo/compiled/quantize-2-bit/ORIGINAL/Unet.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/sd-turbo/compiled/quantize-2-bit/ORIGINAL/Unet.mlmodelc/model.mil" @@ -0,0 +1,4754 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.5"}, {"coremlc-version", "1877.40.3"}})] +{ + func main(tensor encoder_hidden_states, tensor sample, tensor timestep) { + tensor var_25 = const()[name = tensor("op_25"), val = tensor(-1)]; + tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; + tensor var_42_cast_fp16 = expand_dims(axes = var_42_axes_0, x = timestep)[name = tensor("op_42_cast_fp16")]; + tensor var_44_to_fp16 = const()[name = tensor("op_44_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast_fp16 = mul(x = var_42_cast_fp16, y = var_44_to_fp16)[name = tensor("emb_3_cast_fp16")]; + tensor var_49_cast_fp16 = sin(x = emb_3_cast_fp16)[name = tensor("op_49_cast_fp16")]; + tensor var_50_cast_fp16 = cos(x = emb_3_cast_fp16)[name = tensor("op_50_cast_fp16")]; + tensor emb_interleave_0 = const()[name = tensor("emb_interleave_0"), val = tensor(false)]; + tensor emb_cast_fp16 = concat(axis = var_25, interleave = emb_interleave_0, values = (var_49_cast_fp16, var_50_cast_fp16))[name = tensor("emb_cast_fp16")]; + tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 160])]; + tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([2, 320])]; + tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, true])]; + tensor var_54_cast_fp16 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = emb_cast_fp16)[name = tensor("op_54_cast_fp16")]; + tensor var_56_begin_0 = const()[name = tensor("op_56_begin_0"), val = tensor([0, 0])]; + tensor var_56_end_0 = const()[name = tensor("op_56_end_0"), val = tensor([2, 160])]; + tensor var_56_end_mask_0 = const()[name = tensor("op_56_end_mask_0"), val = tensor([true, false])]; + tensor var_56_cast_fp16 = slice_by_index(begin = var_56_begin_0, end = var_56_end_0, end_mask = var_56_end_mask_0, x = emb_cast_fp16)[name = tensor("op_56_cast_fp16")]; + tensor sample_interleave_0 = const()[name = tensor("sample_interleave_0"), val = tensor(false)]; + tensor sample_cast_fp16 = concat(axis = var_25, interleave = sample_interleave_0, values = (var_54_cast_fp16, var_56_cast_fp16))[name = tensor("sample_cast_fp16")]; + tensor var_59 = const()[name = tensor("op_59"), val = tensor(1)]; + tensor var_66_axes_0 = const()[name = tensor("op_66_axes_0"), val = tensor([-1])]; + tensor var_66_cast_fp16 = expand_dims(axes = var_66_axes_0, x = sample_cast_fp16)[name = tensor("op_66_cast_fp16")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = var_66_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor var_70 = const()[name = tensor("op_70"), val = tensor([1, 1])]; + tensor var_72 = const()[name = tensor("op_72"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448))), lut = tensor([-0x1.7e4p-4, -0x1.868p-8, 0x1.1dp-8, 0x1.0ccp-4]), name = tensor("time_embedding_linear_1_weight_to_fp16_palettized"), shape = tensor([1280, 320, 1, 1])]; + tensor time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102912)))]; + tensor input_3_cast_fp16 = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_72, groups = var_59, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_70, weight = time_embedding_linear_1_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = silu(x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor var_78 = const()[name = tensor("op_78"), val = tensor([1, 1])]; + tensor var_80 = const()[name = tensor("op_80"), val = tensor([1, 1])]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105536))), lut = tensor([-0x1.3dp-4, -0x1.4dcp-10, 0x1.48p-10, 0x1.4a4p-4]), name = tensor("time_embedding_linear_2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(515200)))]; + tensor input_13_cast_fp16 = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_80, groups = var_59, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_78, weight = time_embedding_linear_2_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor(1)]; + tensor var_89 = const()[name = tensor("op_89"), val = tensor([1, 1])]; + tensor var_91 = const()[name = tensor("op_91"), val = tensor([1, 1])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517824))), lut = tensor([-0x1.f9p-4, -0x1.ef8p-6, 0x1.5fp-6, 0x1.b4p-4]), name = tensor("conv_in_weight_to_fp16_palettized"), shape = tensor([320, 4, 3, 3])]; + tensor conv_in_bias_to_fp16 = const()[name = tensor("conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520768)))]; + tensor input_7_cast_fp16 = conv(bias = conv_in_bias_to_fp16, dilations = var_91, groups = var_86, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_89, weight = conv_in_weight_to_fp16_palettized, x = sample)[name = tensor("input_7_cast_fp16")]; + tensor var_95 = const()[name = tensor("op_95"), val = tensor(3)]; + tensor var_106 = const()[name = tensor("op_106"), val = tensor(true)]; + tensor var_111 = const()[name = tensor("op_111"), val = tensor(1)]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_7_cast_fp16)[name = tensor("reshape_0_cast_fp16")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; + tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; + tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; + tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; + tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521472)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522176)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522880)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(523584)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = tensor("add_1_cast_fp16")]; + tensor input_11_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor var_133 = const()[name = tensor("op_133"), val = tensor([1, 1])]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(524288))), lut = tensor([-0x1.8dcp-4, -0x1.fa4p-6, 0x1.21p-6, 0x1.3d4p-4]), name = tensor("down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754752)))]; + tensor hidden_states_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_135, groups = var_111, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_133, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor input_15_cast_fp16 = silu(x = input_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; + tensor var_141 = const()[name = tensor("op_141"), val = tensor([1, 1])]; + tensor var_143 = const()[name = tensor("op_143"), val = tensor([1, 1])]; + tensor temb_1_pad_type_0 = const()[name = tensor("temb_1_pad_type_0"), val = tensor("custom")]; + tensor temb_1_pad_0 = const()[name = tensor("temb_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755456))), lut = tensor([-0x1.154p-1, -0x1.418p-4, -0x1.7ap-16, 0x1.514p-4]), name = tensor("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(857920)))]; + tensor temb_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_143, groups = var_111, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_141, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_1_cast_fp16")]; + tensor input_17_cast_fp16 = add(x = hidden_states_1_cast_fp16, y = temb_1_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_17_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; + tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; + tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; + tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; + tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858624)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(859328)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = tensor("add_3_cast_fp16")]; + tensor input_21_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor var_153 = const()[name = tensor("op_153"), val = tensor([1, 1])]; + tensor var_155 = const()[name = tensor("op_155"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860032))), lut = tensor([-0x1.4fcp-4, -0x1.4f8p-6, 0x1.508p-6, 0x1.4d8p-4]), name = tensor("down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1090496)))]; + tensor hidden_states_3_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_155, groups = var_111, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_153, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("hidden_states_3_cast_fp16")]; + tensor hidden_states_5_cast_fp16 = add(x = input_7_cast_fp16, y = hidden_states_3_cast_fp16)[name = tensor("hidden_states_5_cast_fp16")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = hidden_states_5_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; + tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; + tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; + tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; + tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1091200)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1091904)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast_fp16 = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast_fp16)[name = tensor("add_5_cast_fp16")]; + tensor var_175 = const()[name = tensor("op_175"), val = tensor([1, 1])]; + tensor var_177 = const()[name = tensor("op_177"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_type_0 = const()[name = tensor("hidden_states_7_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1092608))), lut = tensor([-0x1.7bcp-4, -0x1.c9cp-6, 0x1.b28p-6, 0x1.74p-4]), name = tensor("down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1118272)))]; + tensor hidden_states_7_cast_fp16 = conv(bias = down_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_177, groups = var_111, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_175, weight = down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized, x = add_5_cast_fp16)[name = tensor("hidden_states_7_cast_fp16")]; + tensor var_182 = const()[name = tensor("op_182"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_1_cast_fp16 = reshape(shape = var_182, x = hidden_states_7_cast_fp16)[name = tensor("inputs_1_cast_fp16")]; + tensor var_192 = const()[name = tensor("op_192"), val = tensor([1])]; + tensor channels_mean_1_cast_fp16 = reduce_mean(axes = var_192, keep_dims = var_106, x = inputs_1_cast_fp16)[name = tensor("channels_mean_1_cast_fp16")]; + tensor zero_mean_1_cast_fp16 = sub(x = inputs_1_cast_fp16, y = channels_mean_1_cast_fp16)[name = tensor("zero_mean_1_cast_fp16")]; + tensor zero_mean_sq_1_cast_fp16 = mul(x = zero_mean_1_cast_fp16, y = zero_mean_1_cast_fp16)[name = tensor("zero_mean_sq_1_cast_fp16")]; + tensor var_196 = const()[name = tensor("op_196"), val = tensor([1])]; + tensor var_197_cast_fp16 = reduce_mean(axes = var_196, keep_dims = var_106, x = zero_mean_sq_1_cast_fp16)[name = tensor("op_197_cast_fp16")]; + tensor var_198_to_fp16 = const()[name = tensor("op_198_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_199_cast_fp16 = add(x = var_197_cast_fp16, y = var_198_to_fp16)[name = tensor("op_199_cast_fp16")]; + tensor denom_1_epsilon_0_to_fp16 = const()[name = tensor("denom_1_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_1_cast_fp16 = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_199_cast_fp16)[name = tensor("denom_1_cast_fp16")]; + tensor out_1_cast_fp16 = mul(x = zero_mean_1_cast_fp16, y = denom_1_cast_fp16)[name = tensor("out_1_cast_fp16")]; + tensor var_203_to_fp16 = const()[name = tensor("op_203_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1118976)))]; + tensor var_204_cast_fp16 = add(x = out_1_cast_fp16, y = var_203_to_fp16)[name = tensor("op_204_cast_fp16")]; + tensor var_206_to_fp16 = const()[name = tensor("op_206_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1119680)))]; + tensor hidden_states_9_cast_fp16 = mul(x = var_204_cast_fp16, y = var_206_to_fp16)[name = tensor("hidden_states_9_cast_fp16")]; + tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; + tensor var_215 = const()[name = tensor("op_215"), val = tensor([1, 1])]; + tensor q_1_pad_type_0 = const()[name = tensor("q_1_pad_type_0"), val = tensor("custom")]; + tensor q_1_pad_0 = const()[name = tensor("q_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1120384))), lut = tensor([-0x1.584p-3, -0x1.8d4p-5, 0x1.8a8p-5, 0x1.57cp-3]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_1_cast_fp16 = conv(dilations = var_215, groups = var_111, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_213, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_9_cast_fp16)[name = tensor("q_1_cast_fp16")]; + tensor var_219 = const()[name = tensor("op_219"), val = tensor([1, 1])]; + tensor var_221 = const()[name = tensor("op_221"), val = tensor([1, 1])]; + tensor k_1_pad_type_0 = const()[name = tensor("k_1_pad_type_0"), val = tensor("custom")]; + tensor k_1_pad_0 = const()[name = tensor("k_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1146048))), lut = tensor([-0x1.3c4p-3, -0x1.654p-5, 0x1.704p-5, 0x1.428p-3]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_1_cast_fp16 = conv(dilations = var_221, groups = var_111, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_219, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_9_cast_fp16)[name = tensor("k_1_cast_fp16")]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor([1, 1])]; + tensor var_227 = const()[name = tensor("op_227"), val = tensor([1, 1])]; + tensor v_1_pad_type_0 = const()[name = tensor("v_1_pad_type_0"), val = tensor("custom")]; + tensor v_1_pad_0 = const()[name = tensor("v_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1171712))), lut = tensor([-0x1.644p-4, -0x1.918p-6, 0x1.8f4p-6, 0x1.62p-4]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_1_cast_fp16 = conv(dilations = var_227, groups = var_111, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_225, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_9_cast_fp16)[name = tensor("v_1_cast_fp16")]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([2, 5, 64, -1])]; + tensor var_232_cast_fp16 = reshape(shape = var_231, x = q_1_cast_fp16)[name = tensor("op_232_cast_fp16")]; + tensor var_233 = const()[name = tensor("op_233"), val = tensor([2, 5, 64, -1])]; + tensor var_234_cast_fp16 = reshape(shape = var_233, x = k_1_cast_fp16)[name = tensor("op_234_cast_fp16")]; + tensor var_235 = const()[name = tensor("op_235"), val = tensor([2, 5, 64, -1])]; + tensor var_236_cast_fp16 = reshape(shape = var_235, x = v_1_cast_fp16)[name = tensor("op_236_cast_fp16")]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_232_cast_fp16, y = var_234_cast_fp16)[name = tensor("attn_weights_1_cast_fp16")]; + tensor var_102_to_fp16 = const()[name = tensor("op_102_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_3_cast_fp16")]; + tensor var_240_cast_fp16 = softmax(axis = var_95, x = attn_weights_3_cast_fp16)[name = tensor("op_240_cast_fp16")]; + tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; + tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_236_cast_fp16, y = var_240_cast_fp16)[name = tensor("attn_1_cast_fp16")]; + tensor var_244 = const()[name = tensor("op_244"), val = tensor([2, 320, 1, -1])]; + tensor input_25_cast_fp16 = reshape(shape = var_244, x = attn_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor var_249 = const()[name = tensor("op_249"), val = tensor([1, 1])]; + tensor var_251 = const()[name = tensor("op_251"), val = tensor([1, 1])]; + tensor var_253_pad_type_0 = const()[name = tensor("op_253_pad_type_0"), val = tensor("custom")]; + tensor var_253_pad_0 = const()[name = tensor("op_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1197376))), lut = tensor([-0x1.3c8p-4, -0x1.748p-6, 0x1.7a4p-6, 0x1.3fp-4]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1223040)))]; + tensor var_253_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_251, groups = var_111, pad = var_253_pad_0, pad_type = var_253_pad_type_0, strides = var_249, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_25_cast_fp16)[name = tensor("op_253_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = var_253_cast_fp16, y = inputs_1_cast_fp16)[name = tensor("inputs_3_cast_fp16")]; + tensor var_257 = const()[name = tensor("op_257"), val = tensor([1])]; + tensor channels_mean_3_cast_fp16 = reduce_mean(axes = var_257, keep_dims = var_106, x = inputs_3_cast_fp16)[name = tensor("channels_mean_3_cast_fp16")]; + tensor zero_mean_3_cast_fp16 = sub(x = inputs_3_cast_fp16, y = channels_mean_3_cast_fp16)[name = tensor("zero_mean_3_cast_fp16")]; + tensor zero_mean_sq_3_cast_fp16 = mul(x = zero_mean_3_cast_fp16, y = zero_mean_3_cast_fp16)[name = tensor("zero_mean_sq_3_cast_fp16")]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1])]; + tensor var_262_cast_fp16 = reduce_mean(axes = var_261, keep_dims = var_106, x = zero_mean_sq_3_cast_fp16)[name = tensor("op_262_cast_fp16")]; + tensor var_263_to_fp16 = const()[name = tensor("op_263_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_264_cast_fp16 = add(x = var_262_cast_fp16, y = var_263_to_fp16)[name = tensor("op_264_cast_fp16")]; + tensor denom_3_epsilon_0_to_fp16 = const()[name = tensor("denom_3_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_3_cast_fp16 = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_264_cast_fp16)[name = tensor("denom_3_cast_fp16")]; + tensor out_3_cast_fp16 = mul(x = zero_mean_3_cast_fp16, y = denom_3_cast_fp16)[name = tensor("out_3_cast_fp16")]; + tensor var_268_to_fp16 = const()[name = tensor("op_268_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1223744)))]; + tensor var_269_cast_fp16 = add(x = out_3_cast_fp16, y = var_268_to_fp16)[name = tensor("op_269_cast_fp16")]; + tensor var_271_to_fp16 = const()[name = tensor("op_271_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1224448)))]; + tensor hidden_states_11_cast_fp16 = mul(x = var_269_cast_fp16, y = var_271_to_fp16)[name = tensor("hidden_states_11_cast_fp16")]; + tensor var_278 = const()[name = tensor("op_278"), val = tensor([1, 1])]; + tensor var_280 = const()[name = tensor("op_280"), val = tensor([1, 1])]; + tensor q_3_pad_type_0 = const()[name = tensor("q_3_pad_type_0"), val = tensor("custom")]; + tensor q_3_pad_0 = const()[name = tensor("q_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1225152))), lut = tensor([-0x1.ca8p-4, -0x1.0f8p-5, 0x1.15cp-5, 0x1.dp-4]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_3_cast_fp16 = conv(dilations = var_280, groups = var_111, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_278, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_11_cast_fp16)[name = tensor("q_3_cast_fp16")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; + tensor k_3_pad_type_0 = const()[name = tensor("k_3_pad_type_0"), val = tensor("custom")]; + tensor k_3_pad_0 = const()[name = tensor("k_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1250816))), lut = tensor([-0x1.824p-4, -0x1.c4cp-6, 0x1.c04p-6, 0x1.81p-4]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor k_3_cast_fp16 = conv(dilations = var_286, groups = var_111, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_284, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_3_cast_fp16")]; + tensor var_290 = const()[name = tensor("op_290"), val = tensor([1, 1])]; + tensor var_292 = const()[name = tensor("op_292"), val = tensor([1, 1])]; + tensor v_3_pad_type_0 = const()[name = tensor("v_3_pad_type_0"), val = tensor("custom")]; + tensor v_3_pad_0 = const()[name = tensor("v_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1332800))), lut = tensor([-0x1.c7cp-6, -0x1.fep-8, 0x1.0ccp-7, 0x1.cfp-6]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor v_3_cast_fp16 = conv(dilations = var_292, groups = var_111, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_290, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_3_cast_fp16")]; + tensor var_296 = const()[name = tensor("op_296"), val = tensor([2, 5, 64, -1])]; + tensor var_297_cast_fp16 = reshape(shape = var_296, x = q_3_cast_fp16)[name = tensor("op_297_cast_fp16")]; + tensor var_298 = const()[name = tensor("op_298"), val = tensor([2, 5, 64, -1])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = k_3_cast_fp16)[name = tensor("op_299_cast_fp16")]; + tensor var_300 = const()[name = tensor("op_300"), val = tensor([2, 5, 64, -1])]; + tensor var_301_cast_fp16 = reshape(shape = var_300, x = v_3_cast_fp16)[name = tensor("op_301_cast_fp16")]; + tensor attn_weights_5_transpose_x_0 = const()[name = tensor("attn_weights_5_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_5_transpose_y_0 = const()[name = tensor("attn_weights_5_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_5_cast_fp16 = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_297_cast_fp16, y = var_299_cast_fp16)[name = tensor("attn_weights_5_cast_fp16")]; + tensor attn_weights_7_cast_fp16 = mul(x = attn_weights_5_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_7_cast_fp16")]; + tensor var_305_cast_fp16 = softmax(axis = var_95, x = attn_weights_7_cast_fp16)[name = tensor("op_305_cast_fp16")]; + tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; + tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_301_cast_fp16, y = var_305_cast_fp16)[name = tensor("attn_3_cast_fp16")]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([2, 320, 1, -1])]; + tensor input_27_cast_fp16 = reshape(shape = var_309, x = attn_3_cast_fp16)[name = tensor("input_27_cast_fp16")]; + tensor var_314 = const()[name = tensor("op_314"), val = tensor([1, 1])]; + tensor var_316 = const()[name = tensor("op_316"), val = tensor([1, 1])]; + tensor var_318_pad_type_0 = const()[name = tensor("op_318_pad_type_0"), val = tensor("custom")]; + tensor var_318_pad_0 = const()[name = tensor("op_318_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1414784))), lut = tensor([-0x1.32p-6, -0x1.9c8p-9, 0x1.ad4p-9, 0x1.388p-6]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1440448)))]; + tensor var_318_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_316, groups = var_111, pad = var_318_pad_0, pad_type = var_318_pad_type_0, strides = var_314, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = tensor("op_318_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = var_318_cast_fp16, y = inputs_3_cast_fp16)[name = tensor("inputs_5_cast_fp16")]; + tensor var_322 = const()[name = tensor("op_322"), val = tensor([1])]; + tensor channels_mean_5_cast_fp16 = reduce_mean(axes = var_322, keep_dims = var_106, x = inputs_5_cast_fp16)[name = tensor("channels_mean_5_cast_fp16")]; + tensor zero_mean_5_cast_fp16 = sub(x = inputs_5_cast_fp16, y = channels_mean_5_cast_fp16)[name = tensor("zero_mean_5_cast_fp16")]; + tensor zero_mean_sq_5_cast_fp16 = mul(x = zero_mean_5_cast_fp16, y = zero_mean_5_cast_fp16)[name = tensor("zero_mean_sq_5_cast_fp16")]; + tensor var_326 = const()[name = tensor("op_326"), val = tensor([1])]; + tensor var_327_cast_fp16 = reduce_mean(axes = var_326, keep_dims = var_106, x = zero_mean_sq_5_cast_fp16)[name = tensor("op_327_cast_fp16")]; + tensor var_328_to_fp16 = const()[name = tensor("op_328_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_329_cast_fp16 = add(x = var_327_cast_fp16, y = var_328_to_fp16)[name = tensor("op_329_cast_fp16")]; + tensor denom_5_epsilon_0_to_fp16 = const()[name = tensor("denom_5_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_5_cast_fp16 = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_329_cast_fp16)[name = tensor("denom_5_cast_fp16")]; + tensor out_5_cast_fp16 = mul(x = zero_mean_5_cast_fp16, y = denom_5_cast_fp16)[name = tensor("out_5_cast_fp16")]; + tensor var_333_to_fp16 = const()[name = tensor("op_333_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1441152)))]; + tensor var_334_cast_fp16 = add(x = out_5_cast_fp16, y = var_333_to_fp16)[name = tensor("op_334_cast_fp16")]; + tensor var_336_to_fp16 = const()[name = tensor("op_336_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1441856)))]; + tensor input_29_cast_fp16 = mul(x = var_334_cast_fp16, y = var_336_to_fp16)[name = tensor("input_29_cast_fp16")]; + tensor var_344 = const()[name = tensor("op_344"), val = tensor([1, 1])]; + tensor var_346 = const()[name = tensor("op_346"), val = tensor([1, 1])]; + tensor var_348_pad_type_0 = const()[name = tensor("op_348_pad_type_0"), val = tensor("custom")]; + tensor var_348_pad_0 = const()[name = tensor("op_348_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1442560))), lut = tensor([-0x1.9f4p-4, -0x1.de8p-6, 0x1.e0cp-6, 0x1.9fp-4]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1647424))), lut = tensor([-0x1.6ccp-7, -0x1.9ep-3, 0x1.1ep-5, -0x1.174p-4]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_348_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_346, groups = var_111, pad = var_348_pad_0, pad_type = var_348_pad_type_0, strides = var_344, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_29_cast_fp16)[name = tensor("op_348_cast_fp16")]; + tensor var_349_split_sizes_0 = const()[name = tensor("op_349_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_349_axis_0 = const()[name = tensor("op_349_axis_0"), val = tensor(1)]; + tensor var_349_cast_fp16_0, tensor var_349_cast_fp16_1 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = var_348_cast_fp16)[name = tensor("op_349_cast_fp16")]; + tensor var_351_mode_0 = const()[name = tensor("op_351_mode_0"), val = tensor("EXACT")]; + tensor var_351_cast_fp16 = gelu(mode = var_351_mode_0, x = var_349_cast_fp16_1)[name = tensor("op_351_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = var_349_cast_fp16_0, y = var_351_cast_fp16)[name = tensor("input_31_cast_fp16")]; + tensor var_355 = const()[name = tensor("op_355"), val = tensor([1, 1])]; + tensor var_357 = const()[name = tensor("op_357"), val = tensor([1, 1])]; + tensor var_359_pad_type_0 = const()[name = tensor("op_359_pad_type_0"), val = tensor("custom")]; + tensor var_359_pad_0 = const()[name = tensor("op_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648128))), lut = tensor([-0x1.748p-4, -0x1.b5p-6, 0x1.c4p-6, 0x1.778p-4]), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1750592)))]; + tensor var_359_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_357, groups = var_111, pad = var_359_pad_0, pad_type = var_359_pad_type_0, strides = var_355, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_31_cast_fp16)[name = tensor("op_359_cast_fp16")]; + tensor hidden_states_15_cast_fp16 = add(x = var_359_cast_fp16, y = inputs_5_cast_fp16)[name = tensor("hidden_states_15_cast_fp16")]; + tensor var_361 = const()[name = tensor("op_361"), val = tensor([2, 320, 64, 64])]; + tensor input_33_cast_fp16 = reshape(shape = var_361, x = hidden_states_15_cast_fp16)[name = tensor("input_33_cast_fp16")]; + tensor var_365 = const()[name = tensor("op_365"), val = tensor([1, 1])]; + tensor var_367 = const()[name = tensor("op_367"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1751296))), lut = tensor([-0x1.394p-4, -0x1.754p-6, 0x1.748p-6, 0x1.3b4p-4]), name = tensor("down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1776960)))]; + tensor hidden_states_17_cast_fp16 = conv(bias = down_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_367, groups = var_111, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_365, weight = down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized, x = input_33_cast_fp16)[name = tensor("hidden_states_17_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = hidden_states_17_cast_fp16, y = hidden_states_5_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_35_cast_fp16)[name = tensor("reshape_12_cast_fp16")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; + tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; + tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; + tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; + tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1777664)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1778368)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = tensor("add_7_cast_fp16")]; + tensor input_39_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor("input_39_cast_fp16")]; + tensor var_382 = const()[name = tensor("op_382"), val = tensor([1, 1])]; + tensor var_384 = const()[name = tensor("op_384"), val = tensor([1, 1])]; + tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779072))), lut = tensor([-0x1.a58p-4, -0x1.b54p-6, 0x1.a58p-6, 0x1.9f8p-4]), name = tensor("down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2009536)))]; + tensor hidden_states_19_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_384, groups = var_111, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_382, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; + tensor var_390 = const()[name = tensor("op_390"), val = tensor([1, 1])]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor temb_3_pad_type_0 = const()[name = tensor("temb_3_pad_type_0"), val = tensor("custom")]; + tensor temb_3_pad_0 = const()[name = tensor("temb_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2010240))), lut = tensor([-0x1.6ccp-5, -0x1.c28p-9, 0x1.abcp-9, 0x1.184p-5]), name = tensor("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2112704)))]; + tensor temb_3_cast_fp16 = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_392, groups = var_111, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_390, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_3_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = temb_3_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_43_cast_fp16)[name = tensor("reshape_16_cast_fp16")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor("reduce_mean_12_cast_fp16")]; + tensor sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor("sub_8_cast_fp16")]; + tensor square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor("square_4_cast_fp16")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor("reduce_mean_14_cast_fp16")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast_fp16")]; + tensor sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor("sqrt_4_cast_fp16")]; + tensor real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor("real_div_4_cast_fp16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor("reshape_17_cast_fp16")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2113408)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2114112)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = tensor("add_9_cast_fp16")]; + tensor input_47_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor("input_47_cast_fp16")]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, 1])]; + tensor var_404 = const()[name = tensor("op_404"), val = tensor([1, 1])]; + tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2114816))), lut = tensor([-0x1.64cp-4, -0x1.73p-6, 0x1.54cp-6, 0x1.594p-4]), name = tensor("down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2345280)))]; + tensor hidden_states_21_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_404, groups = var_111, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_402, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = tensor("hidden_states_21_cast_fp16")]; + tensor hidden_states_23_cast_fp16 = add(x = input_35_cast_fp16, y = hidden_states_21_cast_fp16)[name = tensor("hidden_states_23_cast_fp16")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = hidden_states_23_cast_fp16)[name = tensor("reshape_20_cast_fp16")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor("reduce_mean_15_cast_fp16")]; + tensor sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor("sub_10_cast_fp16")]; + tensor square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor("square_5_cast_fp16")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor("reduce_mean_17_cast_fp16")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast_fp16")]; + tensor sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor("sqrt_5_cast_fp16")]; + tensor real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor("real_div_5_cast_fp16")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor("reshape_21_cast_fp16")]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2345984)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2346688)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor("add_11_cast_fp16")]; + tensor var_424 = const()[name = tensor("op_424"), val = tensor([1, 1])]; + tensor var_426 = const()[name = tensor("op_426"), val = tensor([1, 1])]; + tensor hidden_states_25_pad_type_0 = const()[name = tensor("hidden_states_25_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_25_pad_0 = const()[name = tensor("hidden_states_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2347392))), lut = tensor([-0x1.784p-4, -0x1.bd8p-6, 0x1.b6p-6, 0x1.744p-4]), name = tensor("down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2373056)))]; + tensor hidden_states_25_cast_fp16 = conv(bias = down_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_426, groups = var_111, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_424, weight = down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized, x = add_11_cast_fp16)[name = tensor("hidden_states_25_cast_fp16")]; + tensor var_431 = const()[name = tensor("op_431"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_7_cast_fp16 = reshape(shape = var_431, x = hidden_states_25_cast_fp16)[name = tensor("inputs_7_cast_fp16")]; + tensor var_441 = const()[name = tensor("op_441"), val = tensor([1])]; + tensor channels_mean_7_cast_fp16 = reduce_mean(axes = var_441, keep_dims = var_106, x = inputs_7_cast_fp16)[name = tensor("channels_mean_7_cast_fp16")]; + tensor zero_mean_7_cast_fp16 = sub(x = inputs_7_cast_fp16, y = channels_mean_7_cast_fp16)[name = tensor("zero_mean_7_cast_fp16")]; + tensor zero_mean_sq_7_cast_fp16 = mul(x = zero_mean_7_cast_fp16, y = zero_mean_7_cast_fp16)[name = tensor("zero_mean_sq_7_cast_fp16")]; + tensor var_445 = const()[name = tensor("op_445"), val = tensor([1])]; + tensor var_446_cast_fp16 = reduce_mean(axes = var_445, keep_dims = var_106, x = zero_mean_sq_7_cast_fp16)[name = tensor("op_446_cast_fp16")]; + tensor var_447_to_fp16 = const()[name = tensor("op_447_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_448_cast_fp16 = add(x = var_446_cast_fp16, y = var_447_to_fp16)[name = tensor("op_448_cast_fp16")]; + tensor denom_7_epsilon_0_to_fp16 = const()[name = tensor("denom_7_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_7_cast_fp16 = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_448_cast_fp16)[name = tensor("denom_7_cast_fp16")]; + tensor out_7_cast_fp16 = mul(x = zero_mean_7_cast_fp16, y = denom_7_cast_fp16)[name = tensor("out_7_cast_fp16")]; + tensor var_452_to_fp16 = const()[name = tensor("op_452_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2373760)))]; + tensor var_453_cast_fp16 = add(x = out_7_cast_fp16, y = var_452_to_fp16)[name = tensor("op_453_cast_fp16")]; + tensor var_455_to_fp16 = const()[name = tensor("op_455_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2374464)))]; + tensor hidden_states_27_cast_fp16 = mul(x = var_453_cast_fp16, y = var_455_to_fp16)[name = tensor("hidden_states_27_cast_fp16")]; + tensor var_462 = const()[name = tensor("op_462"), val = tensor([1, 1])]; + tensor var_464 = const()[name = tensor("op_464"), val = tensor([1, 1])]; + tensor q_5_pad_type_0 = const()[name = tensor("q_5_pad_type_0"), val = tensor("custom")]; + tensor q_5_pad_0 = const()[name = tensor("q_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2375168))), lut = tensor([-0x1.3d4p-3, -0x1.7fcp-5, 0x1.6e8p-5, 0x1.39p-3]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_5_cast_fp16 = conv(dilations = var_464, groups = var_111, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_462, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_27_cast_fp16)[name = tensor("q_5_cast_fp16")]; + tensor var_468 = const()[name = tensor("op_468"), val = tensor([1, 1])]; + tensor var_470 = const()[name = tensor("op_470"), val = tensor([1, 1])]; + tensor k_5_pad_type_0 = const()[name = tensor("k_5_pad_type_0"), val = tensor("custom")]; + tensor k_5_pad_0 = const()[name = tensor("k_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2400832))), lut = tensor([-0x1.238p-3, -0x1.58p-5, 0x1.53cp-5, 0x1.208p-3]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_5_cast_fp16 = conv(dilations = var_470, groups = var_111, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_468, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_27_cast_fp16)[name = tensor("k_5_cast_fp16")]; + tensor var_474 = const()[name = tensor("op_474"), val = tensor([1, 1])]; + tensor var_476 = const()[name = tensor("op_476"), val = tensor([1, 1])]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("custom")]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2426496))), lut = tensor([-0x1.62p-4, -0x1.95cp-6, 0x1.9bp-6, 0x1.61p-4]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_5_cast_fp16 = conv(dilations = var_476, groups = var_111, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_474, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_27_cast_fp16)[name = tensor("v_5_cast_fp16")]; + tensor var_480 = const()[name = tensor("op_480"), val = tensor([2, 5, 64, -1])]; + tensor var_481_cast_fp16 = reshape(shape = var_480, x = q_5_cast_fp16)[name = tensor("op_481_cast_fp16")]; + tensor var_482 = const()[name = tensor("op_482"), val = tensor([2, 5, 64, -1])]; + tensor var_483_cast_fp16 = reshape(shape = var_482, x = k_5_cast_fp16)[name = tensor("op_483_cast_fp16")]; + tensor var_484 = const()[name = tensor("op_484"), val = tensor([2, 5, 64, -1])]; + tensor var_485_cast_fp16 = reshape(shape = var_484, x = v_5_cast_fp16)[name = tensor("op_485_cast_fp16")]; + tensor attn_weights_9_transpose_x_0 = const()[name = tensor("attn_weights_9_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_9_transpose_y_0 = const()[name = tensor("attn_weights_9_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_481_cast_fp16, y = var_483_cast_fp16)[name = tensor("attn_weights_9_cast_fp16")]; + tensor attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_11_cast_fp16")]; + tensor var_489_cast_fp16 = softmax(axis = var_95, x = attn_weights_11_cast_fp16)[name = tensor("op_489_cast_fp16")]; + tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; + tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_485_cast_fp16, y = var_489_cast_fp16)[name = tensor("attn_5_cast_fp16")]; + tensor var_493 = const()[name = tensor("op_493"), val = tensor([2, 320, 1, -1])]; + tensor input_51_cast_fp16 = reshape(shape = var_493, x = attn_5_cast_fp16)[name = tensor("input_51_cast_fp16")]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor([1, 1])]; + tensor var_500 = const()[name = tensor("op_500"), val = tensor([1, 1])]; + tensor var_502_pad_type_0 = const()[name = tensor("op_502_pad_type_0"), val = tensor("custom")]; + tensor var_502_pad_0 = const()[name = tensor("op_502_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2452160))), lut = tensor([-0x1.3f8p-4, -0x1.7b4p-6, 0x1.70cp-6, 0x1.3c4p-4]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2477824)))]; + tensor var_502_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_500, groups = var_111, pad = var_502_pad_0, pad_type = var_502_pad_type_0, strides = var_498, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = tensor("op_502_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = var_502_cast_fp16, y = inputs_7_cast_fp16)[name = tensor("inputs_9_cast_fp16")]; + tensor var_506 = const()[name = tensor("op_506"), val = tensor([1])]; + tensor channels_mean_9_cast_fp16 = reduce_mean(axes = var_506, keep_dims = var_106, x = inputs_9_cast_fp16)[name = tensor("channels_mean_9_cast_fp16")]; + tensor zero_mean_9_cast_fp16 = sub(x = inputs_9_cast_fp16, y = channels_mean_9_cast_fp16)[name = tensor("zero_mean_9_cast_fp16")]; + tensor zero_mean_sq_9_cast_fp16 = mul(x = zero_mean_9_cast_fp16, y = zero_mean_9_cast_fp16)[name = tensor("zero_mean_sq_9_cast_fp16")]; + tensor var_510 = const()[name = tensor("op_510"), val = tensor([1])]; + tensor var_511_cast_fp16 = reduce_mean(axes = var_510, keep_dims = var_106, x = zero_mean_sq_9_cast_fp16)[name = tensor("op_511_cast_fp16")]; + tensor var_512_to_fp16 = const()[name = tensor("op_512_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_513_cast_fp16 = add(x = var_511_cast_fp16, y = var_512_to_fp16)[name = tensor("op_513_cast_fp16")]; + tensor denom_9_epsilon_0_to_fp16 = const()[name = tensor("denom_9_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_9_cast_fp16 = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_513_cast_fp16)[name = tensor("denom_9_cast_fp16")]; + tensor out_9_cast_fp16 = mul(x = zero_mean_9_cast_fp16, y = denom_9_cast_fp16)[name = tensor("out_9_cast_fp16")]; + tensor var_517_to_fp16 = const()[name = tensor("op_517_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2478528)))]; + tensor var_518_cast_fp16 = add(x = out_9_cast_fp16, y = var_517_to_fp16)[name = tensor("op_518_cast_fp16")]; + tensor var_520_to_fp16 = const()[name = tensor("op_520_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2479232)))]; + tensor hidden_states_29_cast_fp16 = mul(x = var_518_cast_fp16, y = var_520_to_fp16)[name = tensor("hidden_states_29_cast_fp16")]; + tensor var_527 = const()[name = tensor("op_527"), val = tensor([1, 1])]; + tensor var_529 = const()[name = tensor("op_529"), val = tensor([1, 1])]; + tensor q_7_pad_type_0 = const()[name = tensor("q_7_pad_type_0"), val = tensor("custom")]; + tensor q_7_pad_0 = const()[name = tensor("q_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2479936))), lut = tensor([-0x1.854p-4, -0x1.d18p-6, 0x1.cap-6, 0x1.80cp-4]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_7_cast_fp16 = conv(dilations = var_529, groups = var_111, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_527, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_29_cast_fp16)[name = tensor("q_7_cast_fp16")]; + tensor var_533 = const()[name = tensor("op_533"), val = tensor([1, 1])]; + tensor var_535 = const()[name = tensor("op_535"), val = tensor([1, 1])]; + tensor k_7_pad_type_0 = const()[name = tensor("k_7_pad_type_0"), val = tensor("custom")]; + tensor k_7_pad_0 = const()[name = tensor("k_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2505600))), lut = tensor([-0x1.32cp-4, -0x1.5e4p-6, 0x1.6a8p-6, 0x1.37p-4]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor k_7_cast_fp16 = conv(dilations = var_535, groups = var_111, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_533, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_7_cast_fp16")]; + tensor var_539 = const()[name = tensor("op_539"), val = tensor([1, 1])]; + tensor var_541 = const()[name = tensor("op_541"), val = tensor([1, 1])]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("custom")]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2587584))), lut = tensor([-0x1.7f8p-6, -0x1.7b8p-8, 0x1.88p-8, 0x1.8a8p-6]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor v_7_cast_fp16 = conv(dilations = var_541, groups = var_111, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_539, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_7_cast_fp16")]; + tensor var_545 = const()[name = tensor("op_545"), val = tensor([2, 5, 64, -1])]; + tensor var_546_cast_fp16 = reshape(shape = var_545, x = q_7_cast_fp16)[name = tensor("op_546_cast_fp16")]; + tensor var_547 = const()[name = tensor("op_547"), val = tensor([2, 5, 64, -1])]; + tensor var_548_cast_fp16 = reshape(shape = var_547, x = k_7_cast_fp16)[name = tensor("op_548_cast_fp16")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([2, 5, 64, -1])]; + tensor var_550_cast_fp16 = reshape(shape = var_549, x = v_7_cast_fp16)[name = tensor("op_550_cast_fp16")]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_13_cast_fp16 = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_546_cast_fp16, y = var_548_cast_fp16)[name = tensor("attn_weights_13_cast_fp16")]; + tensor attn_weights_15_cast_fp16 = mul(x = attn_weights_13_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_15_cast_fp16")]; + tensor var_554_cast_fp16 = softmax(axis = var_95, x = attn_weights_15_cast_fp16)[name = tensor("op_554_cast_fp16")]; + tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; + tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_550_cast_fp16, y = var_554_cast_fp16)[name = tensor("attn_7_cast_fp16")]; + tensor var_558 = const()[name = tensor("op_558"), val = tensor([2, 320, 1, -1])]; + tensor input_53_cast_fp16 = reshape(shape = var_558, x = attn_7_cast_fp16)[name = tensor("input_53_cast_fp16")]; + tensor var_563 = const()[name = tensor("op_563"), val = tensor([1, 1])]; + tensor var_565 = const()[name = tensor("op_565"), val = tensor([1, 1])]; + tensor var_567_pad_type_0 = const()[name = tensor("op_567_pad_type_0"), val = tensor("custom")]; + tensor var_567_pad_0 = const()[name = tensor("op_567_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2669568))), lut = tensor([-0x1.8c8p-6, -0x1.d3cp-10, 0x1.144p-9, 0x1.894p-6]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2695232)))]; + tensor var_567_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_565, groups = var_111, pad = var_567_pad_0, pad_type = var_567_pad_type_0, strides = var_563, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_53_cast_fp16)[name = tensor("op_567_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = var_567_cast_fp16, y = inputs_9_cast_fp16)[name = tensor("inputs_11_cast_fp16")]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor([1])]; + tensor channels_mean_11_cast_fp16 = reduce_mean(axes = var_571, keep_dims = var_106, x = inputs_11_cast_fp16)[name = tensor("channels_mean_11_cast_fp16")]; + tensor zero_mean_11_cast_fp16 = sub(x = inputs_11_cast_fp16, y = channels_mean_11_cast_fp16)[name = tensor("zero_mean_11_cast_fp16")]; + tensor zero_mean_sq_11_cast_fp16 = mul(x = zero_mean_11_cast_fp16, y = zero_mean_11_cast_fp16)[name = tensor("zero_mean_sq_11_cast_fp16")]; + tensor var_575 = const()[name = tensor("op_575"), val = tensor([1])]; + tensor var_576_cast_fp16 = reduce_mean(axes = var_575, keep_dims = var_106, x = zero_mean_sq_11_cast_fp16)[name = tensor("op_576_cast_fp16")]; + tensor var_577_to_fp16 = const()[name = tensor("op_577_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_578_cast_fp16 = add(x = var_576_cast_fp16, y = var_577_to_fp16)[name = tensor("op_578_cast_fp16")]; + tensor denom_11_epsilon_0_to_fp16 = const()[name = tensor("denom_11_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_11_cast_fp16 = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_578_cast_fp16)[name = tensor("denom_11_cast_fp16")]; + tensor out_11_cast_fp16 = mul(x = zero_mean_11_cast_fp16, y = denom_11_cast_fp16)[name = tensor("out_11_cast_fp16")]; + tensor var_582_to_fp16 = const()[name = tensor("op_582_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2695936)))]; + tensor var_583_cast_fp16 = add(x = out_11_cast_fp16, y = var_582_to_fp16)[name = tensor("op_583_cast_fp16")]; + tensor var_585_to_fp16 = const()[name = tensor("op_585_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2696640)))]; + tensor input_55_cast_fp16 = mul(x = var_583_cast_fp16, y = var_585_to_fp16)[name = tensor("input_55_cast_fp16")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([1, 1])]; + tensor var_595 = const()[name = tensor("op_595"), val = tensor([1, 1])]; + tensor var_597_pad_type_0 = const()[name = tensor("op_597_pad_type_0"), val = tensor("custom")]; + tensor var_597_pad_0 = const()[name = tensor("op_597_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2697344))), lut = tensor([-0x1.7a8p-4, -0x1.c4p-6, 0x1.b08p-6, 0x1.754p-4]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2902208))), lut = tensor([0x1.68p-5, -0x1.438p-8, -0x1.158p-4, 0x1.85p-4]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_597_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_595, groups = var_111, pad = var_597_pad_0, pad_type = var_597_pad_type_0, strides = var_593, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = tensor("op_597_cast_fp16")]; + tensor var_598_split_sizes_0 = const()[name = tensor("op_598_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_598_axis_0 = const()[name = tensor("op_598_axis_0"), val = tensor(1)]; + tensor var_598_cast_fp16_0, tensor var_598_cast_fp16_1 = split(axis = var_598_axis_0, split_sizes = var_598_split_sizes_0, x = var_597_cast_fp16)[name = tensor("op_598_cast_fp16")]; + tensor var_600_mode_0 = const()[name = tensor("op_600_mode_0"), val = tensor("EXACT")]; + tensor var_600_cast_fp16 = gelu(mode = var_600_mode_0, x = var_598_cast_fp16_1)[name = tensor("op_600_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = var_598_cast_fp16_0, y = var_600_cast_fp16)[name = tensor("input_57_cast_fp16")]; + tensor var_604 = const()[name = tensor("op_604"), val = tensor([1, 1])]; + tensor var_606 = const()[name = tensor("op_606"), val = tensor([1, 1])]; + tensor var_608_pad_type_0 = const()[name = tensor("op_608_pad_type_0"), val = tensor("custom")]; + tensor var_608_pad_0 = const()[name = tensor("op_608_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2902912))), lut = tensor([-0x1.94cp-4, -0x1.e8cp-6, 0x1.d6cp-6, 0x1.9p-4]), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3005376)))]; + tensor var_608_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_606, groups = var_111, pad = var_608_pad_0, pad_type = var_608_pad_type_0, strides = var_604, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_57_cast_fp16)[name = tensor("op_608_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = add(x = var_608_cast_fp16, y = inputs_11_cast_fp16)[name = tensor("hidden_states_33_cast_fp16")]; + tensor var_610 = const()[name = tensor("op_610"), val = tensor([2, 320, 64, 64])]; + tensor input_59_cast_fp16 = reshape(shape = var_610, x = hidden_states_33_cast_fp16)[name = tensor("input_59_cast_fp16")]; + tensor var_614 = const()[name = tensor("op_614"), val = tensor([1, 1])]; + tensor var_616 = const()[name = tensor("op_616"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3006080))), lut = tensor([-0x1.b78p-4, -0x1.f5p-6, 0x1.118p-5, 0x1.c2p-4]), name = tensor("down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3031744)))]; + tensor hidden_states_35_cast_fp16 = conv(bias = down_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_616, groups = var_111, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_614, weight = down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = tensor("hidden_states_35_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = hidden_states_23_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([2, 2])]; + tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; + tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("custom")]; + tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3032448))), lut = tensor([-0x1.b5cp-5, -0x1.cdcp-7, 0x1.f8cp-7, 0x1.c7p-5]), name = tensor("down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3262912)))]; + tensor input_63_cast_fp16 = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_625, groups = var_111, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_623, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = tensor("input_63_cast_fp16")]; + tensor var_633 = const()[name = tensor("op_633"), val = tensor(3)]; + tensor var_644 = const()[name = tensor("op_644"), val = tensor(true)]; + tensor var_649 = const()[name = tensor("op_649"), val = tensor(1)]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([2, 32, 10, 32, 32])]; + tensor reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_63_cast_fp16)[name = tensor("reshape_24_cast_fp16")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor("reduce_mean_18_cast_fp16")]; + tensor sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor("sub_12_cast_fp16")]; + tensor square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor("square_6_cast_fp16")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor("reduce_mean_20_cast_fp16")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast_fp16")]; + tensor sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor("sqrt_6_cast_fp16")]; + tensor real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor("real_div_6_cast_fp16")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([2, 320, 32, 32])]; + tensor reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor("reshape_25_cast_fp16")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3263616)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3264320)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor("add_13_cast_fp16")]; + tensor input_67_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 1])]; + tensor var_674 = const()[name = tensor("op_674"), val = tensor([1, 1])]; + tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3265024))), lut = tensor([-0x1.c58p-4, -0x1.ed4p-6, 0x1.9bcp-6, 0x1.ac4p-4]), name = tensor("down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([640, 320, 3, 3])]; + tensor down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3725888)))]; + tensor hidden_states_37_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_674, groups = var_649, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_672, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor var_680 = const()[name = tensor("op_680"), val = tensor([1, 1])]; + tensor var_682 = const()[name = tensor("op_682"), val = tensor([1, 1])]; + tensor temb_5_pad_type_0 = const()[name = tensor("temb_5_pad_type_0"), val = tensor("custom")]; + tensor temb_5_pad_0 = const()[name = tensor("temb_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3727232))), lut = tensor([-0x1.16p+1, -0x1.87p-2, -0x1.35p-8, 0x1.414p-8]), name = tensor("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3932096)))]; + tensor temb_5_cast_fp16 = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_682, groups = var_649, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_680, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_5_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = hidden_states_37_cast_fp16, y = temb_5_cast_fp16)[name = tensor("input_71_cast_fp16")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = input_71_cast_fp16)[name = tensor("reshape_28_cast_fp16")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor("reduce_mean_21_cast_fp16")]; + tensor sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor("sub_14_cast_fp16")]; + tensor square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor("square_7_cast_fp16")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor("reduce_mean_23_cast_fp16")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast_fp16")]; + tensor sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor("sqrt_7_cast_fp16")]; + tensor real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor("real_div_7_cast_fp16")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor("reshape_29_cast_fp16")]; + tensor add_15_mean_0_to_fp16 = const()[name = tensor("add_15_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3933440)))]; + tensor add_15_variance_0_to_fp16 = const()[name = tensor("add_15_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3934784)))]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3936128)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3937472)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor("add_15_cast_fp16")]; + tensor input_75_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor var_692 = const()[name = tensor("op_692"), val = tensor([1, 1])]; + tensor var_694 = const()[name = tensor("op_694"), val = tensor([1, 1])]; + tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3938816))), lut = tensor([-0x1.64cp-4, -0x1.734p-6, 0x1.768p-6, 0x1.65p-4]), name = tensor("down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4860480)))]; + tensor hidden_states_39_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_694, groups = var_649, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_692, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; + tensor var_699 = const()[name = tensor("op_699"), val = tensor([1, 1])]; + tensor var_701 = const()[name = tensor("op_701"), val = tensor([1, 1])]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4861824))), lut = tensor([-0x1.728p-5, -0x1.acp-7, 0x1.c0cp-7, 0x1.784p-5]), name = tensor("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 320, 1, 1])]; + tensor down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4913088)))]; + tensor x_1_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_701, groups = var_649, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_699, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor("x_1_cast_fp16")]; + tensor hidden_states_41_cast_fp16 = add(x = x_1_cast_fp16, y = hidden_states_39_cast_fp16)[name = tensor("hidden_states_41_cast_fp16")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = hidden_states_41_cast_fp16)[name = tensor("reshape_32_cast_fp16")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor("reduce_mean_24_cast_fp16")]; + tensor sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor("sub_16_cast_fp16")]; + tensor square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor("square_8_cast_fp16")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor("reduce_mean_26_cast_fp16")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast_fp16")]; + tensor sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor("sqrt_8_cast_fp16")]; + tensor real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor("real_div_8_cast_fp16")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor("reshape_33_cast_fp16")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4914432)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4915776)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor("add_17_cast_fp16")]; + tensor var_721 = const()[name = tensor("op_721"), val = tensor([1, 1])]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4917120))), lut = tensor([-0x1.944p-4, -0x1.e2cp-6, 0x1.da8p-6, 0x1.914p-4]), name = tensor("down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5019584)))]; + tensor hidden_states_43_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_723, groups = var_649, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_721, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_17_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor var_728 = const()[name = tensor("op_728"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_13_cast_fp16 = reshape(shape = var_728, x = hidden_states_43_cast_fp16)[name = tensor("inputs_13_cast_fp16")]; + tensor var_738 = const()[name = tensor("op_738"), val = tensor([1])]; + tensor channels_mean_13_cast_fp16 = reduce_mean(axes = var_738, keep_dims = var_644, x = inputs_13_cast_fp16)[name = tensor("channels_mean_13_cast_fp16")]; + tensor zero_mean_13_cast_fp16 = sub(x = inputs_13_cast_fp16, y = channels_mean_13_cast_fp16)[name = tensor("zero_mean_13_cast_fp16")]; + tensor zero_mean_sq_13_cast_fp16 = mul(x = zero_mean_13_cast_fp16, y = zero_mean_13_cast_fp16)[name = tensor("zero_mean_sq_13_cast_fp16")]; + tensor var_742 = const()[name = tensor("op_742"), val = tensor([1])]; + tensor var_743_cast_fp16 = reduce_mean(axes = var_742, keep_dims = var_644, x = zero_mean_sq_13_cast_fp16)[name = tensor("op_743_cast_fp16")]; + tensor var_744_to_fp16 = const()[name = tensor("op_744_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_745_cast_fp16 = add(x = var_743_cast_fp16, y = var_744_to_fp16)[name = tensor("op_745_cast_fp16")]; + tensor denom_13_epsilon_0_to_fp16 = const()[name = tensor("denom_13_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_13_cast_fp16 = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_745_cast_fp16)[name = tensor("denom_13_cast_fp16")]; + tensor out_13_cast_fp16 = mul(x = zero_mean_13_cast_fp16, y = denom_13_cast_fp16)[name = tensor("out_13_cast_fp16")]; + tensor var_749_to_fp16 = const()[name = tensor("op_749_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5020928)))]; + tensor var_750_cast_fp16 = add(x = out_13_cast_fp16, y = var_749_to_fp16)[name = tensor("op_750_cast_fp16")]; + tensor var_752_to_fp16 = const()[name = tensor("op_752_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5022272)))]; + tensor hidden_states_45_cast_fp16 = mul(x = var_750_cast_fp16, y = var_752_to_fp16)[name = tensor("hidden_states_45_cast_fp16")]; + tensor var_759 = const()[name = tensor("op_759"), val = tensor([1, 1])]; + tensor var_761 = const()[name = tensor("op_761"), val = tensor([1, 1])]; + tensor q_9_pad_type_0 = const()[name = tensor("q_9_pad_type_0"), val = tensor("custom")]; + tensor q_9_pad_0 = const()[name = tensor("q_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5023616))), lut = tensor([-0x1.034p-3, -0x1.24cp-5, 0x1.34p-5, 0x1.07p-3]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_9_cast_fp16 = conv(dilations = var_761, groups = var_649, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_759, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_45_cast_fp16)[name = tensor("q_9_cast_fp16")]; + tensor var_765 = const()[name = tensor("op_765"), val = tensor([1, 1])]; + tensor var_767 = const()[name = tensor("op_767"), val = tensor([1, 1])]; + tensor k_9_pad_type_0 = const()[name = tensor("k_9_pad_type_0"), val = tensor("custom")]; + tensor k_9_pad_0 = const()[name = tensor("k_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5126080))), lut = tensor([-0x1.f74p-4, -0x1.21p-5, 0x1.248p-5, 0x1.f9p-4]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_9_cast_fp16 = conv(dilations = var_767, groups = var_649, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_765, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_45_cast_fp16)[name = tensor("k_9_cast_fp16")]; + tensor var_771 = const()[name = tensor("op_771"), val = tensor([1, 1])]; + tensor var_773 = const()[name = tensor("op_773"), val = tensor([1, 1])]; + tensor v_9_pad_type_0 = const()[name = tensor("v_9_pad_type_0"), val = tensor("custom")]; + tensor v_9_pad_0 = const()[name = tensor("v_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5228544))), lut = tensor([-0x1.684p-4, -0x1.a68p-6, 0x1.98p-6, 0x1.638p-4]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_9_cast_fp16 = conv(dilations = var_773, groups = var_649, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_771, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_45_cast_fp16)[name = tensor("v_9_cast_fp16")]; + tensor var_777 = const()[name = tensor("op_777"), val = tensor([2, 10, 64, -1])]; + tensor var_778_cast_fp16 = reshape(shape = var_777, x = q_9_cast_fp16)[name = tensor("op_778_cast_fp16")]; + tensor var_779 = const()[name = tensor("op_779"), val = tensor([2, 10, 64, -1])]; + tensor var_780_cast_fp16 = reshape(shape = var_779, x = k_9_cast_fp16)[name = tensor("op_780_cast_fp16")]; + tensor var_781 = const()[name = tensor("op_781"), val = tensor([2, 10, 64, -1])]; + tensor var_782_cast_fp16 = reshape(shape = var_781, x = v_9_cast_fp16)[name = tensor("op_782_cast_fp16")]; + tensor attn_weights_17_transpose_x_0 = const()[name = tensor("attn_weights_17_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_17_transpose_y_0 = const()[name = tensor("attn_weights_17_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_778_cast_fp16, y = var_780_cast_fp16)[name = tensor("attn_weights_17_cast_fp16")]; + tensor var_640_to_fp16 = const()[name = tensor("op_640_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_19_cast_fp16")]; + tensor var_786_cast_fp16 = softmax(axis = var_633, x = attn_weights_19_cast_fp16)[name = tensor("op_786_cast_fp16")]; + tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; + tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_782_cast_fp16, y = var_786_cast_fp16)[name = tensor("attn_9_cast_fp16")]; + tensor var_790 = const()[name = tensor("op_790"), val = tensor([2, 640, 1, -1])]; + tensor input_79_cast_fp16 = reshape(shape = var_790, x = attn_9_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor var_795 = const()[name = tensor("op_795"), val = tensor([1, 1])]; + tensor var_797 = const()[name = tensor("op_797"), val = tensor([1, 1])]; + tensor var_799_pad_type_0 = const()[name = tensor("op_799_pad_type_0"), val = tensor("custom")]; + tensor var_799_pad_0 = const()[name = tensor("op_799_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5331008))), lut = tensor([-0x1.5d4p-4, -0x1.a2p-6, 0x1.9acp-6, 0x1.5bp-4]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5433472)))]; + tensor var_799_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_797, groups = var_649, pad = var_799_pad_0, pad_type = var_799_pad_type_0, strides = var_795, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_79_cast_fp16)[name = tensor("op_799_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = var_799_cast_fp16, y = inputs_13_cast_fp16)[name = tensor("inputs_15_cast_fp16")]; + tensor var_803 = const()[name = tensor("op_803"), val = tensor([1])]; + tensor channels_mean_15_cast_fp16 = reduce_mean(axes = var_803, keep_dims = var_644, x = inputs_15_cast_fp16)[name = tensor("channels_mean_15_cast_fp16")]; + tensor zero_mean_15_cast_fp16 = sub(x = inputs_15_cast_fp16, y = channels_mean_15_cast_fp16)[name = tensor("zero_mean_15_cast_fp16")]; + tensor zero_mean_sq_15_cast_fp16 = mul(x = zero_mean_15_cast_fp16, y = zero_mean_15_cast_fp16)[name = tensor("zero_mean_sq_15_cast_fp16")]; + tensor var_807 = const()[name = tensor("op_807"), val = tensor([1])]; + tensor var_808_cast_fp16 = reduce_mean(axes = var_807, keep_dims = var_644, x = zero_mean_sq_15_cast_fp16)[name = tensor("op_808_cast_fp16")]; + tensor var_809_to_fp16 = const()[name = tensor("op_809_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_810_cast_fp16 = add(x = var_808_cast_fp16, y = var_809_to_fp16)[name = tensor("op_810_cast_fp16")]; + tensor denom_15_epsilon_0_to_fp16 = const()[name = tensor("denom_15_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_15_cast_fp16 = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_810_cast_fp16)[name = tensor("denom_15_cast_fp16")]; + tensor out_15_cast_fp16 = mul(x = zero_mean_15_cast_fp16, y = denom_15_cast_fp16)[name = tensor("out_15_cast_fp16")]; + tensor var_814_to_fp16 = const()[name = tensor("op_814_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5434816)))]; + tensor var_815_cast_fp16 = add(x = out_15_cast_fp16, y = var_814_to_fp16)[name = tensor("op_815_cast_fp16")]; + tensor var_817_to_fp16 = const()[name = tensor("op_817_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5436160)))]; + tensor hidden_states_47_cast_fp16 = mul(x = var_815_cast_fp16, y = var_817_to_fp16)[name = tensor("hidden_states_47_cast_fp16")]; + tensor var_824 = const()[name = tensor("op_824"), val = tensor([1, 1])]; + tensor var_826 = const()[name = tensor("op_826"), val = tensor([1, 1])]; + tensor q_11_pad_type_0 = const()[name = tensor("q_11_pad_type_0"), val = tensor("custom")]; + tensor q_11_pad_0 = const()[name = tensor("q_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5437504))), lut = tensor([-0x1.84p-4, -0x1.cap-6, 0x1.d18p-6, 0x1.854p-4]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_11_cast_fp16 = conv(dilations = var_826, groups = var_649, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_824, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_47_cast_fp16)[name = tensor("q_11_cast_fp16")]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, 1])]; + tensor var_832 = const()[name = tensor("op_832"), val = tensor([1, 1])]; + tensor k_11_pad_type_0 = const()[name = tensor("k_11_pad_type_0"), val = tensor("custom")]; + tensor k_11_pad_0 = const()[name = tensor("k_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5539968))), lut = tensor([-0x1.664p-4, -0x1.a14p-6, 0x1.af4p-6, 0x1.69cp-4]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor k_11_cast_fp16 = conv(dilations = var_832, groups = var_649, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_830, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_11_cast_fp16")]; + tensor var_836 = const()[name = tensor("op_836"), val = tensor([1, 1])]; + tensor var_838 = const()[name = tensor("op_838"), val = tensor([1, 1])]; + tensor v_11_pad_type_0 = const()[name = tensor("v_11_pad_type_0"), val = tensor("custom")]; + tensor v_11_pad_0 = const()[name = tensor("v_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5703872))), lut = tensor([-0x1.39p-5, -0x1.71cp-7, 0x1.6dp-7, 0x1.374p-5]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor v_11_cast_fp16 = conv(dilations = var_838, groups = var_649, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_836, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_11_cast_fp16")]; + tensor var_842 = const()[name = tensor("op_842"), val = tensor([2, 10, 64, -1])]; + tensor var_843_cast_fp16 = reshape(shape = var_842, x = q_11_cast_fp16)[name = tensor("op_843_cast_fp16")]; + tensor var_844 = const()[name = tensor("op_844"), val = tensor([2, 10, 64, -1])]; + tensor var_845_cast_fp16 = reshape(shape = var_844, x = k_11_cast_fp16)[name = tensor("op_845_cast_fp16")]; + tensor var_846 = const()[name = tensor("op_846"), val = tensor([2, 10, 64, -1])]; + tensor var_847_cast_fp16 = reshape(shape = var_846, x = v_11_cast_fp16)[name = tensor("op_847_cast_fp16")]; + tensor attn_weights_21_transpose_x_0 = const()[name = tensor("attn_weights_21_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_21_transpose_y_0 = const()[name = tensor("attn_weights_21_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_21_cast_fp16 = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_843_cast_fp16, y = var_845_cast_fp16)[name = tensor("attn_weights_21_cast_fp16")]; + tensor attn_weights_23_cast_fp16 = mul(x = attn_weights_21_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_23_cast_fp16")]; + tensor var_851_cast_fp16 = softmax(axis = var_633, x = attn_weights_23_cast_fp16)[name = tensor("op_851_cast_fp16")]; + tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; + tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_847_cast_fp16, y = var_851_cast_fp16)[name = tensor("attn_11_cast_fp16")]; + tensor var_855 = const()[name = tensor("op_855"), val = tensor([2, 640, 1, -1])]; + tensor input_81_cast_fp16 = reshape(shape = var_855, x = attn_11_cast_fp16)[name = tensor("input_81_cast_fp16")]; + tensor var_860 = const()[name = tensor("op_860"), val = tensor([1, 1])]; + tensor var_862 = const()[name = tensor("op_862"), val = tensor([1, 1])]; + tensor var_864_pad_type_0 = const()[name = tensor("op_864_pad_type_0"), val = tensor("custom")]; + tensor var_864_pad_0 = const()[name = tensor("op_864_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5867776))), lut = tensor([-0x1.8bp-6, -0x1.ba8p-8, 0x1.b88p-8, 0x1.894p-6]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5970240)))]; + tensor var_864_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_862, groups = var_649, pad = var_864_pad_0, pad_type = var_864_pad_type_0, strides = var_860, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_81_cast_fp16)[name = tensor("op_864_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = var_864_cast_fp16, y = inputs_15_cast_fp16)[name = tensor("inputs_17_cast_fp16")]; + tensor var_868 = const()[name = tensor("op_868"), val = tensor([1])]; + tensor channels_mean_17_cast_fp16 = reduce_mean(axes = var_868, keep_dims = var_644, x = inputs_17_cast_fp16)[name = tensor("channels_mean_17_cast_fp16")]; + tensor zero_mean_17_cast_fp16 = sub(x = inputs_17_cast_fp16, y = channels_mean_17_cast_fp16)[name = tensor("zero_mean_17_cast_fp16")]; + tensor zero_mean_sq_17_cast_fp16 = mul(x = zero_mean_17_cast_fp16, y = zero_mean_17_cast_fp16)[name = tensor("zero_mean_sq_17_cast_fp16")]; + tensor var_872 = const()[name = tensor("op_872"), val = tensor([1])]; + tensor var_873_cast_fp16 = reduce_mean(axes = var_872, keep_dims = var_644, x = zero_mean_sq_17_cast_fp16)[name = tensor("op_873_cast_fp16")]; + tensor var_874_to_fp16 = const()[name = tensor("op_874_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_875_cast_fp16 = add(x = var_873_cast_fp16, y = var_874_to_fp16)[name = tensor("op_875_cast_fp16")]; + tensor denom_17_epsilon_0_to_fp16 = const()[name = tensor("denom_17_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_17_cast_fp16 = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_875_cast_fp16)[name = tensor("denom_17_cast_fp16")]; + tensor out_17_cast_fp16 = mul(x = zero_mean_17_cast_fp16, y = denom_17_cast_fp16)[name = tensor("out_17_cast_fp16")]; + tensor var_879_to_fp16 = const()[name = tensor("op_879_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5971584)))]; + tensor var_880_cast_fp16 = add(x = out_17_cast_fp16, y = var_879_to_fp16)[name = tensor("op_880_cast_fp16")]; + tensor var_882_to_fp16 = const()[name = tensor("op_882_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5972928)))]; + tensor input_83_cast_fp16 = mul(x = var_880_cast_fp16, y = var_882_to_fp16)[name = tensor("input_83_cast_fp16")]; + tensor var_890 = const()[name = tensor("op_890"), val = tensor([1, 1])]; + tensor var_892 = const()[name = tensor("op_892"), val = tensor([1, 1])]; + tensor var_894_pad_type_0 = const()[name = tensor("op_894_pad_type_0"), val = tensor("custom")]; + tensor var_894_pad_0 = const()[name = tensor("op_894_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5974272))), lut = tensor([-0x1.8a8p-4, -0x1.ccp-6, 0x1.d74p-6, 0x1.8fp-4]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6793536))), lut = tensor([0x1.c44p-8, -0x1.c24p-5, -0x1.accp-3, 0x1.114p-4]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_894_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_892, groups = var_649, pad = var_894_pad_0, pad_type = var_894_pad_type_0, strides = var_890, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = tensor("op_894_cast_fp16")]; + tensor var_895_split_sizes_0 = const()[name = tensor("op_895_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_895_axis_0 = const()[name = tensor("op_895_axis_0"), val = tensor(1)]; + tensor var_895_cast_fp16_0, tensor var_895_cast_fp16_1 = split(axis = var_895_axis_0, split_sizes = var_895_split_sizes_0, x = var_894_cast_fp16)[name = tensor("op_895_cast_fp16")]; + tensor var_897_mode_0 = const()[name = tensor("op_897_mode_0"), val = tensor("EXACT")]; + tensor var_897_cast_fp16 = gelu(mode = var_897_mode_0, x = var_895_cast_fp16_1)[name = tensor("op_897_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = var_895_cast_fp16_0, y = var_897_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor var_901 = const()[name = tensor("op_901"), val = tensor([1, 1])]; + tensor var_903 = const()[name = tensor("op_903"), val = tensor([1, 1])]; + tensor var_905_pad_type_0 = const()[name = tensor("op_905_pad_type_0"), val = tensor("custom")]; + tensor var_905_pad_0 = const()[name = tensor("op_905_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6794880))), lut = tensor([-0x1.834p-4, -0x1.cd4p-6, 0x1.d14p-6, 0x1.854p-4]), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7204544)))]; + tensor var_905_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_903, groups = var_649, pad = var_905_pad_0, pad_type = var_905_pad_type_0, strides = var_901, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = tensor("op_905_cast_fp16")]; + tensor hidden_states_51_cast_fp16 = add(x = var_905_cast_fp16, y = inputs_17_cast_fp16)[name = tensor("hidden_states_51_cast_fp16")]; + tensor var_907 = const()[name = tensor("op_907"), val = tensor([2, 640, 32, 32])]; + tensor input_87_cast_fp16 = reshape(shape = var_907, x = hidden_states_51_cast_fp16)[name = tensor("input_87_cast_fp16")]; + tensor var_911 = const()[name = tensor("op_911"), val = tensor([1, 1])]; + tensor var_913 = const()[name = tensor("op_913"), val = tensor([1, 1])]; + tensor hidden_states_53_pad_type_0 = const()[name = tensor("hidden_states_53_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_53_pad_0 = const()[name = tensor("hidden_states_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7205888))), lut = tensor([-0x1.32cp-4, -0x1.6e8p-6, 0x1.6f8p-6, 0x1.338p-4]), name = tensor("down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7308352)))]; + tensor hidden_states_53_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_913, groups = var_649, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_911, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor("hidden_states_53_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = hidden_states_53_cast_fp16, y = hidden_states_41_cast_fp16)[name = tensor("input_89_cast_fp16")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = input_89_cast_fp16)[name = tensor("reshape_36_cast_fp16")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor("reduce_mean_27_cast_fp16")]; + tensor sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor("sub_18_cast_fp16")]; + tensor square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor("square_9_cast_fp16")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor("reduce_mean_29_cast_fp16")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast_fp16")]; + tensor sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor("sqrt_9_cast_fp16")]; + tensor real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor("real_div_9_cast_fp16")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor("reshape_37_cast_fp16")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7309696)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7311040)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor("add_19_cast_fp16")]; + tensor input_93_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor("input_93_cast_fp16")]; + tensor var_928 = const()[name = tensor("op_928"), val = tensor([1, 1])]; + tensor var_930 = const()[name = tensor("op_930"), val = tensor([1, 1])]; + tensor hidden_states_55_pad_type_0 = const()[name = tensor("hidden_states_55_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_55_pad_0 = const()[name = tensor("hidden_states_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7312384))), lut = tensor([-0x1.b14p-4, -0x1.aap-6, 0x1.da4p-6, 0x1.cbcp-4]), name = tensor("down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8234048)))]; + tensor hidden_states_55_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_930, groups = var_649, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_928, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor var_936 = const()[name = tensor("op_936"), val = tensor([1, 1])]; + tensor var_938 = const()[name = tensor("op_938"), val = tensor([1, 1])]; + tensor temb_7_pad_type_0 = const()[name = tensor("temb_7_pad_type_0"), val = tensor("custom")]; + tensor temb_7_pad_0 = const()[name = tensor("temb_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8235392))), lut = tensor([-0x1.94cp-5, -0x1.eccp-8, 0x1.82p-11, 0x1.37p-7]), name = tensor("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8440256)))]; + tensor temb_7_cast_fp16 = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_938, groups = var_649, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_936, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_7_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = temb_7_cast_fp16)[name = tensor("input_97_cast_fp16")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_97_cast_fp16)[name = tensor("reshape_40_cast_fp16")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor("reduce_mean_30_cast_fp16")]; + tensor sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor("sub_20_cast_fp16")]; + tensor square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor("square_10_cast_fp16")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor("reduce_mean_32_cast_fp16")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast_fp16")]; + tensor sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor("sqrt_10_cast_fp16")]; + tensor real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor("real_div_10_cast_fp16")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor("reshape_41_cast_fp16")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8441600)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8442944)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor("add_21_cast_fp16")]; + tensor input_101_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor("input_101_cast_fp16")]; + tensor var_948 = const()[name = tensor("op_948"), val = tensor([1, 1])]; + tensor var_950 = const()[name = tensor("op_950"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_type_0 = const()[name = tensor("hidden_states_57_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_57_pad_0 = const()[name = tensor("hidden_states_57_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8444288))), lut = tensor([-0x1.838p-4, -0x1.88p-6, 0x1.6f4p-6, 0x1.77cp-4]), name = tensor("down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9365952)))]; + tensor hidden_states_57_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_950, groups = var_649, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_948, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = tensor("hidden_states_57_cast_fp16")]; + tensor hidden_states_59_cast_fp16 = add(x = input_89_cast_fp16, y = hidden_states_57_cast_fp16)[name = tensor("hidden_states_59_cast_fp16")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = hidden_states_59_cast_fp16)[name = tensor("reshape_44_cast_fp16")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor("reduce_mean_33_cast_fp16")]; + tensor sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor("sub_22_cast_fp16")]; + tensor square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor("square_11_cast_fp16")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor("reduce_mean_35_cast_fp16")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast_fp16")]; + tensor sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor("sqrt_11_cast_fp16")]; + tensor real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor("real_div_11_cast_fp16")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor("reshape_45_cast_fp16")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9367296)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9368640)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor("add_23_cast_fp16")]; + tensor var_970 = const()[name = tensor("op_970"), val = tensor([1, 1])]; + tensor var_972 = const()[name = tensor("op_972"), val = tensor([1, 1])]; + tensor hidden_states_61_pad_type_0 = const()[name = tensor("hidden_states_61_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_61_pad_0 = const()[name = tensor("hidden_states_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9369984))), lut = tensor([-0x1.a58p-4, -0x1.f0cp-6, 0x1.06p-5, 0x1.adcp-4]), name = tensor("down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9472448)))]; + tensor hidden_states_61_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_972, groups = var_649, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = var_970, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_23_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor var_977 = const()[name = tensor("op_977"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_19_cast_fp16 = reshape(shape = var_977, x = hidden_states_61_cast_fp16)[name = tensor("inputs_19_cast_fp16")]; + tensor var_987 = const()[name = tensor("op_987"), val = tensor([1])]; + tensor channels_mean_19_cast_fp16 = reduce_mean(axes = var_987, keep_dims = var_644, x = inputs_19_cast_fp16)[name = tensor("channels_mean_19_cast_fp16")]; + tensor zero_mean_19_cast_fp16 = sub(x = inputs_19_cast_fp16, y = channels_mean_19_cast_fp16)[name = tensor("zero_mean_19_cast_fp16")]; + tensor zero_mean_sq_19_cast_fp16 = mul(x = zero_mean_19_cast_fp16, y = zero_mean_19_cast_fp16)[name = tensor("zero_mean_sq_19_cast_fp16")]; + tensor var_991 = const()[name = tensor("op_991"), val = tensor([1])]; + tensor var_992_cast_fp16 = reduce_mean(axes = var_991, keep_dims = var_644, x = zero_mean_sq_19_cast_fp16)[name = tensor("op_992_cast_fp16")]; + tensor var_993_to_fp16 = const()[name = tensor("op_993_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_994_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_to_fp16)[name = tensor("op_994_cast_fp16")]; + tensor denom_19_epsilon_0_to_fp16 = const()[name = tensor("denom_19_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_19_cast_fp16 = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_994_cast_fp16)[name = tensor("denom_19_cast_fp16")]; + tensor out_19_cast_fp16 = mul(x = zero_mean_19_cast_fp16, y = denom_19_cast_fp16)[name = tensor("out_19_cast_fp16")]; + tensor var_998_to_fp16 = const()[name = tensor("op_998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9473792)))]; + tensor var_999_cast_fp16 = add(x = out_19_cast_fp16, y = var_998_to_fp16)[name = tensor("op_999_cast_fp16")]; + tensor var_1001_to_fp16 = const()[name = tensor("op_1001_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9475136)))]; + tensor hidden_states_63_cast_fp16 = mul(x = var_999_cast_fp16, y = var_1001_to_fp16)[name = tensor("hidden_states_63_cast_fp16")]; + tensor var_1008 = const()[name = tensor("op_1008"), val = tensor([1, 1])]; + tensor var_1010 = const()[name = tensor("op_1010"), val = tensor([1, 1])]; + tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; + tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9476480))), lut = tensor([-0x1.fb4p-4, -0x1.3p-5, 0x1.2a8p-5, 0x1.f7cp-4]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_13_cast_fp16 = conv(dilations = var_1010, groups = var_649, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1008, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_63_cast_fp16)[name = tensor("q_13_cast_fp16")]; + tensor var_1014 = const()[name = tensor("op_1014"), val = tensor([1, 1])]; + tensor var_1016 = const()[name = tensor("op_1016"), val = tensor([1, 1])]; + tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; + tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9578944))), lut = tensor([-0x1.f58p-4, -0x1.26cp-5, 0x1.32p-5, 0x1.f9cp-4]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_13_cast_fp16 = conv(dilations = var_1016, groups = var_649, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1014, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_63_cast_fp16)[name = tensor("k_13_cast_fp16")]; + tensor var_1020 = const()[name = tensor("op_1020"), val = tensor([1, 1])]; + tensor var_1022 = const()[name = tensor("op_1022"), val = tensor([1, 1])]; + tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; + tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9681408))), lut = tensor([-0x1.5p-4, -0x1.8a4p-6, 0x1.8ep-6, 0x1.51p-4]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_13_cast_fp16 = conv(dilations = var_1022, groups = var_649, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1020, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_63_cast_fp16)[name = tensor("v_13_cast_fp16")]; + tensor var_1026 = const()[name = tensor("op_1026"), val = tensor([2, 10, 64, -1])]; + tensor var_1027_cast_fp16 = reshape(shape = var_1026, x = q_13_cast_fp16)[name = tensor("op_1027_cast_fp16")]; + tensor var_1028 = const()[name = tensor("op_1028"), val = tensor([2, 10, 64, -1])]; + tensor var_1029_cast_fp16 = reshape(shape = var_1028, x = k_13_cast_fp16)[name = tensor("op_1029_cast_fp16")]; + tensor var_1030 = const()[name = tensor("op_1030"), val = tensor([2, 10, 64, -1])]; + tensor var_1031_cast_fp16 = reshape(shape = var_1030, x = v_13_cast_fp16)[name = tensor("op_1031_cast_fp16")]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1027_cast_fp16, y = var_1029_cast_fp16)[name = tensor("attn_weights_25_cast_fp16")]; + tensor attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_27_cast_fp16")]; + tensor var_1035_cast_fp16 = softmax(axis = var_633, x = attn_weights_27_cast_fp16)[name = tensor("op_1035_cast_fp16")]; + tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; + tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1031_cast_fp16, y = var_1035_cast_fp16)[name = tensor("attn_13_cast_fp16")]; + tensor var_1039 = const()[name = tensor("op_1039"), val = tensor([2, 640, 1, -1])]; + tensor input_105_cast_fp16 = reshape(shape = var_1039, x = attn_13_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor var_1044 = const()[name = tensor("op_1044"), val = tensor([1, 1])]; + tensor var_1046 = const()[name = tensor("op_1046"), val = tensor([1, 1])]; + tensor var_1048_pad_type_0 = const()[name = tensor("op_1048_pad_type_0"), val = tensor("custom")]; + tensor var_1048_pad_0 = const()[name = tensor("op_1048_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9783872))), lut = tensor([-0x1.564p-4, -0x1.998p-6, 0x1.914p-6, 0x1.544p-4]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9886336)))]; + tensor var_1048_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1046, groups = var_649, pad = var_1048_pad_0, pad_type = var_1048_pad_type_0, strides = var_1044, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_105_cast_fp16)[name = tensor("op_1048_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = var_1048_cast_fp16, y = inputs_19_cast_fp16)[name = tensor("inputs_21_cast_fp16")]; + tensor var_1052 = const()[name = tensor("op_1052"), val = tensor([1])]; + tensor channels_mean_21_cast_fp16 = reduce_mean(axes = var_1052, keep_dims = var_644, x = inputs_21_cast_fp16)[name = tensor("channels_mean_21_cast_fp16")]; + tensor zero_mean_21_cast_fp16 = sub(x = inputs_21_cast_fp16, y = channels_mean_21_cast_fp16)[name = tensor("zero_mean_21_cast_fp16")]; + tensor zero_mean_sq_21_cast_fp16 = mul(x = zero_mean_21_cast_fp16, y = zero_mean_21_cast_fp16)[name = tensor("zero_mean_sq_21_cast_fp16")]; + tensor var_1056 = const()[name = tensor("op_1056"), val = tensor([1])]; + tensor var_1057_cast_fp16 = reduce_mean(axes = var_1056, keep_dims = var_644, x = zero_mean_sq_21_cast_fp16)[name = tensor("op_1057_cast_fp16")]; + tensor var_1058_to_fp16 = const()[name = tensor("op_1058_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1059_cast_fp16 = add(x = var_1057_cast_fp16, y = var_1058_to_fp16)[name = tensor("op_1059_cast_fp16")]; + tensor denom_21_epsilon_0_to_fp16 = const()[name = tensor("denom_21_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_21_cast_fp16 = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1059_cast_fp16)[name = tensor("denom_21_cast_fp16")]; + tensor out_21_cast_fp16 = mul(x = zero_mean_21_cast_fp16, y = denom_21_cast_fp16)[name = tensor("out_21_cast_fp16")]; + tensor var_1063_to_fp16 = const()[name = tensor("op_1063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9887680)))]; + tensor var_1064_cast_fp16 = add(x = out_21_cast_fp16, y = var_1063_to_fp16)[name = tensor("op_1064_cast_fp16")]; + tensor var_1066_to_fp16 = const()[name = tensor("op_1066_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9889024)))]; + tensor hidden_states_65_cast_fp16 = mul(x = var_1064_cast_fp16, y = var_1066_to_fp16)[name = tensor("hidden_states_65_cast_fp16")]; + tensor var_1073 = const()[name = tensor("op_1073"), val = tensor([1, 1])]; + tensor var_1075 = const()[name = tensor("op_1075"), val = tensor([1, 1])]; + tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; + tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9890368))), lut = tensor([-0x1.728p-4, -0x1.bb8p-6, 0x1.bb4p-6, 0x1.72cp-4]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_15_cast_fp16 = conv(dilations = var_1075, groups = var_649, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1073, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_65_cast_fp16)[name = tensor("q_15_cast_fp16")]; + tensor var_1079 = const()[name = tensor("op_1079"), val = tensor([1, 1])]; + tensor var_1081 = const()[name = tensor("op_1081"), val = tensor([1, 1])]; + tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; + tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9992832))), lut = tensor([-0x1.5p-4, -0x1.8d8p-6, 0x1.8e8p-6, 0x1.51p-4]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor k_15_cast_fp16 = conv(dilations = var_1081, groups = var_649, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1079, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_15_cast_fp16")]; + tensor var_1085 = const()[name = tensor("op_1085"), val = tensor([1, 1])]; + tensor var_1087 = const()[name = tensor("op_1087"), val = tensor([1, 1])]; + tensor v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; + tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10156736))), lut = tensor([-0x1.41cp-5, -0x1.6fp-7, 0x1.6bcp-7, 0x1.418p-5]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor v_15_cast_fp16 = conv(dilations = var_1087, groups = var_649, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1085, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_15_cast_fp16")]; + tensor var_1091 = const()[name = tensor("op_1091"), val = tensor([2, 10, 64, -1])]; + tensor var_1092_cast_fp16 = reshape(shape = var_1091, x = q_15_cast_fp16)[name = tensor("op_1092_cast_fp16")]; + tensor var_1093 = const()[name = tensor("op_1093"), val = tensor([2, 10, 64, -1])]; + tensor var_1094_cast_fp16 = reshape(shape = var_1093, x = k_15_cast_fp16)[name = tensor("op_1094_cast_fp16")]; + tensor var_1095 = const()[name = tensor("op_1095"), val = tensor([2, 10, 64, -1])]; + tensor var_1096_cast_fp16 = reshape(shape = var_1095, x = v_15_cast_fp16)[name = tensor("op_1096_cast_fp16")]; + tensor attn_weights_29_transpose_x_0 = const()[name = tensor("attn_weights_29_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_29_transpose_y_0 = const()[name = tensor("attn_weights_29_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_29_cast_fp16 = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1092_cast_fp16, y = var_1094_cast_fp16)[name = tensor("attn_weights_29_cast_fp16")]; + tensor attn_weights_31_cast_fp16 = mul(x = attn_weights_29_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_31_cast_fp16")]; + tensor var_1100_cast_fp16 = softmax(axis = var_633, x = attn_weights_31_cast_fp16)[name = tensor("op_1100_cast_fp16")]; + tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; + tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; + tensor attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1096_cast_fp16, y = var_1100_cast_fp16)[name = tensor("attn_15_cast_fp16")]; + tensor var_1104 = const()[name = tensor("op_1104"), val = tensor([2, 640, 1, -1])]; + tensor input_107_cast_fp16 = reshape(shape = var_1104, x = attn_15_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor var_1109 = const()[name = tensor("op_1109"), val = tensor([1, 1])]; + tensor var_1111 = const()[name = tensor("op_1111"), val = tensor([1, 1])]; + tensor var_1113_pad_type_0 = const()[name = tensor("op_1113_pad_type_0"), val = tensor("custom")]; + tensor var_1113_pad_0 = const()[name = tensor("op_1113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10320640))), lut = tensor([-0x1.6fp-6, -0x1.4d4p-8, 0x1.54p-8, 0x1.738p-6]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10423104)))]; + tensor var_1113_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1111, groups = var_649, pad = var_1113_pad_0, pad_type = var_1113_pad_type_0, strides = var_1109, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = tensor("op_1113_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = var_1113_cast_fp16, y = inputs_21_cast_fp16)[name = tensor("inputs_23_cast_fp16")]; + tensor var_1117 = const()[name = tensor("op_1117"), val = tensor([1])]; + tensor channels_mean_23_cast_fp16 = reduce_mean(axes = var_1117, keep_dims = var_644, x = inputs_23_cast_fp16)[name = tensor("channels_mean_23_cast_fp16")]; + tensor zero_mean_23_cast_fp16 = sub(x = inputs_23_cast_fp16, y = channels_mean_23_cast_fp16)[name = tensor("zero_mean_23_cast_fp16")]; + tensor zero_mean_sq_23_cast_fp16 = mul(x = zero_mean_23_cast_fp16, y = zero_mean_23_cast_fp16)[name = tensor("zero_mean_sq_23_cast_fp16")]; + tensor var_1121 = const()[name = tensor("op_1121"), val = tensor([1])]; + tensor var_1122_cast_fp16 = reduce_mean(axes = var_1121, keep_dims = var_644, x = zero_mean_sq_23_cast_fp16)[name = tensor("op_1122_cast_fp16")]; + tensor var_1123_to_fp16 = const()[name = tensor("op_1123_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = tensor("op_1124_cast_fp16")]; + tensor denom_23_epsilon_0_to_fp16 = const()[name = tensor("denom_23_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_23_cast_fp16 = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1124_cast_fp16)[name = tensor("denom_23_cast_fp16")]; + tensor out_23_cast_fp16 = mul(x = zero_mean_23_cast_fp16, y = denom_23_cast_fp16)[name = tensor("out_23_cast_fp16")]; + tensor var_1128_to_fp16 = const()[name = tensor("op_1128_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10424448)))]; + tensor var_1129_cast_fp16 = add(x = out_23_cast_fp16, y = var_1128_to_fp16)[name = tensor("op_1129_cast_fp16")]; + tensor var_1131_to_fp16 = const()[name = tensor("op_1131_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10425792)))]; + tensor input_109_cast_fp16 = mul(x = var_1129_cast_fp16, y = var_1131_to_fp16)[name = tensor("input_109_cast_fp16")]; + tensor var_1139 = const()[name = tensor("op_1139"), val = tensor([1, 1])]; + tensor var_1141 = const()[name = tensor("op_1141"), val = tensor([1, 1])]; + tensor var_1143_pad_type_0 = const()[name = tensor("op_1143_pad_type_0"), val = tensor("custom")]; + tensor var_1143_pad_0 = const()[name = tensor("op_1143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10427136))), lut = tensor([-0x1.89p-4, -0x1.ce8p-6, 0x1.d24p-6, 0x1.8ap-4]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11246400))), lut = tensor([-0x1.434p-6, 0x1.4dp-5, -0x1.188p-2, -0x1.a8cp-4]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_1143_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1141, groups = var_649, pad = var_1143_pad_0, pad_type = var_1143_pad_type_0, strides = var_1139, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_109_cast_fp16)[name = tensor("op_1143_cast_fp16")]; + tensor var_1144_split_sizes_0 = const()[name = tensor("op_1144_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_1144_axis_0 = const()[name = tensor("op_1144_axis_0"), val = tensor(1)]; + tensor var_1144_cast_fp16_0, tensor var_1144_cast_fp16_1 = split(axis = var_1144_axis_0, split_sizes = var_1144_split_sizes_0, x = var_1143_cast_fp16)[name = tensor("op_1144_cast_fp16")]; + tensor var_1146_mode_0 = const()[name = tensor("op_1146_mode_0"), val = tensor("EXACT")]; + tensor var_1146_cast_fp16 = gelu(mode = var_1146_mode_0, x = var_1144_cast_fp16_1)[name = tensor("op_1146_cast_fp16")]; + tensor input_111_cast_fp16 = mul(x = var_1144_cast_fp16_0, y = var_1146_cast_fp16)[name = tensor("input_111_cast_fp16")]; + tensor var_1150 = const()[name = tensor("op_1150"), val = tensor([1, 1])]; + tensor var_1152 = const()[name = tensor("op_1152"), val = tensor([1, 1])]; + tensor var_1154_pad_type_0 = const()[name = tensor("op_1154_pad_type_0"), val = tensor("custom")]; + tensor var_1154_pad_0 = const()[name = tensor("op_1154_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11247744))), lut = tensor([-0x1.98p-4, -0x1.e6cp-6, 0x1.e8p-6, 0x1.99p-4]), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11657408)))]; + tensor var_1154_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1152, groups = var_649, pad = var_1154_pad_0, pad_type = var_1154_pad_type_0, strides = var_1150, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = tensor("op_1154_cast_fp16")]; + tensor hidden_states_69_cast_fp16 = add(x = var_1154_cast_fp16, y = inputs_23_cast_fp16)[name = tensor("hidden_states_69_cast_fp16")]; + tensor var_1156 = const()[name = tensor("op_1156"), val = tensor([2, 640, 32, 32])]; + tensor input_113_cast_fp16 = reshape(shape = var_1156, x = hidden_states_69_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([1, 1])]; + tensor var_1162 = const()[name = tensor("op_1162"), val = tensor([1, 1])]; + tensor hidden_states_71_pad_type_0 = const()[name = tensor("hidden_states_71_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_71_pad_0 = const()[name = tensor("hidden_states_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11658752))), lut = tensor([-0x1.7b4p-4, -0x1.c8cp-6, 0x1.c3cp-6, 0x1.798p-4]), name = tensor("down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11761216)))]; + tensor hidden_states_71_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1162, groups = var_649, pad = hidden_states_71_pad_0, pad_type = hidden_states_71_pad_type_0, strides = var_1160, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = tensor("hidden_states_71_cast_fp16")]; + tensor input_115_cast_fp16 = add(x = hidden_states_71_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor var_1169 = const()[name = tensor("op_1169"), val = tensor([2, 2])]; + tensor var_1171 = const()[name = tensor("op_1171"), val = tensor([1, 1])]; + tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("custom")]; + tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11762560))), lut = tensor([-0x1.47cp-5, -0x1.5bcp-7, 0x1.7ccp-7, 0x1.574p-5]), name = tensor("down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12684224)))]; + tensor input_117_cast_fp16 = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1171, groups = var_649, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_1169, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized, x = input_115_cast_fp16)[name = tensor("input_117_cast_fp16")]; + tensor var_1179 = const()[name = tensor("op_1179"), val = tensor(3)]; + tensor var_1190 = const()[name = tensor("op_1190"), val = tensor(true)]; + tensor var_1195 = const()[name = tensor("op_1195"), val = tensor(1)]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([2, 32, 20, 16, 16])]; + tensor reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_117_cast_fp16)[name = tensor("reshape_48_cast_fp16")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor("reduce_mean_36_cast_fp16")]; + tensor sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor("sub_24_cast_fp16")]; + tensor square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor("square_12_cast_fp16")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor("reduce_mean_38_cast_fp16")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast_fp16")]; + tensor sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor("sqrt_12_cast_fp16")]; + tensor real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor("real_div_12_cast_fp16")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([2, 640, 16, 16])]; + tensor reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor("reshape_49_cast_fp16")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12685568)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12686912)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor("add_25_cast_fp16")]; + tensor input_121_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor("input_121_cast_fp16")]; + tensor var_1218 = const()[name = tensor("op_1218"), val = tensor([1, 1])]; + tensor var_1220 = const()[name = tensor("op_1220"), val = tensor([1, 1])]; + tensor hidden_states_73_pad_type_0 = const()[name = tensor("hidden_states_73_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_73_pad_0 = const()[name = tensor("hidden_states_73_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12688256))), lut = tensor([-0x1.61cp-4, -0x1.8b4p-6, 0x1.6acp-6, 0x1.59cp-4]), name = tensor("down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 640, 3, 3])]; + tensor down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14531520)))]; + tensor hidden_states_73_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1220, groups = var_1195, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = var_1218, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_121_cast_fp16)[name = tensor("hidden_states_73_cast_fp16")]; + tensor var_1226 = const()[name = tensor("op_1226"), val = tensor([1, 1])]; + tensor var_1228 = const()[name = tensor("op_1228"), val = tensor([1, 1])]; + tensor temb_9_pad_type_0 = const()[name = tensor("temb_9_pad_type_0"), val = tensor("custom")]; + tensor temb_9_pad_0 = const()[name = tensor("temb_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14534144))), lut = tensor([-0x1.23p+2, -0x1.0fp-4, -0x1.d04p-9, 0x1.3f8p-8]), name = tensor("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14943808)))]; + tensor temb_9_cast_fp16 = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1228, groups = var_1195, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1226, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_9_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = hidden_states_73_cast_fp16, y = temb_9_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = input_125_cast_fp16)[name = tensor("reshape_52_cast_fp16")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor("reduce_mean_39_cast_fp16")]; + tensor sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor("sub_26_cast_fp16")]; + tensor square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor("square_13_cast_fp16")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor("reduce_mean_41_cast_fp16")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast_fp16")]; + tensor sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor("sqrt_13_cast_fp16")]; + tensor real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor("real_div_13_cast_fp16")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor("reshape_53_cast_fp16")]; + tensor add_27_mean_0_to_fp16 = const()[name = tensor("add_27_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14946432)))]; + tensor add_27_variance_0_to_fp16 = const()[name = tensor("add_27_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14949056)))]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14951680)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14954304)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor("add_27_cast_fp16")]; + tensor input_129_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor("input_129_cast_fp16")]; + tensor var_1238 = const()[name = tensor("op_1238"), val = tensor([1, 1])]; + tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, 1])]; + tensor hidden_states_75_pad_type_0 = const()[name = tensor("hidden_states_75_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_75_pad_0 = const()[name = tensor("hidden_states_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14956928))), lut = tensor([-0x1.1fp-4, -0x1.45cp-6, 0x1.454p-6, 0x1.1f4p-4]), name = tensor("down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18643392)))]; + tensor hidden_states_75_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1240, groups = var_1195, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_1238, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_129_cast_fp16)[name = tensor("hidden_states_75_cast_fp16")]; + tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, 1])]; + tensor var_1247 = const()[name = tensor("op_1247"), val = tensor([1, 1])]; + tensor x_3_pad_type_0 = const()[name = tensor("x_3_pad_type_0"), val = tensor("custom")]; + tensor x_3_pad_0 = const()[name = tensor("x_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18646016))), lut = tensor([-0x1.edp-6, -0x1.148p-7, 0x1.314p-7, 0x1.fccp-6]), name = tensor("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 640, 1, 1])]; + tensor down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18850880)))]; + tensor x_3_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1247, groups = var_1195, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1245, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_117_cast_fp16)[name = tensor("x_3_cast_fp16")]; + tensor hidden_states_77_cast_fp16 = add(x = x_3_cast_fp16, y = hidden_states_75_cast_fp16)[name = tensor("hidden_states_77_cast_fp16")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = hidden_states_77_cast_fp16)[name = tensor("reshape_56_cast_fp16")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor("reduce_mean_42_cast_fp16")]; + tensor sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor("sub_28_cast_fp16")]; + tensor square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor("square_14_cast_fp16")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor("reduce_mean_44_cast_fp16")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast_fp16")]; + tensor sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor("sqrt_14_cast_fp16")]; + tensor real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor("real_div_14_cast_fp16")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor("reshape_57_cast_fp16")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18853504)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18856128)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor("add_29_cast_fp16")]; + tensor var_1267 = const()[name = tensor("op_1267"), val = tensor([1, 1])]; + tensor var_1269 = const()[name = tensor("op_1269"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_type_0 = const()[name = tensor("hidden_states_79_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_79_pad_0 = const()[name = tensor("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18858752))), lut = tensor([-0x1.8bcp-4, -0x1.dap-6, 0x1.ddcp-6, 0x1.8d8p-4]), name = tensor("down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19268416)))]; + tensor hidden_states_79_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1269, groups = var_1195, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = var_1267, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_29_cast_fp16)[name = tensor("hidden_states_79_cast_fp16")]; + tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_25_cast_fp16 = reshape(shape = var_1274, x = hidden_states_79_cast_fp16)[name = tensor("inputs_25_cast_fp16")]; + tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([1])]; + tensor channels_mean_25_cast_fp16 = reduce_mean(axes = var_1284, keep_dims = var_1190, x = inputs_25_cast_fp16)[name = tensor("channels_mean_25_cast_fp16")]; + tensor zero_mean_25_cast_fp16 = sub(x = inputs_25_cast_fp16, y = channels_mean_25_cast_fp16)[name = tensor("zero_mean_25_cast_fp16")]; + tensor zero_mean_sq_25_cast_fp16 = mul(x = zero_mean_25_cast_fp16, y = zero_mean_25_cast_fp16)[name = tensor("zero_mean_sq_25_cast_fp16")]; + tensor var_1288 = const()[name = tensor("op_1288"), val = tensor([1])]; + tensor var_1289_cast_fp16 = reduce_mean(axes = var_1288, keep_dims = var_1190, x = zero_mean_sq_25_cast_fp16)[name = tensor("op_1289_cast_fp16")]; + tensor var_1290_to_fp16 = const()[name = tensor("op_1290_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1291_cast_fp16 = add(x = var_1289_cast_fp16, y = var_1290_to_fp16)[name = tensor("op_1291_cast_fp16")]; + tensor denom_25_epsilon_0_to_fp16 = const()[name = tensor("denom_25_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_25_cast_fp16 = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1291_cast_fp16)[name = tensor("denom_25_cast_fp16")]; + tensor out_25_cast_fp16 = mul(x = zero_mean_25_cast_fp16, y = denom_25_cast_fp16)[name = tensor("out_25_cast_fp16")]; + tensor var_1295_to_fp16 = const()[name = tensor("op_1295_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19271040)))]; + tensor var_1296_cast_fp16 = add(x = out_25_cast_fp16, y = var_1295_to_fp16)[name = tensor("op_1296_cast_fp16")]; + tensor var_1298_to_fp16 = const()[name = tensor("op_1298_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19273664)))]; + tensor hidden_states_81_cast_fp16 = mul(x = var_1296_cast_fp16, y = var_1298_to_fp16)[name = tensor("hidden_states_81_cast_fp16")]; + tensor var_1305 = const()[name = tensor("op_1305"), val = tensor([1, 1])]; + tensor var_1307 = const()[name = tensor("op_1307"), val = tensor([1, 1])]; + tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; + tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19276288))), lut = tensor([-0x1.90cp-4, -0x1.dap-6, 0x1.dap-6, 0x1.904p-4]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_17_cast_fp16 = conv(dilations = var_1307, groups = var_1195, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1305, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_81_cast_fp16)[name = tensor("q_17_cast_fp16")]; + tensor var_1311 = const()[name = tensor("op_1311"), val = tensor([1, 1])]; + tensor var_1313 = const()[name = tensor("op_1313"), val = tensor([1, 1])]; + tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; + tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19685952))), lut = tensor([-0x1.90cp-4, -0x1.d98p-6, 0x1.e08p-6, 0x1.92cp-4]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_17_cast_fp16 = conv(dilations = var_1313, groups = var_1195, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1311, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_81_cast_fp16)[name = tensor("k_17_cast_fp16")]; + tensor var_1317 = const()[name = tensor("op_1317"), val = tensor([1, 1])]; + tensor var_1319 = const()[name = tensor("op_1319"), val = tensor([1, 1])]; + tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; + tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20095616))), lut = tensor([-0x1.308p-4, -0x1.6acp-6, 0x1.698p-6, 0x1.2f4p-4]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_17_cast_fp16 = conv(dilations = var_1319, groups = var_1195, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1317, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_81_cast_fp16)[name = tensor("v_17_cast_fp16")]; + tensor var_1323 = const()[name = tensor("op_1323"), val = tensor([2, 20, 64, -1])]; + tensor var_1324_cast_fp16 = reshape(shape = var_1323, x = q_17_cast_fp16)[name = tensor("op_1324_cast_fp16")]; + tensor var_1325 = const()[name = tensor("op_1325"), val = tensor([2, 20, 64, -1])]; + tensor var_1326_cast_fp16 = reshape(shape = var_1325, x = k_17_cast_fp16)[name = tensor("op_1326_cast_fp16")]; + tensor var_1327 = const()[name = tensor("op_1327"), val = tensor([2, 20, 64, -1])]; + tensor var_1328_cast_fp16 = reshape(shape = var_1327, x = v_17_cast_fp16)[name = tensor("op_1328_cast_fp16")]; + tensor attn_weights_33_transpose_x_0 = const()[name = tensor("attn_weights_33_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_33_transpose_y_0 = const()[name = tensor("attn_weights_33_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1324_cast_fp16, y = var_1326_cast_fp16)[name = tensor("attn_weights_33_cast_fp16")]; + tensor var_1186_to_fp16 = const()[name = tensor("op_1186_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_35_cast_fp16")]; + tensor var_1332_cast_fp16 = softmax(axis = var_1179, x = attn_weights_35_cast_fp16)[name = tensor("op_1332_cast_fp16")]; + tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; + tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; + tensor attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1328_cast_fp16, y = var_1332_cast_fp16)[name = tensor("attn_17_cast_fp16")]; + tensor var_1336 = const()[name = tensor("op_1336"), val = tensor([2, 1280, 1, -1])]; + tensor input_133_cast_fp16 = reshape(shape = var_1336, x = attn_17_cast_fp16)[name = tensor("input_133_cast_fp16")]; + tensor var_1341 = const()[name = tensor("op_1341"), val = tensor([1, 1])]; + tensor var_1343 = const()[name = tensor("op_1343"), val = tensor([1, 1])]; + tensor var_1345_pad_type_0 = const()[name = tensor("op_1345_pad_type_0"), val = tensor("custom")]; + tensor var_1345_pad_0 = const()[name = tensor("op_1345_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20505280))), lut = tensor([-0x1.4ecp-4, -0x1.8ecp-6, 0x1.954p-6, 0x1.514p-4]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20914944)))]; + tensor var_1345_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1343, groups = var_1195, pad = var_1345_pad_0, pad_type = var_1345_pad_type_0, strides = var_1341, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = tensor("op_1345_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = var_1345_cast_fp16, y = inputs_25_cast_fp16)[name = tensor("inputs_27_cast_fp16")]; + tensor var_1349 = const()[name = tensor("op_1349"), val = tensor([1])]; + tensor channels_mean_27_cast_fp16 = reduce_mean(axes = var_1349, keep_dims = var_1190, x = inputs_27_cast_fp16)[name = tensor("channels_mean_27_cast_fp16")]; + tensor zero_mean_27_cast_fp16 = sub(x = inputs_27_cast_fp16, y = channels_mean_27_cast_fp16)[name = tensor("zero_mean_27_cast_fp16")]; + tensor zero_mean_sq_27_cast_fp16 = mul(x = zero_mean_27_cast_fp16, y = zero_mean_27_cast_fp16)[name = tensor("zero_mean_sq_27_cast_fp16")]; + tensor var_1353 = const()[name = tensor("op_1353"), val = tensor([1])]; + tensor var_1354_cast_fp16 = reduce_mean(axes = var_1353, keep_dims = var_1190, x = zero_mean_sq_27_cast_fp16)[name = tensor("op_1354_cast_fp16")]; + tensor var_1355_to_fp16 = const()[name = tensor("op_1355_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1356_cast_fp16 = add(x = var_1354_cast_fp16, y = var_1355_to_fp16)[name = tensor("op_1356_cast_fp16")]; + tensor denom_27_epsilon_0_to_fp16 = const()[name = tensor("denom_27_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_27_cast_fp16 = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1356_cast_fp16)[name = tensor("denom_27_cast_fp16")]; + tensor out_27_cast_fp16 = mul(x = zero_mean_27_cast_fp16, y = denom_27_cast_fp16)[name = tensor("out_27_cast_fp16")]; + tensor var_1360_to_fp16 = const()[name = tensor("op_1360_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20917568)))]; + tensor var_1361_cast_fp16 = add(x = out_27_cast_fp16, y = var_1360_to_fp16)[name = tensor("op_1361_cast_fp16")]; + tensor var_1363_to_fp16 = const()[name = tensor("op_1363_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20920192)))]; + tensor hidden_states_83_cast_fp16 = mul(x = var_1361_cast_fp16, y = var_1363_to_fp16)[name = tensor("hidden_states_83_cast_fp16")]; + tensor var_1370 = const()[name = tensor("op_1370"), val = tensor([1, 1])]; + tensor var_1372 = const()[name = tensor("op_1372"), val = tensor([1, 1])]; + tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; + tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20922816))), lut = tensor([-0x1.348p-4, -0x1.6dp-6, 0x1.6fp-6, 0x1.34cp-4]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_19_cast_fp16 = conv(dilations = var_1372, groups = var_1195, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1370, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_83_cast_fp16)[name = tensor("q_19_cast_fp16")]; + tensor var_1376 = const()[name = tensor("op_1376"), val = tensor([1, 1])]; + tensor var_1378 = const()[name = tensor("op_1378"), val = tensor([1, 1])]; + tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; + tensor k_19_pad_0 = const()[name = tensor("k_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21332480))), lut = tensor([-0x1.43p-4, -0x1.7dcp-6, 0x1.794p-6, 0x1.424p-4]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor k_19_cast_fp16 = conv(dilations = var_1378, groups = var_1195, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1376, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_19_cast_fp16")]; + tensor var_1382 = const()[name = tensor("op_1382"), val = tensor([1, 1])]; + tensor var_1384 = const()[name = tensor("op_1384"), val = tensor([1, 1])]; + tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; + tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21660224))), lut = tensor([-0x1.a18p-5, -0x1.eccp-7, 0x1.ef4p-7, 0x1.a3p-5]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor v_19_cast_fp16 = conv(dilations = var_1384, groups = var_1195, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1382, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_19_cast_fp16")]; + tensor var_1388 = const()[name = tensor("op_1388"), val = tensor([2, 20, 64, -1])]; + tensor var_1389_cast_fp16 = reshape(shape = var_1388, x = q_19_cast_fp16)[name = tensor("op_1389_cast_fp16")]; + tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([2, 20, 64, -1])]; + tensor var_1391_cast_fp16 = reshape(shape = var_1390, x = k_19_cast_fp16)[name = tensor("op_1391_cast_fp16")]; + tensor var_1392 = const()[name = tensor("op_1392"), val = tensor([2, 20, 64, -1])]; + tensor var_1393_cast_fp16 = reshape(shape = var_1392, x = v_19_cast_fp16)[name = tensor("op_1393_cast_fp16")]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_37_cast_fp16 = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1389_cast_fp16, y = var_1391_cast_fp16)[name = tensor("attn_weights_37_cast_fp16")]; + tensor attn_weights_39_cast_fp16 = mul(x = attn_weights_37_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_39_cast_fp16")]; + tensor var_1397_cast_fp16 = softmax(axis = var_1179, x = attn_weights_39_cast_fp16)[name = tensor("op_1397_cast_fp16")]; + tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; + tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; + tensor attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1393_cast_fp16, y = var_1397_cast_fp16)[name = tensor("attn_19_cast_fp16")]; + tensor var_1401 = const()[name = tensor("op_1401"), val = tensor([2, 1280, 1, -1])]; + tensor input_135_cast_fp16 = reshape(shape = var_1401, x = attn_19_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor var_1406 = const()[name = tensor("op_1406"), val = tensor([1, 1])]; + tensor var_1408 = const()[name = tensor("op_1408"), val = tensor([1, 1])]; + tensor var_1410_pad_type_0 = const()[name = tensor("op_1410_pad_type_0"), val = tensor("custom")]; + tensor var_1410_pad_0 = const()[name = tensor("op_1410_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21987968))), lut = tensor([-0x1.57p-5, -0x1.9ap-7, 0x1.95cp-7, 0x1.568p-5]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22397632)))]; + tensor var_1410_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1408, groups = var_1195, pad = var_1410_pad_0, pad_type = var_1410_pad_type_0, strides = var_1406, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = tensor("op_1410_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = var_1410_cast_fp16, y = inputs_27_cast_fp16)[name = tensor("inputs_29_cast_fp16")]; + tensor var_1414 = const()[name = tensor("op_1414"), val = tensor([1])]; + tensor channels_mean_29_cast_fp16 = reduce_mean(axes = var_1414, keep_dims = var_1190, x = inputs_29_cast_fp16)[name = tensor("channels_mean_29_cast_fp16")]; + tensor zero_mean_29_cast_fp16 = sub(x = inputs_29_cast_fp16, y = channels_mean_29_cast_fp16)[name = tensor("zero_mean_29_cast_fp16")]; + tensor zero_mean_sq_29_cast_fp16 = mul(x = zero_mean_29_cast_fp16, y = zero_mean_29_cast_fp16)[name = tensor("zero_mean_sq_29_cast_fp16")]; + tensor var_1418 = const()[name = tensor("op_1418"), val = tensor([1])]; + tensor var_1419_cast_fp16 = reduce_mean(axes = var_1418, keep_dims = var_1190, x = zero_mean_sq_29_cast_fp16)[name = tensor("op_1419_cast_fp16")]; + tensor var_1420_to_fp16 = const()[name = tensor("op_1420_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1421_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_to_fp16)[name = tensor("op_1421_cast_fp16")]; + tensor denom_29_epsilon_0_to_fp16 = const()[name = tensor("denom_29_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_29_cast_fp16 = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1421_cast_fp16)[name = tensor("denom_29_cast_fp16")]; + tensor out_29_cast_fp16 = mul(x = zero_mean_29_cast_fp16, y = denom_29_cast_fp16)[name = tensor("out_29_cast_fp16")]; + tensor var_1425_to_fp16 = const()[name = tensor("op_1425_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22400256)))]; + tensor var_1426_cast_fp16 = add(x = out_29_cast_fp16, y = var_1425_to_fp16)[name = tensor("op_1426_cast_fp16")]; + tensor var_1428_to_fp16 = const()[name = tensor("op_1428_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22402880)))]; + tensor input_137_cast_fp16 = mul(x = var_1426_cast_fp16, y = var_1428_to_fp16)[name = tensor("input_137_cast_fp16")]; + tensor var_1436 = const()[name = tensor("op_1436"), val = tensor([1, 1])]; + tensor var_1438 = const()[name = tensor("op_1438"), val = tensor([1, 1])]; + tensor var_1440_pad_type_0 = const()[name = tensor("op_1440_pad_type_0"), val = tensor("custom")]; + tensor var_1440_pad_0 = const()[name = tensor("op_1440_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22405504))), lut = tensor([-0x1.6acp-4, -0x1.abp-6, 0x1.a88p-6, 0x1.6a8p-4]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25682368))), lut = tensor([-0x1.7ecp-3, -0x1.6a8p-4, -0x1.64p-6, 0x1.2a8p-6]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1440_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1438, groups = var_1195, pad = var_1440_pad_0, pad_type = var_1440_pad_type_0, strides = var_1436, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_137_cast_fp16)[name = tensor("op_1440_cast_fp16")]; + tensor var_1441_split_sizes_0 = const()[name = tensor("op_1441_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1441_axis_0 = const()[name = tensor("op_1441_axis_0"), val = tensor(1)]; + tensor var_1441_cast_fp16_0, tensor var_1441_cast_fp16_1 = split(axis = var_1441_axis_0, split_sizes = var_1441_split_sizes_0, x = var_1440_cast_fp16)[name = tensor("op_1441_cast_fp16")]; + tensor var_1443_mode_0 = const()[name = tensor("op_1443_mode_0"), val = tensor("EXACT")]; + tensor var_1443_cast_fp16 = gelu(mode = var_1443_mode_0, x = var_1441_cast_fp16_1)[name = tensor("op_1443_cast_fp16")]; + tensor input_139_cast_fp16 = mul(x = var_1441_cast_fp16_0, y = var_1443_cast_fp16)[name = tensor("input_139_cast_fp16")]; + tensor var_1447 = const()[name = tensor("op_1447"), val = tensor([1, 1])]; + tensor var_1449 = const()[name = tensor("op_1449"), val = tensor([1, 1])]; + tensor var_1451_pad_type_0 = const()[name = tensor("op_1451_pad_type_0"), val = tensor("custom")]; + tensor var_1451_pad_0 = const()[name = tensor("op_1451_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25684992))), lut = tensor([-0x1.5e4p-4, -0x1.a1cp-6, 0x1.a14p-6, 0x1.5ep-4]), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27323456)))]; + tensor var_1451_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1449, groups = var_1195, pad = var_1451_pad_0, pad_type = var_1451_pad_type_0, strides = var_1447, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_139_cast_fp16)[name = tensor("op_1451_cast_fp16")]; + tensor hidden_states_87_cast_fp16 = add(x = var_1451_cast_fp16, y = inputs_29_cast_fp16)[name = tensor("hidden_states_87_cast_fp16")]; + tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([2, 1280, 16, 16])]; + tensor input_141_cast_fp16 = reshape(shape = var_1453, x = hidden_states_87_cast_fp16)[name = tensor("input_141_cast_fp16")]; + tensor var_1457 = const()[name = tensor("op_1457"), val = tensor([1, 1])]; + tensor var_1459 = const()[name = tensor("op_1459"), val = tensor([1, 1])]; + tensor hidden_states_89_pad_type_0 = const()[name = tensor("hidden_states_89_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_89_pad_0 = const()[name = tensor("hidden_states_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27326080))), lut = tensor([-0x1.088p-4, -0x1.3c8p-6, 0x1.3dp-6, 0x1.084p-4]), name = tensor("down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27735744)))]; + tensor hidden_states_89_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_1459, groups = var_1195, pad = hidden_states_89_pad_0, pad_type = hidden_states_89_pad_type_0, strides = var_1457, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_141_cast_fp16)[name = tensor("hidden_states_89_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = hidden_states_89_cast_fp16, y = hidden_states_77_cast_fp16)[name = tensor("input_143_cast_fp16")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = input_143_cast_fp16)[name = tensor("reshape_60_cast_fp16")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor("reduce_mean_45_cast_fp16")]; + tensor sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor("sub_30_cast_fp16")]; + tensor square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor("square_15_cast_fp16")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor("reduce_mean_47_cast_fp16")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast_fp16")]; + tensor sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor("sqrt_15_cast_fp16")]; + tensor real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor("real_div_15_cast_fp16")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor("reshape_61_cast_fp16")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27738368)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27740992)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor("add_31_cast_fp16")]; + tensor input_147_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor("input_147_cast_fp16")]; + tensor var_1474 = const()[name = tensor("op_1474"), val = tensor([1, 1])]; + tensor var_1476 = const()[name = tensor("op_1476"), val = tensor([1, 1])]; + tensor hidden_states_91_pad_type_0 = const()[name = tensor("hidden_states_91_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_91_pad_0 = const()[name = tensor("hidden_states_91_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27743616))), lut = tensor([-0x1.188p-4, -0x1.4f8p-6, 0x1.334p-6, 0x1.0ecp-4]), name = tensor("down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31430080)))]; + tensor hidden_states_91_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_1476, groups = var_1195, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = var_1474, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = tensor("hidden_states_91_cast_fp16")]; + tensor var_1482 = const()[name = tensor("op_1482"), val = tensor([1, 1])]; + tensor var_1484 = const()[name = tensor("op_1484"), val = tensor([1, 1])]; + tensor temb_11_pad_type_0 = const()[name = tensor("temb_11_pad_type_0"), val = tensor("custom")]; + tensor temb_11_pad_0 = const()[name = tensor("temb_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31432704))), lut = tensor([-0x1.d78p+0, -0x1.018p-8, 0x1.a3p-9, 0x1.658p-5]), name = tensor("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31842368)))]; + tensor temb_11_cast_fp16 = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1484, groups = var_1195, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_1482, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_11_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = hidden_states_91_cast_fp16, y = temb_11_cast_fp16)[name = tensor("input_151_cast_fp16")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_151_cast_fp16)[name = tensor("reshape_64_cast_fp16")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor("reduce_mean_48_cast_fp16")]; + tensor sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor("sub_32_cast_fp16")]; + tensor square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor("square_16_cast_fp16")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor("reduce_mean_50_cast_fp16")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast_fp16")]; + tensor sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor("sqrt_16_cast_fp16")]; + tensor real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor("real_div_16_cast_fp16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor("reshape_65_cast_fp16")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31844992)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31847616)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor("add_33_cast_fp16")]; + tensor input_155_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor("input_155_cast_fp16")]; + tensor var_1494 = const()[name = tensor("op_1494"), val = tensor([1, 1])]; + tensor var_1496 = const()[name = tensor("op_1496"), val = tensor([1, 1])]; + tensor hidden_states_93_pad_type_0 = const()[name = tensor("hidden_states_93_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_93_pad_0 = const()[name = tensor("hidden_states_93_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31850240))), lut = tensor([-0x1.fb8p-5, -0x1.2acp-6, 0x1.26p-6, 0x1.f8p-5]), name = tensor("down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35536704)))]; + tensor hidden_states_93_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_1496, groups = var_1195, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = var_1494, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_155_cast_fp16)[name = tensor("hidden_states_93_cast_fp16")]; + tensor hidden_states_95_cast_fp16 = add(x = input_143_cast_fp16, y = hidden_states_93_cast_fp16)[name = tensor("hidden_states_95_cast_fp16")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = hidden_states_95_cast_fp16)[name = tensor("reshape_68_cast_fp16")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor("reduce_mean_51_cast_fp16")]; + tensor sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor("sub_34_cast_fp16")]; + tensor square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor("square_17_cast_fp16")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor("reduce_mean_53_cast_fp16")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast_fp16")]; + tensor sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor("sqrt_17_cast_fp16")]; + tensor real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor("real_div_17_cast_fp16")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor("reshape_69_cast_fp16")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35539328)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35541952)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor("add_35_cast_fp16")]; + tensor var_1516 = const()[name = tensor("op_1516"), val = tensor([1, 1])]; + tensor var_1518 = const()[name = tensor("op_1518"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_type_0 = const()[name = tensor("hidden_states_97_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_97_pad_0 = const()[name = tensor("hidden_states_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35544576))), lut = tensor([-0x1.944p-4, -0x1.e84p-6, 0x1.d9p-6, 0x1.8ep-4]), name = tensor("down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35954240)))]; + tensor hidden_states_97_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_1518, groups = var_1195, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_1516, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_35_cast_fp16)[name = tensor("hidden_states_97_cast_fp16")]; + tensor var_1523 = const()[name = tensor("op_1523"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_31_cast_fp16 = reshape(shape = var_1523, x = hidden_states_97_cast_fp16)[name = tensor("inputs_31_cast_fp16")]; + tensor var_1533 = const()[name = tensor("op_1533"), val = tensor([1])]; + tensor channels_mean_31_cast_fp16 = reduce_mean(axes = var_1533, keep_dims = var_1190, x = inputs_31_cast_fp16)[name = tensor("channels_mean_31_cast_fp16")]; + tensor zero_mean_31_cast_fp16 = sub(x = inputs_31_cast_fp16, y = channels_mean_31_cast_fp16)[name = tensor("zero_mean_31_cast_fp16")]; + tensor zero_mean_sq_31_cast_fp16 = mul(x = zero_mean_31_cast_fp16, y = zero_mean_31_cast_fp16)[name = tensor("zero_mean_sq_31_cast_fp16")]; + tensor var_1537 = const()[name = tensor("op_1537"), val = tensor([1])]; + tensor var_1538_cast_fp16 = reduce_mean(axes = var_1537, keep_dims = var_1190, x = zero_mean_sq_31_cast_fp16)[name = tensor("op_1538_cast_fp16")]; + tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1540_cast_fp16 = add(x = var_1538_cast_fp16, y = var_1539_to_fp16)[name = tensor("op_1540_cast_fp16")]; + tensor denom_31_epsilon_0_to_fp16 = const()[name = tensor("denom_31_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_31_cast_fp16 = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1540_cast_fp16)[name = tensor("denom_31_cast_fp16")]; + tensor out_31_cast_fp16 = mul(x = zero_mean_31_cast_fp16, y = denom_31_cast_fp16)[name = tensor("out_31_cast_fp16")]; + tensor var_1544_to_fp16 = const()[name = tensor("op_1544_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35956864)))]; + tensor var_1545_cast_fp16 = add(x = out_31_cast_fp16, y = var_1544_to_fp16)[name = tensor("op_1545_cast_fp16")]; + tensor var_1547_to_fp16 = const()[name = tensor("op_1547_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35959488)))]; + tensor hidden_states_99_cast_fp16 = mul(x = var_1545_cast_fp16, y = var_1547_to_fp16)[name = tensor("hidden_states_99_cast_fp16")]; + tensor var_1554 = const()[name = tensor("op_1554"), val = tensor([1, 1])]; + tensor var_1556 = const()[name = tensor("op_1556"), val = tensor([1, 1])]; + tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; + tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35962112))), lut = tensor([-0x1.888p-4, -0x1.d6cp-6, 0x1.cfp-6, 0x1.85cp-4]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_21_cast_fp16 = conv(dilations = var_1556, groups = var_1195, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_99_cast_fp16)[name = tensor("q_21_cast_fp16")]; + tensor var_1560 = const()[name = tensor("op_1560"), val = tensor([1, 1])]; + tensor var_1562 = const()[name = tensor("op_1562"), val = tensor([1, 1])]; + tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; + tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36371776))), lut = tensor([-0x1.89p-4, -0x1.d5cp-6, 0x1.d4p-6, 0x1.888p-4]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_21_cast_fp16 = conv(dilations = var_1562, groups = var_1195, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_99_cast_fp16)[name = tensor("k_21_cast_fp16")]; + tensor var_1566 = const()[name = tensor("op_1566"), val = tensor([1, 1])]; + tensor var_1568 = const()[name = tensor("op_1568"), val = tensor([1, 1])]; + tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; + tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36781440))), lut = tensor([-0x1.344p-4, -0x1.718p-6, 0x1.6dp-6, 0x1.334p-4]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_21_cast_fp16 = conv(dilations = var_1568, groups = var_1195, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_99_cast_fp16)[name = tensor("v_21_cast_fp16")]; + tensor var_1572 = const()[name = tensor("op_1572"), val = tensor([2, 20, 64, -1])]; + tensor var_1573_cast_fp16 = reshape(shape = var_1572, x = q_21_cast_fp16)[name = tensor("op_1573_cast_fp16")]; + tensor var_1574 = const()[name = tensor("op_1574"), val = tensor([2, 20, 64, -1])]; + tensor var_1575_cast_fp16 = reshape(shape = var_1574, x = k_21_cast_fp16)[name = tensor("op_1575_cast_fp16")]; + tensor var_1576 = const()[name = tensor("op_1576"), val = tensor([2, 20, 64, -1])]; + tensor var_1577_cast_fp16 = reshape(shape = var_1576, x = v_21_cast_fp16)[name = tensor("op_1577_cast_fp16")]; + tensor attn_weights_41_transpose_x_0 = const()[name = tensor("attn_weights_41_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_41_transpose_y_0 = const()[name = tensor("attn_weights_41_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1573_cast_fp16, y = var_1575_cast_fp16)[name = tensor("attn_weights_41_cast_fp16")]; + tensor attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_43_cast_fp16")]; + tensor var_1581_cast_fp16 = softmax(axis = var_1179, x = attn_weights_43_cast_fp16)[name = tensor("op_1581_cast_fp16")]; + tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; + tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; + tensor attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1577_cast_fp16, y = var_1581_cast_fp16)[name = tensor("attn_21_cast_fp16")]; + tensor var_1585 = const()[name = tensor("op_1585"), val = tensor([2, 1280, 1, -1])]; + tensor input_159_cast_fp16 = reshape(shape = var_1585, x = attn_21_cast_fp16)[name = tensor("input_159_cast_fp16")]; + tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1, 1])]; + tensor var_1592 = const()[name = tensor("op_1592"), val = tensor([1, 1])]; + tensor var_1594_pad_type_0 = const()[name = tensor("op_1594_pad_type_0"), val = tensor("custom")]; + tensor var_1594_pad_0 = const()[name = tensor("op_1594_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37191104))), lut = tensor([-0x1.4ecp-4, -0x1.928p-6, 0x1.8dp-6, 0x1.4dp-4]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37600768)))]; + tensor var_1594_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1195, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_159_cast_fp16)[name = tensor("op_1594_cast_fp16")]; + tensor inputs_33_cast_fp16 = add(x = var_1594_cast_fp16, y = inputs_31_cast_fp16)[name = tensor("inputs_33_cast_fp16")]; + tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1])]; + tensor channels_mean_33_cast_fp16 = reduce_mean(axes = var_1598, keep_dims = var_1190, x = inputs_33_cast_fp16)[name = tensor("channels_mean_33_cast_fp16")]; + tensor zero_mean_33_cast_fp16 = sub(x = inputs_33_cast_fp16, y = channels_mean_33_cast_fp16)[name = tensor("zero_mean_33_cast_fp16")]; + tensor zero_mean_sq_33_cast_fp16 = mul(x = zero_mean_33_cast_fp16, y = zero_mean_33_cast_fp16)[name = tensor("zero_mean_sq_33_cast_fp16")]; + tensor var_1602 = const()[name = tensor("op_1602"), val = tensor([1])]; + tensor var_1603_cast_fp16 = reduce_mean(axes = var_1602, keep_dims = var_1190, x = zero_mean_sq_33_cast_fp16)[name = tensor("op_1603_cast_fp16")]; + tensor var_1604_to_fp16 = const()[name = tensor("op_1604_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1605_cast_fp16 = add(x = var_1603_cast_fp16, y = var_1604_to_fp16)[name = tensor("op_1605_cast_fp16")]; + tensor denom_33_epsilon_0_to_fp16 = const()[name = tensor("denom_33_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_33_cast_fp16 = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1605_cast_fp16)[name = tensor("denom_33_cast_fp16")]; + tensor out_33_cast_fp16 = mul(x = zero_mean_33_cast_fp16, y = denom_33_cast_fp16)[name = tensor("out_33_cast_fp16")]; + tensor var_1609_to_fp16 = const()[name = tensor("op_1609_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37603392)))]; + tensor var_1610_cast_fp16 = add(x = out_33_cast_fp16, y = var_1609_to_fp16)[name = tensor("op_1610_cast_fp16")]; + tensor var_1612_to_fp16 = const()[name = tensor("op_1612_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37606016)))]; + tensor hidden_states_101_cast_fp16 = mul(x = var_1610_cast_fp16, y = var_1612_to_fp16)[name = tensor("hidden_states_101_cast_fp16")]; + tensor var_1619 = const()[name = tensor("op_1619"), val = tensor([1, 1])]; + tensor var_1621 = const()[name = tensor("op_1621"), val = tensor([1, 1])]; + tensor q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; + tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37608640))), lut = tensor([-0x1.2fp-4, -0x1.6ap-6, 0x1.698p-6, 0x1.2f4p-4]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_23_cast_fp16 = conv(dilations = var_1621, groups = var_1195, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1619, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_101_cast_fp16)[name = tensor("q_23_cast_fp16")]; + tensor var_1625 = const()[name = tensor("op_1625"), val = tensor([1, 1])]; + tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 1])]; + tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; + tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38018304))), lut = tensor([-0x1.3cp-4, -0x1.76p-6, 0x1.75cp-6, 0x1.3cp-4]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor k_23_cast_fp16 = conv(dilations = var_1627, groups = var_1195, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1625, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_23_cast_fp16")]; + tensor var_1631 = const()[name = tensor("op_1631"), val = tensor([1, 1])]; + tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([1, 1])]; + tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; + tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38346048))), lut = tensor([-0x1.cp-5, -0x1.05cp-6, 0x1.09p-6, 0x1.c24p-5]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor v_23_cast_fp16 = conv(dilations = var_1633, groups = var_1195, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_23_cast_fp16")]; + tensor var_1637 = const()[name = tensor("op_1637"), val = tensor([2, 20, 64, -1])]; + tensor var_1638_cast_fp16 = reshape(shape = var_1637, x = q_23_cast_fp16)[name = tensor("op_1638_cast_fp16")]; + tensor var_1639 = const()[name = tensor("op_1639"), val = tensor([2, 20, 64, -1])]; + tensor var_1640_cast_fp16 = reshape(shape = var_1639, x = k_23_cast_fp16)[name = tensor("op_1640_cast_fp16")]; + tensor var_1641 = const()[name = tensor("op_1641"), val = tensor([2, 20, 64, -1])]; + tensor var_1642_cast_fp16 = reshape(shape = var_1641, x = v_23_cast_fp16)[name = tensor("op_1642_cast_fp16")]; + tensor attn_weights_45_transpose_x_0 = const()[name = tensor("attn_weights_45_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_45_transpose_y_0 = const()[name = tensor("attn_weights_45_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_45_cast_fp16 = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1638_cast_fp16, y = var_1640_cast_fp16)[name = tensor("attn_weights_45_cast_fp16")]; + tensor attn_weights_47_cast_fp16 = mul(x = attn_weights_45_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_47_cast_fp16")]; + tensor var_1646_cast_fp16 = softmax(axis = var_1179, x = attn_weights_47_cast_fp16)[name = tensor("op_1646_cast_fp16")]; + tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; + tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; + tensor attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1642_cast_fp16, y = var_1646_cast_fp16)[name = tensor("attn_23_cast_fp16")]; + tensor var_1650 = const()[name = tensor("op_1650"), val = tensor([2, 1280, 1, -1])]; + tensor input_161_cast_fp16 = reshape(shape = var_1650, x = attn_23_cast_fp16)[name = tensor("input_161_cast_fp16")]; + tensor var_1655 = const()[name = tensor("op_1655"), val = tensor([1, 1])]; + tensor var_1657 = const()[name = tensor("op_1657"), val = tensor([1, 1])]; + tensor var_1659_pad_type_0 = const()[name = tensor("op_1659_pad_type_0"), val = tensor("custom")]; + tensor var_1659_pad_0 = const()[name = tensor("op_1659_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38673792))), lut = tensor([-0x1.4dcp-5, -0x1.8bp-7, 0x1.92cp-7, 0x1.4fcp-5]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39083456)))]; + tensor var_1659_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1657, groups = var_1195, pad = var_1659_pad_0, pad_type = var_1659_pad_type_0, strides = var_1655, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = tensor("op_1659_cast_fp16")]; + tensor inputs_35_cast_fp16 = add(x = var_1659_cast_fp16, y = inputs_33_cast_fp16)[name = tensor("inputs_35_cast_fp16")]; + tensor var_1663 = const()[name = tensor("op_1663"), val = tensor([1])]; + tensor channels_mean_35_cast_fp16 = reduce_mean(axes = var_1663, keep_dims = var_1190, x = inputs_35_cast_fp16)[name = tensor("channels_mean_35_cast_fp16")]; + tensor zero_mean_35_cast_fp16 = sub(x = inputs_35_cast_fp16, y = channels_mean_35_cast_fp16)[name = tensor("zero_mean_35_cast_fp16")]; + tensor zero_mean_sq_35_cast_fp16 = mul(x = zero_mean_35_cast_fp16, y = zero_mean_35_cast_fp16)[name = tensor("zero_mean_sq_35_cast_fp16")]; + tensor var_1667 = const()[name = tensor("op_1667"), val = tensor([1])]; + tensor var_1668_cast_fp16 = reduce_mean(axes = var_1667, keep_dims = var_1190, x = zero_mean_sq_35_cast_fp16)[name = tensor("op_1668_cast_fp16")]; + tensor var_1669_to_fp16 = const()[name = tensor("op_1669_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1670_cast_fp16 = add(x = var_1668_cast_fp16, y = var_1669_to_fp16)[name = tensor("op_1670_cast_fp16")]; + tensor denom_35_epsilon_0_to_fp16 = const()[name = tensor("denom_35_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_35_cast_fp16 = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1670_cast_fp16)[name = tensor("denom_35_cast_fp16")]; + tensor out_35_cast_fp16 = mul(x = zero_mean_35_cast_fp16, y = denom_35_cast_fp16)[name = tensor("out_35_cast_fp16")]; + tensor var_1674_to_fp16 = const()[name = tensor("op_1674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39086080)))]; + tensor var_1675_cast_fp16 = add(x = out_35_cast_fp16, y = var_1674_to_fp16)[name = tensor("op_1675_cast_fp16")]; + tensor var_1677_to_fp16 = const()[name = tensor("op_1677_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39088704)))]; + tensor input_163_cast_fp16 = mul(x = var_1675_cast_fp16, y = var_1677_to_fp16)[name = tensor("input_163_cast_fp16")]; + tensor var_1685 = const()[name = tensor("op_1685"), val = tensor([1, 1])]; + tensor var_1687 = const()[name = tensor("op_1687"), val = tensor([1, 1])]; + tensor var_1689_pad_type_0 = const()[name = tensor("op_1689_pad_type_0"), val = tensor("custom")]; + tensor var_1689_pad_0 = const()[name = tensor("op_1689_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39091328))), lut = tensor([-0x1.5ep-4, -0x1.9dcp-6, 0x1.9fp-6, 0x1.5ecp-4]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42368192))), lut = tensor([-0x1.484p-3, -0x1.20cp-4, -0x1.aecp-7, 0x1.25p-5]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1689_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1687, groups = var_1195, pad = var_1689_pad_0, pad_type = var_1689_pad_type_0, strides = var_1685, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_163_cast_fp16)[name = tensor("op_1689_cast_fp16")]; + tensor var_1690_split_sizes_0 = const()[name = tensor("op_1690_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1690_axis_0 = const()[name = tensor("op_1690_axis_0"), val = tensor(1)]; + tensor var_1690_cast_fp16_0, tensor var_1690_cast_fp16_1 = split(axis = var_1690_axis_0, split_sizes = var_1690_split_sizes_0, x = var_1689_cast_fp16)[name = tensor("op_1690_cast_fp16")]; + tensor var_1692_mode_0 = const()[name = tensor("op_1692_mode_0"), val = tensor("EXACT")]; + tensor var_1692_cast_fp16 = gelu(mode = var_1692_mode_0, x = var_1690_cast_fp16_1)[name = tensor("op_1692_cast_fp16")]; + tensor input_165_cast_fp16 = mul(x = var_1690_cast_fp16_0, y = var_1692_cast_fp16)[name = tensor("input_165_cast_fp16")]; + tensor var_1696 = const()[name = tensor("op_1696"), val = tensor([1, 1])]; + tensor var_1698 = const()[name = tensor("op_1698"), val = tensor([1, 1])]; + tensor var_1700_pad_type_0 = const()[name = tensor("op_1700_pad_type_0"), val = tensor("custom")]; + tensor var_1700_pad_0 = const()[name = tensor("op_1700_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42370816))), lut = tensor([-0x1.4acp-4, -0x1.8bp-6, 0x1.8a8p-6, 0x1.4bp-4]), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44009280)))]; + tensor var_1700_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1698, groups = var_1195, pad = var_1700_pad_0, pad_type = var_1700_pad_type_0, strides = var_1696, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = tensor("op_1700_cast_fp16")]; + tensor hidden_states_105_cast_fp16 = add(x = var_1700_cast_fp16, y = inputs_35_cast_fp16)[name = tensor("hidden_states_105_cast_fp16")]; + tensor var_1702 = const()[name = tensor("op_1702"), val = tensor([2, 1280, 16, 16])]; + tensor input_167_cast_fp16 = reshape(shape = var_1702, x = hidden_states_105_cast_fp16)[name = tensor("input_167_cast_fp16")]; + tensor var_1706 = const()[name = tensor("op_1706"), val = tensor([1, 1])]; + tensor var_1708 = const()[name = tensor("op_1708"), val = tensor([1, 1])]; + tensor hidden_states_107_pad_type_0 = const()[name = tensor("hidden_states_107_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_107_pad_0 = const()[name = tensor("hidden_states_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44011904))), lut = tensor([-0x1.18p-4, -0x1.4d8p-6, 0x1.534p-6, 0x1.198p-4]), name = tensor("down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44421568)))]; + tensor hidden_states_107_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_1708, groups = var_1195, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = var_1706, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("hidden_states_107_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = hidden_states_107_cast_fp16, y = hidden_states_95_cast_fp16)[name = tensor("input_169_cast_fp16")]; + tensor var_1715 = const()[name = tensor("op_1715"), val = tensor([2, 2])]; + tensor var_1717 = const()[name = tensor("op_1717"), val = tensor([1, 1])]; + tensor input_171_pad_type_0 = const()[name = tensor("input_171_pad_type_0"), val = tensor("custom")]; + tensor input_171_pad_0 = const()[name = tensor("input_171_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44424192))), lut = tensor([-0x1.b6p-6, -0x1.f3p-8, 0x1.0ep-7, 0x1.c5cp-6]), name = tensor("down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48110656)))]; + tensor input_171_cast_fp16 = conv(bias = down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_1717, groups = var_1195, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1715, weight = down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized, x = input_169_cast_fp16)[name = tensor("input_171_cast_fp16")]; + tensor var_1729 = const()[name = tensor("op_1729"), val = tensor(1)]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_171_cast_fp16)[name = tensor("reshape_72_cast_fp16")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor("reduce_mean_54_cast_fp16")]; + tensor sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor("sub_36_cast_fp16")]; + tensor square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor("square_18_cast_fp16")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor("reduce_mean_56_cast_fp16")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast_fp16")]; + tensor sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor("sqrt_18_cast_fp16")]; + tensor real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor("real_div_18_cast_fp16")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor("reshape_73_cast_fp16")]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48113280)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48115904)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor("add_37_cast_fp16")]; + tensor input_175_cast_fp16 = silu(x = add_37_cast_fp16)[name = tensor("input_175_cast_fp16")]; + tensor var_1745 = const()[name = tensor("op_1745"), val = tensor([1, 1])]; + tensor var_1747 = const()[name = tensor("op_1747"), val = tensor([1, 1])]; + tensor hidden_states_109_pad_type_0 = const()[name = tensor("hidden_states_109_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_109_pad_0 = const()[name = tensor("hidden_states_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48118528))), lut = tensor([-0x1.608p-5, -0x1.408p-7, 0x1.484p-7, 0x1.6e8p-5]), name = tensor("down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51804992)))]; + tensor hidden_states_109_cast_fp16 = conv(bias = down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_1747, groups = var_1729, pad = hidden_states_109_pad_0, pad_type = hidden_states_109_pad_type_0, strides = var_1745, weight = down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = tensor("hidden_states_109_cast_fp16")]; + tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 1])]; + tensor var_1755 = const()[name = tensor("op_1755"), val = tensor([1, 1])]; + tensor temb_13_pad_type_0 = const()[name = tensor("temb_13_pad_type_0"), val = tensor("custom")]; + tensor temb_13_pad_0 = const()[name = tensor("temb_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51807616))), lut = tensor([-0x1.144p-5, -0x1.768p-9, 0x1.898p-9, 0x1.53cp-5]), name = tensor("down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52217280)))]; + tensor temb_13_cast_fp16 = conv(bias = down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1755, groups = var_1729, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_1753, weight = down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_13_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = temb_13_cast_fp16)[name = tensor("input_179_cast_fp16")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = input_179_cast_fp16)[name = tensor("reshape_76_cast_fp16")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor("reduce_mean_57_cast_fp16")]; + tensor sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor("sub_38_cast_fp16")]; + tensor square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor("square_19_cast_fp16")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor("reduce_mean_59_cast_fp16")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast_fp16")]; + tensor sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor("sqrt_19_cast_fp16")]; + tensor real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor("real_div_19_cast_fp16")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor("reshape_77_cast_fp16")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52219904)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52222528)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor("add_39_cast_fp16")]; + tensor input_183_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor("input_183_cast_fp16")]; + tensor var_1765 = const()[name = tensor("op_1765"), val = tensor([1, 1])]; + tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, 1])]; + tensor hidden_states_111_pad_type_0 = const()[name = tensor("hidden_states_111_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_111_pad_0 = const()[name = tensor("hidden_states_111_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(52225152))), lut = tensor([-0x1.8a8p-5, -0x1.5fcp-7, 0x1.73p-7, 0x1.91p-5]), name = tensor("down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55911616)))]; + tensor hidden_states_111_cast_fp16 = conv(bias = down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_1767, groups = var_1729, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = var_1765, weight = down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = tensor("hidden_states_111_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = input_171_cast_fp16, y = hidden_states_111_cast_fp16)[name = tensor("input_185_cast_fp16")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_185_cast_fp16)[name = tensor("reshape_80_cast_fp16")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor("reduce_mean_60_cast_fp16")]; + tensor sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor("sub_40_cast_fp16")]; + tensor square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor("square_20_cast_fp16")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor("reduce_mean_62_cast_fp16")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast_fp16")]; + tensor sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor("sqrt_20_cast_fp16")]; + tensor real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor("real_div_20_cast_fp16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor("reshape_81_cast_fp16")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55914240)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55916864)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor("add_41_cast_fp16")]; + tensor input_189_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor("input_189_cast_fp16")]; + tensor var_1782 = const()[name = tensor("op_1782"), val = tensor([1, 1])]; + tensor var_1784 = const()[name = tensor("op_1784"), val = tensor([1, 1])]; + tensor hidden_states_113_pad_type_0 = const()[name = tensor("hidden_states_113_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_113_pad_0 = const()[name = tensor("hidden_states_113_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55919488))), lut = tensor([-0x1.39cp-5, -0x1.1p-7, 0x1.0b4p-7, 0x1.3dcp-5]), name = tensor("down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59605952)))]; + tensor hidden_states_113_cast_fp16 = conv(bias = down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_1784, groups = var_1729, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = var_1782, weight = down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = tensor("hidden_states_113_cast_fp16")]; + tensor var_1790 = const()[name = tensor("op_1790"), val = tensor([1, 1])]; + tensor var_1792 = const()[name = tensor("op_1792"), val = tensor([1, 1])]; + tensor temb_15_pad_type_0 = const()[name = tensor("temb_15_pad_type_0"), val = tensor("custom")]; + tensor temb_15_pad_0 = const()[name = tensor("temb_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59608576))), lut = tensor([-0x1.10cp-8, -0x1.f14p-14, 0x1.018p-8, 0x1.06p-5]), name = tensor("down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60018240)))]; + tensor temb_15_cast_fp16 = conv(bias = down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1792, groups = var_1729, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_1790, weight = down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_15_cast_fp16")]; + tensor input_193_cast_fp16 = add(x = hidden_states_113_cast_fp16, y = temb_15_cast_fp16)[name = tensor("input_193_cast_fp16")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = input_193_cast_fp16)[name = tensor("reshape_84_cast_fp16")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor("reduce_mean_63_cast_fp16")]; + tensor sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor("sub_42_cast_fp16")]; + tensor square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor("square_21_cast_fp16")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor("reduce_mean_65_cast_fp16")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast_fp16")]; + tensor sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor("sqrt_21_cast_fp16")]; + tensor real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor("real_div_21_cast_fp16")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor("reshape_85_cast_fp16")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60020864)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60023488)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = tensor("add_43_cast_fp16")]; + tensor input_197_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor("input_197_cast_fp16")]; + tensor var_1802 = const()[name = tensor("op_1802"), val = tensor([1, 1])]; + tensor var_1804 = const()[name = tensor("op_1804"), val = tensor([1, 1])]; + tensor hidden_states_115_pad_type_0 = const()[name = tensor("hidden_states_115_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_115_pad_0 = const()[name = tensor("hidden_states_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60026112))), lut = tensor([-0x1.4acp-5, -0x1.0bp-7, 0x1.26p-7, 0x1.5ap-5]), name = tensor("down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63712576)))]; + tensor hidden_states_115_cast_fp16 = conv(bias = down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_1804, groups = var_1729, pad = hidden_states_115_pad_0, pad_type = hidden_states_115_pad_type_0, strides = var_1802, weight = down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized, x = input_197_cast_fp16)[name = tensor("hidden_states_115_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = input_185_cast_fp16, y = hidden_states_115_cast_fp16)[name = tensor("input_199_cast_fp16")]; + tensor var_1812 = const()[name = tensor("op_1812"), val = tensor(3)]; + tensor var_1823 = const()[name = tensor("op_1823"), val = tensor(true)]; + tensor var_1828 = const()[name = tensor("op_1828"), val = tensor(1)]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_199_cast_fp16)[name = tensor("reshape_88_cast_fp16")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66_cast_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = tensor("reduce_mean_66_cast_fp16")]; + tensor sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor("sub_44_cast_fp16")]; + tensor square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor("square_22_cast_fp16")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68_cast_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = tensor("reduce_mean_68_cast_fp16")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast_fp16")]; + tensor sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor("sqrt_22_cast_fp16")]; + tensor real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor("real_div_22_cast_fp16")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor("reshape_89_cast_fp16")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63715200)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63717824)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_45_cast_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor("add_45_cast_fp16")]; + tensor input_203_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor("input_203_cast_fp16")]; + tensor var_1846 = const()[name = tensor("op_1846"), val = tensor([1, 1])]; + tensor var_1848 = const()[name = tensor("op_1848"), val = tensor([1, 1])]; + tensor hidden_states_117_pad_type_0 = const()[name = tensor("hidden_states_117_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_117_pad_0 = const()[name = tensor("hidden_states_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63720448))), lut = tensor([-0x1.21cp-5, -0x1.adcp-8, 0x1.acp-8, 0x1.25p-5]), name = tensor("mid_block_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67406912)))]; + tensor hidden_states_117_cast_fp16 = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_1848, groups = var_1828, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = var_1846, weight = mid_block_resnets_0_conv1_weight_to_fp16_palettized, x = input_203_cast_fp16)[name = tensor("hidden_states_117_cast_fp16")]; + tensor var_1854 = const()[name = tensor("op_1854"), val = tensor([1, 1])]; + tensor var_1856 = const()[name = tensor("op_1856"), val = tensor([1, 1])]; + tensor temb_17_pad_type_0 = const()[name = tensor("temb_17_pad_type_0"), val = tensor("custom")]; + tensor temb_17_pad_0 = const()[name = tensor("temb_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67409536))), lut = tensor([-0x1.0bp-8, -0x1.6f8p-14, 0x1.ffcp-9, 0x1.298p-5]), name = tensor("mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67819200)))]; + tensor temb_17_cast_fp16 = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1856, groups = var_1828, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_1854, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_17_cast_fp16")]; + tensor input_207_cast_fp16 = add(x = hidden_states_117_cast_fp16, y = temb_17_cast_fp16)[name = tensor("input_207_cast_fp16")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_207_cast_fp16)[name = tensor("reshape_92_cast_fp16")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = tensor("reduce_mean_69_cast_fp16")]; + tensor sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor("sub_46_cast_fp16")]; + tensor square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor("square_23_cast_fp16")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = tensor("reduce_mean_71_cast_fp16")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast_fp16")]; + tensor sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor("sqrt_23_cast_fp16")]; + tensor real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor("real_div_23_cast_fp16")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor("reshape_93_cast_fp16")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67821824)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67824448)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_47_cast_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor("add_47_cast_fp16")]; + tensor input_211_cast_fp16 = silu(x = add_47_cast_fp16)[name = tensor("input_211_cast_fp16")]; + tensor var_1866 = const()[name = tensor("op_1866"), val = tensor([1, 1])]; + tensor var_1868 = const()[name = tensor("op_1868"), val = tensor([1, 1])]; + tensor hidden_states_119_pad_type_0 = const()[name = tensor("hidden_states_119_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_119_pad_0 = const()[name = tensor("hidden_states_119_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67827072))), lut = tensor([-0x1.32p-5, -0x1.b24p-8, 0x1.9fcp-8, 0x1.368p-5]), name = tensor("mid_block_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71513536)))]; + tensor hidden_states_119_cast_fp16 = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_1868, groups = var_1828, pad = hidden_states_119_pad_0, pad_type = hidden_states_119_pad_type_0, strides = var_1866, weight = mid_block_resnets_0_conv2_weight_to_fp16_palettized, x = input_211_cast_fp16)[name = tensor("hidden_states_119_cast_fp16")]; + tensor hidden_states_121_cast_fp16 = add(x = input_199_cast_fp16, y = hidden_states_119_cast_fp16)[name = tensor("hidden_states_121_cast_fp16")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = hidden_states_121_cast_fp16)[name = tensor("reshape_96_cast_fp16")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = tensor("reduce_mean_72_cast_fp16")]; + tensor sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor("sub_48_cast_fp16")]; + tensor square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor("square_24_cast_fp16")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = tensor("reduce_mean_74_cast_fp16")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast_fp16")]; + tensor sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor("sqrt_24_cast_fp16")]; + tensor real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor("real_div_24_cast_fp16")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor("reshape_97_cast_fp16")]; + tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71516160)))]; + tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71518784)))]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_97_cast_fp16)[name = tensor("add_49_cast_fp16")]; + tensor var_1888 = const()[name = tensor("op_1888"), val = tensor([1, 1])]; + tensor var_1890 = const()[name = tensor("op_1890"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_type_0 = const()[name = tensor("hidden_states_123_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_123_pad_0 = const()[name = tensor("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71521408))), lut = tensor([-0x1.758p-5, -0x1.b64p-7, 0x1.b78p-7, 0x1.74cp-5]), name = tensor("mid_block_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71931072)))]; + tensor hidden_states_123_cast_fp16 = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_1890, groups = var_1828, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = var_1888, weight = mid_block_attentions_0_proj_in_weight_to_fp16_palettized, x = add_49_cast_fp16)[name = tensor("hidden_states_123_cast_fp16")]; + tensor var_1895 = const()[name = tensor("op_1895"), val = tensor([2, 1280, 1, 64])]; + tensor inputs_37_cast_fp16 = reshape(shape = var_1895, x = hidden_states_123_cast_fp16)[name = tensor("inputs_37_cast_fp16")]; + tensor var_1905 = const()[name = tensor("op_1905"), val = tensor([1])]; + tensor channels_mean_37_cast_fp16 = reduce_mean(axes = var_1905, keep_dims = var_1823, x = inputs_37_cast_fp16)[name = tensor("channels_mean_37_cast_fp16")]; + tensor zero_mean_37_cast_fp16 = sub(x = inputs_37_cast_fp16, y = channels_mean_37_cast_fp16)[name = tensor("zero_mean_37_cast_fp16")]; + tensor zero_mean_sq_37_cast_fp16 = mul(x = zero_mean_37_cast_fp16, y = zero_mean_37_cast_fp16)[name = tensor("zero_mean_sq_37_cast_fp16")]; + tensor var_1909 = const()[name = tensor("op_1909"), val = tensor([1])]; + tensor var_1910_cast_fp16 = reduce_mean(axes = var_1909, keep_dims = var_1823, x = zero_mean_sq_37_cast_fp16)[name = tensor("op_1910_cast_fp16")]; + tensor var_1911_to_fp16 = const()[name = tensor("op_1911_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1912_cast_fp16 = add(x = var_1910_cast_fp16, y = var_1911_to_fp16)[name = tensor("op_1912_cast_fp16")]; + tensor denom_37_epsilon_0_to_fp16 = const()[name = tensor("denom_37_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_37_cast_fp16 = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1912_cast_fp16)[name = tensor("denom_37_cast_fp16")]; + tensor out_37_cast_fp16 = mul(x = zero_mean_37_cast_fp16, y = denom_37_cast_fp16)[name = tensor("out_37_cast_fp16")]; + tensor var_1916_to_fp16 = const()[name = tensor("op_1916_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71933696)))]; + tensor var_1917_cast_fp16 = add(x = out_37_cast_fp16, y = var_1916_to_fp16)[name = tensor("op_1917_cast_fp16")]; + tensor var_1919_to_fp16 = const()[name = tensor("op_1919_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71936320)))]; + tensor hidden_states_125_cast_fp16 = mul(x = var_1917_cast_fp16, y = var_1919_to_fp16)[name = tensor("hidden_states_125_cast_fp16")]; + tensor var_1926 = const()[name = tensor("op_1926"), val = tensor([1, 1])]; + tensor var_1928 = const()[name = tensor("op_1928"), val = tensor([1, 1])]; + tensor q_25_pad_type_0 = const()[name = tensor("q_25_pad_type_0"), val = tensor("custom")]; + tensor q_25_pad_0 = const()[name = tensor("q_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71938944))), lut = tensor([-0x1.abp-6, -0x1.d78p-8, 0x1.d48p-8, 0x1.a9p-6]), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_25_cast_fp16 = conv(dilations = var_1928, groups = var_1828, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1926, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_125_cast_fp16)[name = tensor("q_25_cast_fp16")]; + tensor var_1932 = const()[name = tensor("op_1932"), val = tensor([1, 1])]; + tensor var_1934 = const()[name = tensor("op_1934"), val = tensor([1, 1])]; + tensor k_25_pad_type_0 = const()[name = tensor("k_25_pad_type_0"), val = tensor("custom")]; + tensor k_25_pad_0 = const()[name = tensor("k_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72348608))), lut = tensor([-0x1.c14p-6, -0x1.fp-8, 0x1.f7p-8, 0x1.c34p-6]), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_25_cast_fp16 = conv(dilations = var_1934, groups = var_1828, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1932, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_125_cast_fp16)[name = tensor("k_25_cast_fp16")]; + tensor var_1938 = const()[name = tensor("op_1938"), val = tensor([1, 1])]; + tensor var_1940 = const()[name = tensor("op_1940"), val = tensor([1, 1])]; + tensor v_25_pad_type_0 = const()[name = tensor("v_25_pad_type_0"), val = tensor("custom")]; + tensor v_25_pad_0 = const()[name = tensor("v_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72758272))), lut = tensor([-0x1.2p-5, -0x1.428p-7, 0x1.474p-7, 0x1.21cp-5]), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_25_cast_fp16 = conv(dilations = var_1940, groups = var_1828, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1938, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_125_cast_fp16)[name = tensor("v_25_cast_fp16")]; + tensor var_1944 = const()[name = tensor("op_1944"), val = tensor([2, 20, 64, -1])]; + tensor var_1945_cast_fp16 = reshape(shape = var_1944, x = q_25_cast_fp16)[name = tensor("op_1945_cast_fp16")]; + tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([2, 20, 64, -1])]; + tensor var_1947_cast_fp16 = reshape(shape = var_1946, x = k_25_cast_fp16)[name = tensor("op_1947_cast_fp16")]; + tensor var_1948 = const()[name = tensor("op_1948"), val = tensor([2, 20, 64, -1])]; + tensor var_1949_cast_fp16 = reshape(shape = var_1948, x = v_25_cast_fp16)[name = tensor("op_1949_cast_fp16")]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1945_cast_fp16, y = var_1947_cast_fp16)[name = tensor("attn_weights_49_cast_fp16")]; + tensor var_1819_to_fp16 = const()[name = tensor("op_1819_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_1819_to_fp16)[name = tensor("attn_weights_51_cast_fp16")]; + tensor var_1953_cast_fp16 = softmax(axis = var_1812, x = attn_weights_51_cast_fp16)[name = tensor("op_1953_cast_fp16")]; + tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; + tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; + tensor attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1949_cast_fp16, y = var_1953_cast_fp16)[name = tensor("attn_25_cast_fp16")]; + tensor var_1957 = const()[name = tensor("op_1957"), val = tensor([2, 1280, 1, -1])]; + tensor input_215_cast_fp16 = reshape(shape = var_1957, x = attn_25_cast_fp16)[name = tensor("input_215_cast_fp16")]; + tensor var_1962 = const()[name = tensor("op_1962"), val = tensor([1, 1])]; + tensor var_1964 = const()[name = tensor("op_1964"), val = tensor([1, 1])]; + tensor var_1966_pad_type_0 = const()[name = tensor("op_1966_pad_type_0"), val = tensor("custom")]; + tensor var_1966_pad_0 = const()[name = tensor("op_1966_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73167936))), lut = tensor([-0x1.16cp-5, -0x1.3ecp-7, 0x1.45p-7, 0x1.184p-5]), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73577600)))]; + tensor var_1966_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1964, groups = var_1828, pad = var_1966_pad_0, pad_type = var_1966_pad_type_0, strides = var_1962, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_215_cast_fp16)[name = tensor("op_1966_cast_fp16")]; + tensor inputs_39_cast_fp16 = add(x = var_1966_cast_fp16, y = inputs_37_cast_fp16)[name = tensor("inputs_39_cast_fp16")]; + tensor var_1970 = const()[name = tensor("op_1970"), val = tensor([1])]; + tensor channels_mean_39_cast_fp16 = reduce_mean(axes = var_1970, keep_dims = var_1823, x = inputs_39_cast_fp16)[name = tensor("channels_mean_39_cast_fp16")]; + tensor zero_mean_39_cast_fp16 = sub(x = inputs_39_cast_fp16, y = channels_mean_39_cast_fp16)[name = tensor("zero_mean_39_cast_fp16")]; + tensor zero_mean_sq_39_cast_fp16 = mul(x = zero_mean_39_cast_fp16, y = zero_mean_39_cast_fp16)[name = tensor("zero_mean_sq_39_cast_fp16")]; + tensor var_1974 = const()[name = tensor("op_1974"), val = tensor([1])]; + tensor var_1975_cast_fp16 = reduce_mean(axes = var_1974, keep_dims = var_1823, x = zero_mean_sq_39_cast_fp16)[name = tensor("op_1975_cast_fp16")]; + tensor var_1976_to_fp16 = const()[name = tensor("op_1976_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1977_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1976_to_fp16)[name = tensor("op_1977_cast_fp16")]; + tensor denom_39_epsilon_0_to_fp16 = const()[name = tensor("denom_39_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_39_cast_fp16 = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1977_cast_fp16)[name = tensor("denom_39_cast_fp16")]; + tensor out_39_cast_fp16 = mul(x = zero_mean_39_cast_fp16, y = denom_39_cast_fp16)[name = tensor("out_39_cast_fp16")]; + tensor var_1981_to_fp16 = const()[name = tensor("op_1981_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73580224)))]; + tensor var_1982_cast_fp16 = add(x = out_39_cast_fp16, y = var_1981_to_fp16)[name = tensor("op_1982_cast_fp16")]; + tensor var_1984_to_fp16 = const()[name = tensor("op_1984_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73582848)))]; + tensor hidden_states_127_cast_fp16 = mul(x = var_1982_cast_fp16, y = var_1984_to_fp16)[name = tensor("hidden_states_127_cast_fp16")]; + tensor var_1991 = const()[name = tensor("op_1991"), val = tensor([1, 1])]; + tensor var_1993 = const()[name = tensor("op_1993"), val = tensor([1, 1])]; + tensor q_27_pad_type_0 = const()[name = tensor("q_27_pad_type_0"), val = tensor("custom")]; + tensor q_27_pad_0 = const()[name = tensor("q_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73585472))), lut = tensor([-0x1.8ap-6, -0x1.a1cp-8, 0x1.a14p-8, 0x1.8ap-6]), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_27_cast_fp16 = conv(dilations = var_1993, groups = var_1828, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1991, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_127_cast_fp16)[name = tensor("q_27_cast_fp16")]; + tensor var_1997 = const()[name = tensor("op_1997"), val = tensor([1, 1])]; + tensor var_1999 = const()[name = tensor("op_1999"), val = tensor([1, 1])]; + tensor k_27_pad_type_0 = const()[name = tensor("k_27_pad_type_0"), val = tensor("custom")]; + tensor k_27_pad_0 = const()[name = tensor("k_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73995136))), lut = tensor([-0x1.1b4p-5, -0x1.294p-7, 0x1.2c4p-7, 0x1.1d8p-5]), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor k_27_cast_fp16 = conv(dilations = var_1999, groups = var_1828, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1997, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_27_cast_fp16")]; + tensor var_2003 = const()[name = tensor("op_2003"), val = tensor([1, 1])]; + tensor var_2005 = const()[name = tensor("op_2005"), val = tensor([1, 1])]; + tensor v_27_pad_type_0 = const()[name = tensor("v_27_pad_type_0"), val = tensor("custom")]; + tensor v_27_pad_0 = const()[name = tensor("v_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74322880))), lut = tensor([-0x1.95p-5, -0x1.d04p-7, 0x1.d34p-7, 0x1.96p-5]), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor v_27_cast_fp16 = conv(dilations = var_2005, groups = var_1828, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_2003, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_27_cast_fp16")]; + tensor var_2009 = const()[name = tensor("op_2009"), val = tensor([2, 20, 64, -1])]; + tensor var_2010_cast_fp16 = reshape(shape = var_2009, x = q_27_cast_fp16)[name = tensor("op_2010_cast_fp16")]; + tensor var_2011 = const()[name = tensor("op_2011"), val = tensor([2, 20, 64, -1])]; + tensor var_2012_cast_fp16 = reshape(shape = var_2011, x = k_27_cast_fp16)[name = tensor("op_2012_cast_fp16")]; + tensor var_2013 = const()[name = tensor("op_2013"), val = tensor([2, 20, 64, -1])]; + tensor var_2014_cast_fp16 = reshape(shape = var_2013, x = v_27_cast_fp16)[name = tensor("op_2014_cast_fp16")]; + tensor attn_weights_53_transpose_x_0 = const()[name = tensor("attn_weights_53_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_53_transpose_y_0 = const()[name = tensor("attn_weights_53_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_53_cast_fp16 = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_2010_cast_fp16, y = var_2012_cast_fp16)[name = tensor("attn_weights_53_cast_fp16")]; + tensor attn_weights_55_cast_fp16 = mul(x = attn_weights_53_cast_fp16, y = var_1819_to_fp16)[name = tensor("attn_weights_55_cast_fp16")]; + tensor var_2018_cast_fp16 = softmax(axis = var_1812, x = attn_weights_55_cast_fp16)[name = tensor("op_2018_cast_fp16")]; + tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; + tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; + tensor attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_2014_cast_fp16, y = var_2018_cast_fp16)[name = tensor("attn_27_cast_fp16")]; + tensor var_2022 = const()[name = tensor("op_2022"), val = tensor([2, 1280, 1, -1])]; + tensor input_217_cast_fp16 = reshape(shape = var_2022, x = attn_27_cast_fp16)[name = tensor("input_217_cast_fp16")]; + tensor var_2027 = const()[name = tensor("op_2027"), val = tensor([1, 1])]; + tensor var_2029 = const()[name = tensor("op_2029"), val = tensor([1, 1])]; + tensor var_2031_pad_type_0 = const()[name = tensor("op_2031_pad_type_0"), val = tensor("custom")]; + tensor var_2031_pad_0 = const()[name = tensor("op_2031_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74650624))), lut = tensor([-0x1.ee4p-6, -0x1.1d4p-7, 0x1.1ccp-7, 0x1.ee8p-6]), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75060288)))]; + tensor var_2031_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2029, groups = var_1828, pad = var_2031_pad_0, pad_type = var_2031_pad_type_0, strides = var_2027, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = tensor("op_2031_cast_fp16")]; + tensor inputs_41_cast_fp16 = add(x = var_2031_cast_fp16, y = inputs_39_cast_fp16)[name = tensor("inputs_41_cast_fp16")]; + tensor var_2035 = const()[name = tensor("op_2035"), val = tensor([1])]; + tensor channels_mean_41_cast_fp16 = reduce_mean(axes = var_2035, keep_dims = var_1823, x = inputs_41_cast_fp16)[name = tensor("channels_mean_41_cast_fp16")]; + tensor zero_mean_41_cast_fp16 = sub(x = inputs_41_cast_fp16, y = channels_mean_41_cast_fp16)[name = tensor("zero_mean_41_cast_fp16")]; + tensor zero_mean_sq_41_cast_fp16 = mul(x = zero_mean_41_cast_fp16, y = zero_mean_41_cast_fp16)[name = tensor("zero_mean_sq_41_cast_fp16")]; + tensor var_2039 = const()[name = tensor("op_2039"), val = tensor([1])]; + tensor var_2040_cast_fp16 = reduce_mean(axes = var_2039, keep_dims = var_1823, x = zero_mean_sq_41_cast_fp16)[name = tensor("op_2040_cast_fp16")]; + tensor var_2041_to_fp16 = const()[name = tensor("op_2041_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2042_cast_fp16 = add(x = var_2040_cast_fp16, y = var_2041_to_fp16)[name = tensor("op_2042_cast_fp16")]; + tensor denom_41_epsilon_0_to_fp16 = const()[name = tensor("denom_41_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_41_cast_fp16 = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_2042_cast_fp16)[name = tensor("denom_41_cast_fp16")]; + tensor out_41_cast_fp16 = mul(x = zero_mean_41_cast_fp16, y = denom_41_cast_fp16)[name = tensor("out_41_cast_fp16")]; + tensor var_2046_to_fp16 = const()[name = tensor("op_2046_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75062912)))]; + tensor var_2047_cast_fp16 = add(x = out_41_cast_fp16, y = var_2046_to_fp16)[name = tensor("op_2047_cast_fp16")]; + tensor var_2049_to_fp16 = const()[name = tensor("op_2049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75065536)))]; + tensor input_219_cast_fp16 = mul(x = var_2047_cast_fp16, y = var_2049_to_fp16)[name = tensor("input_219_cast_fp16")]; + tensor var_2057 = const()[name = tensor("op_2057"), val = tensor([1, 1])]; + tensor var_2059 = const()[name = tensor("op_2059"), val = tensor([1, 1])]; + tensor var_2061_pad_type_0 = const()[name = tensor("op_2061_pad_type_0"), val = tensor("custom")]; + tensor var_2061_pad_0 = const()[name = tensor("op_2061_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75068160))), lut = tensor([-0x1.67p-5, -0x1.a8cp-9, 0x1.a8cp-9, 0x1.67p-5]), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78345024))), lut = tensor([-0x1.d3p-4, -0x1.4e4p-5, 0x1.75cp-11, 0x1.568p-5]), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2061_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2059, groups = var_1828, pad = var_2061_pad_0, pad_type = var_2061_pad_type_0, strides = var_2057, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_219_cast_fp16)[name = tensor("op_2061_cast_fp16")]; + tensor var_2062_split_sizes_0 = const()[name = tensor("op_2062_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2062_axis_0 = const()[name = tensor("op_2062_axis_0"), val = tensor(1)]; + tensor var_2062_cast_fp16_0, tensor var_2062_cast_fp16_1 = split(axis = var_2062_axis_0, split_sizes = var_2062_split_sizes_0, x = var_2061_cast_fp16)[name = tensor("op_2062_cast_fp16")]; + tensor var_2064_mode_0 = const()[name = tensor("op_2064_mode_0"), val = tensor("EXACT")]; + tensor var_2064_cast_fp16 = gelu(mode = var_2064_mode_0, x = var_2062_cast_fp16_1)[name = tensor("op_2064_cast_fp16")]; + tensor input_221_cast_fp16 = mul(x = var_2062_cast_fp16_0, y = var_2064_cast_fp16)[name = tensor("input_221_cast_fp16")]; + tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([1, 1])]; + tensor var_2070 = const()[name = tensor("op_2070"), val = tensor([1, 1])]; + tensor var_2072_pad_type_0 = const()[name = tensor("op_2072_pad_type_0"), val = tensor("custom")]; + tensor var_2072_pad_0 = const()[name = tensor("op_2072_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78347648))), lut = tensor([-0x1.47cp-5, -0x1.2e4p-13, 0x1.7a4p-6, 0x1.dacp-5]), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79986112)))]; + tensor var_2072_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2070, groups = var_1828, pad = var_2072_pad_0, pad_type = var_2072_pad_type_0, strides = var_2068, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_221_cast_fp16)[name = tensor("op_2072_cast_fp16")]; + tensor hidden_states_131_cast_fp16 = add(x = var_2072_cast_fp16, y = inputs_41_cast_fp16)[name = tensor("hidden_states_131_cast_fp16")]; + tensor var_2074 = const()[name = tensor("op_2074"), val = tensor([2, 1280, 8, 8])]; + tensor input_223_cast_fp16 = reshape(shape = var_2074, x = hidden_states_131_cast_fp16)[name = tensor("input_223_cast_fp16")]; + tensor var_2078 = const()[name = tensor("op_2078"), val = tensor([1, 1])]; + tensor var_2080 = const()[name = tensor("op_2080"), val = tensor([1, 1])]; + tensor hidden_states_133_pad_type_0 = const()[name = tensor("hidden_states_133_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_133_pad_0 = const()[name = tensor("hidden_states_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79988736))), lut = tensor([-0x1.28p-5, -0x1.518p-7, 0x1.5d8p-7, 0x1.2c4p-5]), name = tensor("mid_block_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80398400)))]; + tensor hidden_states_133_cast_fp16 = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_2080, groups = var_1828, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = var_2078, weight = mid_block_attentions_0_proj_out_weight_to_fp16_palettized, x = input_223_cast_fp16)[name = tensor("hidden_states_133_cast_fp16")]; + tensor input_225_cast_fp16 = add(x = hidden_states_133_cast_fp16, y = hidden_states_121_cast_fp16)[name = tensor("input_225_cast_fp16")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = input_225_cast_fp16)[name = tensor("reshape_100_cast_fp16")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = tensor("reduce_mean_75_cast_fp16")]; + tensor sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor("sub_50_cast_fp16")]; + tensor square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor("square_25_cast_fp16")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = tensor("reduce_mean_77_cast_fp16")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast_fp16")]; + tensor sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor("sqrt_25_cast_fp16")]; + tensor real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor("real_div_25_cast_fp16")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor("reshape_101_cast_fp16")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80401024)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80403648)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_51_cast_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor("add_51_cast_fp16")]; + tensor input_229_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor("input_229_cast_fp16")]; + tensor var_2095 = const()[name = tensor("op_2095"), val = tensor([1, 1])]; + tensor var_2097 = const()[name = tensor("op_2097"), val = tensor([1, 1])]; + tensor hidden_states_135_pad_type_0 = const()[name = tensor("hidden_states_135_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_135_pad_0 = const()[name = tensor("hidden_states_135_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80406272))), lut = tensor([-0x1.058p-5, -0x1.588p-8, 0x1.804p-8, 0x1.0d4p-5]), name = tensor("mid_block_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84092736)))]; + tensor hidden_states_135_cast_fp16 = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_2097, groups = var_1828, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_2095, weight = mid_block_resnets_1_conv1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = tensor("hidden_states_135_cast_fp16")]; + tensor var_2103 = const()[name = tensor("op_2103"), val = tensor([1, 1])]; + tensor var_2105 = const()[name = tensor("op_2105"), val = tensor([1, 1])]; + tensor temb_19_pad_type_0 = const()[name = tensor("temb_19_pad_type_0"), val = tensor("custom")]; + tensor temb_19_pad_0 = const()[name = tensor("temb_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84095360))), lut = tensor([-0x1.034p-8, -0x1.5ep-14, 0x1.f08p-9, 0x1.b68p-6]), name = tensor("mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84505024)))]; + tensor temb_19_cast_fp16 = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2105, groups = var_1828, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_2103, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_19_cast_fp16")]; + tensor input_233_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = temb_19_cast_fp16)[name = tensor("input_233_cast_fp16")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_233_cast_fp16)[name = tensor("reshape_104_cast_fp16")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78_cast_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = tensor("reduce_mean_78_cast_fp16")]; + tensor sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor("sub_52_cast_fp16")]; + tensor square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor("square_26_cast_fp16")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80_cast_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = tensor("reduce_mean_80_cast_fp16")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast_fp16")]; + tensor sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor("sqrt_26_cast_fp16")]; + tensor real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor("real_div_26_cast_fp16")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor("reshape_105_cast_fp16")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84507648)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84510272)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_53_cast_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor("add_53_cast_fp16")]; + tensor input_237_cast_fp16 = silu(x = add_53_cast_fp16)[name = tensor("input_237_cast_fp16")]; + tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, 1])]; + tensor var_2117 = const()[name = tensor("op_2117"), val = tensor([1, 1])]; + tensor hidden_states_137_pad_type_0 = const()[name = tensor("hidden_states_137_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_137_pad_0 = const()[name = tensor("hidden_states_137_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84512896))), lut = tensor([-0x1.018p-5, -0x1.394p-8, 0x1.4cp-8, 0x1p-5]), name = tensor("mid_block_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88199360)))]; + tensor hidden_states_137_cast_fp16 = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_2117, groups = var_1828, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = var_2115, weight = mid_block_resnets_1_conv2_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = tensor("hidden_states_137_cast_fp16")]; + tensor hidden_states_139_cast_fp16 = add(x = input_225_cast_fp16, y = hidden_states_137_cast_fp16)[name = tensor("hidden_states_139_cast_fp16")]; + tensor var_2128 = const()[name = tensor("op_2128"), val = tensor(1)]; + tensor input_239_interleave_0 = const()[name = tensor("input_239_interleave_0"), val = tensor(false)]; + tensor input_239_cast_fp16 = concat(axis = var_2128, interleave = input_239_interleave_0, values = (hidden_states_139_cast_fp16, input_199_cast_fp16))[name = tensor("input_239_cast_fp16")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([2, 32, 80, 8, 8])]; + tensor reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = input_239_cast_fp16)[name = tensor("reshape_108_cast_fp16")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = tensor("reduce_mean_81_cast_fp16")]; + tensor sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor("sub_54_cast_fp16")]; + tensor square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor("square_27_cast_fp16")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = tensor("reduce_mean_83_cast_fp16")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast_fp16")]; + tensor sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor("sqrt_27_cast_fp16")]; + tensor real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor("real_div_27_cast_fp16")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([2, 2560, 8, 8])]; + tensor reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor("reshape_109_cast_fp16")]; + tensor add_55_mean_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88201984))), lut = tensor([0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0]), name = tensor("add_55_mean_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_variance_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88202688))), lut = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0]), name = tensor("add_55_variance_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88203392))), lut = tensor([0x1.78cp-4, 0x1.894p-2, 0x1.4d8p-3, 0x1.2dcp-2]), name = tensor("add_55_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88204096))), lut = tensor([-0x1.2a8p-7, -0x1.a14p-3, -0x1.27cp-4, -0x1.038p-5]), name = tensor("add_55_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_55_cast_fp16 = batch_norm(beta = add_55_beta_0_to_fp16_palettized, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_109_cast_fp16)[name = tensor("add_55_cast_fp16")]; + tensor input_243_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor("input_243_cast_fp16")]; + tensor var_2151 = const()[name = tensor("op_2151"), val = tensor([1, 1])]; + tensor var_2153 = const()[name = tensor("op_2153"), val = tensor([1, 1])]; + tensor hidden_states_141_pad_type_0 = const()[name = tensor("hidden_states_141_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_141_pad_0 = const()[name = tensor("hidden_states_141_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88204800))), lut = tensor([-0x1.c38p-6, -0x1.f64p-9, 0x1.69cp-8, 0x1.ee8p-6]), name = tensor("up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95577664)))]; + tensor hidden_states_141_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_2153, groups = var_2128, pad = hidden_states_141_pad_0, pad_type = hidden_states_141_pad_type_0, strides = var_2151, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_243_cast_fp16)[name = tensor("hidden_states_141_cast_fp16")]; + tensor var_2159 = const()[name = tensor("op_2159"), val = tensor([1, 1])]; + tensor var_2161 = const()[name = tensor("op_2161"), val = tensor([1, 1])]; + tensor temb_21_pad_type_0 = const()[name = tensor("temb_21_pad_type_0"), val = tensor("custom")]; + tensor temb_21_pad_0 = const()[name = tensor("temb_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95580288))), lut = tensor([-0x1.058p-8, -0x1.b9cp-14, 0x1.ef8p-9, 0x1.f6cp-6]), name = tensor("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95989952)))]; + tensor temb_21_cast_fp16 = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2161, groups = var_2128, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_2159, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_21_cast_fp16")]; + tensor input_247_cast_fp16 = add(x = hidden_states_141_cast_fp16, y = temb_21_cast_fp16)[name = tensor("input_247_cast_fp16")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_247_cast_fp16)[name = tensor("reshape_112_cast_fp16")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84_cast_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = tensor("reduce_mean_84_cast_fp16")]; + tensor sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor("sub_56_cast_fp16")]; + tensor square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor("square_28_cast_fp16")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86_cast_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = tensor("reduce_mean_86_cast_fp16")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast_fp16")]; + tensor sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor("sqrt_28_cast_fp16")]; + tensor real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor("real_div_28_cast_fp16")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor("reshape_113_cast_fp16")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95992576)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95995200)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_57_cast_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor("add_57_cast_fp16")]; + tensor input_251_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor("input_251_cast_fp16")]; + tensor var_2171 = const()[name = tensor("op_2171"), val = tensor([1, 1])]; + tensor var_2173 = const()[name = tensor("op_2173"), val = tensor([1, 1])]; + tensor hidden_states_143_pad_type_0 = const()[name = tensor("hidden_states_143_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_143_pad_0 = const()[name = tensor("hidden_states_143_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95997824))), lut = tensor([-0x1.488p-5, -0x1.f84p-11, 0x1.84cp-6, 0x1.d5cp-4]), name = tensor("up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99684288)))]; + tensor hidden_states_143_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_2173, groups = var_2128, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = var_2171, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = tensor("hidden_states_143_cast_fp16")]; + tensor var_2178 = const()[name = tensor("op_2178"), val = tensor([1, 1])]; + tensor var_2180 = const()[name = tensor("op_2180"), val = tensor([1, 1])]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99686912))), lut = tensor([-0x1.3dcp-6, -0x1.374p-8, 0x1.3acp-8, 0x1.3fp-6]), name = tensor("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100506176)))]; + tensor x_5_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2180, groups = var_2128, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_2178, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_239_cast_fp16)[name = tensor("x_5_cast_fp16")]; + tensor hidden_states_145_cast_fp16 = add(x = x_5_cast_fp16, y = hidden_states_143_cast_fp16)[name = tensor("hidden_states_145_cast_fp16")]; + tensor input_253_interleave_0 = const()[name = tensor("input_253_interleave_0"), val = tensor(false)]; + tensor input_253_cast_fp16 = concat(axis = var_2128, interleave = input_253_interleave_0, values = (hidden_states_145_cast_fp16, input_185_cast_fp16))[name = tensor("input_253_cast_fp16")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([2, 32, 80, 8, 8])]; + tensor reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = input_253_cast_fp16)[name = tensor("reshape_116_cast_fp16")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = tensor("reduce_mean_87_cast_fp16")]; + tensor sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor("sub_58_cast_fp16")]; + tensor square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor("square_29_cast_fp16")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = tensor("reduce_mean_89_cast_fp16")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast_fp16")]; + tensor sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor("sqrt_29_cast_fp16")]; + tensor real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor("real_div_29_cast_fp16")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([2, 2560, 8, 8])]; + tensor reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor("reshape_117_cast_fp16")]; + tensor add_59_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100508800))), lut = tensor([0x1.858p-3, 0x1.724p-2, 0x1.144p-1, 0x1.1ccp-2]), name = tensor("add_59_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_59_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100509504))), lut = tensor([-0x1.9fcp-6, -0x1.87cp-2, -0x1.2fcp-3, -0x1.35cp-1]), name = tensor("add_59_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_59_cast_fp16 = batch_norm(beta = add_59_beta_0_to_fp16_palettized, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_117_cast_fp16)[name = tensor("add_59_cast_fp16")]; + tensor input_257_cast_fp16 = silu(x = add_59_cast_fp16)[name = tensor("input_257_cast_fp16")]; + tensor var_2198 = const()[name = tensor("op_2198"), val = tensor([1, 1])]; + tensor var_2200 = const()[name = tensor("op_2200"), val = tensor([1, 1])]; + tensor hidden_states_147_pad_type_0 = const()[name = tensor("hidden_states_147_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_147_pad_0 = const()[name = tensor("hidden_states_147_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100510208))), lut = tensor([-0x1.04p-5, -0x1.238p-8, 0x1.2e8p-7, 0x1.3ecp-5]), name = tensor("up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107883072)))]; + tensor hidden_states_147_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_2200, groups = var_2128, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = var_2198, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_257_cast_fp16)[name = tensor("hidden_states_147_cast_fp16")]; + tensor var_2206 = const()[name = tensor("op_2206"), val = tensor([1, 1])]; + tensor var_2208 = const()[name = tensor("op_2208"), val = tensor([1, 1])]; + tensor temb_23_pad_type_0 = const()[name = tensor("temb_23_pad_type_0"), val = tensor("custom")]; + tensor temb_23_pad_0 = const()[name = tensor("temb_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107885696))), lut = tensor([-0x1.13cp-8, -0x1.5bp-13, 0x1.fc4p-9, 0x1.20cp-5]), name = tensor("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108295360)))]; + tensor temb_23_cast_fp16 = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2208, groups = var_2128, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_2206, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_23_cast_fp16")]; + tensor input_261_cast_fp16 = add(x = hidden_states_147_cast_fp16, y = temb_23_cast_fp16)[name = tensor("input_261_cast_fp16")]; + tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_120_cast_fp16 = reshape(shape = reshape_120_shape_0, x = input_261_cast_fp16)[name = tensor("reshape_120_cast_fp16")]; + tensor reduce_mean_90_axes_0 = const()[name = tensor("reduce_mean_90_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_90_keep_dims_0 = const()[name = tensor("reduce_mean_90_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_90_cast_fp16 = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast_fp16)[name = tensor("reduce_mean_90_cast_fp16")]; + tensor sub_60_cast_fp16 = sub(x = reshape_120_cast_fp16, y = reduce_mean_90_cast_fp16)[name = tensor("sub_60_cast_fp16")]; + tensor square_30_cast_fp16 = square(x = sub_60_cast_fp16)[name = tensor("square_30_cast_fp16")]; + tensor reduce_mean_92_axes_0 = const()[name = tensor("reduce_mean_92_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_92_keep_dims_0 = const()[name = tensor("reduce_mean_92_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_92_cast_fp16 = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast_fp16)[name = tensor("reduce_mean_92_cast_fp16")]; + tensor add_60_y_0_to_fp16 = const()[name = tensor("add_60_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_60_cast_fp16 = add(x = reduce_mean_92_cast_fp16, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast_fp16")]; + tensor sqrt_30_cast_fp16 = sqrt(x = add_60_cast_fp16)[name = tensor("sqrt_30_cast_fp16")]; + tensor real_div_30_cast_fp16 = real_div(x = sub_60_cast_fp16, y = sqrt_30_cast_fp16)[name = tensor("real_div_30_cast_fp16")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_121_cast_fp16 = reshape(shape = reshape_121_shape_0, x = real_div_30_cast_fp16)[name = tensor("reshape_121_cast_fp16")]; + tensor add_61_gamma_0_to_fp16 = const()[name = tensor("add_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108297984)))]; + tensor add_61_beta_0_to_fp16 = const()[name = tensor("add_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108300608)))]; + tensor add_61_epsilon_0_to_fp16 = const()[name = tensor("add_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_61_cast_fp16 = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_121_cast_fp16)[name = tensor("add_61_cast_fp16")]; + tensor input_265_cast_fp16 = silu(x = add_61_cast_fp16)[name = tensor("input_265_cast_fp16")]; + tensor var_2218 = const()[name = tensor("op_2218"), val = tensor([1, 1])]; + tensor var_2220 = const()[name = tensor("op_2220"), val = tensor([1, 1])]; + tensor hidden_states_149_pad_type_0 = const()[name = tensor("hidden_states_149_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_149_pad_0 = const()[name = tensor("hidden_states_149_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108303232))), lut = tensor([-0x1.9ecp-5, -0x1.9bcp-9, 0x1.44p-6, 0x1.438p-4]), name = tensor("up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111989696)))]; + tensor hidden_states_149_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_2220, groups = var_2128, pad = hidden_states_149_pad_0, pad_type = hidden_states_149_pad_type_0, strides = var_2218, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_265_cast_fp16)[name = tensor("hidden_states_149_cast_fp16")]; + tensor var_2225 = const()[name = tensor("op_2225"), val = tensor([1, 1])]; + tensor var_2227 = const()[name = tensor("op_2227"), val = tensor([1, 1])]; + tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; + tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111992320))), lut = tensor([-0x1.9bp-7, -0x1.8ap-9, 0x1.95cp-9, 0x1.a14p-7]), name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112811584)))]; + tensor x_7_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2227, groups = var_2128, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_2225, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_253_cast_fp16)[name = tensor("x_7_cast_fp16")]; + tensor hidden_states_151_cast_fp16 = add(x = x_7_cast_fp16, y = hidden_states_149_cast_fp16)[name = tensor("hidden_states_151_cast_fp16")]; + tensor input_267_interleave_0 = const()[name = tensor("input_267_interleave_0"), val = tensor(false)]; + tensor input_267_cast_fp16 = concat(axis = var_2128, interleave = input_267_interleave_0, values = (hidden_states_151_cast_fp16, input_171_cast_fp16))[name = tensor("input_267_cast_fp16")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([2, 32, 80, 8, 8])]; + tensor reshape_124_cast_fp16 = reshape(shape = reshape_124_shape_0, x = input_267_cast_fp16)[name = tensor("reshape_124_cast_fp16")]; + tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_93_cast_fp16 = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast_fp16)[name = tensor("reduce_mean_93_cast_fp16")]; + tensor sub_62_cast_fp16 = sub(x = reshape_124_cast_fp16, y = reduce_mean_93_cast_fp16)[name = tensor("sub_62_cast_fp16")]; + tensor square_31_cast_fp16 = square(x = sub_62_cast_fp16)[name = tensor("square_31_cast_fp16")]; + tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_95_cast_fp16 = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast_fp16)[name = tensor("reduce_mean_95_cast_fp16")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast_fp16 = add(x = reduce_mean_95_cast_fp16, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast_fp16")]; + tensor sqrt_31_cast_fp16 = sqrt(x = add_62_cast_fp16)[name = tensor("sqrt_31_cast_fp16")]; + tensor real_div_31_cast_fp16 = real_div(x = sub_62_cast_fp16, y = sqrt_31_cast_fp16)[name = tensor("real_div_31_cast_fp16")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([2, 2560, 8, 8])]; + tensor reshape_125_cast_fp16 = reshape(shape = reshape_125_shape_0, x = real_div_31_cast_fp16)[name = tensor("reshape_125_cast_fp16")]; + tensor add_63_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112814208))), lut = tensor([0x1.33cp-2, 0x1.a08p-3, 0x1.448p-1, 0x1.ac8p-2]), name = tensor("add_63_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_63_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112814912))), lut = tensor([-0x1.f1p-4, -0x1.d34p-7, -0x1.4bcp-1, -0x1.59cp-2]), name = tensor("add_63_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_63_cast_fp16 = batch_norm(beta = add_63_beta_0_to_fp16_palettized, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_125_cast_fp16)[name = tensor("add_63_cast_fp16")]; + tensor input_271_cast_fp16 = silu(x = add_63_cast_fp16)[name = tensor("input_271_cast_fp16")]; + tensor var_2245 = const()[name = tensor("op_2245"), val = tensor([1, 1])]; + tensor var_2247 = const()[name = tensor("op_2247"), val = tensor([1, 1])]; + tensor hidden_states_153_pad_type_0 = const()[name = tensor("hidden_states_153_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_153_pad_0 = const()[name = tensor("hidden_states_153_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112815616))), lut = tensor([-0x1.964p-5, -0x1.c2cp-7, 0x1.fc4p-9, 0x1.27cp-5]), name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120188480)))]; + tensor hidden_states_153_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_2247, groups = var_2128, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = var_2245, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized, x = input_271_cast_fp16)[name = tensor("hidden_states_153_cast_fp16")]; + tensor var_2253 = const()[name = tensor("op_2253"), val = tensor([1, 1])]; + tensor var_2255 = const()[name = tensor("op_2255"), val = tensor([1, 1])]; + tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; + tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120191104))), lut = tensor([-0x1.164p-5, -0x1.74cp-9, 0x1.7ep-9, 0x1.3f4p-5]), name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120600768)))]; + tensor temb_25_cast_fp16 = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2255, groups = var_2128, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_2253, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_25_cast_fp16")]; + tensor input_275_cast_fp16 = add(x = hidden_states_153_cast_fp16, y = temb_25_cast_fp16)[name = tensor("input_275_cast_fp16")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_128_cast_fp16 = reshape(shape = reshape_128_shape_0, x = input_275_cast_fp16)[name = tensor("reshape_128_cast_fp16")]; + tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_96_cast_fp16 = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast_fp16)[name = tensor("reduce_mean_96_cast_fp16")]; + tensor sub_64_cast_fp16 = sub(x = reshape_128_cast_fp16, y = reduce_mean_96_cast_fp16)[name = tensor("sub_64_cast_fp16")]; + tensor square_32_cast_fp16 = square(x = sub_64_cast_fp16)[name = tensor("square_32_cast_fp16")]; + tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_98_cast_fp16 = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast_fp16)[name = tensor("reduce_mean_98_cast_fp16")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_64_cast_fp16 = add(x = reduce_mean_98_cast_fp16, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast_fp16")]; + tensor sqrt_32_cast_fp16 = sqrt(x = add_64_cast_fp16)[name = tensor("sqrt_32_cast_fp16")]; + tensor real_div_32_cast_fp16 = real_div(x = sub_64_cast_fp16, y = sqrt_32_cast_fp16)[name = tensor("real_div_32_cast_fp16")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_129_cast_fp16 = reshape(shape = reshape_129_shape_0, x = real_div_32_cast_fp16)[name = tensor("reshape_129_cast_fp16")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120603392)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120606016)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_65_cast_fp16 = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_129_cast_fp16)[name = tensor("add_65_cast_fp16")]; + tensor input_279_cast_fp16 = silu(x = add_65_cast_fp16)[name = tensor("input_279_cast_fp16")]; + tensor var_2265 = const()[name = tensor("op_2265"), val = tensor([1, 1])]; + tensor var_2267 = const()[name = tensor("op_2267"), val = tensor([1, 1])]; + tensor hidden_states_155_pad_type_0 = const()[name = tensor("hidden_states_155_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_155_pad_0 = const()[name = tensor("hidden_states_155_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120608640))), lut = tensor([-0x1.424p-5, -0x1.384p-9, 0x1.4fp-6, 0x1.16cp-4]), name = tensor("up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124295104)))]; + tensor hidden_states_155_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_2267, groups = var_2128, pad = hidden_states_155_pad_0, pad_type = hidden_states_155_pad_type_0, strides = var_2265, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = tensor("hidden_states_155_cast_fp16")]; + tensor var_2272 = const()[name = tensor("op_2272"), val = tensor([1, 1])]; + tensor var_2274 = const()[name = tensor("op_2274"), val = tensor([1, 1])]; + tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; + tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124297728))), lut = tensor([-0x1.a88p-8, -0x1.e9p-10, 0x1.e7p-10, 0x1.a78p-8]), name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125116992)))]; + tensor x_9_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2274, groups = var_2128, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_2272, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = tensor("x_9_cast_fp16")]; + tensor input_281_cast_fp16 = add(x = x_9_cast_fp16, y = hidden_states_155_cast_fp16)[name = tensor("input_281_cast_fp16")]; + tensor input_283_scale_factor_height_0 = const()[name = tensor("input_283_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_283_scale_factor_width_0 = const()[name = tensor("input_283_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_283_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_283_scale_factor_height_0, scale_factor_width = input_283_scale_factor_width_0, x = input_281_cast_fp16)[name = tensor("input_283_cast_fp16")]; + tensor var_2283 = const()[name = tensor("op_2283"), val = tensor([1, 1])]; + tensor var_2285 = const()[name = tensor("op_2285"), val = tensor([1, 1])]; + tensor hidden_states_157_pad_type_0 = const()[name = tensor("hidden_states_157_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_157_pad_0 = const()[name = tensor("hidden_states_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125119616))), lut = tensor([-0x1.b8cp-7, -0x1.004p-8, 0x1.f78p-9, 0x1.b5p-7]), name = tensor("up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(128806080)))]; + tensor hidden_states_157_cast_fp16 = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_2285, groups = var_2128, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = var_2283, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = tensor("hidden_states_157_cast_fp16")]; + tensor var_2290 = const()[name = tensor("op_2290"), val = tensor(3)]; + tensor var_2301 = const()[name = tensor("op_2301"), val = tensor(true)]; + tensor var_2306 = const()[name = tensor("op_2306"), val = tensor(1)]; + tensor input_285_interleave_0 = const()[name = tensor("input_285_interleave_0"), val = tensor(false)]; + tensor input_285_cast_fp16 = concat(axis = var_2306, interleave = input_285_interleave_0, values = (hidden_states_157_cast_fp16, input_169_cast_fp16))[name = tensor("input_285_cast_fp16")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([2, 32, 80, 16, 16])]; + tensor reshape_132_cast_fp16 = reshape(shape = reshape_132_shape_0, x = input_285_cast_fp16)[name = tensor("reshape_132_cast_fp16")]; + tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_99_cast_fp16 = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast_fp16)[name = tensor("reduce_mean_99_cast_fp16")]; + tensor sub_66_cast_fp16 = sub(x = reshape_132_cast_fp16, y = reduce_mean_99_cast_fp16)[name = tensor("sub_66_cast_fp16")]; + tensor square_33_cast_fp16 = square(x = sub_66_cast_fp16)[name = tensor("square_33_cast_fp16")]; + tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_101_cast_fp16 = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast_fp16)[name = tensor("reduce_mean_101_cast_fp16")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_66_cast_fp16 = add(x = reduce_mean_101_cast_fp16, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast_fp16")]; + tensor sqrt_33_cast_fp16 = sqrt(x = add_66_cast_fp16)[name = tensor("sqrt_33_cast_fp16")]; + tensor real_div_33_cast_fp16 = real_div(x = sub_66_cast_fp16, y = sqrt_33_cast_fp16)[name = tensor("real_div_33_cast_fp16")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([2, 2560, 16, 16])]; + tensor reshape_133_cast_fp16 = reshape(shape = reshape_133_shape_0, x = real_div_33_cast_fp16)[name = tensor("reshape_133_cast_fp16")]; + tensor add_67_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(128808704))), lut = tensor([0x1.438p-2, 0x1.70cp-8, 0x1.378p-1, 0x1.aecp-2]), name = tensor("add_67_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_67_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(128809408))), lut = tensor([-0x1.9c4p-6, -0x1.288p-1, -0x1.438p+0, -0x1.f7cp-3]), name = tensor("add_67_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_67_cast_fp16 = batch_norm(beta = add_67_beta_0_to_fp16_palettized, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_133_cast_fp16)[name = tensor("add_67_cast_fp16")]; + tensor input_289_cast_fp16 = silu(x = add_67_cast_fp16)[name = tensor("input_289_cast_fp16")]; + tensor var_2335 = const()[name = tensor("op_2335"), val = tensor([1, 1])]; + tensor var_2337 = const()[name = tensor("op_2337"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_type_0 = const()[name = tensor("hidden_states_159_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_159_pad_0 = const()[name = tensor("hidden_states_159_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(128810112))), lut = tensor([-0x1.448p-4, -0x1.ec4p-6, 0x1.048p-8, 0x1.b94p-5]), name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136182976)))]; + tensor hidden_states_159_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_2337, groups = var_2306, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = var_2335, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_289_cast_fp16)[name = tensor("hidden_states_159_cast_fp16")]; + tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, 1])]; + tensor var_2345 = const()[name = tensor("op_2345"), val = tensor([1, 1])]; + tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; + tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136185600))), lut = tensor([-0x1.588p+1, -0x1.01cp-8, 0x1.88cp-9, 0x1.a64p-5]), name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136595264)))]; + tensor temb_27_cast_fp16 = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2345, groups = var_2306, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_2343, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_27_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = temb_27_cast_fp16)[name = tensor("input_293_cast_fp16")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_136_cast_fp16 = reshape(shape = reshape_136_shape_0, x = input_293_cast_fp16)[name = tensor("reshape_136_cast_fp16")]; + tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_102_cast_fp16 = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast_fp16)[name = tensor("reduce_mean_102_cast_fp16")]; + tensor sub_68_cast_fp16 = sub(x = reshape_136_cast_fp16, y = reduce_mean_102_cast_fp16)[name = tensor("sub_68_cast_fp16")]; + tensor square_34_cast_fp16 = square(x = sub_68_cast_fp16)[name = tensor("square_34_cast_fp16")]; + tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_104_cast_fp16 = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast_fp16)[name = tensor("reduce_mean_104_cast_fp16")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast_fp16 = add(x = reduce_mean_104_cast_fp16, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast_fp16")]; + tensor sqrt_34_cast_fp16 = sqrt(x = add_68_cast_fp16)[name = tensor("sqrt_34_cast_fp16")]; + tensor real_div_34_cast_fp16 = real_div(x = sub_68_cast_fp16, y = sqrt_34_cast_fp16)[name = tensor("real_div_34_cast_fp16")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_137_cast_fp16 = reshape(shape = reshape_137_shape_0, x = real_div_34_cast_fp16)[name = tensor("reshape_137_cast_fp16")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136597888)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136600512)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_69_cast_fp16 = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_137_cast_fp16)[name = tensor("add_69_cast_fp16")]; + tensor input_297_cast_fp16 = silu(x = add_69_cast_fp16)[name = tensor("input_297_cast_fp16")]; + tensor var_2355 = const()[name = tensor("op_2355"), val = tensor([1, 1])]; + tensor var_2357 = const()[name = tensor("op_2357"), val = tensor([1, 1])]; + tensor hidden_states_161_pad_type_0 = const()[name = tensor("hidden_states_161_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_161_pad_0 = const()[name = tensor("hidden_states_161_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136603136))), lut = tensor([-0x1.1ecp-4, -0x1.47p-6, 0x1.488p-6, 0x1.1f8p-4]), name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140289600)))]; + tensor hidden_states_161_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_2357, groups = var_2306, pad = hidden_states_161_pad_0, pad_type = hidden_states_161_pad_type_0, strides = var_2355, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_297_cast_fp16)[name = tensor("hidden_states_161_cast_fp16")]; + tensor var_2362 = const()[name = tensor("op_2362"), val = tensor([1, 1])]; + tensor var_2364 = const()[name = tensor("op_2364"), val = tensor([1, 1])]; + tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; + tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140292224))), lut = tensor([-0x1.1b8p-5, -0x1.2c4p-10, 0x1.48p-6, 0x1.87cp-5]), name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141111488)))]; + tensor x_11_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2364, groups = var_2306, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_2362, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_285_cast_fp16)[name = tensor("x_11_cast_fp16")]; + tensor hidden_states_163_cast_fp16 = add(x = x_11_cast_fp16, y = hidden_states_161_cast_fp16)[name = tensor("hidden_states_163_cast_fp16")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_140_cast_fp16 = reshape(shape = reshape_140_shape_0, x = hidden_states_163_cast_fp16)[name = tensor("reshape_140_cast_fp16")]; + tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_105_cast_fp16 = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast_fp16)[name = tensor("reduce_mean_105_cast_fp16")]; + tensor sub_70_cast_fp16 = sub(x = reshape_140_cast_fp16, y = reduce_mean_105_cast_fp16)[name = tensor("sub_70_cast_fp16")]; + tensor square_35_cast_fp16 = square(x = sub_70_cast_fp16)[name = tensor("square_35_cast_fp16")]; + tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_107_cast_fp16 = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast_fp16)[name = tensor("reduce_mean_107_cast_fp16")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_70_cast_fp16 = add(x = reduce_mean_107_cast_fp16, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast_fp16")]; + tensor sqrt_35_cast_fp16 = sqrt(x = add_70_cast_fp16)[name = tensor("sqrt_35_cast_fp16")]; + tensor real_div_35_cast_fp16 = real_div(x = sub_70_cast_fp16, y = sqrt_35_cast_fp16)[name = tensor("real_div_35_cast_fp16")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_141_cast_fp16 = reshape(shape = reshape_141_shape_0, x = real_div_35_cast_fp16)[name = tensor("reshape_141_cast_fp16")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141114112)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141116736)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_71_cast_fp16 = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_141_cast_fp16)[name = tensor("add_71_cast_fp16")]; + tensor var_2384 = const()[name = tensor("op_2384"), val = tensor([1, 1])]; + tensor var_2386 = const()[name = tensor("op_2386"), val = tensor([1, 1])]; + tensor hidden_states_165_pad_type_0 = const()[name = tensor("hidden_states_165_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_165_pad_0 = const()[name = tensor("hidden_states_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141119360))), lut = tensor([-0x1.8fp-4, -0x1.dc8p-6, 0x1.e1cp-6, 0x1.908p-4]), name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141529024)))]; + tensor hidden_states_165_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_2386, groups = var_2306, pad = hidden_states_165_pad_0, pad_type = hidden_states_165_pad_type_0, strides = var_2384, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_71_cast_fp16)[name = tensor("hidden_states_165_cast_fp16")]; + tensor var_2391 = const()[name = tensor("op_2391"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_43_cast_fp16 = reshape(shape = var_2391, x = hidden_states_165_cast_fp16)[name = tensor("inputs_43_cast_fp16")]; + tensor var_2401 = const()[name = tensor("op_2401"), val = tensor([1])]; + tensor channels_mean_43_cast_fp16 = reduce_mean(axes = var_2401, keep_dims = var_2301, x = inputs_43_cast_fp16)[name = tensor("channels_mean_43_cast_fp16")]; + tensor zero_mean_43_cast_fp16 = sub(x = inputs_43_cast_fp16, y = channels_mean_43_cast_fp16)[name = tensor("zero_mean_43_cast_fp16")]; + tensor zero_mean_sq_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = zero_mean_43_cast_fp16)[name = tensor("zero_mean_sq_43_cast_fp16")]; + tensor var_2405 = const()[name = tensor("op_2405"), val = tensor([1])]; + tensor var_2406_cast_fp16 = reduce_mean(axes = var_2405, keep_dims = var_2301, x = zero_mean_sq_43_cast_fp16)[name = tensor("op_2406_cast_fp16")]; + tensor var_2407_to_fp16 = const()[name = tensor("op_2407_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2408_cast_fp16 = add(x = var_2406_cast_fp16, y = var_2407_to_fp16)[name = tensor("op_2408_cast_fp16")]; + tensor denom_43_epsilon_0_to_fp16 = const()[name = tensor("denom_43_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_43_cast_fp16 = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_2408_cast_fp16)[name = tensor("denom_43_cast_fp16")]; + tensor out_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = denom_43_cast_fp16)[name = tensor("out_43_cast_fp16")]; + tensor var_2412_to_fp16 = const()[name = tensor("op_2412_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141531648)))]; + tensor var_2413_cast_fp16 = add(x = out_43_cast_fp16, y = var_2412_to_fp16)[name = tensor("op_2413_cast_fp16")]; + tensor var_2415_to_fp16 = const()[name = tensor("op_2415_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141534272)))]; + tensor hidden_states_167_cast_fp16 = mul(x = var_2413_cast_fp16, y = var_2415_to_fp16)[name = tensor("hidden_states_167_cast_fp16")]; + tensor var_2422 = const()[name = tensor("op_2422"), val = tensor([1, 1])]; + tensor var_2424 = const()[name = tensor("op_2424"), val = tensor([1, 1])]; + tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; + tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141536896))), lut = tensor([-0x1.754p-4, -0x1.b9p-6, 0x1.b6p-6, 0x1.75p-4]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_29_cast_fp16 = conv(dilations = var_2424, groups = var_2306, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_2422, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_167_cast_fp16)[name = tensor("q_29_cast_fp16")]; + tensor var_2428 = const()[name = tensor("op_2428"), val = tensor([1, 1])]; + tensor var_2430 = const()[name = tensor("op_2430"), val = tensor([1, 1])]; + tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; + tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141946560))), lut = tensor([-0x1.758p-4, -0x1.b88p-6, 0x1.b5cp-6, 0x1.75p-4]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_29_cast_fp16 = conv(dilations = var_2430, groups = var_2306, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_2428, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_167_cast_fp16)[name = tensor("k_29_cast_fp16")]; + tensor var_2434 = const()[name = tensor("op_2434"), val = tensor([1, 1])]; + tensor var_2436 = const()[name = tensor("op_2436"), val = tensor([1, 1])]; + tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; + tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142356224))), lut = tensor([-0x1.56p-4, -0x1.95cp-6, 0x1.958p-6, 0x1.55cp-4]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_29_cast_fp16 = conv(dilations = var_2436, groups = var_2306, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_2434, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_167_cast_fp16)[name = tensor("v_29_cast_fp16")]; + tensor var_2440 = const()[name = tensor("op_2440"), val = tensor([2, 20, 64, -1])]; + tensor var_2441_cast_fp16 = reshape(shape = var_2440, x = q_29_cast_fp16)[name = tensor("op_2441_cast_fp16")]; + tensor var_2442 = const()[name = tensor("op_2442"), val = tensor([2, 20, 64, -1])]; + tensor var_2443_cast_fp16 = reshape(shape = var_2442, x = k_29_cast_fp16)[name = tensor("op_2443_cast_fp16")]; + tensor var_2444 = const()[name = tensor("op_2444"), val = tensor([2, 20, 64, -1])]; + tensor var_2445_cast_fp16 = reshape(shape = var_2444, x = v_29_cast_fp16)[name = tensor("op_2445_cast_fp16")]; + tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2441_cast_fp16, y = var_2443_cast_fp16)[name = tensor("attn_weights_57_cast_fp16")]; + tensor var_2297_to_fp16 = const()[name = tensor("op_2297_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_59_cast_fp16")]; + tensor var_2449_cast_fp16 = softmax(axis = var_2290, x = attn_weights_59_cast_fp16)[name = tensor("op_2449_cast_fp16")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_2445_cast_fp16, y = var_2449_cast_fp16)[name = tensor("attn_29_cast_fp16")]; + tensor var_2453 = const()[name = tensor("op_2453"), val = tensor([2, 1280, 1, -1])]; + tensor input_301_cast_fp16 = reshape(shape = var_2453, x = attn_29_cast_fp16)[name = tensor("input_301_cast_fp16")]; + tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, 1])]; + tensor var_2460 = const()[name = tensor("op_2460"), val = tensor([1, 1])]; + tensor var_2462_pad_type_0 = const()[name = tensor("op_2462_pad_type_0"), val = tensor("custom")]; + tensor var_2462_pad_0 = const()[name = tensor("op_2462_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142765888))), lut = tensor([-0x1.68p-4, -0x1.adcp-6, 0x1.b0cp-6, 0x1.684p-4]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143175552)))]; + tensor var_2462_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2460, groups = var_2306, pad = var_2462_pad_0, pad_type = var_2462_pad_type_0, strides = var_2458, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_301_cast_fp16)[name = tensor("op_2462_cast_fp16")]; + tensor inputs_45_cast_fp16 = add(x = var_2462_cast_fp16, y = inputs_43_cast_fp16)[name = tensor("inputs_45_cast_fp16")]; + tensor var_2466 = const()[name = tensor("op_2466"), val = tensor([1])]; + tensor channels_mean_45_cast_fp16 = reduce_mean(axes = var_2466, keep_dims = var_2301, x = inputs_45_cast_fp16)[name = tensor("channels_mean_45_cast_fp16")]; + tensor zero_mean_45_cast_fp16 = sub(x = inputs_45_cast_fp16, y = channels_mean_45_cast_fp16)[name = tensor("zero_mean_45_cast_fp16")]; + tensor zero_mean_sq_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = zero_mean_45_cast_fp16)[name = tensor("zero_mean_sq_45_cast_fp16")]; + tensor var_2470 = const()[name = tensor("op_2470"), val = tensor([1])]; + tensor var_2471_cast_fp16 = reduce_mean(axes = var_2470, keep_dims = var_2301, x = zero_mean_sq_45_cast_fp16)[name = tensor("op_2471_cast_fp16")]; + tensor var_2472_to_fp16 = const()[name = tensor("op_2472_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2473_cast_fp16 = add(x = var_2471_cast_fp16, y = var_2472_to_fp16)[name = tensor("op_2473_cast_fp16")]; + tensor denom_45_epsilon_0_to_fp16 = const()[name = tensor("denom_45_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_45_cast_fp16 = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_2473_cast_fp16)[name = tensor("denom_45_cast_fp16")]; + tensor out_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = denom_45_cast_fp16)[name = tensor("out_45_cast_fp16")]; + tensor var_2477_to_fp16 = const()[name = tensor("op_2477_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143178176)))]; + tensor var_2478_cast_fp16 = add(x = out_45_cast_fp16, y = var_2477_to_fp16)[name = tensor("op_2478_cast_fp16")]; + tensor var_2480_to_fp16 = const()[name = tensor("op_2480_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143180800)))]; + tensor hidden_states_169_cast_fp16 = mul(x = var_2478_cast_fp16, y = var_2480_to_fp16)[name = tensor("hidden_states_169_cast_fp16")]; + tensor var_2487 = const()[name = tensor("op_2487"), val = tensor([1, 1])]; + tensor var_2489 = const()[name = tensor("op_2489"), val = tensor([1, 1])]; + tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; + tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143183424))), lut = tensor([-0x1.10cp-4, -0x1.464p-6, 0x1.46p-6, 0x1.11p-4]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_31_cast_fp16 = conv(dilations = var_2489, groups = var_2306, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_2487, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_169_cast_fp16)[name = tensor("q_31_cast_fp16")]; + tensor var_2493 = const()[name = tensor("op_2493"), val = tensor([1, 1])]; + tensor var_2495 = const()[name = tensor("op_2495"), val = tensor([1, 1])]; + tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; + tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143593088))), lut = tensor([-0x1.1f4p-4, -0x1.55p-6, 0x1.54cp-6, 0x1.1f8p-4]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor k_31_cast_fp16 = conv(dilations = var_2495, groups = var_2306, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_2493, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_31_cast_fp16")]; + tensor var_2499 = const()[name = tensor("op_2499"), val = tensor([1, 1])]; + tensor var_2501 = const()[name = tensor("op_2501"), val = tensor([1, 1])]; + tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; + tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143920832))), lut = tensor([-0x1.ba8p-5, -0x1.014p-6, 0x1.04p-6, 0x1.bccp-5]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor v_31_cast_fp16 = conv(dilations = var_2501, groups = var_2306, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_2499, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_31_cast_fp16")]; + tensor var_2505 = const()[name = tensor("op_2505"), val = tensor([2, 20, 64, -1])]; + tensor var_2506_cast_fp16 = reshape(shape = var_2505, x = q_31_cast_fp16)[name = tensor("op_2506_cast_fp16")]; + tensor var_2507 = const()[name = tensor("op_2507"), val = tensor([2, 20, 64, -1])]; + tensor var_2508_cast_fp16 = reshape(shape = var_2507, x = k_31_cast_fp16)[name = tensor("op_2508_cast_fp16")]; + tensor var_2509 = const()[name = tensor("op_2509"), val = tensor([2, 20, 64, -1])]; + tensor var_2510_cast_fp16 = reshape(shape = var_2509, x = v_31_cast_fp16)[name = tensor("op_2510_cast_fp16")]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_61_cast_fp16 = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_2506_cast_fp16, y = var_2508_cast_fp16)[name = tensor("attn_weights_61_cast_fp16")]; + tensor attn_weights_63_cast_fp16 = mul(x = attn_weights_61_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_63_cast_fp16")]; + tensor var_2514_cast_fp16 = softmax(axis = var_2290, x = attn_weights_63_cast_fp16)[name = tensor("op_2514_cast_fp16")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_2510_cast_fp16, y = var_2514_cast_fp16)[name = tensor("attn_31_cast_fp16")]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([2, 1280, 1, -1])]; + tensor input_303_cast_fp16 = reshape(shape = var_2518, x = attn_31_cast_fp16)[name = tensor("input_303_cast_fp16")]; + tensor var_2523 = const()[name = tensor("op_2523"), val = tensor([1, 1])]; + tensor var_2525 = const()[name = tensor("op_2525"), val = tensor([1, 1])]; + tensor var_2527_pad_type_0 = const()[name = tensor("op_2527_pad_type_0"), val = tensor("custom")]; + tensor var_2527_pad_0 = const()[name = tensor("op_2527_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144248576))), lut = tensor([-0x1.3dcp-5, -0x1.774p-7, 0x1.7cp-7, 0x1.3e8p-5]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144658240)))]; + tensor var_2527_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2525, groups = var_2306, pad = var_2527_pad_0, pad_type = var_2527_pad_type_0, strides = var_2523, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = tensor("op_2527_cast_fp16")]; + tensor inputs_47_cast_fp16 = add(x = var_2527_cast_fp16, y = inputs_45_cast_fp16)[name = tensor("inputs_47_cast_fp16")]; + tensor var_2531 = const()[name = tensor("op_2531"), val = tensor([1])]; + tensor channels_mean_47_cast_fp16 = reduce_mean(axes = var_2531, keep_dims = var_2301, x = inputs_47_cast_fp16)[name = tensor("channels_mean_47_cast_fp16")]; + tensor zero_mean_47_cast_fp16 = sub(x = inputs_47_cast_fp16, y = channels_mean_47_cast_fp16)[name = tensor("zero_mean_47_cast_fp16")]; + tensor zero_mean_sq_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = zero_mean_47_cast_fp16)[name = tensor("zero_mean_sq_47_cast_fp16")]; + tensor var_2535 = const()[name = tensor("op_2535"), val = tensor([1])]; + tensor var_2536_cast_fp16 = reduce_mean(axes = var_2535, keep_dims = var_2301, x = zero_mean_sq_47_cast_fp16)[name = tensor("op_2536_cast_fp16")]; + tensor var_2537_to_fp16 = const()[name = tensor("op_2537_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2538_cast_fp16 = add(x = var_2536_cast_fp16, y = var_2537_to_fp16)[name = tensor("op_2538_cast_fp16")]; + tensor denom_47_epsilon_0_to_fp16 = const()[name = tensor("denom_47_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_47_cast_fp16 = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_2538_cast_fp16)[name = tensor("denom_47_cast_fp16")]; + tensor out_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = denom_47_cast_fp16)[name = tensor("out_47_cast_fp16")]; + tensor var_2542_to_fp16 = const()[name = tensor("op_2542_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144660864)))]; + tensor var_2543_cast_fp16 = add(x = out_47_cast_fp16, y = var_2542_to_fp16)[name = tensor("op_2543_cast_fp16")]; + tensor var_2545_to_fp16 = const()[name = tensor("op_2545_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144663488)))]; + tensor input_305_cast_fp16 = mul(x = var_2543_cast_fp16, y = var_2545_to_fp16)[name = tensor("input_305_cast_fp16")]; + tensor var_2553 = const()[name = tensor("op_2553"), val = tensor([1, 1])]; + tensor var_2555 = const()[name = tensor("op_2555"), val = tensor([1, 1])]; + tensor var_2557_pad_type_0 = const()[name = tensor("op_2557_pad_type_0"), val = tensor("custom")]; + tensor var_2557_pad_0 = const()[name = tensor("op_2557_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144666112))), lut = tensor([-0x1.584p-4, -0x1.984p-6, 0x1.95p-6, 0x1.574p-4]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147942976))), lut = tensor([-0x1.4f8p-3, -0x1.f2cp-5, -0x1.57p-9, 0x1.118p-4]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2557_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2555, groups = var_2306, pad = var_2557_pad_0, pad_type = var_2557_pad_type_0, strides = var_2553, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_305_cast_fp16)[name = tensor("op_2557_cast_fp16")]; + tensor var_2558_split_sizes_0 = const()[name = tensor("op_2558_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2558_axis_0 = const()[name = tensor("op_2558_axis_0"), val = tensor(1)]; + tensor var_2558_cast_fp16_0, tensor var_2558_cast_fp16_1 = split(axis = var_2558_axis_0, split_sizes = var_2558_split_sizes_0, x = var_2557_cast_fp16)[name = tensor("op_2558_cast_fp16")]; + tensor var_2560_mode_0 = const()[name = tensor("op_2560_mode_0"), val = tensor("EXACT")]; + tensor var_2560_cast_fp16 = gelu(mode = var_2560_mode_0, x = var_2558_cast_fp16_1)[name = tensor("op_2560_cast_fp16")]; + tensor input_307_cast_fp16 = mul(x = var_2558_cast_fp16_0, y = var_2560_cast_fp16)[name = tensor("input_307_cast_fp16")]; + tensor var_2564 = const()[name = tensor("op_2564"), val = tensor([1, 1])]; + tensor var_2566 = const()[name = tensor("op_2566"), val = tensor([1, 1])]; + tensor var_2568_pad_type_0 = const()[name = tensor("op_2568_pad_type_0"), val = tensor("custom")]; + tensor var_2568_pad_0 = const()[name = tensor("op_2568_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147945600))), lut = tensor([-0x1.56cp-4, -0x1.9a8p-6, 0x1.988p-6, 0x1.568p-4]), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149584064)))]; + tensor var_2568_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2566, groups = var_2306, pad = var_2568_pad_0, pad_type = var_2568_pad_type_0, strides = var_2564, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = tensor("op_2568_cast_fp16")]; + tensor hidden_states_173_cast_fp16 = add(x = var_2568_cast_fp16, y = inputs_47_cast_fp16)[name = tensor("hidden_states_173_cast_fp16")]; + tensor var_2570 = const()[name = tensor("op_2570"), val = tensor([2, 1280, 16, 16])]; + tensor input_309_cast_fp16 = reshape(shape = var_2570, x = hidden_states_173_cast_fp16)[name = tensor("input_309_cast_fp16")]; + tensor var_2574 = const()[name = tensor("op_2574"), val = tensor([1, 1])]; + tensor var_2576 = const()[name = tensor("op_2576"), val = tensor([1, 1])]; + tensor hidden_states_175_pad_type_0 = const()[name = tensor("hidden_states_175_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_175_pad_0 = const()[name = tensor("hidden_states_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149586688))), lut = tensor([-0x1.56cp-4, -0x1.9acp-6, 0x1.9bcp-6, 0x1.578p-4]), name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149996352)))]; + tensor hidden_states_175_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_2576, groups = var_2306, pad = hidden_states_175_pad_0, pad_type = hidden_states_175_pad_type_0, strides = var_2574, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_309_cast_fp16)[name = tensor("hidden_states_175_cast_fp16")]; + tensor hidden_states_177_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_163_cast_fp16)[name = tensor("hidden_states_177_cast_fp16")]; + tensor input_311_interleave_0 = const()[name = tensor("input_311_interleave_0"), val = tensor(false)]; + tensor input_311_cast_fp16 = concat(axis = var_2306, interleave = input_311_interleave_0, values = (hidden_states_177_cast_fp16, input_143_cast_fp16))[name = tensor("input_311_cast_fp16")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([2, 32, 80, 16, 16])]; + tensor reshape_144_cast_fp16 = reshape(shape = reshape_144_shape_0, x = input_311_cast_fp16)[name = tensor("reshape_144_cast_fp16")]; + tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_108_cast_fp16 = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast_fp16)[name = tensor("reduce_mean_108_cast_fp16")]; + tensor sub_72_cast_fp16 = sub(x = reshape_144_cast_fp16, y = reduce_mean_108_cast_fp16)[name = tensor("sub_72_cast_fp16")]; + tensor square_36_cast_fp16 = square(x = sub_72_cast_fp16)[name = tensor("square_36_cast_fp16")]; + tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_110_cast_fp16 = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast_fp16)[name = tensor("reduce_mean_110_cast_fp16")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_72_cast_fp16 = add(x = reduce_mean_110_cast_fp16, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast_fp16")]; + tensor sqrt_36_cast_fp16 = sqrt(x = add_72_cast_fp16)[name = tensor("sqrt_36_cast_fp16")]; + tensor real_div_36_cast_fp16 = real_div(x = sub_72_cast_fp16, y = sqrt_36_cast_fp16)[name = tensor("real_div_36_cast_fp16")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([2, 2560, 16, 16])]; + tensor reshape_145_cast_fp16 = reshape(shape = reshape_145_shape_0, x = real_div_36_cast_fp16)[name = tensor("reshape_145_cast_fp16")]; + tensor add_73_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149998976))), lut = tensor([0x1.a5p-3, 0x1.0bp-1, 0x1.6d4p-2, 0x1.79cp-1]), name = tensor("add_73_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_73_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149999680))), lut = tensor([-0x1.cacp-6, -0x1.638p-1, -0x1.928p-2, -0x1.688p-3]), name = tensor("add_73_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_73_cast_fp16 = batch_norm(beta = add_73_beta_0_to_fp16_palettized, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_145_cast_fp16)[name = tensor("add_73_cast_fp16")]; + tensor input_315_cast_fp16 = silu(x = add_73_cast_fp16)[name = tensor("input_315_cast_fp16")]; + tensor var_2594 = const()[name = tensor("op_2594"), val = tensor([1, 1])]; + tensor var_2596 = const()[name = tensor("op_2596"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_type_0 = const()[name = tensor("hidden_states_179_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_179_pad_0 = const()[name = tensor("hidden_states_179_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150000384))), lut = tensor([-0x1.ffp-5, -0x1.21cp-6, 0x1.29p-6, 0x1.04p-4]), name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157373248)))]; + tensor hidden_states_179_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_2596, groups = var_2306, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = var_2594, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_315_cast_fp16)[name = tensor("hidden_states_179_cast_fp16")]; + tensor var_2602 = const()[name = tensor("op_2602"), val = tensor([1, 1])]; + tensor var_2604 = const()[name = tensor("op_2604"), val = tensor([1, 1])]; + tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; + tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157375872))), lut = tensor([-0x1.b98p-5, -0x1.ad4p-9, 0x1.a84p-9, 0x1.794p-5]), name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157785536)))]; + tensor temb_29_cast_fp16 = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2604, groups = var_2306, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_2602, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_29_cast_fp16")]; + tensor input_319_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = temb_29_cast_fp16)[name = tensor("input_319_cast_fp16")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_148_cast_fp16 = reshape(shape = reshape_148_shape_0, x = input_319_cast_fp16)[name = tensor("reshape_148_cast_fp16")]; + tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_111_cast_fp16 = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast_fp16)[name = tensor("reduce_mean_111_cast_fp16")]; + tensor sub_74_cast_fp16 = sub(x = reshape_148_cast_fp16, y = reduce_mean_111_cast_fp16)[name = tensor("sub_74_cast_fp16")]; + tensor square_37_cast_fp16 = square(x = sub_74_cast_fp16)[name = tensor("square_37_cast_fp16")]; + tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_113_cast_fp16 = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast_fp16)[name = tensor("reduce_mean_113_cast_fp16")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast_fp16 = add(x = reduce_mean_113_cast_fp16, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast_fp16")]; + tensor sqrt_37_cast_fp16 = sqrt(x = add_74_cast_fp16)[name = tensor("sqrt_37_cast_fp16")]; + tensor real_div_37_cast_fp16 = real_div(x = sub_74_cast_fp16, y = sqrt_37_cast_fp16)[name = tensor("real_div_37_cast_fp16")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_149_cast_fp16 = reshape(shape = reshape_149_shape_0, x = real_div_37_cast_fp16)[name = tensor("reshape_149_cast_fp16")]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157788160)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157790784)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_75_cast_fp16 = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_149_cast_fp16)[name = tensor("add_75_cast_fp16")]; + tensor input_323_cast_fp16 = silu(x = add_75_cast_fp16)[name = tensor("input_323_cast_fp16")]; + tensor var_2614 = const()[name = tensor("op_2614"), val = tensor([1, 1])]; + tensor var_2616 = const()[name = tensor("op_2616"), val = tensor([1, 1])]; + tensor hidden_states_181_pad_type_0 = const()[name = tensor("hidden_states_181_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_181_pad_0 = const()[name = tensor("hidden_states_181_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157793408))), lut = tensor([-0x1.3a4p-4, -0x1.658p-6, 0x1.69cp-6, 0x1.3cp-4]), name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161479872)))]; + tensor hidden_states_181_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_2616, groups = var_2306, pad = hidden_states_181_pad_0, pad_type = hidden_states_181_pad_type_0, strides = var_2614, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = tensor("hidden_states_181_cast_fp16")]; + tensor var_2621 = const()[name = tensor("op_2621"), val = tensor([1, 1])]; + tensor var_2623 = const()[name = tensor("op_2623"), val = tensor([1, 1])]; + tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; + tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161482496))), lut = tensor([-0x1.e88p-6, -0x1.1dp-7, 0x1.294p-7, 0x1.f04p-6]), name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162301760)))]; + tensor x_13_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2623, groups = var_2306, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_2621, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = tensor("x_13_cast_fp16")]; + tensor hidden_states_183_cast_fp16 = add(x = x_13_cast_fp16, y = hidden_states_181_cast_fp16)[name = tensor("hidden_states_183_cast_fp16")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_152_cast_fp16 = reshape(shape = reshape_152_shape_0, x = hidden_states_183_cast_fp16)[name = tensor("reshape_152_cast_fp16")]; + tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_114_cast_fp16 = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast_fp16)[name = tensor("reduce_mean_114_cast_fp16")]; + tensor sub_76_cast_fp16 = sub(x = reshape_152_cast_fp16, y = reduce_mean_114_cast_fp16)[name = tensor("sub_76_cast_fp16")]; + tensor square_38_cast_fp16 = square(x = sub_76_cast_fp16)[name = tensor("square_38_cast_fp16")]; + tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_116_cast_fp16 = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast_fp16)[name = tensor("reduce_mean_116_cast_fp16")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_76_cast_fp16 = add(x = reduce_mean_116_cast_fp16, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast_fp16")]; + tensor sqrt_38_cast_fp16 = sqrt(x = add_76_cast_fp16)[name = tensor("sqrt_38_cast_fp16")]; + tensor real_div_38_cast_fp16 = real_div(x = sub_76_cast_fp16, y = sqrt_38_cast_fp16)[name = tensor("real_div_38_cast_fp16")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_153_cast_fp16 = reshape(shape = reshape_153_shape_0, x = real_div_38_cast_fp16)[name = tensor("reshape_153_cast_fp16")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162304384)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162307008)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_77_cast_fp16 = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_153_cast_fp16)[name = tensor("add_77_cast_fp16")]; + tensor var_2643 = const()[name = tensor("op_2643"), val = tensor([1, 1])]; + tensor var_2645 = const()[name = tensor("op_2645"), val = tensor([1, 1])]; + tensor hidden_states_185_pad_type_0 = const()[name = tensor("hidden_states_185_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_185_pad_0 = const()[name = tensor("hidden_states_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162309632))), lut = tensor([-0x1.9bp-4, -0x1.eccp-6, 0x1.eb4p-6, 0x1.9a4p-4]), name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162719296)))]; + tensor hidden_states_185_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_2645, groups = var_2306, pad = hidden_states_185_pad_0, pad_type = hidden_states_185_pad_type_0, strides = var_2643, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_77_cast_fp16)[name = tensor("hidden_states_185_cast_fp16")]; + tensor var_2650 = const()[name = tensor("op_2650"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_49_cast_fp16 = reshape(shape = var_2650, x = hidden_states_185_cast_fp16)[name = tensor("inputs_49_cast_fp16")]; + tensor var_2660 = const()[name = tensor("op_2660"), val = tensor([1])]; + tensor channels_mean_49_cast_fp16 = reduce_mean(axes = var_2660, keep_dims = var_2301, x = inputs_49_cast_fp16)[name = tensor("channels_mean_49_cast_fp16")]; + tensor zero_mean_49_cast_fp16 = sub(x = inputs_49_cast_fp16, y = channels_mean_49_cast_fp16)[name = tensor("zero_mean_49_cast_fp16")]; + tensor zero_mean_sq_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = zero_mean_49_cast_fp16)[name = tensor("zero_mean_sq_49_cast_fp16")]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor([1])]; + tensor var_2665_cast_fp16 = reduce_mean(axes = var_2664, keep_dims = var_2301, x = zero_mean_sq_49_cast_fp16)[name = tensor("op_2665_cast_fp16")]; + tensor var_2666_to_fp16 = const()[name = tensor("op_2666_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2667_cast_fp16 = add(x = var_2665_cast_fp16, y = var_2666_to_fp16)[name = tensor("op_2667_cast_fp16")]; + tensor denom_49_epsilon_0_to_fp16 = const()[name = tensor("denom_49_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_49_cast_fp16 = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_2667_cast_fp16)[name = tensor("denom_49_cast_fp16")]; + tensor out_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = denom_49_cast_fp16)[name = tensor("out_49_cast_fp16")]; + tensor var_2671_to_fp16 = const()[name = tensor("op_2671_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162721920)))]; + tensor var_2672_cast_fp16 = add(x = out_49_cast_fp16, y = var_2671_to_fp16)[name = tensor("op_2672_cast_fp16")]; + tensor var_2674_to_fp16 = const()[name = tensor("op_2674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162724544)))]; + tensor hidden_states_187_cast_fp16 = mul(x = var_2672_cast_fp16, y = var_2674_to_fp16)[name = tensor("hidden_states_187_cast_fp16")]; + tensor var_2681 = const()[name = tensor("op_2681"), val = tensor([1, 1])]; + tensor var_2683 = const()[name = tensor("op_2683"), val = tensor([1, 1])]; + tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; + tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162727168))), lut = tensor([-0x1.7ep-4, -0x1.cap-6, 0x1.c5cp-6, 0x1.7d4p-4]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_33_cast_fp16 = conv(dilations = var_2683, groups = var_2306, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2681, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_187_cast_fp16)[name = tensor("q_33_cast_fp16")]; + tensor var_2687 = const()[name = tensor("op_2687"), val = tensor([1, 1])]; + tensor var_2689 = const()[name = tensor("op_2689"), val = tensor([1, 1])]; + tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; + tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163136832))), lut = tensor([-0x1.7f8p-4, -0x1.c74p-6, 0x1.cb4p-6, 0x1.80cp-4]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_33_cast_fp16 = conv(dilations = var_2689, groups = var_2306, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2687, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_187_cast_fp16)[name = tensor("k_33_cast_fp16")]; + tensor var_2693 = const()[name = tensor("op_2693"), val = tensor([1, 1])]; + tensor var_2695 = const()[name = tensor("op_2695"), val = tensor([1, 1])]; + tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; + tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163546496))), lut = tensor([-0x1.52p-4, -0x1.92p-6, 0x1.95p-6, 0x1.524p-4]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_33_cast_fp16 = conv(dilations = var_2695, groups = var_2306, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2693, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_187_cast_fp16)[name = tensor("v_33_cast_fp16")]; + tensor var_2699 = const()[name = tensor("op_2699"), val = tensor([2, 20, 64, -1])]; + tensor var_2700_cast_fp16 = reshape(shape = var_2699, x = q_33_cast_fp16)[name = tensor("op_2700_cast_fp16")]; + tensor var_2701 = const()[name = tensor("op_2701"), val = tensor([2, 20, 64, -1])]; + tensor var_2702_cast_fp16 = reshape(shape = var_2701, x = k_33_cast_fp16)[name = tensor("op_2702_cast_fp16")]; + tensor var_2703 = const()[name = tensor("op_2703"), val = tensor([2, 20, 64, -1])]; + tensor var_2704_cast_fp16 = reshape(shape = var_2703, x = v_33_cast_fp16)[name = tensor("op_2704_cast_fp16")]; + tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2700_cast_fp16, y = var_2702_cast_fp16)[name = tensor("attn_weights_65_cast_fp16")]; + tensor attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_67_cast_fp16")]; + tensor var_2708_cast_fp16 = softmax(axis = var_2290, x = attn_weights_67_cast_fp16)[name = tensor("op_2708_cast_fp16")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast_fp16 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2704_cast_fp16, y = var_2708_cast_fp16)[name = tensor("attn_33_cast_fp16")]; + tensor var_2712 = const()[name = tensor("op_2712"), val = tensor([2, 1280, 1, -1])]; + tensor input_327_cast_fp16 = reshape(shape = var_2712, x = attn_33_cast_fp16)[name = tensor("input_327_cast_fp16")]; + tensor var_2717 = const()[name = tensor("op_2717"), val = tensor([1, 1])]; + tensor var_2719 = const()[name = tensor("op_2719"), val = tensor([1, 1])]; + tensor var_2721_pad_type_0 = const()[name = tensor("op_2721_pad_type_0"), val = tensor("custom")]; + tensor var_2721_pad_0 = const()[name = tensor("op_2721_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163956160))), lut = tensor([-0x1.64p-4, -0x1.aa8p-6, 0x1.a88p-6, 0x1.638p-4]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164365824)))]; + tensor var_2721_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2719, groups = var_2306, pad = var_2721_pad_0, pad_type = var_2721_pad_type_0, strides = var_2717, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_327_cast_fp16)[name = tensor("op_2721_cast_fp16")]; + tensor inputs_51_cast_fp16 = add(x = var_2721_cast_fp16, y = inputs_49_cast_fp16)[name = tensor("inputs_51_cast_fp16")]; + tensor var_2725 = const()[name = tensor("op_2725"), val = tensor([1])]; + tensor channels_mean_51_cast_fp16 = reduce_mean(axes = var_2725, keep_dims = var_2301, x = inputs_51_cast_fp16)[name = tensor("channels_mean_51_cast_fp16")]; + tensor zero_mean_51_cast_fp16 = sub(x = inputs_51_cast_fp16, y = channels_mean_51_cast_fp16)[name = tensor("zero_mean_51_cast_fp16")]; + tensor zero_mean_sq_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = zero_mean_51_cast_fp16)[name = tensor("zero_mean_sq_51_cast_fp16")]; + tensor var_2729 = const()[name = tensor("op_2729"), val = tensor([1])]; + tensor var_2730_cast_fp16 = reduce_mean(axes = var_2729, keep_dims = var_2301, x = zero_mean_sq_51_cast_fp16)[name = tensor("op_2730_cast_fp16")]; + tensor var_2731_to_fp16 = const()[name = tensor("op_2731_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2732_cast_fp16 = add(x = var_2730_cast_fp16, y = var_2731_to_fp16)[name = tensor("op_2732_cast_fp16")]; + tensor denom_51_epsilon_0_to_fp16 = const()[name = tensor("denom_51_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_51_cast_fp16 = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2732_cast_fp16)[name = tensor("denom_51_cast_fp16")]; + tensor out_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = denom_51_cast_fp16)[name = tensor("out_51_cast_fp16")]; + tensor var_2736_to_fp16 = const()[name = tensor("op_2736_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164368448)))]; + tensor var_2737_cast_fp16 = add(x = out_51_cast_fp16, y = var_2736_to_fp16)[name = tensor("op_2737_cast_fp16")]; + tensor var_2739_to_fp16 = const()[name = tensor("op_2739_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164371072)))]; + tensor hidden_states_189_cast_fp16 = mul(x = var_2737_cast_fp16, y = var_2739_to_fp16)[name = tensor("hidden_states_189_cast_fp16")]; + tensor var_2746 = const()[name = tensor("op_2746"), val = tensor([1, 1])]; + tensor var_2748 = const()[name = tensor("op_2748"), val = tensor([1, 1])]; + tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; + tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164373696))), lut = tensor([-0x1.3c4p-4, -0x1.7dp-6, 0x1.724p-6, 0x1.3ap-4]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_35_cast_fp16 = conv(dilations = var_2748, groups = var_2306, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2746, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_189_cast_fp16)[name = tensor("q_35_cast_fp16")]; + tensor var_2752 = const()[name = tensor("op_2752"), val = tensor([1, 1])]; + tensor var_2754 = const()[name = tensor("op_2754"), val = tensor([1, 1])]; + tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; + tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164783360))), lut = tensor([-0x1.488p-4, -0x1.844p-6, 0x1.864p-6, 0x1.49p-4]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor k_35_cast_fp16 = conv(dilations = var_2754, groups = var_2306, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2752, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_35_cast_fp16")]; + tensor var_2758 = const()[name = tensor("op_2758"), val = tensor([1, 1])]; + tensor var_2760 = const()[name = tensor("op_2760"), val = tensor([1, 1])]; + tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; + tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165111104))), lut = tensor([-0x1.ff8p-5, -0x1.2acp-6, 0x1.2c8p-6, 0x1.008p-4]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor v_35_cast_fp16 = conv(dilations = var_2760, groups = var_2306, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2758, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_35_cast_fp16")]; + tensor var_2764 = const()[name = tensor("op_2764"), val = tensor([2, 20, 64, -1])]; + tensor var_2765_cast_fp16 = reshape(shape = var_2764, x = q_35_cast_fp16)[name = tensor("op_2765_cast_fp16")]; + tensor var_2766 = const()[name = tensor("op_2766"), val = tensor([2, 20, 64, -1])]; + tensor var_2767_cast_fp16 = reshape(shape = var_2766, x = k_35_cast_fp16)[name = tensor("op_2767_cast_fp16")]; + tensor var_2768 = const()[name = tensor("op_2768"), val = tensor([2, 20, 64, -1])]; + tensor var_2769_cast_fp16 = reshape(shape = var_2768, x = v_35_cast_fp16)[name = tensor("op_2769_cast_fp16")]; + tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_69_cast_fp16 = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2765_cast_fp16, y = var_2767_cast_fp16)[name = tensor("attn_weights_69_cast_fp16")]; + tensor attn_weights_71_cast_fp16 = mul(x = attn_weights_69_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_71_cast_fp16")]; + tensor var_2773_cast_fp16 = softmax(axis = var_2290, x = attn_weights_71_cast_fp16)[name = tensor("op_2773_cast_fp16")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast_fp16 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2769_cast_fp16, y = var_2773_cast_fp16)[name = tensor("attn_35_cast_fp16")]; + tensor var_2777 = const()[name = tensor("op_2777"), val = tensor([2, 1280, 1, -1])]; + tensor input_329_cast_fp16 = reshape(shape = var_2777, x = attn_35_cast_fp16)[name = tensor("input_329_cast_fp16")]; + tensor var_2782 = const()[name = tensor("op_2782"), val = tensor([1, 1])]; + tensor var_2784 = const()[name = tensor("op_2784"), val = tensor([1, 1])]; + tensor var_2786_pad_type_0 = const()[name = tensor("op_2786_pad_type_0"), val = tensor("custom")]; + tensor var_2786_pad_0 = const()[name = tensor("op_2786_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165438848))), lut = tensor([-0x1.8a8p-5, -0x1.d48p-7, 0x1.d78p-7, 0x1.8bp-5]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165848512)))]; + tensor var_2786_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2784, groups = var_2306, pad = var_2786_pad_0, pad_type = var_2786_pad_type_0, strides = var_2782, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_329_cast_fp16)[name = tensor("op_2786_cast_fp16")]; + tensor inputs_53_cast_fp16 = add(x = var_2786_cast_fp16, y = inputs_51_cast_fp16)[name = tensor("inputs_53_cast_fp16")]; + tensor var_2790 = const()[name = tensor("op_2790"), val = tensor([1])]; + tensor channels_mean_53_cast_fp16 = reduce_mean(axes = var_2790, keep_dims = var_2301, x = inputs_53_cast_fp16)[name = tensor("channels_mean_53_cast_fp16")]; + tensor zero_mean_53_cast_fp16 = sub(x = inputs_53_cast_fp16, y = channels_mean_53_cast_fp16)[name = tensor("zero_mean_53_cast_fp16")]; + tensor zero_mean_sq_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = zero_mean_53_cast_fp16)[name = tensor("zero_mean_sq_53_cast_fp16")]; + tensor var_2794 = const()[name = tensor("op_2794"), val = tensor([1])]; + tensor var_2795_cast_fp16 = reduce_mean(axes = var_2794, keep_dims = var_2301, x = zero_mean_sq_53_cast_fp16)[name = tensor("op_2795_cast_fp16")]; + tensor var_2796_to_fp16 = const()[name = tensor("op_2796_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2797_cast_fp16 = add(x = var_2795_cast_fp16, y = var_2796_to_fp16)[name = tensor("op_2797_cast_fp16")]; + tensor denom_53_epsilon_0_to_fp16 = const()[name = tensor("denom_53_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_53_cast_fp16 = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2797_cast_fp16)[name = tensor("denom_53_cast_fp16")]; + tensor out_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = denom_53_cast_fp16)[name = tensor("out_53_cast_fp16")]; + tensor var_2801_to_fp16 = const()[name = tensor("op_2801_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165851136)))]; + tensor var_2802_cast_fp16 = add(x = out_53_cast_fp16, y = var_2801_to_fp16)[name = tensor("op_2802_cast_fp16")]; + tensor var_2804_to_fp16 = const()[name = tensor("op_2804_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165853760)))]; + tensor input_331_cast_fp16 = mul(x = var_2802_cast_fp16, y = var_2804_to_fp16)[name = tensor("input_331_cast_fp16")]; + tensor var_2812 = const()[name = tensor("op_2812"), val = tensor([1, 1])]; + tensor var_2814 = const()[name = tensor("op_2814"), val = tensor([1, 1])]; + tensor var_2816_pad_type_0 = const()[name = tensor("op_2816_pad_type_0"), val = tensor("custom")]; + tensor var_2816_pad_0 = const()[name = tensor("op_2816_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165856384))), lut = tensor([-0x1.6b8p-4, -0x1.abcp-6, 0x1.a88p-6, 0x1.6a4p-4]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169133248))), lut = tensor([-0x1.734p-3, -0x1.56p-4, -0x1.11p-6, 0x1.d8p-6]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2816_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2814, groups = var_2306, pad = var_2816_pad_0, pad_type = var_2816_pad_type_0, strides = var_2812, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = tensor("op_2816_cast_fp16")]; + tensor var_2817_split_sizes_0 = const()[name = tensor("op_2817_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2817_axis_0 = const()[name = tensor("op_2817_axis_0"), val = tensor(1)]; + tensor var_2817_cast_fp16_0, tensor var_2817_cast_fp16_1 = split(axis = var_2817_axis_0, split_sizes = var_2817_split_sizes_0, x = var_2816_cast_fp16)[name = tensor("op_2817_cast_fp16")]; + tensor var_2819_mode_0 = const()[name = tensor("op_2819_mode_0"), val = tensor("EXACT")]; + tensor var_2819_cast_fp16 = gelu(mode = var_2819_mode_0, x = var_2817_cast_fp16_1)[name = tensor("op_2819_cast_fp16")]; + tensor input_333_cast_fp16 = mul(x = var_2817_cast_fp16_0, y = var_2819_cast_fp16)[name = tensor("input_333_cast_fp16")]; + tensor var_2823 = const()[name = tensor("op_2823"), val = tensor([1, 1])]; + tensor var_2825 = const()[name = tensor("op_2825"), val = tensor([1, 1])]; + tensor var_2827_pad_type_0 = const()[name = tensor("op_2827_pad_type_0"), val = tensor("custom")]; + tensor var_2827_pad_0 = const()[name = tensor("op_2827_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169135872))), lut = tensor([-0x1.6bp-4, -0x1.b14p-6, 0x1.adcp-6, 0x1.6ap-4]), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170774336)))]; + tensor var_2827_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2825, groups = var_2306, pad = var_2827_pad_0, pad_type = var_2827_pad_type_0, strides = var_2823, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_333_cast_fp16)[name = tensor("op_2827_cast_fp16")]; + tensor hidden_states_193_cast_fp16 = add(x = var_2827_cast_fp16, y = inputs_53_cast_fp16)[name = tensor("hidden_states_193_cast_fp16")]; + tensor var_2829 = const()[name = tensor("op_2829"), val = tensor([2, 1280, 16, 16])]; + tensor input_335_cast_fp16 = reshape(shape = var_2829, x = hidden_states_193_cast_fp16)[name = tensor("input_335_cast_fp16")]; + tensor var_2833 = const()[name = tensor("op_2833"), val = tensor([1, 1])]; + tensor var_2835 = const()[name = tensor("op_2835"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_type_0 = const()[name = tensor("hidden_states_195_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_195_pad_0 = const()[name = tensor("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170776960))), lut = tensor([-0x1.60cp-4, -0x1.a28p-6, 0x1.acp-6, 0x1.628p-4]), name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171186624)))]; + tensor hidden_states_195_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_2835, groups = var_2306, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = var_2833, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = tensor("hidden_states_195_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_183_cast_fp16)[name = tensor("hidden_states_197_cast_fp16")]; + tensor input_337_interleave_0 = const()[name = tensor("input_337_interleave_0"), val = tensor(false)]; + tensor input_337_cast_fp16 = concat(axis = var_2306, interleave = input_337_interleave_0, values = (hidden_states_197_cast_fp16, input_117_cast_fp16))[name = tensor("input_337_cast_fp16")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([2, 32, 60, 16, 16])]; + tensor reshape_156_cast_fp16 = reshape(shape = reshape_156_shape_0, x = input_337_cast_fp16)[name = tensor("reshape_156_cast_fp16")]; + tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_117_cast_fp16 = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast_fp16)[name = tensor("reduce_mean_117_cast_fp16")]; + tensor sub_78_cast_fp16 = sub(x = reshape_156_cast_fp16, y = reduce_mean_117_cast_fp16)[name = tensor("sub_78_cast_fp16")]; + tensor square_39_cast_fp16 = square(x = sub_78_cast_fp16)[name = tensor("square_39_cast_fp16")]; + tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_119_cast_fp16 = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast_fp16)[name = tensor("reduce_mean_119_cast_fp16")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_78_cast_fp16 = add(x = reduce_mean_119_cast_fp16, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast_fp16")]; + tensor sqrt_39_cast_fp16 = sqrt(x = add_78_cast_fp16)[name = tensor("sqrt_39_cast_fp16")]; + tensor real_div_39_cast_fp16 = real_div(x = sub_78_cast_fp16, y = sqrt_39_cast_fp16)[name = tensor("real_div_39_cast_fp16")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([2, 1920, 16, 16])]; + tensor reshape_157_cast_fp16 = reshape(shape = reshape_157_shape_0, x = real_div_39_cast_fp16)[name = tensor("reshape_157_cast_fp16")]; + tensor add_79_mean_0_to_fp16 = const()[name = tensor("add_79_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171189248)))]; + tensor add_79_variance_0_to_fp16 = const()[name = tensor("add_79_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171193152)))]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171197056)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171200960)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_79_cast_fp16 = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_157_cast_fp16)[name = tensor("add_79_cast_fp16")]; + tensor input_341_cast_fp16 = silu(x = add_79_cast_fp16)[name = tensor("input_341_cast_fp16")]; + tensor var_2853 = const()[name = tensor("op_2853"), val = tensor([1, 1])]; + tensor var_2855 = const()[name = tensor("op_2855"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171204864))), lut = tensor([-0x1.254p-4, -0x1.45cp-6, 0x1.3e8p-6, 0x1.23p-4]), name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 3, 3])]; + tensor up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176734528)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_2855, groups = var_2306, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_2853, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized, x = input_341_cast_fp16)[name = tensor("hidden_states_199_cast_fp16")]; + tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, 1])]; + tensor var_2863 = const()[name = tensor("op_2863"), val = tensor([1, 1])]; + tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; + tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176737152))), lut = tensor([-0x1.228p-4, -0x1.b08p-9, 0x1.a4cp-9, 0x1.b24p-5]), name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177146816)))]; + tensor temb_31_cast_fp16 = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2863, groups = var_2306, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_2861, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_31_cast_fp16")]; + tensor input_345_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = temb_31_cast_fp16)[name = tensor("input_345_cast_fp16")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_160_cast_fp16 = reshape(shape = reshape_160_shape_0, x = input_345_cast_fp16)[name = tensor("reshape_160_cast_fp16")]; + tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_120_cast_fp16 = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast_fp16)[name = tensor("reduce_mean_120_cast_fp16")]; + tensor sub_80_cast_fp16 = sub(x = reshape_160_cast_fp16, y = reduce_mean_120_cast_fp16)[name = tensor("sub_80_cast_fp16")]; + tensor square_40_cast_fp16 = square(x = sub_80_cast_fp16)[name = tensor("square_40_cast_fp16")]; + tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_122_cast_fp16 = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast_fp16)[name = tensor("reduce_mean_122_cast_fp16")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast_fp16 = add(x = reduce_mean_122_cast_fp16, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast_fp16")]; + tensor sqrt_40_cast_fp16 = sqrt(x = add_80_cast_fp16)[name = tensor("sqrt_40_cast_fp16")]; + tensor real_div_40_cast_fp16 = real_div(x = sub_80_cast_fp16, y = sqrt_40_cast_fp16)[name = tensor("real_div_40_cast_fp16")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_161_cast_fp16 = reshape(shape = reshape_161_shape_0, x = real_div_40_cast_fp16)[name = tensor("reshape_161_cast_fp16")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177149440)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177152064)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_81_cast_fp16 = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_161_cast_fp16)[name = tensor("add_81_cast_fp16")]; + tensor input_349_cast_fp16 = silu(x = add_81_cast_fp16)[name = tensor("input_349_cast_fp16")]; + tensor var_2873 = const()[name = tensor("op_2873"), val = tensor([1, 1])]; + tensor var_2875 = const()[name = tensor("op_2875"), val = tensor([1, 1])]; + tensor hidden_states_201_pad_type_0 = const()[name = tensor("hidden_states_201_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_201_pad_0 = const()[name = tensor("hidden_states_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177154688))), lut = tensor([-0x1.574p-4, -0x1.84cp-6, 0x1.86p-6, 0x1.58p-4]), name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180841152)))]; + tensor hidden_states_201_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_2875, groups = var_2306, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_2873, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = tensor("hidden_states_201_cast_fp16")]; + tensor var_2880 = const()[name = tensor("op_2880"), val = tensor([1, 1])]; + tensor var_2882 = const()[name = tensor("op_2882"), val = tensor([1, 1])]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180843776))), lut = tensor([-0x1.934p-6, -0x1.becp-8, 0x1.bbcp-8, 0x1.924p-6]), name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 1, 1])]; + tensor up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181458240)))]; + tensor x_15_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2882, groups = var_2306, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_2880, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_337_cast_fp16)[name = tensor("x_15_cast_fp16")]; + tensor hidden_states_203_cast_fp16 = add(x = x_15_cast_fp16, y = hidden_states_201_cast_fp16)[name = tensor("hidden_states_203_cast_fp16")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_164_cast_fp16 = reshape(shape = reshape_164_shape_0, x = hidden_states_203_cast_fp16)[name = tensor("reshape_164_cast_fp16")]; + tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_123_cast_fp16 = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast_fp16)[name = tensor("reduce_mean_123_cast_fp16")]; + tensor sub_82_cast_fp16 = sub(x = reshape_164_cast_fp16, y = reduce_mean_123_cast_fp16)[name = tensor("sub_82_cast_fp16")]; + tensor square_41_cast_fp16 = square(x = sub_82_cast_fp16)[name = tensor("square_41_cast_fp16")]; + tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_125_cast_fp16 = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast_fp16)[name = tensor("reduce_mean_125_cast_fp16")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_82_cast_fp16 = add(x = reduce_mean_125_cast_fp16, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast_fp16")]; + tensor sqrt_41_cast_fp16 = sqrt(x = add_82_cast_fp16)[name = tensor("sqrt_41_cast_fp16")]; + tensor real_div_41_cast_fp16 = real_div(x = sub_82_cast_fp16, y = sqrt_41_cast_fp16)[name = tensor("real_div_41_cast_fp16")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_165_cast_fp16 = reshape(shape = reshape_165_shape_0, x = real_div_41_cast_fp16)[name = tensor("reshape_165_cast_fp16")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181460864)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181463488)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_83_cast_fp16 = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_165_cast_fp16)[name = tensor("add_83_cast_fp16")]; + tensor var_2902 = const()[name = tensor("op_2902"), val = tensor([1, 1])]; + tensor var_2904 = const()[name = tensor("op_2904"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_type_0 = const()[name = tensor("hidden_states_205_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_205_pad_0 = const()[name = tensor("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181466112))), lut = tensor([-0x1.988p-4, -0x1.ebp-6, 0x1.f24p-6, 0x1.9a4p-4]), name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181875776)))]; + tensor hidden_states_205_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_2904, groups = var_2306, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = var_2902, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized, x = add_83_cast_fp16)[name = tensor("hidden_states_205_cast_fp16")]; + tensor var_2909 = const()[name = tensor("op_2909"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_55_cast_fp16 = reshape(shape = var_2909, x = hidden_states_205_cast_fp16)[name = tensor("inputs_55_cast_fp16")]; + tensor var_2919 = const()[name = tensor("op_2919"), val = tensor([1])]; + tensor channels_mean_55_cast_fp16 = reduce_mean(axes = var_2919, keep_dims = var_2301, x = inputs_55_cast_fp16)[name = tensor("channels_mean_55_cast_fp16")]; + tensor zero_mean_55_cast_fp16 = sub(x = inputs_55_cast_fp16, y = channels_mean_55_cast_fp16)[name = tensor("zero_mean_55_cast_fp16")]; + tensor zero_mean_sq_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = zero_mean_55_cast_fp16)[name = tensor("zero_mean_sq_55_cast_fp16")]; + tensor var_2923 = const()[name = tensor("op_2923"), val = tensor([1])]; + tensor var_2924_cast_fp16 = reduce_mean(axes = var_2923, keep_dims = var_2301, x = zero_mean_sq_55_cast_fp16)[name = tensor("op_2924_cast_fp16")]; + tensor var_2925_to_fp16 = const()[name = tensor("op_2925_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2926_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2925_to_fp16)[name = tensor("op_2926_cast_fp16")]; + tensor denom_55_epsilon_0_to_fp16 = const()[name = tensor("denom_55_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_55_cast_fp16 = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2926_cast_fp16)[name = tensor("denom_55_cast_fp16")]; + tensor out_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = denom_55_cast_fp16)[name = tensor("out_55_cast_fp16")]; + tensor var_2930_to_fp16 = const()[name = tensor("op_2930_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181878400)))]; + tensor var_2931_cast_fp16 = add(x = out_55_cast_fp16, y = var_2930_to_fp16)[name = tensor("op_2931_cast_fp16")]; + tensor var_2933_to_fp16 = const()[name = tensor("op_2933_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181881024)))]; + tensor hidden_states_207_cast_fp16 = mul(x = var_2931_cast_fp16, y = var_2933_to_fp16)[name = tensor("hidden_states_207_cast_fp16")]; + tensor var_2940 = const()[name = tensor("op_2940"), val = tensor([1, 1])]; + tensor var_2942 = const()[name = tensor("op_2942"), val = tensor([1, 1])]; + tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; + tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181883648))), lut = tensor([-0x1.9cp-4, -0x1.ec4p-6, 0x1.ebcp-6, 0x1.9c4p-4]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_37_cast_fp16 = conv(dilations = var_2942, groups = var_2306, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2940, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_207_cast_fp16)[name = tensor("q_37_cast_fp16")]; + tensor var_2946 = const()[name = tensor("op_2946"), val = tensor([1, 1])]; + tensor var_2948 = const()[name = tensor("op_2948"), val = tensor([1, 1])]; + tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; + tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182293312))), lut = tensor([-0x1.a04p-4, -0x1.f24p-6, 0x1.eccp-6, 0x1.9e8p-4]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_37_cast_fp16 = conv(dilations = var_2948, groups = var_2306, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2946, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_207_cast_fp16)[name = tensor("k_37_cast_fp16")]; + tensor var_2952 = const()[name = tensor("op_2952"), val = tensor([1, 1])]; + tensor var_2954 = const()[name = tensor("op_2954"), val = tensor([1, 1])]; + tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; + tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182702976))), lut = tensor([-0x1.404p-4, -0x1.7d4p-6, 0x1.7fcp-6, 0x1.408p-4]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_37_cast_fp16 = conv(dilations = var_2954, groups = var_2306, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2952, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_207_cast_fp16)[name = tensor("v_37_cast_fp16")]; + tensor var_2958 = const()[name = tensor("op_2958"), val = tensor([2, 20, 64, -1])]; + tensor var_2959_cast_fp16 = reshape(shape = var_2958, x = q_37_cast_fp16)[name = tensor("op_2959_cast_fp16")]; + tensor var_2960 = const()[name = tensor("op_2960"), val = tensor([2, 20, 64, -1])]; + tensor var_2961_cast_fp16 = reshape(shape = var_2960, x = k_37_cast_fp16)[name = tensor("op_2961_cast_fp16")]; + tensor var_2962 = const()[name = tensor("op_2962"), val = tensor([2, 20, 64, -1])]; + tensor var_2963_cast_fp16 = reshape(shape = var_2962, x = v_37_cast_fp16)[name = tensor("op_2963_cast_fp16")]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2959_cast_fp16, y = var_2961_cast_fp16)[name = tensor("attn_weights_73_cast_fp16")]; + tensor attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_75_cast_fp16")]; + tensor var_2967_cast_fp16 = softmax(axis = var_2290, x = attn_weights_75_cast_fp16)[name = tensor("op_2967_cast_fp16")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast_fp16 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2963_cast_fp16, y = var_2967_cast_fp16)[name = tensor("attn_37_cast_fp16")]; + tensor var_2971 = const()[name = tensor("op_2971"), val = tensor([2, 1280, 1, -1])]; + tensor input_353_cast_fp16 = reshape(shape = var_2971, x = attn_37_cast_fp16)[name = tensor("input_353_cast_fp16")]; + tensor var_2976 = const()[name = tensor("op_2976"), val = tensor([1, 1])]; + tensor var_2978 = const()[name = tensor("op_2978"), val = tensor([1, 1])]; + tensor var_2980_pad_type_0 = const()[name = tensor("op_2980_pad_type_0"), val = tensor("custom")]; + tensor var_2980_pad_0 = const()[name = tensor("op_2980_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183112640))), lut = tensor([-0x1.55cp-4, -0x1.9acp-6, 0x1.948p-6, 0x1.544p-4]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183522304)))]; + tensor var_2980_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2978, groups = var_2306, pad = var_2980_pad_0, pad_type = var_2980_pad_type_0, strides = var_2976, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = tensor("op_2980_cast_fp16")]; + tensor inputs_57_cast_fp16 = add(x = var_2980_cast_fp16, y = inputs_55_cast_fp16)[name = tensor("inputs_57_cast_fp16")]; + tensor var_2984 = const()[name = tensor("op_2984"), val = tensor([1])]; + tensor channels_mean_57_cast_fp16 = reduce_mean(axes = var_2984, keep_dims = var_2301, x = inputs_57_cast_fp16)[name = tensor("channels_mean_57_cast_fp16")]; + tensor zero_mean_57_cast_fp16 = sub(x = inputs_57_cast_fp16, y = channels_mean_57_cast_fp16)[name = tensor("zero_mean_57_cast_fp16")]; + tensor zero_mean_sq_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = zero_mean_57_cast_fp16)[name = tensor("zero_mean_sq_57_cast_fp16")]; + tensor var_2988 = const()[name = tensor("op_2988"), val = tensor([1])]; + tensor var_2989_cast_fp16 = reduce_mean(axes = var_2988, keep_dims = var_2301, x = zero_mean_sq_57_cast_fp16)[name = tensor("op_2989_cast_fp16")]; + tensor var_2990_to_fp16 = const()[name = tensor("op_2990_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2991_cast_fp16 = add(x = var_2989_cast_fp16, y = var_2990_to_fp16)[name = tensor("op_2991_cast_fp16")]; + tensor denom_57_epsilon_0_to_fp16 = const()[name = tensor("denom_57_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_57_cast_fp16 = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2991_cast_fp16)[name = tensor("denom_57_cast_fp16")]; + tensor out_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = denom_57_cast_fp16)[name = tensor("out_57_cast_fp16")]; + tensor var_2995_to_fp16 = const()[name = tensor("op_2995_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183524928)))]; + tensor var_2996_cast_fp16 = add(x = out_57_cast_fp16, y = var_2995_to_fp16)[name = tensor("op_2996_cast_fp16")]; + tensor var_2998_to_fp16 = const()[name = tensor("op_2998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183527552)))]; + tensor hidden_states_209_cast_fp16 = mul(x = var_2996_cast_fp16, y = var_2998_to_fp16)[name = tensor("hidden_states_209_cast_fp16")]; + tensor var_3005 = const()[name = tensor("op_3005"), val = tensor([1, 1])]; + tensor var_3007 = const()[name = tensor("op_3007"), val = tensor([1, 1])]; + tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; + tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183530176))), lut = tensor([-0x1.454p-4, -0x1.854p-6, 0x1.85p-6, 0x1.454p-4]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_39_cast_fp16 = conv(dilations = var_3007, groups = var_2306, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_3005, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_209_cast_fp16)[name = tensor("q_39_cast_fp16")]; + tensor var_3011 = const()[name = tensor("op_3011"), val = tensor([1, 1])]; + tensor var_3013 = const()[name = tensor("op_3013"), val = tensor([1, 1])]; + tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; + tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183939840))), lut = tensor([-0x1.54p-4, -0x1.95cp-6, 0x1.8f8p-6, 0x1.528p-4]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor k_39_cast_fp16 = conv(dilations = var_3013, groups = var_2306, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_3011, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_39_cast_fp16")]; + tensor var_3017 = const()[name = tensor("op_3017"), val = tensor([1, 1])]; + tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 1])]; + tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; + tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184267584))), lut = tensor([-0x1.09cp-4, -0x1.37p-6, 0x1.37p-6, 0x1.09cp-4]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1024, 1, 1])]; + tensor v_39_cast_fp16 = conv(dilations = var_3019, groups = var_2306, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_3017, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_39_cast_fp16")]; + tensor var_3023 = const()[name = tensor("op_3023"), val = tensor([2, 20, 64, -1])]; + tensor var_3024_cast_fp16 = reshape(shape = var_3023, x = q_39_cast_fp16)[name = tensor("op_3024_cast_fp16")]; + tensor var_3025 = const()[name = tensor("op_3025"), val = tensor([2, 20, 64, -1])]; + tensor var_3026_cast_fp16 = reshape(shape = var_3025, x = k_39_cast_fp16)[name = tensor("op_3026_cast_fp16")]; + tensor var_3027 = const()[name = tensor("op_3027"), val = tensor([2, 20, 64, -1])]; + tensor var_3028_cast_fp16 = reshape(shape = var_3027, x = v_39_cast_fp16)[name = tensor("op_3028_cast_fp16")]; + tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_77_cast_fp16 = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_3024_cast_fp16, y = var_3026_cast_fp16)[name = tensor("attn_weights_77_cast_fp16")]; + tensor attn_weights_79_cast_fp16 = mul(x = attn_weights_77_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_79_cast_fp16")]; + tensor var_3032_cast_fp16 = softmax(axis = var_2290, x = attn_weights_79_cast_fp16)[name = tensor("op_3032_cast_fp16")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast_fp16 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_3028_cast_fp16, y = var_3032_cast_fp16)[name = tensor("attn_39_cast_fp16")]; + tensor var_3036 = const()[name = tensor("op_3036"), val = tensor([2, 1280, 1, -1])]; + tensor input_355_cast_fp16 = reshape(shape = var_3036, x = attn_39_cast_fp16)[name = tensor("input_355_cast_fp16")]; + tensor var_3041 = const()[name = tensor("op_3041"), val = tensor([1, 1])]; + tensor var_3043 = const()[name = tensor("op_3043"), val = tensor([1, 1])]; + tensor var_3045_pad_type_0 = const()[name = tensor("op_3045_pad_type_0"), val = tensor("custom")]; + tensor var_3045_pad_0 = const()[name = tensor("op_3045_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184595328))), lut = tensor([-0x1.9c4p-5, -0x1.ec8p-7, 0x1.e3p-7, 0x1.9a8p-5]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185004992)))]; + tensor var_3045_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3043, groups = var_2306, pad = var_3045_pad_0, pad_type = var_3045_pad_type_0, strides = var_3041, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = tensor("op_3045_cast_fp16")]; + tensor inputs_59_cast_fp16 = add(x = var_3045_cast_fp16, y = inputs_57_cast_fp16)[name = tensor("inputs_59_cast_fp16")]; + tensor var_3049 = const()[name = tensor("op_3049"), val = tensor([1])]; + tensor channels_mean_59_cast_fp16 = reduce_mean(axes = var_3049, keep_dims = var_2301, x = inputs_59_cast_fp16)[name = tensor("channels_mean_59_cast_fp16")]; + tensor zero_mean_59_cast_fp16 = sub(x = inputs_59_cast_fp16, y = channels_mean_59_cast_fp16)[name = tensor("zero_mean_59_cast_fp16")]; + tensor zero_mean_sq_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = zero_mean_59_cast_fp16)[name = tensor("zero_mean_sq_59_cast_fp16")]; + tensor var_3053 = const()[name = tensor("op_3053"), val = tensor([1])]; + tensor var_3054_cast_fp16 = reduce_mean(axes = var_3053, keep_dims = var_2301, x = zero_mean_sq_59_cast_fp16)[name = tensor("op_3054_cast_fp16")]; + tensor var_3055_to_fp16 = const()[name = tensor("op_3055_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3056_cast_fp16 = add(x = var_3054_cast_fp16, y = var_3055_to_fp16)[name = tensor("op_3056_cast_fp16")]; + tensor denom_59_epsilon_0_to_fp16 = const()[name = tensor("denom_59_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_59_cast_fp16 = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_3056_cast_fp16)[name = tensor("denom_59_cast_fp16")]; + tensor out_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = denom_59_cast_fp16)[name = tensor("out_59_cast_fp16")]; + tensor var_3060_to_fp16 = const()[name = tensor("op_3060_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185007616)))]; + tensor var_3061_cast_fp16 = add(x = out_59_cast_fp16, y = var_3060_to_fp16)[name = tensor("op_3061_cast_fp16")]; + tensor var_3063_to_fp16 = const()[name = tensor("op_3063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185010240)))]; + tensor input_357_cast_fp16 = mul(x = var_3061_cast_fp16, y = var_3063_to_fp16)[name = tensor("input_357_cast_fp16")]; + tensor var_3071 = const()[name = tensor("op_3071"), val = tensor([1, 1])]; + tensor var_3073 = const()[name = tensor("op_3073"), val = tensor([1, 1])]; + tensor var_3075_pad_type_0 = const()[name = tensor("op_3075_pad_type_0"), val = tensor("custom")]; + tensor var_3075_pad_0 = const()[name = tensor("op_3075_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185012864))), lut = tensor([-0x1.6c4p-4, -0x1.bp-6, 0x1.b28p-6, 0x1.6dp-4]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188289728))), lut = tensor([-0x1.638p-3, -0x1.35p-4, -0x1.15cp-6, 0x1.adp-6]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3075_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3073, groups = var_2306, pad = var_3075_pad_0, pad_type = var_3075_pad_type_0, strides = var_3071, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_357_cast_fp16)[name = tensor("op_3075_cast_fp16")]; + tensor var_3076_split_sizes_0 = const()[name = tensor("op_3076_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3076_axis_0 = const()[name = tensor("op_3076_axis_0"), val = tensor(1)]; + tensor var_3076_cast_fp16_0, tensor var_3076_cast_fp16_1 = split(axis = var_3076_axis_0, split_sizes = var_3076_split_sizes_0, x = var_3075_cast_fp16)[name = tensor("op_3076_cast_fp16")]; + tensor var_3078_mode_0 = const()[name = tensor("op_3078_mode_0"), val = tensor("EXACT")]; + tensor var_3078_cast_fp16 = gelu(mode = var_3078_mode_0, x = var_3076_cast_fp16_1)[name = tensor("op_3078_cast_fp16")]; + tensor input_359_cast_fp16 = mul(x = var_3076_cast_fp16_0, y = var_3078_cast_fp16)[name = tensor("input_359_cast_fp16")]; + tensor var_3082 = const()[name = tensor("op_3082"), val = tensor([1, 1])]; + tensor var_3084 = const()[name = tensor("op_3084"), val = tensor([1, 1])]; + tensor var_3086_pad_type_0 = const()[name = tensor("op_3086_pad_type_0"), val = tensor("custom")]; + tensor var_3086_pad_0 = const()[name = tensor("op_3086_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188292352))), lut = tensor([-0x1.5e8p-4, -0x1.a3cp-6, 0x1.a14p-6, 0x1.5ep-4]), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189930816)))]; + tensor var_3086_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3084, groups = var_2306, pad = var_3086_pad_0, pad_type = var_3086_pad_type_0, strides = var_3082, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_359_cast_fp16)[name = tensor("op_3086_cast_fp16")]; + tensor hidden_states_213_cast_fp16 = add(x = var_3086_cast_fp16, y = inputs_59_cast_fp16)[name = tensor("hidden_states_213_cast_fp16")]; + tensor var_3088 = const()[name = tensor("op_3088"), val = tensor([2, 1280, 16, 16])]; + tensor input_361_cast_fp16 = reshape(shape = var_3088, x = hidden_states_213_cast_fp16)[name = tensor("input_361_cast_fp16")]; + tensor var_3092 = const()[name = tensor("op_3092"), val = tensor([1, 1])]; + tensor var_3094 = const()[name = tensor("op_3094"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_type_0 = const()[name = tensor("hidden_states_215_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_215_pad_0 = const()[name = tensor("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189933440))), lut = tensor([-0x1.4bcp-4, -0x1.928p-6, 0x1.8ep-6, 0x1.4bp-4]), name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190343104)))]; + tensor hidden_states_215_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_3094, groups = var_2306, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = var_3092, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = tensor("hidden_states_215_cast_fp16")]; + tensor input_363_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_203_cast_fp16)[name = tensor("input_363_cast_fp16")]; + tensor input_365_scale_factor_height_0 = const()[name = tensor("input_365_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_365_scale_factor_width_0 = const()[name = tensor("input_365_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_365_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_365_scale_factor_height_0, scale_factor_width = input_365_scale_factor_width_0, x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; + tensor var_3103 = const()[name = tensor("op_3103"), val = tensor([1, 1])]; + tensor var_3105 = const()[name = tensor("op_3105"), val = tensor([1, 1])]; + tensor hidden_states_217_pad_type_0 = const()[name = tensor("hidden_states_217_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_217_pad_0 = const()[name = tensor("hidden_states_217_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190345728))), lut = tensor([-0x1.504p-5, -0x1.81p-7, 0x1.824p-7, 0x1.50cp-5]), name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194032192)))]; + tensor hidden_states_217_cast_fp16 = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_3105, groups = var_2306, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = var_3103, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized, x = input_365_cast_fp16)[name = tensor("hidden_states_217_cast_fp16")]; + tensor var_3110 = const()[name = tensor("op_3110"), val = tensor(3)]; + tensor var_3121 = const()[name = tensor("op_3121"), val = tensor(true)]; + tensor var_3126 = const()[name = tensor("op_3126"), val = tensor(1)]; + tensor input_367_interleave_0 = const()[name = tensor("input_367_interleave_0"), val = tensor(false)]; + tensor input_367_cast_fp16 = concat(axis = var_3126, interleave = input_367_interleave_0, values = (hidden_states_217_cast_fp16, input_115_cast_fp16))[name = tensor("input_367_cast_fp16")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([2, 32, 60, 32, 32])]; + tensor reshape_168_cast_fp16 = reshape(shape = reshape_168_shape_0, x = input_367_cast_fp16)[name = tensor("reshape_168_cast_fp16")]; + tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_126_cast_fp16 = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast_fp16)[name = tensor("reduce_mean_126_cast_fp16")]; + tensor sub_84_cast_fp16 = sub(x = reshape_168_cast_fp16, y = reduce_mean_126_cast_fp16)[name = tensor("sub_84_cast_fp16")]; + tensor square_42_cast_fp16 = square(x = sub_84_cast_fp16)[name = tensor("square_42_cast_fp16")]; + tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_128_cast_fp16 = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast_fp16)[name = tensor("reduce_mean_128_cast_fp16")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_84_cast_fp16 = add(x = reduce_mean_128_cast_fp16, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast_fp16")]; + tensor sqrt_42_cast_fp16 = sqrt(x = add_84_cast_fp16)[name = tensor("sqrt_42_cast_fp16")]; + tensor real_div_42_cast_fp16 = real_div(x = sub_84_cast_fp16, y = sqrt_42_cast_fp16)[name = tensor("real_div_42_cast_fp16")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([2, 1920, 32, 32])]; + tensor reshape_169_cast_fp16 = reshape(shape = reshape_169_shape_0, x = real_div_42_cast_fp16)[name = tensor("reshape_169_cast_fp16")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194034816)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194038720)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_85_cast_fp16 = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_169_cast_fp16)[name = tensor("add_85_cast_fp16")]; + tensor input_371_cast_fp16 = silu(x = add_85_cast_fp16)[name = tensor("input_371_cast_fp16")]; + tensor var_3155 = const()[name = tensor("op_3155"), val = tensor([1, 1])]; + tensor var_3157 = const()[name = tensor("op_3157"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_type_0 = const()[name = tensor("hidden_states_219_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_219_pad_0 = const()[name = tensor("hidden_states_219_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194042624))), lut = tensor([-0x1.8f4p-4, -0x1.a9cp-6, 0x1.75cp-6, 0x1.79p-4]), name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1920, 3, 3])]; + tensor up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196807488)))]; + tensor hidden_states_219_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_3157, groups = var_3126, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = var_3155, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_371_cast_fp16)[name = tensor("hidden_states_219_cast_fp16")]; + tensor var_3163 = const()[name = tensor("op_3163"), val = tensor([1, 1])]; + tensor var_3165 = const()[name = tensor("op_3165"), val = tensor([1, 1])]; + tensor temb_33_pad_type_0 = const()[name = tensor("temb_33_pad_type_0"), val = tensor("custom")]; + tensor temb_33_pad_0 = const()[name = tensor("temb_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196808832))), lut = tensor([-0x1.04p-4, -0x1.cc8p-9, 0x1.d7cp-9, 0x1.30cp-4]), name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197013696)))]; + tensor temb_33_cast_fp16 = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3165, groups = var_3126, pad = temb_33_pad_0, pad_type = temb_33_pad_type_0, strides = var_3163, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_33_cast_fp16")]; + tensor input_375_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = temb_33_cast_fp16)[name = tensor("input_375_cast_fp16")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_172_cast_fp16 = reshape(shape = reshape_172_shape_0, x = input_375_cast_fp16)[name = tensor("reshape_172_cast_fp16")]; + tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_129_cast_fp16 = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast_fp16)[name = tensor("reduce_mean_129_cast_fp16")]; + tensor sub_86_cast_fp16 = sub(x = reshape_172_cast_fp16, y = reduce_mean_129_cast_fp16)[name = tensor("sub_86_cast_fp16")]; + tensor square_43_cast_fp16 = square(x = sub_86_cast_fp16)[name = tensor("square_43_cast_fp16")]; + tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_131_cast_fp16 = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast_fp16)[name = tensor("reduce_mean_131_cast_fp16")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast_fp16 = add(x = reduce_mean_131_cast_fp16, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast_fp16")]; + tensor sqrt_43_cast_fp16 = sqrt(x = add_86_cast_fp16)[name = tensor("sqrt_43_cast_fp16")]; + tensor real_div_43_cast_fp16 = real_div(x = sub_86_cast_fp16, y = sqrt_43_cast_fp16)[name = tensor("real_div_43_cast_fp16")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_173_cast_fp16 = reshape(shape = reshape_173_shape_0, x = real_div_43_cast_fp16)[name = tensor("reshape_173_cast_fp16")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197015040)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197016384)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_87_cast_fp16 = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_173_cast_fp16)[name = tensor("add_87_cast_fp16")]; + tensor input_379_cast_fp16 = silu(x = add_87_cast_fp16)[name = tensor("input_379_cast_fp16")]; + tensor var_3175 = const()[name = tensor("op_3175"), val = tensor([1, 1])]; + tensor var_3177 = const()[name = tensor("op_3177"), val = tensor([1, 1])]; + tensor hidden_states_221_pad_type_0 = const()[name = tensor("hidden_states_221_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_221_pad_0 = const()[name = tensor("hidden_states_221_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197017728))), lut = tensor([-0x1.1ecp-3, -0x1.1acp-5, 0x1.214p-5, 0x1.24p-3]), name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197939392)))]; + tensor hidden_states_221_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_3177, groups = var_3126, pad = hidden_states_221_pad_0, pad_type = hidden_states_221_pad_type_0, strides = var_3175, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_379_cast_fp16)[name = tensor("hidden_states_221_cast_fp16")]; + tensor var_3182 = const()[name = tensor("op_3182"), val = tensor([1, 1])]; + tensor var_3184 = const()[name = tensor("op_3184"), val = tensor([1, 1])]; + tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; + tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197940736))), lut = tensor([-0x1.5ccp-4, -0x1.c6p-10, 0x1.858p-5, 0x1.dep-4]), name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1920, 1, 1])]; + tensor up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198248000)))]; + tensor x_17_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_3184, groups = var_3126, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_3182, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_367_cast_fp16)[name = tensor("x_17_cast_fp16")]; + tensor hidden_states_223_cast_fp16 = add(x = x_17_cast_fp16, y = hidden_states_221_cast_fp16)[name = tensor("hidden_states_223_cast_fp16")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_176_cast_fp16 = reshape(shape = reshape_176_shape_0, x = hidden_states_223_cast_fp16)[name = tensor("reshape_176_cast_fp16")]; + tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_132_cast_fp16 = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast_fp16)[name = tensor("reduce_mean_132_cast_fp16")]; + tensor sub_88_cast_fp16 = sub(x = reshape_176_cast_fp16, y = reduce_mean_132_cast_fp16)[name = tensor("sub_88_cast_fp16")]; + tensor square_44_cast_fp16 = square(x = sub_88_cast_fp16)[name = tensor("square_44_cast_fp16")]; + tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_134_cast_fp16 = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast_fp16)[name = tensor("reduce_mean_134_cast_fp16")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_88_cast_fp16 = add(x = reduce_mean_134_cast_fp16, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast_fp16")]; + tensor sqrt_44_cast_fp16 = sqrt(x = add_88_cast_fp16)[name = tensor("sqrt_44_cast_fp16")]; + tensor real_div_44_cast_fp16 = real_div(x = sub_88_cast_fp16, y = sqrt_44_cast_fp16)[name = tensor("real_div_44_cast_fp16")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_177_cast_fp16 = reshape(shape = reshape_177_shape_0, x = real_div_44_cast_fp16)[name = tensor("reshape_177_cast_fp16")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198249344)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198250688)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_89_cast_fp16 = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_177_cast_fp16)[name = tensor("add_89_cast_fp16")]; + tensor var_3204 = const()[name = tensor("op_3204"), val = tensor([1, 1])]; + tensor var_3206 = const()[name = tensor("op_3206"), val = tensor([1, 1])]; + tensor hidden_states_225_pad_type_0 = const()[name = tensor("hidden_states_225_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_225_pad_0 = const()[name = tensor("hidden_states_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198252032))), lut = tensor([-0x1.d7p-4, -0x1.1b8p-5, 0x1.1ap-5, 0x1.d84p-4]), name = tensor("up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198354496)))]; + tensor hidden_states_225_cast_fp16 = conv(bias = up_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_3206, groups = var_3126, pad = hidden_states_225_pad_0, pad_type = hidden_states_225_pad_type_0, strides = var_3204, weight = up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_89_cast_fp16)[name = tensor("hidden_states_225_cast_fp16")]; + tensor var_3211 = const()[name = tensor("op_3211"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_61_cast_fp16 = reshape(shape = var_3211, x = hidden_states_225_cast_fp16)[name = tensor("inputs_61_cast_fp16")]; + tensor var_3221 = const()[name = tensor("op_3221"), val = tensor([1])]; + tensor channels_mean_61_cast_fp16 = reduce_mean(axes = var_3221, keep_dims = var_3121, x = inputs_61_cast_fp16)[name = tensor("channels_mean_61_cast_fp16")]; + tensor zero_mean_61_cast_fp16 = sub(x = inputs_61_cast_fp16, y = channels_mean_61_cast_fp16)[name = tensor("zero_mean_61_cast_fp16")]; + tensor zero_mean_sq_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = zero_mean_61_cast_fp16)[name = tensor("zero_mean_sq_61_cast_fp16")]; + tensor var_3225 = const()[name = tensor("op_3225"), val = tensor([1])]; + tensor var_3226_cast_fp16 = reduce_mean(axes = var_3225, keep_dims = var_3121, x = zero_mean_sq_61_cast_fp16)[name = tensor("op_3226_cast_fp16")]; + tensor var_3227_to_fp16 = const()[name = tensor("op_3227_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3228_cast_fp16 = add(x = var_3226_cast_fp16, y = var_3227_to_fp16)[name = tensor("op_3228_cast_fp16")]; + tensor denom_61_epsilon_0_to_fp16 = const()[name = tensor("denom_61_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_61_cast_fp16 = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_3228_cast_fp16)[name = tensor("denom_61_cast_fp16")]; + tensor out_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = denom_61_cast_fp16)[name = tensor("out_61_cast_fp16")]; + tensor var_3232_to_fp16 = const()[name = tensor("op_3232_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198355840)))]; + tensor var_3233_cast_fp16 = add(x = out_61_cast_fp16, y = var_3232_to_fp16)[name = tensor("op_3233_cast_fp16")]; + tensor var_3235_to_fp16 = const()[name = tensor("op_3235_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198357184)))]; + tensor hidden_states_227_cast_fp16 = mul(x = var_3233_cast_fp16, y = var_3235_to_fp16)[name = tensor("hidden_states_227_cast_fp16")]; + tensor var_3242 = const()[name = tensor("op_3242"), val = tensor([1, 1])]; + tensor var_3244 = const()[name = tensor("op_3244"), val = tensor([1, 1])]; + tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; + tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198358528))), lut = tensor([-0x1.bd4p-4, -0x1.07cp-5, 0x1.06p-5, 0x1.bdcp-4]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_41_cast_fp16 = conv(dilations = var_3244, groups = var_3126, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_3242, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_227_cast_fp16)[name = tensor("q_41_cast_fp16")]; + tensor var_3248 = const()[name = tensor("op_3248"), val = tensor([1, 1])]; + tensor var_3250 = const()[name = tensor("op_3250"), val = tensor([1, 1])]; + tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; + tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198460992))), lut = tensor([-0x1.b84p-4, -0x1.01cp-5, 0x1.04p-5, 0x1.b94p-4]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_41_cast_fp16 = conv(dilations = var_3250, groups = var_3126, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_3248, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_227_cast_fp16)[name = tensor("k_41_cast_fp16")]; + tensor var_3254 = const()[name = tensor("op_3254"), val = tensor([1, 1])]; + tensor var_3256 = const()[name = tensor("op_3256"), val = tensor([1, 1])]; + tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; + tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198563456))), lut = tensor([-0x1.ad8p-4, -0x1.f68p-6, 0x1.014p-5, 0x1.ae4p-4]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_41_cast_fp16 = conv(dilations = var_3256, groups = var_3126, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_3254, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_227_cast_fp16)[name = tensor("v_41_cast_fp16")]; + tensor var_3260 = const()[name = tensor("op_3260"), val = tensor([2, 10, 64, -1])]; + tensor var_3261_cast_fp16 = reshape(shape = var_3260, x = q_41_cast_fp16)[name = tensor("op_3261_cast_fp16")]; + tensor var_3262 = const()[name = tensor("op_3262"), val = tensor([2, 10, 64, -1])]; + tensor var_3263_cast_fp16 = reshape(shape = var_3262, x = k_41_cast_fp16)[name = tensor("op_3263_cast_fp16")]; + tensor var_3264 = const()[name = tensor("op_3264"), val = tensor([2, 10, 64, -1])]; + tensor var_3265_cast_fp16 = reshape(shape = var_3264, x = v_41_cast_fp16)[name = tensor("op_3265_cast_fp16")]; + tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_3261_cast_fp16, y = var_3263_cast_fp16)[name = tensor("attn_weights_81_cast_fp16")]; + tensor var_3117_to_fp16 = const()[name = tensor("op_3117_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_83_cast_fp16")]; + tensor var_3269_cast_fp16 = softmax(axis = var_3110, x = attn_weights_83_cast_fp16)[name = tensor("op_3269_cast_fp16")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast_fp16 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_3265_cast_fp16, y = var_3269_cast_fp16)[name = tensor("attn_41_cast_fp16")]; + tensor var_3273 = const()[name = tensor("op_3273"), val = tensor([2, 640, 1, -1])]; + tensor input_383_cast_fp16 = reshape(shape = var_3273, x = attn_41_cast_fp16)[name = tensor("input_383_cast_fp16")]; + tensor var_3278 = const()[name = tensor("op_3278"), val = tensor([1, 1])]; + tensor var_3280 = const()[name = tensor("op_3280"), val = tensor([1, 1])]; + tensor var_3282_pad_type_0 = const()[name = tensor("op_3282_pad_type_0"), val = tensor("custom")]; + tensor var_3282_pad_0 = const()[name = tensor("op_3282_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198665920))), lut = tensor([-0x1.9d8p-4, -0x1.edcp-6, 0x1.e9p-6, 0x1.9ap-4]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198768384)))]; + tensor var_3282_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3280, groups = var_3126, pad = var_3282_pad_0, pad_type = var_3282_pad_type_0, strides = var_3278, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_383_cast_fp16)[name = tensor("op_3282_cast_fp16")]; + tensor inputs_63_cast_fp16 = add(x = var_3282_cast_fp16, y = inputs_61_cast_fp16)[name = tensor("inputs_63_cast_fp16")]; + tensor var_3286 = const()[name = tensor("op_3286"), val = tensor([1])]; + tensor channels_mean_63_cast_fp16 = reduce_mean(axes = var_3286, keep_dims = var_3121, x = inputs_63_cast_fp16)[name = tensor("channels_mean_63_cast_fp16")]; + tensor zero_mean_63_cast_fp16 = sub(x = inputs_63_cast_fp16, y = channels_mean_63_cast_fp16)[name = tensor("zero_mean_63_cast_fp16")]; + tensor zero_mean_sq_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = zero_mean_63_cast_fp16)[name = tensor("zero_mean_sq_63_cast_fp16")]; + tensor var_3290 = const()[name = tensor("op_3290"), val = tensor([1])]; + tensor var_3291_cast_fp16 = reduce_mean(axes = var_3290, keep_dims = var_3121, x = zero_mean_sq_63_cast_fp16)[name = tensor("op_3291_cast_fp16")]; + tensor var_3292_to_fp16 = const()[name = tensor("op_3292_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3293_cast_fp16 = add(x = var_3291_cast_fp16, y = var_3292_to_fp16)[name = tensor("op_3293_cast_fp16")]; + tensor denom_63_epsilon_0_to_fp16 = const()[name = tensor("denom_63_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_63_cast_fp16 = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_3293_cast_fp16)[name = tensor("denom_63_cast_fp16")]; + tensor out_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = denom_63_cast_fp16)[name = tensor("out_63_cast_fp16")]; + tensor var_3297_to_fp16 = const()[name = tensor("op_3297_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198769728)))]; + tensor var_3298_cast_fp16 = add(x = out_63_cast_fp16, y = var_3297_to_fp16)[name = tensor("op_3298_cast_fp16")]; + tensor var_3300_to_fp16 = const()[name = tensor("op_3300_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198771072)))]; + tensor hidden_states_229_cast_fp16 = mul(x = var_3298_cast_fp16, y = var_3300_to_fp16)[name = tensor("hidden_states_229_cast_fp16")]; + tensor var_3307 = const()[name = tensor("op_3307"), val = tensor([1, 1])]; + tensor var_3309 = const()[name = tensor("op_3309"), val = tensor([1, 1])]; + tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; + tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198772416))), lut = tensor([-0x1.88cp-4, -0x1.d68p-6, 0x1.d58p-6, 0x1.888p-4]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_43_cast_fp16 = conv(dilations = var_3309, groups = var_3126, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_3307, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_229_cast_fp16)[name = tensor("q_43_cast_fp16")]; + tensor var_3313 = const()[name = tensor("op_3313"), val = tensor([1, 1])]; + tensor var_3315 = const()[name = tensor("op_3315"), val = tensor([1, 1])]; + tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; + tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198874880))), lut = tensor([-0x1.67p-4, -0x1.ad4p-6, 0x1.a24p-6, 0x1.63cp-4]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor k_43_cast_fp16 = conv(dilations = var_3315, groups = var_3126, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_3313, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_43_cast_fp16")]; + tensor var_3319 = const()[name = tensor("op_3319"), val = tensor([1, 1])]; + tensor var_3321 = const()[name = tensor("op_3321"), val = tensor([1, 1])]; + tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; + tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199038784))), lut = tensor([-0x1.408p-4, -0x1.6fcp-6, 0x1.6f4p-6, 0x1.408p-4]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor v_43_cast_fp16 = conv(dilations = var_3321, groups = var_3126, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_3319, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_43_cast_fp16")]; + tensor var_3325 = const()[name = tensor("op_3325"), val = tensor([2, 10, 64, -1])]; + tensor var_3326_cast_fp16 = reshape(shape = var_3325, x = q_43_cast_fp16)[name = tensor("op_3326_cast_fp16")]; + tensor var_3327 = const()[name = tensor("op_3327"), val = tensor([2, 10, 64, -1])]; + tensor var_3328_cast_fp16 = reshape(shape = var_3327, x = k_43_cast_fp16)[name = tensor("op_3328_cast_fp16")]; + tensor var_3329 = const()[name = tensor("op_3329"), val = tensor([2, 10, 64, -1])]; + tensor var_3330_cast_fp16 = reshape(shape = var_3329, x = v_43_cast_fp16)[name = tensor("op_3330_cast_fp16")]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_85_cast_fp16 = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_3326_cast_fp16, y = var_3328_cast_fp16)[name = tensor("attn_weights_85_cast_fp16")]; + tensor attn_weights_87_cast_fp16 = mul(x = attn_weights_85_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_87_cast_fp16")]; + tensor var_3334_cast_fp16 = softmax(axis = var_3110, x = attn_weights_87_cast_fp16)[name = tensor("op_3334_cast_fp16")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast_fp16 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_3330_cast_fp16, y = var_3334_cast_fp16)[name = tensor("attn_43_cast_fp16")]; + tensor var_3338 = const()[name = tensor("op_3338"), val = tensor([2, 640, 1, -1])]; + tensor input_385_cast_fp16 = reshape(shape = var_3338, x = attn_43_cast_fp16)[name = tensor("input_385_cast_fp16")]; + tensor var_3343 = const()[name = tensor("op_3343"), val = tensor([1, 1])]; + tensor var_3345 = const()[name = tensor("op_3345"), val = tensor([1, 1])]; + tensor var_3347_pad_type_0 = const()[name = tensor("op_3347_pad_type_0"), val = tensor("custom")]; + tensor var_3347_pad_0 = const()[name = tensor("op_3347_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199202688))), lut = tensor([-0x1.48p-5, -0x1.824p-7, 0x1.7bcp-7, 0x1.464p-5]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199305152)))]; + tensor var_3347_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3345, groups = var_3126, pad = var_3347_pad_0, pad_type = var_3347_pad_type_0, strides = var_3343, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_385_cast_fp16)[name = tensor("op_3347_cast_fp16")]; + tensor inputs_65_cast_fp16 = add(x = var_3347_cast_fp16, y = inputs_63_cast_fp16)[name = tensor("inputs_65_cast_fp16")]; + tensor var_3351 = const()[name = tensor("op_3351"), val = tensor([1])]; + tensor channels_mean_65_cast_fp16 = reduce_mean(axes = var_3351, keep_dims = var_3121, x = inputs_65_cast_fp16)[name = tensor("channels_mean_65_cast_fp16")]; + tensor zero_mean_65_cast_fp16 = sub(x = inputs_65_cast_fp16, y = channels_mean_65_cast_fp16)[name = tensor("zero_mean_65_cast_fp16")]; + tensor zero_mean_sq_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = zero_mean_65_cast_fp16)[name = tensor("zero_mean_sq_65_cast_fp16")]; + tensor var_3355 = const()[name = tensor("op_3355"), val = tensor([1])]; + tensor var_3356_cast_fp16 = reduce_mean(axes = var_3355, keep_dims = var_3121, x = zero_mean_sq_65_cast_fp16)[name = tensor("op_3356_cast_fp16")]; + tensor var_3357_to_fp16 = const()[name = tensor("op_3357_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3358_cast_fp16 = add(x = var_3356_cast_fp16, y = var_3357_to_fp16)[name = tensor("op_3358_cast_fp16")]; + tensor denom_65_epsilon_0_to_fp16 = const()[name = tensor("denom_65_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_65_cast_fp16 = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_3358_cast_fp16)[name = tensor("denom_65_cast_fp16")]; + tensor out_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = denom_65_cast_fp16)[name = tensor("out_65_cast_fp16")]; + tensor var_3362_to_fp16 = const()[name = tensor("op_3362_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199306496)))]; + tensor var_3363_cast_fp16 = add(x = out_65_cast_fp16, y = var_3362_to_fp16)[name = tensor("op_3363_cast_fp16")]; + tensor var_3365_to_fp16 = const()[name = tensor("op_3365_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199307840)))]; + tensor input_387_cast_fp16 = mul(x = var_3363_cast_fp16, y = var_3365_to_fp16)[name = tensor("input_387_cast_fp16")]; + tensor var_3373 = const()[name = tensor("op_3373"), val = tensor([1, 1])]; + tensor var_3375 = const()[name = tensor("op_3375"), val = tensor([1, 1])]; + tensor var_3377_pad_type_0 = const()[name = tensor("op_3377_pad_type_0"), val = tensor("custom")]; + tensor var_3377_pad_0 = const()[name = tensor("op_3377_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199309184))), lut = tensor([-0x1.9e8p-4, -0x1.e54p-6, 0x1.d9cp-6, 0x1.98cp-4]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200128448))), lut = tensor([0x1.0e8p-4, -0x1.5e8p-4, -0x1.8dp-9, -0x1.2a4p-2]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_3377_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3375, groups = var_3126, pad = var_3377_pad_0, pad_type = var_3377_pad_type_0, strides = var_3373, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_387_cast_fp16)[name = tensor("op_3377_cast_fp16")]; + tensor var_3378_split_sizes_0 = const()[name = tensor("op_3378_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3378_axis_0 = const()[name = tensor("op_3378_axis_0"), val = tensor(1)]; + tensor var_3378_cast_fp16_0, tensor var_3378_cast_fp16_1 = split(axis = var_3378_axis_0, split_sizes = var_3378_split_sizes_0, x = var_3377_cast_fp16)[name = tensor("op_3378_cast_fp16")]; + tensor var_3380_mode_0 = const()[name = tensor("op_3380_mode_0"), val = tensor("EXACT")]; + tensor var_3380_cast_fp16 = gelu(mode = var_3380_mode_0, x = var_3378_cast_fp16_1)[name = tensor("op_3380_cast_fp16")]; + tensor input_389_cast_fp16 = mul(x = var_3378_cast_fp16_0, y = var_3380_cast_fp16)[name = tensor("input_389_cast_fp16")]; + tensor var_3384 = const()[name = tensor("op_3384"), val = tensor([1, 1])]; + tensor var_3386 = const()[name = tensor("op_3386"), val = tensor([1, 1])]; + tensor var_3388_pad_type_0 = const()[name = tensor("op_3388_pad_type_0"), val = tensor("custom")]; + tensor var_3388_pad_0 = const()[name = tensor("op_3388_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200129792))), lut = tensor([-0x1.acp-4, -0x1.fb8p-6, 0x1.ff8p-6, 0x1.adp-4]), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200539456)))]; + tensor var_3388_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3386, groups = var_3126, pad = var_3388_pad_0, pad_type = var_3388_pad_type_0, strides = var_3384, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = tensor("op_3388_cast_fp16")]; + tensor hidden_states_233_cast_fp16 = add(x = var_3388_cast_fp16, y = inputs_65_cast_fp16)[name = tensor("hidden_states_233_cast_fp16")]; + tensor var_3390 = const()[name = tensor("op_3390"), val = tensor([2, 640, 32, 32])]; + tensor input_391_cast_fp16 = reshape(shape = var_3390, x = hidden_states_233_cast_fp16)[name = tensor("input_391_cast_fp16")]; + tensor var_3394 = const()[name = tensor("op_3394"), val = tensor([1, 1])]; + tensor var_3396 = const()[name = tensor("op_3396"), val = tensor([1, 1])]; + tensor hidden_states_235_pad_type_0 = const()[name = tensor("hidden_states_235_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_235_pad_0 = const()[name = tensor("hidden_states_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200540800))), lut = tensor([-0x1.2c4p-3, -0x1.658p-5, 0x1.6b4p-5, 0x1.2e8p-3]), name = tensor("up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200643264)))]; + tensor hidden_states_235_cast_fp16 = conv(bias = up_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3396, groups = var_3126, pad = hidden_states_235_pad_0, pad_type = hidden_states_235_pad_type_0, strides = var_3394, weight = up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_391_cast_fp16)[name = tensor("hidden_states_235_cast_fp16")]; + tensor hidden_states_237_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_223_cast_fp16)[name = tensor("hidden_states_237_cast_fp16")]; + tensor input_393_interleave_0 = const()[name = tensor("input_393_interleave_0"), val = tensor(false)]; + tensor input_393_cast_fp16 = concat(axis = var_3126, interleave = input_393_interleave_0, values = (hidden_states_237_cast_fp16, input_89_cast_fp16))[name = tensor("input_393_cast_fp16")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_180_cast_fp16 = reshape(shape = reshape_180_shape_0, x = input_393_cast_fp16)[name = tensor("reshape_180_cast_fp16")]; + tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_135_cast_fp16 = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast_fp16)[name = tensor("reduce_mean_135_cast_fp16")]; + tensor sub_90_cast_fp16 = sub(x = reshape_180_cast_fp16, y = reduce_mean_135_cast_fp16)[name = tensor("sub_90_cast_fp16")]; + tensor square_45_cast_fp16 = square(x = sub_90_cast_fp16)[name = tensor("square_45_cast_fp16")]; + tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_137_cast_fp16 = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast_fp16)[name = tensor("reduce_mean_137_cast_fp16")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_90_cast_fp16 = add(x = reduce_mean_137_cast_fp16, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast_fp16")]; + tensor sqrt_45_cast_fp16 = sqrt(x = add_90_cast_fp16)[name = tensor("sqrt_45_cast_fp16")]; + tensor real_div_45_cast_fp16 = real_div(x = sub_90_cast_fp16, y = sqrt_45_cast_fp16)[name = tensor("real_div_45_cast_fp16")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_181_cast_fp16 = reshape(shape = reshape_181_shape_0, x = real_div_45_cast_fp16)[name = tensor("reshape_181_cast_fp16")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200644608)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200647232)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_91_cast_fp16 = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_181_cast_fp16)[name = tensor("add_91_cast_fp16")]; + tensor input_397_cast_fp16 = silu(x = add_91_cast_fp16)[name = tensor("input_397_cast_fp16")]; + tensor var_3414 = const()[name = tensor("op_3414"), val = tensor([1, 1])]; + tensor var_3416 = const()[name = tensor("op_3416"), val = tensor([1, 1])]; + tensor hidden_states_239_pad_type_0 = const()[name = tensor("hidden_states_239_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_239_pad_0 = const()[name = tensor("hidden_states_239_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200649856))), lut = tensor([-0x1.988p-4, -0x1.84cp-6, 0x1.a2p-6, 0x1.aacp-4]), name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1280, 3, 3])]; + tensor up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202493120)))]; + tensor hidden_states_239_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3416, groups = var_3126, pad = hidden_states_239_pad_0, pad_type = hidden_states_239_pad_type_0, strides = var_3414, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_397_cast_fp16)[name = tensor("hidden_states_239_cast_fp16")]; + tensor var_3422 = const()[name = tensor("op_3422"), val = tensor([1, 1])]; + tensor var_3424 = const()[name = tensor("op_3424"), val = tensor([1, 1])]; + tensor temb_35_pad_type_0 = const()[name = tensor("temb_35_pad_type_0"), val = tensor("custom")]; + tensor temb_35_pad_0 = const()[name = tensor("temb_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202494464))), lut = tensor([-0x1.84p-5, -0x1.824p-8, 0x1.7fcp-11, 0x1.048p-7]), name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202699328)))]; + tensor temb_35_cast_fp16 = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3424, groups = var_3126, pad = temb_35_pad_0, pad_type = temb_35_pad_type_0, strides = var_3422, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_35_cast_fp16")]; + tensor input_401_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = temb_35_cast_fp16)[name = tensor("input_401_cast_fp16")]; + tensor reshape_184_shape_0 = const()[name = tensor("reshape_184_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_184_cast_fp16 = reshape(shape = reshape_184_shape_0, x = input_401_cast_fp16)[name = tensor("reshape_184_cast_fp16")]; + tensor reduce_mean_138_axes_0 = const()[name = tensor("reduce_mean_138_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_138_keep_dims_0 = const()[name = tensor("reduce_mean_138_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_138_cast_fp16 = reduce_mean(axes = reduce_mean_138_axes_0, keep_dims = reduce_mean_138_keep_dims_0, x = reshape_184_cast_fp16)[name = tensor("reduce_mean_138_cast_fp16")]; + tensor sub_92_cast_fp16 = sub(x = reshape_184_cast_fp16, y = reduce_mean_138_cast_fp16)[name = tensor("sub_92_cast_fp16")]; + tensor square_46_cast_fp16 = square(x = sub_92_cast_fp16)[name = tensor("square_46_cast_fp16")]; + tensor reduce_mean_140_axes_0 = const()[name = tensor("reduce_mean_140_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_140_keep_dims_0 = const()[name = tensor("reduce_mean_140_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_140_cast_fp16 = reduce_mean(axes = reduce_mean_140_axes_0, keep_dims = reduce_mean_140_keep_dims_0, x = square_46_cast_fp16)[name = tensor("reduce_mean_140_cast_fp16")]; + tensor add_92_y_0_to_fp16 = const()[name = tensor("add_92_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_92_cast_fp16 = add(x = reduce_mean_140_cast_fp16, y = add_92_y_0_to_fp16)[name = tensor("add_92_cast_fp16")]; + tensor sqrt_46_cast_fp16 = sqrt(x = add_92_cast_fp16)[name = tensor("sqrt_46_cast_fp16")]; + tensor real_div_46_cast_fp16 = real_div(x = sub_92_cast_fp16, y = sqrt_46_cast_fp16)[name = tensor("real_div_46_cast_fp16")]; + tensor reshape_185_shape_0 = const()[name = tensor("reshape_185_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_185_cast_fp16 = reshape(shape = reshape_185_shape_0, x = real_div_46_cast_fp16)[name = tensor("reshape_185_cast_fp16")]; + tensor add_93_gamma_0_to_fp16 = const()[name = tensor("add_93_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202700672)))]; + tensor add_93_beta_0_to_fp16 = const()[name = tensor("add_93_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202702016)))]; + tensor add_93_epsilon_0_to_fp16 = const()[name = tensor("add_93_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_93_cast_fp16 = batch_norm(beta = add_93_beta_0_to_fp16, epsilon = add_93_epsilon_0_to_fp16, gamma = add_93_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_185_cast_fp16)[name = tensor("add_93_cast_fp16")]; + tensor input_405_cast_fp16 = silu(x = add_93_cast_fp16)[name = tensor("input_405_cast_fp16")]; + tensor var_3434 = const()[name = tensor("op_3434"), val = tensor([1, 1])]; + tensor var_3436 = const()[name = tensor("op_3436"), val = tensor([1, 1])]; + tensor hidden_states_241_pad_type_0 = const()[name = tensor("hidden_states_241_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_241_pad_0 = const()[name = tensor("hidden_states_241_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202703360))), lut = tensor([-0x1.e08p-4, -0x1.b28p-6, 0x1.afp-6, 0x1.dep-4]), name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203625024)))]; + tensor hidden_states_241_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3436, groups = var_3126, pad = hidden_states_241_pad_0, pad_type = hidden_states_241_pad_type_0, strides = var_3434, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = tensor("hidden_states_241_cast_fp16")]; + tensor var_3441 = const()[name = tensor("op_3441"), val = tensor([1, 1])]; + tensor var_3443 = const()[name = tensor("op_3443"), val = tensor([1, 1])]; + tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; + tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203626368))), lut = tensor([-0x1.b3p-5, -0x1.9fcp-7, 0x1.b1p-7, 0x1.b9p-5]), name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203831232)))]; + tensor x_19_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_3443, groups = var_3126, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_3441, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = tensor("x_19_cast_fp16")]; + tensor hidden_states_243_cast_fp16 = add(x = x_19_cast_fp16, y = hidden_states_241_cast_fp16)[name = tensor("hidden_states_243_cast_fp16")]; + tensor reshape_188_shape_0 = const()[name = tensor("reshape_188_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_188_cast_fp16 = reshape(shape = reshape_188_shape_0, x = hidden_states_243_cast_fp16)[name = tensor("reshape_188_cast_fp16")]; + tensor reduce_mean_141_axes_0 = const()[name = tensor("reduce_mean_141_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_141_keep_dims_0 = const()[name = tensor("reduce_mean_141_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_141_cast_fp16 = reduce_mean(axes = reduce_mean_141_axes_0, keep_dims = reduce_mean_141_keep_dims_0, x = reshape_188_cast_fp16)[name = tensor("reduce_mean_141_cast_fp16")]; + tensor sub_94_cast_fp16 = sub(x = reshape_188_cast_fp16, y = reduce_mean_141_cast_fp16)[name = tensor("sub_94_cast_fp16")]; + tensor square_47_cast_fp16 = square(x = sub_94_cast_fp16)[name = tensor("square_47_cast_fp16")]; + tensor reduce_mean_143_axes_0 = const()[name = tensor("reduce_mean_143_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_143_keep_dims_0 = const()[name = tensor("reduce_mean_143_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_143_cast_fp16 = reduce_mean(axes = reduce_mean_143_axes_0, keep_dims = reduce_mean_143_keep_dims_0, x = square_47_cast_fp16)[name = tensor("reduce_mean_143_cast_fp16")]; + tensor add_94_y_0_to_fp16 = const()[name = tensor("add_94_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_94_cast_fp16 = add(x = reduce_mean_143_cast_fp16, y = add_94_y_0_to_fp16)[name = tensor("add_94_cast_fp16")]; + tensor sqrt_47_cast_fp16 = sqrt(x = add_94_cast_fp16)[name = tensor("sqrt_47_cast_fp16")]; + tensor real_div_47_cast_fp16 = real_div(x = sub_94_cast_fp16, y = sqrt_47_cast_fp16)[name = tensor("real_div_47_cast_fp16")]; + tensor reshape_189_shape_0 = const()[name = tensor("reshape_189_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_189_cast_fp16 = reshape(shape = reshape_189_shape_0, x = real_div_47_cast_fp16)[name = tensor("reshape_189_cast_fp16")]; + tensor add_95_gamma_0_to_fp16 = const()[name = tensor("add_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203832576)))]; + tensor add_95_beta_0_to_fp16 = const()[name = tensor("add_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203833920)))]; + tensor add_95_epsilon_0_to_fp16 = const()[name = tensor("add_95_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_95_cast_fp16 = batch_norm(beta = add_95_beta_0_to_fp16, epsilon = add_95_epsilon_0_to_fp16, gamma = add_95_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_189_cast_fp16)[name = tensor("add_95_cast_fp16")]; + tensor var_3463 = const()[name = tensor("op_3463"), val = tensor([1, 1])]; + tensor var_3465 = const()[name = tensor("op_3465"), val = tensor([1, 1])]; + tensor hidden_states_245_pad_type_0 = const()[name = tensor("hidden_states_245_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_245_pad_0 = const()[name = tensor("hidden_states_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203835264))), lut = tensor([-0x1.a2p-4, -0x1.e7p-6, 0x1.028p-5, 0x1.abcp-4]), name = tensor("up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203937728)))]; + tensor hidden_states_245_cast_fp16 = conv(bias = up_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3465, groups = var_3126, pad = hidden_states_245_pad_0, pad_type = hidden_states_245_pad_type_0, strides = var_3463, weight = up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_95_cast_fp16)[name = tensor("hidden_states_245_cast_fp16")]; + tensor var_3470 = const()[name = tensor("op_3470"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_67_cast_fp16 = reshape(shape = var_3470, x = hidden_states_245_cast_fp16)[name = tensor("inputs_67_cast_fp16")]; + tensor var_3480 = const()[name = tensor("op_3480"), val = tensor([1])]; + tensor channels_mean_67_cast_fp16 = reduce_mean(axes = var_3480, keep_dims = var_3121, x = inputs_67_cast_fp16)[name = tensor("channels_mean_67_cast_fp16")]; + tensor zero_mean_67_cast_fp16 = sub(x = inputs_67_cast_fp16, y = channels_mean_67_cast_fp16)[name = tensor("zero_mean_67_cast_fp16")]; + tensor zero_mean_sq_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = zero_mean_67_cast_fp16)[name = tensor("zero_mean_sq_67_cast_fp16")]; + tensor var_3484 = const()[name = tensor("op_3484"), val = tensor([1])]; + tensor var_3485_cast_fp16 = reduce_mean(axes = var_3484, keep_dims = var_3121, x = zero_mean_sq_67_cast_fp16)[name = tensor("op_3485_cast_fp16")]; + tensor var_3486_to_fp16 = const()[name = tensor("op_3486_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3487_cast_fp16 = add(x = var_3485_cast_fp16, y = var_3486_to_fp16)[name = tensor("op_3487_cast_fp16")]; + tensor denom_67_epsilon_0_to_fp16 = const()[name = tensor("denom_67_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_67_cast_fp16 = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_3487_cast_fp16)[name = tensor("denom_67_cast_fp16")]; + tensor out_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = denom_67_cast_fp16)[name = tensor("out_67_cast_fp16")]; + tensor var_3491_to_fp16 = const()[name = tensor("op_3491_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203939072)))]; + tensor var_3492_cast_fp16 = add(x = out_67_cast_fp16, y = var_3491_to_fp16)[name = tensor("op_3492_cast_fp16")]; + tensor var_3494_to_fp16 = const()[name = tensor("op_3494_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203940416)))]; + tensor hidden_states_247_cast_fp16 = mul(x = var_3492_cast_fp16, y = var_3494_to_fp16)[name = tensor("hidden_states_247_cast_fp16")]; + tensor var_3501 = const()[name = tensor("op_3501"), val = tensor([1, 1])]; + tensor var_3503 = const()[name = tensor("op_3503"), val = tensor([1, 1])]; + tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; + tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203941760))), lut = tensor([-0x1.e54p-4, -0x1.1b4p-5, 0x1.118p-5, 0x1.e2p-4]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_45_cast_fp16 = conv(dilations = var_3503, groups = var_3126, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_3501, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_247_cast_fp16)[name = tensor("q_45_cast_fp16")]; + tensor var_3507 = const()[name = tensor("op_3507"), val = tensor([1, 1])]; + tensor var_3509 = const()[name = tensor("op_3509"), val = tensor([1, 1])]; + tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; + tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204044224))), lut = tensor([-0x1.d54p-4, -0x1.0f8p-5, 0x1.0ap-5, 0x1.d2p-4]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_45_cast_fp16 = conv(dilations = var_3509, groups = var_3126, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_3507, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_247_cast_fp16)[name = tensor("k_45_cast_fp16")]; + tensor var_3513 = const()[name = tensor("op_3513"), val = tensor([1, 1])]; + tensor var_3515 = const()[name = tensor("op_3515"), val = tensor([1, 1])]; + tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; + tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204146688))), lut = tensor([-0x1.a38p-4, -0x1.e7p-6, 0x1.e78p-6, 0x1.a34p-4]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_45_cast_fp16 = conv(dilations = var_3515, groups = var_3126, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_3513, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_247_cast_fp16)[name = tensor("v_45_cast_fp16")]; + tensor var_3519 = const()[name = tensor("op_3519"), val = tensor([2, 10, 64, -1])]; + tensor var_3520_cast_fp16 = reshape(shape = var_3519, x = q_45_cast_fp16)[name = tensor("op_3520_cast_fp16")]; + tensor var_3521 = const()[name = tensor("op_3521"), val = tensor([2, 10, 64, -1])]; + tensor var_3522_cast_fp16 = reshape(shape = var_3521, x = k_45_cast_fp16)[name = tensor("op_3522_cast_fp16")]; + tensor var_3523 = const()[name = tensor("op_3523"), val = tensor([2, 10, 64, -1])]; + tensor var_3524_cast_fp16 = reshape(shape = var_3523, x = v_45_cast_fp16)[name = tensor("op_3524_cast_fp16")]; + tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_3520_cast_fp16, y = var_3522_cast_fp16)[name = tensor("attn_weights_89_cast_fp16")]; + tensor attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_91_cast_fp16")]; + tensor var_3528_cast_fp16 = softmax(axis = var_3110, x = attn_weights_91_cast_fp16)[name = tensor("op_3528_cast_fp16")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast_fp16 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_3524_cast_fp16, y = var_3528_cast_fp16)[name = tensor("attn_45_cast_fp16")]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([2, 640, 1, -1])]; + tensor input_409_cast_fp16 = reshape(shape = var_3532, x = attn_45_cast_fp16)[name = tensor("input_409_cast_fp16")]; + tensor var_3537 = const()[name = tensor("op_3537"), val = tensor([1, 1])]; + tensor var_3539 = const()[name = tensor("op_3539"), val = tensor([1, 1])]; + tensor var_3541_pad_type_0 = const()[name = tensor("op_3541_pad_type_0"), val = tensor("custom")]; + tensor var_3541_pad_0 = const()[name = tensor("op_3541_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204249152))), lut = tensor([-0x1.84p-4, -0x1.c84p-6, 0x1.d3cp-6, 0x1.87cp-4]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204351616)))]; + tensor var_3541_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3539, groups = var_3126, pad = var_3541_pad_0, pad_type = var_3541_pad_type_0, strides = var_3537, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_409_cast_fp16)[name = tensor("op_3541_cast_fp16")]; + tensor inputs_69_cast_fp16 = add(x = var_3541_cast_fp16, y = inputs_67_cast_fp16)[name = tensor("inputs_69_cast_fp16")]; + tensor var_3545 = const()[name = tensor("op_3545"), val = tensor([1])]; + tensor channels_mean_69_cast_fp16 = reduce_mean(axes = var_3545, keep_dims = var_3121, x = inputs_69_cast_fp16)[name = tensor("channels_mean_69_cast_fp16")]; + tensor zero_mean_69_cast_fp16 = sub(x = inputs_69_cast_fp16, y = channels_mean_69_cast_fp16)[name = tensor("zero_mean_69_cast_fp16")]; + tensor zero_mean_sq_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = zero_mean_69_cast_fp16)[name = tensor("zero_mean_sq_69_cast_fp16")]; + tensor var_3549 = const()[name = tensor("op_3549"), val = tensor([1])]; + tensor var_3550_cast_fp16 = reduce_mean(axes = var_3549, keep_dims = var_3121, x = zero_mean_sq_69_cast_fp16)[name = tensor("op_3550_cast_fp16")]; + tensor var_3551_to_fp16 = const()[name = tensor("op_3551_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3552_cast_fp16 = add(x = var_3550_cast_fp16, y = var_3551_to_fp16)[name = tensor("op_3552_cast_fp16")]; + tensor denom_69_epsilon_0_to_fp16 = const()[name = tensor("denom_69_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_69_cast_fp16 = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_3552_cast_fp16)[name = tensor("denom_69_cast_fp16")]; + tensor out_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = denom_69_cast_fp16)[name = tensor("out_69_cast_fp16")]; + tensor var_3556_to_fp16 = const()[name = tensor("op_3556_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204352960)))]; + tensor var_3557_cast_fp16 = add(x = out_69_cast_fp16, y = var_3556_to_fp16)[name = tensor("op_3557_cast_fp16")]; + tensor var_3559_to_fp16 = const()[name = tensor("op_3559_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204354304)))]; + tensor hidden_states_249_cast_fp16 = mul(x = var_3557_cast_fp16, y = var_3559_to_fp16)[name = tensor("hidden_states_249_cast_fp16")]; + tensor var_3566 = const()[name = tensor("op_3566"), val = tensor([1, 1])]; + tensor var_3568 = const()[name = tensor("op_3568"), val = tensor([1, 1])]; + tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; + tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204355648))), lut = tensor([-0x1.79p-4, -0x1.c1cp-6, 0x1.be4p-6, 0x1.77p-4]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_47_cast_fp16 = conv(dilations = var_3568, groups = var_3126, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_3566, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_249_cast_fp16)[name = tensor("q_47_cast_fp16")]; + tensor var_3572 = const()[name = tensor("op_3572"), val = tensor([1, 1])]; + tensor var_3574 = const()[name = tensor("op_3574"), val = tensor([1, 1])]; + tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; + tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204458112))), lut = tensor([-0x1.524p-4, -0x1.8d8p-6, 0x1.96cp-6, 0x1.54p-4]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor k_47_cast_fp16 = conv(dilations = var_3574, groups = var_3126, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_3572, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_47_cast_fp16")]; + tensor var_3578 = const()[name = tensor("op_3578"), val = tensor([1, 1])]; + tensor var_3580 = const()[name = tensor("op_3580"), val = tensor([1, 1])]; + tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; + tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204622016))), lut = tensor([-0x1.c3cp-5, -0x1.028p-6, 0x1.034p-6, 0x1.c44p-5]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor v_47_cast_fp16 = conv(dilations = var_3580, groups = var_3126, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_3578, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_47_cast_fp16")]; + tensor var_3584 = const()[name = tensor("op_3584"), val = tensor([2, 10, 64, -1])]; + tensor var_3585_cast_fp16 = reshape(shape = var_3584, x = q_47_cast_fp16)[name = tensor("op_3585_cast_fp16")]; + tensor var_3586 = const()[name = tensor("op_3586"), val = tensor([2, 10, 64, -1])]; + tensor var_3587_cast_fp16 = reshape(shape = var_3586, x = k_47_cast_fp16)[name = tensor("op_3587_cast_fp16")]; + tensor var_3588 = const()[name = tensor("op_3588"), val = tensor([2, 10, 64, -1])]; + tensor var_3589_cast_fp16 = reshape(shape = var_3588, x = v_47_cast_fp16)[name = tensor("op_3589_cast_fp16")]; + tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_93_cast_fp16 = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_3585_cast_fp16, y = var_3587_cast_fp16)[name = tensor("attn_weights_93_cast_fp16")]; + tensor attn_weights_95_cast_fp16 = mul(x = attn_weights_93_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_95_cast_fp16")]; + tensor var_3593_cast_fp16 = softmax(axis = var_3110, x = attn_weights_95_cast_fp16)[name = tensor("op_3593_cast_fp16")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast_fp16 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_3589_cast_fp16, y = var_3593_cast_fp16)[name = tensor("attn_47_cast_fp16")]; + tensor var_3597 = const()[name = tensor("op_3597"), val = tensor([2, 640, 1, -1])]; + tensor input_411_cast_fp16 = reshape(shape = var_3597, x = attn_47_cast_fp16)[name = tensor("input_411_cast_fp16")]; + tensor var_3602 = const()[name = tensor("op_3602"), val = tensor([1, 1])]; + tensor var_3604 = const()[name = tensor("op_3604"), val = tensor([1, 1])]; + tensor var_3606_pad_type_0 = const()[name = tensor("op_3606_pad_type_0"), val = tensor("custom")]; + tensor var_3606_pad_0 = const()[name = tensor("op_3606_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204785920))), lut = tensor([-0x1.a2p-6, -0x1.e2cp-8, 0x1.ec4p-8, 0x1.a4p-6]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204888384)))]; + tensor var_3606_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3604, groups = var_3126, pad = var_3606_pad_0, pad_type = var_3606_pad_type_0, strides = var_3602, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_411_cast_fp16)[name = tensor("op_3606_cast_fp16")]; + tensor inputs_71_cast_fp16 = add(x = var_3606_cast_fp16, y = inputs_69_cast_fp16)[name = tensor("inputs_71_cast_fp16")]; + tensor var_3610 = const()[name = tensor("op_3610"), val = tensor([1])]; + tensor channels_mean_71_cast_fp16 = reduce_mean(axes = var_3610, keep_dims = var_3121, x = inputs_71_cast_fp16)[name = tensor("channels_mean_71_cast_fp16")]; + tensor zero_mean_71_cast_fp16 = sub(x = inputs_71_cast_fp16, y = channels_mean_71_cast_fp16)[name = tensor("zero_mean_71_cast_fp16")]; + tensor zero_mean_sq_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = zero_mean_71_cast_fp16)[name = tensor("zero_mean_sq_71_cast_fp16")]; + tensor var_3614 = const()[name = tensor("op_3614"), val = tensor([1])]; + tensor var_3615_cast_fp16 = reduce_mean(axes = var_3614, keep_dims = var_3121, x = zero_mean_sq_71_cast_fp16)[name = tensor("op_3615_cast_fp16")]; + tensor var_3616_to_fp16 = const()[name = tensor("op_3616_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3617_cast_fp16 = add(x = var_3615_cast_fp16, y = var_3616_to_fp16)[name = tensor("op_3617_cast_fp16")]; + tensor denom_71_epsilon_0_to_fp16 = const()[name = tensor("denom_71_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_71_cast_fp16 = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_3617_cast_fp16)[name = tensor("denom_71_cast_fp16")]; + tensor out_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = denom_71_cast_fp16)[name = tensor("out_71_cast_fp16")]; + tensor var_3621_to_fp16 = const()[name = tensor("op_3621_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204889728)))]; + tensor var_3622_cast_fp16 = add(x = out_71_cast_fp16, y = var_3621_to_fp16)[name = tensor("op_3622_cast_fp16")]; + tensor var_3624_to_fp16 = const()[name = tensor("op_3624_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204891072)))]; + tensor input_413_cast_fp16 = mul(x = var_3622_cast_fp16, y = var_3624_to_fp16)[name = tensor("input_413_cast_fp16")]; + tensor var_3632 = const()[name = tensor("op_3632"), val = tensor([1, 1])]; + tensor var_3634 = const()[name = tensor("op_3634"), val = tensor([1, 1])]; + tensor var_3636_pad_type_0 = const()[name = tensor("op_3636_pad_type_0"), val = tensor("custom")]; + tensor var_3636_pad_0 = const()[name = tensor("op_3636_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204892416))), lut = tensor([-0x1.918p-4, -0x1.d34p-6, 0x1.cb8p-6, 0x1.8e4p-4]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205711680))), lut = tensor([0x1.144p-6, -0x1.564p-3, -0x1.524p-5, 0x1.588p-4]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_3636_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3634, groups = var_3126, pad = var_3636_pad_0, pad_type = var_3636_pad_type_0, strides = var_3632, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = tensor("op_3636_cast_fp16")]; + tensor var_3637_split_sizes_0 = const()[name = tensor("op_3637_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3637_axis_0 = const()[name = tensor("op_3637_axis_0"), val = tensor(1)]; + tensor var_3637_cast_fp16_0, tensor var_3637_cast_fp16_1 = split(axis = var_3637_axis_0, split_sizes = var_3637_split_sizes_0, x = var_3636_cast_fp16)[name = tensor("op_3637_cast_fp16")]; + tensor var_3639_mode_0 = const()[name = tensor("op_3639_mode_0"), val = tensor("EXACT")]; + tensor var_3639_cast_fp16 = gelu(mode = var_3639_mode_0, x = var_3637_cast_fp16_1)[name = tensor("op_3639_cast_fp16")]; + tensor input_415_cast_fp16 = mul(x = var_3637_cast_fp16_0, y = var_3639_cast_fp16)[name = tensor("input_415_cast_fp16")]; + tensor var_3643 = const()[name = tensor("op_3643"), val = tensor([1, 1])]; + tensor var_3645 = const()[name = tensor("op_3645"), val = tensor([1, 1])]; + tensor var_3647_pad_type_0 = const()[name = tensor("op_3647_pad_type_0"), val = tensor("custom")]; + tensor var_3647_pad_0 = const()[name = tensor("op_3647_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205713024))), lut = tensor([-0x1.968p-4, -0x1.e1cp-6, 0x1.e2cp-6, 0x1.97p-4]), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206122688)))]; + tensor var_3647_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3645, groups = var_3126, pad = var_3647_pad_0, pad_type = var_3647_pad_type_0, strides = var_3643, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_415_cast_fp16)[name = tensor("op_3647_cast_fp16")]; + tensor hidden_states_253_cast_fp16 = add(x = var_3647_cast_fp16, y = inputs_71_cast_fp16)[name = tensor("hidden_states_253_cast_fp16")]; + tensor var_3649 = const()[name = tensor("op_3649"), val = tensor([2, 640, 32, 32])]; + tensor input_417_cast_fp16 = reshape(shape = var_3649, x = hidden_states_253_cast_fp16)[name = tensor("input_417_cast_fp16")]; + tensor var_3653 = const()[name = tensor("op_3653"), val = tensor([1, 1])]; + tensor var_3655 = const()[name = tensor("op_3655"), val = tensor([1, 1])]; + tensor hidden_states_255_pad_type_0 = const()[name = tensor("hidden_states_255_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_255_pad_0 = const()[name = tensor("hidden_states_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206124032))), lut = tensor([-0x1.eb4p-4, -0x1.27p-5, 0x1.1e4p-5, 0x1.e4cp-4]), name = tensor("up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206226496)))]; + tensor hidden_states_255_cast_fp16 = conv(bias = up_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_3655, groups = var_3126, pad = hidden_states_255_pad_0, pad_type = hidden_states_255_pad_type_0, strides = var_3653, weight = up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = tensor("hidden_states_255_cast_fp16")]; + tensor hidden_states_257_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_243_cast_fp16)[name = tensor("hidden_states_257_cast_fp16")]; + tensor input_419_interleave_0 = const()[name = tensor("input_419_interleave_0"), val = tensor(false)]; + tensor input_419_cast_fp16 = concat(axis = var_3126, interleave = input_419_interleave_0, values = (hidden_states_257_cast_fp16, input_63_cast_fp16))[name = tensor("input_419_cast_fp16")]; + tensor reshape_192_shape_0 = const()[name = tensor("reshape_192_shape_0"), val = tensor([2, 32, 30, 32, 32])]; + tensor reshape_192_cast_fp16 = reshape(shape = reshape_192_shape_0, x = input_419_cast_fp16)[name = tensor("reshape_192_cast_fp16")]; + tensor reduce_mean_144_axes_0 = const()[name = tensor("reduce_mean_144_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_144_keep_dims_0 = const()[name = tensor("reduce_mean_144_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_144_cast_fp16 = reduce_mean(axes = reduce_mean_144_axes_0, keep_dims = reduce_mean_144_keep_dims_0, x = reshape_192_cast_fp16)[name = tensor("reduce_mean_144_cast_fp16")]; + tensor sub_96_cast_fp16 = sub(x = reshape_192_cast_fp16, y = reduce_mean_144_cast_fp16)[name = tensor("sub_96_cast_fp16")]; + tensor square_48_cast_fp16 = square(x = sub_96_cast_fp16)[name = tensor("square_48_cast_fp16")]; + tensor reduce_mean_146_axes_0 = const()[name = tensor("reduce_mean_146_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_146_keep_dims_0 = const()[name = tensor("reduce_mean_146_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_146_cast_fp16 = reduce_mean(axes = reduce_mean_146_axes_0, keep_dims = reduce_mean_146_keep_dims_0, x = square_48_cast_fp16)[name = tensor("reduce_mean_146_cast_fp16")]; + tensor add_96_y_0_to_fp16 = const()[name = tensor("add_96_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_96_cast_fp16 = add(x = reduce_mean_146_cast_fp16, y = add_96_y_0_to_fp16)[name = tensor("add_96_cast_fp16")]; + tensor sqrt_48_cast_fp16 = sqrt(x = add_96_cast_fp16)[name = tensor("sqrt_48_cast_fp16")]; + tensor real_div_48_cast_fp16 = real_div(x = sub_96_cast_fp16, y = sqrt_48_cast_fp16)[name = tensor("real_div_48_cast_fp16")]; + tensor reshape_193_shape_0 = const()[name = tensor("reshape_193_shape_0"), val = tensor([2, 960, 32, 32])]; + tensor reshape_193_cast_fp16 = reshape(shape = reshape_193_shape_0, x = real_div_48_cast_fp16)[name = tensor("reshape_193_cast_fp16")]; + tensor add_97_mean_0_to_fp16 = const()[name = tensor("add_97_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206227840)))]; + tensor add_97_variance_0_to_fp16 = const()[name = tensor("add_97_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206229824)))]; + tensor add_97_gamma_0_to_fp16 = const()[name = tensor("add_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206231808)))]; + tensor add_97_beta_0_to_fp16 = const()[name = tensor("add_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206233792)))]; + tensor add_97_epsilon_0_to_fp16 = const()[name = tensor("add_97_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_97_cast_fp16 = batch_norm(beta = add_97_beta_0_to_fp16, epsilon = add_97_epsilon_0_to_fp16, gamma = add_97_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_193_cast_fp16)[name = tensor("add_97_cast_fp16")]; + tensor input_423_cast_fp16 = silu(x = add_97_cast_fp16)[name = tensor("input_423_cast_fp16")]; + tensor var_3673 = const()[name = tensor("op_3673"), val = tensor([1, 1])]; + tensor var_3675 = const()[name = tensor("op_3675"), val = tensor([1, 1])]; + tensor hidden_states_259_pad_type_0 = const()[name = tensor("hidden_states_259_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_259_pad_0 = const()[name = tensor("hidden_states_259_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206235776))), lut = tensor([-0x1.9dcp-4, -0x1.6f8p-6, 0x1.d48p-6, 0x1.d4cp-4]), name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([640, 960, 3, 3])]; + tensor up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207618240)))]; + tensor hidden_states_259_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_3675, groups = var_3126, pad = hidden_states_259_pad_0, pad_type = hidden_states_259_pad_type_0, strides = var_3673, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized, x = input_423_cast_fp16)[name = tensor("hidden_states_259_cast_fp16")]; + tensor var_3681 = const()[name = tensor("op_3681"), val = tensor([1, 1])]; + tensor var_3683 = const()[name = tensor("op_3683"), val = tensor([1, 1])]; + tensor temb_37_pad_type_0 = const()[name = tensor("temb_37_pad_type_0"), val = tensor("custom")]; + tensor temb_37_pad_0 = const()[name = tensor("temb_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207619584))), lut = tensor([-0x1.588p-5, -0x1.70cp-8, 0x1.72p-11, 0x1.f4cp-8]), name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207824448)))]; + tensor temb_37_cast_fp16 = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_3683, groups = var_3126, pad = temb_37_pad_0, pad_type = temb_37_pad_type_0, strides = var_3681, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_37_cast_fp16")]; + tensor input_427_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = temb_37_cast_fp16)[name = tensor("input_427_cast_fp16")]; + tensor reshape_196_shape_0 = const()[name = tensor("reshape_196_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_196_cast_fp16 = reshape(shape = reshape_196_shape_0, x = input_427_cast_fp16)[name = tensor("reshape_196_cast_fp16")]; + tensor reduce_mean_147_axes_0 = const()[name = tensor("reduce_mean_147_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_147_keep_dims_0 = const()[name = tensor("reduce_mean_147_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_147_cast_fp16 = reduce_mean(axes = reduce_mean_147_axes_0, keep_dims = reduce_mean_147_keep_dims_0, x = reshape_196_cast_fp16)[name = tensor("reduce_mean_147_cast_fp16")]; + tensor sub_98_cast_fp16 = sub(x = reshape_196_cast_fp16, y = reduce_mean_147_cast_fp16)[name = tensor("sub_98_cast_fp16")]; + tensor square_49_cast_fp16 = square(x = sub_98_cast_fp16)[name = tensor("square_49_cast_fp16")]; + tensor reduce_mean_149_axes_0 = const()[name = tensor("reduce_mean_149_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_149_keep_dims_0 = const()[name = tensor("reduce_mean_149_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_149_cast_fp16 = reduce_mean(axes = reduce_mean_149_axes_0, keep_dims = reduce_mean_149_keep_dims_0, x = square_49_cast_fp16)[name = tensor("reduce_mean_149_cast_fp16")]; + tensor add_98_y_0_to_fp16 = const()[name = tensor("add_98_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_98_cast_fp16 = add(x = reduce_mean_149_cast_fp16, y = add_98_y_0_to_fp16)[name = tensor("add_98_cast_fp16")]; + tensor sqrt_49_cast_fp16 = sqrt(x = add_98_cast_fp16)[name = tensor("sqrt_49_cast_fp16")]; + tensor real_div_49_cast_fp16 = real_div(x = sub_98_cast_fp16, y = sqrt_49_cast_fp16)[name = tensor("real_div_49_cast_fp16")]; + tensor reshape_197_shape_0 = const()[name = tensor("reshape_197_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_197_cast_fp16 = reshape(shape = reshape_197_shape_0, x = real_div_49_cast_fp16)[name = tensor("reshape_197_cast_fp16")]; + tensor add_99_gamma_0_to_fp16 = const()[name = tensor("add_99_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207825792)))]; + tensor add_99_beta_0_to_fp16 = const()[name = tensor("add_99_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207827136)))]; + tensor add_99_epsilon_0_to_fp16 = const()[name = tensor("add_99_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_99_cast_fp16 = batch_norm(beta = add_99_beta_0_to_fp16, epsilon = add_99_epsilon_0_to_fp16, gamma = add_99_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_197_cast_fp16)[name = tensor("add_99_cast_fp16")]; + tensor input_431_cast_fp16 = silu(x = add_99_cast_fp16)[name = tensor("input_431_cast_fp16")]; + tensor var_3693 = const()[name = tensor("op_3693"), val = tensor([1, 1])]; + tensor var_3695 = const()[name = tensor("op_3695"), val = tensor([1, 1])]; + tensor hidden_states_261_pad_type_0 = const()[name = tensor("hidden_states_261_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_261_pad_0 = const()[name = tensor("hidden_states_261_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207828480))), lut = tensor([-0x1.b88p-4, -0x1.8acp-6, 0x1.8ep-6, 0x1.ba4p-4]), name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208750144)))]; + tensor hidden_states_261_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_3695, groups = var_3126, pad = hidden_states_261_pad_0, pad_type = hidden_states_261_pad_type_0, strides = var_3693, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized, x = input_431_cast_fp16)[name = tensor("hidden_states_261_cast_fp16")]; + tensor var_3700 = const()[name = tensor("op_3700"), val = tensor([1, 1])]; + tensor var_3702 = const()[name = tensor("op_3702"), val = tensor([1, 1])]; + tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("custom")]; + tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208751488))), lut = tensor([-0x1.d44p-6, -0x1.0f4p-7, 0x1.11cp-7, 0x1.d64p-6]), name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 960, 1, 1])]; + tensor up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208905152)))]; + tensor x_21_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_3702, groups = var_3126, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = var_3700, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_419_cast_fp16)[name = tensor("x_21_cast_fp16")]; + tensor hidden_states_263_cast_fp16 = add(x = x_21_cast_fp16, y = hidden_states_261_cast_fp16)[name = tensor("hidden_states_263_cast_fp16")]; + tensor reshape_200_shape_0 = const()[name = tensor("reshape_200_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_200_cast_fp16 = reshape(shape = reshape_200_shape_0, x = hidden_states_263_cast_fp16)[name = tensor("reshape_200_cast_fp16")]; + tensor reduce_mean_150_axes_0 = const()[name = tensor("reduce_mean_150_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_150_keep_dims_0 = const()[name = tensor("reduce_mean_150_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_150_cast_fp16 = reduce_mean(axes = reduce_mean_150_axes_0, keep_dims = reduce_mean_150_keep_dims_0, x = reshape_200_cast_fp16)[name = tensor("reduce_mean_150_cast_fp16")]; + tensor sub_100_cast_fp16 = sub(x = reshape_200_cast_fp16, y = reduce_mean_150_cast_fp16)[name = tensor("sub_100_cast_fp16")]; + tensor square_50_cast_fp16 = square(x = sub_100_cast_fp16)[name = tensor("square_50_cast_fp16")]; + tensor reduce_mean_152_axes_0 = const()[name = tensor("reduce_mean_152_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_152_keep_dims_0 = const()[name = tensor("reduce_mean_152_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_152_cast_fp16 = reduce_mean(axes = reduce_mean_152_axes_0, keep_dims = reduce_mean_152_keep_dims_0, x = square_50_cast_fp16)[name = tensor("reduce_mean_152_cast_fp16")]; + tensor add_100_y_0_to_fp16 = const()[name = tensor("add_100_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_100_cast_fp16 = add(x = reduce_mean_152_cast_fp16, y = add_100_y_0_to_fp16)[name = tensor("add_100_cast_fp16")]; + tensor sqrt_50_cast_fp16 = sqrt(x = add_100_cast_fp16)[name = tensor("sqrt_50_cast_fp16")]; + tensor real_div_50_cast_fp16 = real_div(x = sub_100_cast_fp16, y = sqrt_50_cast_fp16)[name = tensor("real_div_50_cast_fp16")]; + tensor reshape_201_shape_0 = const()[name = tensor("reshape_201_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_201_cast_fp16 = reshape(shape = reshape_201_shape_0, x = real_div_50_cast_fp16)[name = tensor("reshape_201_cast_fp16")]; + tensor add_101_gamma_0_to_fp16 = const()[name = tensor("add_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208906496)))]; + tensor add_101_beta_0_to_fp16 = const()[name = tensor("add_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208907840)))]; + tensor add_101_epsilon_0_to_fp16 = const()[name = tensor("add_101_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_101_cast_fp16 = batch_norm(beta = add_101_beta_0_to_fp16, epsilon = add_101_epsilon_0_to_fp16, gamma = add_101_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_201_cast_fp16)[name = tensor("add_101_cast_fp16")]; + tensor var_3722 = const()[name = tensor("op_3722"), val = tensor([1, 1])]; + tensor var_3724 = const()[name = tensor("op_3724"), val = tensor([1, 1])]; + tensor hidden_states_265_pad_type_0 = const()[name = tensor("hidden_states_265_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_265_pad_0 = const()[name = tensor("hidden_states_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208909184))), lut = tensor([-0x1.9acp-4, -0x1.e9cp-6, 0x1.ea4p-6, 0x1.998p-4]), name = tensor("up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209011648)))]; + tensor hidden_states_265_cast_fp16 = conv(bias = up_blocks_2_attentions_2_proj_in_bias_to_fp16, dilations = var_3724, groups = var_3126, pad = hidden_states_265_pad_0, pad_type = hidden_states_265_pad_type_0, strides = var_3722, weight = up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized, x = add_101_cast_fp16)[name = tensor("hidden_states_265_cast_fp16")]; + tensor var_3729 = const()[name = tensor("op_3729"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_73_cast_fp16 = reshape(shape = var_3729, x = hidden_states_265_cast_fp16)[name = tensor("inputs_73_cast_fp16")]; + tensor var_3739 = const()[name = tensor("op_3739"), val = tensor([1])]; + tensor channels_mean_73_cast_fp16 = reduce_mean(axes = var_3739, keep_dims = var_3121, x = inputs_73_cast_fp16)[name = tensor("channels_mean_73_cast_fp16")]; + tensor zero_mean_73_cast_fp16 = sub(x = inputs_73_cast_fp16, y = channels_mean_73_cast_fp16)[name = tensor("zero_mean_73_cast_fp16")]; + tensor zero_mean_sq_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = zero_mean_73_cast_fp16)[name = tensor("zero_mean_sq_73_cast_fp16")]; + tensor var_3743 = const()[name = tensor("op_3743"), val = tensor([1])]; + tensor var_3744_cast_fp16 = reduce_mean(axes = var_3743, keep_dims = var_3121, x = zero_mean_sq_73_cast_fp16)[name = tensor("op_3744_cast_fp16")]; + tensor var_3745_to_fp16 = const()[name = tensor("op_3745_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3746_cast_fp16 = add(x = var_3744_cast_fp16, y = var_3745_to_fp16)[name = tensor("op_3746_cast_fp16")]; + tensor denom_73_epsilon_0_to_fp16 = const()[name = tensor("denom_73_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_73_cast_fp16 = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_3746_cast_fp16)[name = tensor("denom_73_cast_fp16")]; + tensor out_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = denom_73_cast_fp16)[name = tensor("out_73_cast_fp16")]; + tensor var_3750_to_fp16 = const()[name = tensor("op_3750_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209012992)))]; + tensor var_3751_cast_fp16 = add(x = out_73_cast_fp16, y = var_3750_to_fp16)[name = tensor("op_3751_cast_fp16")]; + tensor var_3753_to_fp16 = const()[name = tensor("op_3753_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209014336)))]; + tensor hidden_states_267_cast_fp16 = mul(x = var_3751_cast_fp16, y = var_3753_to_fp16)[name = tensor("hidden_states_267_cast_fp16")]; + tensor var_3760 = const()[name = tensor("op_3760"), val = tensor([1, 1])]; + tensor var_3762 = const()[name = tensor("op_3762"), val = tensor([1, 1])]; + tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; + tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209015680))), lut = tensor([-0x1.084p-3, -0x1.308p-5, 0x1.39p-5, 0x1.0b4p-3]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_49_cast_fp16 = conv(dilations = var_3762, groups = var_3126, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_3760, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_267_cast_fp16)[name = tensor("q_49_cast_fp16")]; + tensor var_3766 = const()[name = tensor("op_3766"), val = tensor([1, 1])]; + tensor var_3768 = const()[name = tensor("op_3768"), val = tensor([1, 1])]; + tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; + tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209118144))), lut = tensor([-0x1.17p-3, -0x1.358p-5, 0x1.3bcp-5, 0x1.18cp-3]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_49_cast_fp16 = conv(dilations = var_3768, groups = var_3126, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_3766, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_267_cast_fp16)[name = tensor("k_49_cast_fp16")]; + tensor var_3772 = const()[name = tensor("op_3772"), val = tensor([1, 1])]; + tensor var_3774 = const()[name = tensor("op_3774"), val = tensor([1, 1])]; + tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; + tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209220608))), lut = tensor([-0x1.6fp-4, -0x1.a5cp-6, 0x1.acp-6, 0x1.728p-4]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_49_cast_fp16 = conv(dilations = var_3774, groups = var_3126, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_3772, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_267_cast_fp16)[name = tensor("v_49_cast_fp16")]; + tensor var_3778 = const()[name = tensor("op_3778"), val = tensor([2, 10, 64, -1])]; + tensor var_3779_cast_fp16 = reshape(shape = var_3778, x = q_49_cast_fp16)[name = tensor("op_3779_cast_fp16")]; + tensor var_3780 = const()[name = tensor("op_3780"), val = tensor([2, 10, 64, -1])]; + tensor var_3781_cast_fp16 = reshape(shape = var_3780, x = k_49_cast_fp16)[name = tensor("op_3781_cast_fp16")]; + tensor var_3782 = const()[name = tensor("op_3782"), val = tensor([2, 10, 64, -1])]; + tensor var_3783_cast_fp16 = reshape(shape = var_3782, x = v_49_cast_fp16)[name = tensor("op_3783_cast_fp16")]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3779_cast_fp16, y = var_3781_cast_fp16)[name = tensor("attn_weights_97_cast_fp16")]; + tensor attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_99_cast_fp16")]; + tensor var_3787_cast_fp16 = softmax(axis = var_3110, x = attn_weights_99_cast_fp16)[name = tensor("op_3787_cast_fp16")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast_fp16 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_3783_cast_fp16, y = var_3787_cast_fp16)[name = tensor("attn_49_cast_fp16")]; + tensor var_3791 = const()[name = tensor("op_3791"), val = tensor([2, 640, 1, -1])]; + tensor input_435_cast_fp16 = reshape(shape = var_3791, x = attn_49_cast_fp16)[name = tensor("input_435_cast_fp16")]; + tensor var_3796 = const()[name = tensor("op_3796"), val = tensor([1, 1])]; + tensor var_3798 = const()[name = tensor("op_3798"), val = tensor([1, 1])]; + tensor var_3800_pad_type_0 = const()[name = tensor("op_3800_pad_type_0"), val = tensor("custom")]; + tensor var_3800_pad_0 = const()[name = tensor("op_3800_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209323072))), lut = tensor([-0x1.6b4p-4, -0x1.accp-6, 0x1.a6p-6, 0x1.698p-4]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209425536)))]; + tensor var_3800_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3798, groups = var_3126, pad = var_3800_pad_0, pad_type = var_3800_pad_type_0, strides = var_3796, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_435_cast_fp16)[name = tensor("op_3800_cast_fp16")]; + tensor inputs_75_cast_fp16 = add(x = var_3800_cast_fp16, y = inputs_73_cast_fp16)[name = tensor("inputs_75_cast_fp16")]; + tensor var_3804 = const()[name = tensor("op_3804"), val = tensor([1])]; + tensor channels_mean_75_cast_fp16 = reduce_mean(axes = var_3804, keep_dims = var_3121, x = inputs_75_cast_fp16)[name = tensor("channels_mean_75_cast_fp16")]; + tensor zero_mean_75_cast_fp16 = sub(x = inputs_75_cast_fp16, y = channels_mean_75_cast_fp16)[name = tensor("zero_mean_75_cast_fp16")]; + tensor zero_mean_sq_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = zero_mean_75_cast_fp16)[name = tensor("zero_mean_sq_75_cast_fp16")]; + tensor var_3808 = const()[name = tensor("op_3808"), val = tensor([1])]; + tensor var_3809_cast_fp16 = reduce_mean(axes = var_3808, keep_dims = var_3121, x = zero_mean_sq_75_cast_fp16)[name = tensor("op_3809_cast_fp16")]; + tensor var_3810_to_fp16 = const()[name = tensor("op_3810_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3811_cast_fp16 = add(x = var_3809_cast_fp16, y = var_3810_to_fp16)[name = tensor("op_3811_cast_fp16")]; + tensor denom_75_epsilon_0_to_fp16 = const()[name = tensor("denom_75_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_75_cast_fp16 = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_3811_cast_fp16)[name = tensor("denom_75_cast_fp16")]; + tensor out_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = denom_75_cast_fp16)[name = tensor("out_75_cast_fp16")]; + tensor var_3815_to_fp16 = const()[name = tensor("op_3815_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209426880)))]; + tensor var_3816_cast_fp16 = add(x = out_75_cast_fp16, y = var_3815_to_fp16)[name = tensor("op_3816_cast_fp16")]; + tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209428224)))]; + tensor hidden_states_269_cast_fp16 = mul(x = var_3816_cast_fp16, y = var_3818_to_fp16)[name = tensor("hidden_states_269_cast_fp16")]; + tensor var_3825 = const()[name = tensor("op_3825"), val = tensor([1, 1])]; + tensor var_3827 = const()[name = tensor("op_3827"), val = tensor([1, 1])]; + tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; + tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209429568))), lut = tensor([-0x1.69cp-4, -0x1.a6cp-6, 0x1.b8p-6, 0x1.6d8p-4]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_51_cast_fp16 = conv(dilations = var_3827, groups = var_3126, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_3825, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_269_cast_fp16)[name = tensor("q_51_cast_fp16")]; + tensor var_3831 = const()[name = tensor("op_3831"), val = tensor([1, 1])]; + tensor var_3833 = const()[name = tensor("op_3833"), val = tensor([1, 1])]; + tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; + tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209532032))), lut = tensor([-0x1.46p-4, -0x1.7e8p-6, 0x1.83p-6, 0x1.45cp-4]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor k_51_cast_fp16 = conv(dilations = var_3833, groups = var_3126, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_3831, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_51_cast_fp16")]; + tensor var_3837 = const()[name = tensor("op_3837"), val = tensor([1, 1])]; + tensor var_3839 = const()[name = tensor("op_3839"), val = tensor([1, 1])]; + tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; + tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209695936))), lut = tensor([-0x1.7acp-5, -0x1.b68p-7, 0x1.ae8p-7, 0x1.77cp-5]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 1024, 1, 1])]; + tensor v_51_cast_fp16 = conv(dilations = var_3839, groups = var_3126, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_3837, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_51_cast_fp16")]; + tensor var_3843 = const()[name = tensor("op_3843"), val = tensor([2, 10, 64, -1])]; + tensor var_3844_cast_fp16 = reshape(shape = var_3843, x = q_51_cast_fp16)[name = tensor("op_3844_cast_fp16")]; + tensor var_3845 = const()[name = tensor("op_3845"), val = tensor([2, 10, 64, -1])]; + tensor var_3846_cast_fp16 = reshape(shape = var_3845, x = k_51_cast_fp16)[name = tensor("op_3846_cast_fp16")]; + tensor var_3847 = const()[name = tensor("op_3847"), val = tensor([2, 10, 64, -1])]; + tensor var_3848_cast_fp16 = reshape(shape = var_3847, x = v_51_cast_fp16)[name = tensor("op_3848_cast_fp16")]; + tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_101_cast_fp16 = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_3844_cast_fp16, y = var_3846_cast_fp16)[name = tensor("attn_weights_101_cast_fp16")]; + tensor attn_weights_103_cast_fp16 = mul(x = attn_weights_101_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_103_cast_fp16")]; + tensor var_3852_cast_fp16 = softmax(axis = var_3110, x = attn_weights_103_cast_fp16)[name = tensor("op_3852_cast_fp16")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast_fp16 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_3848_cast_fp16, y = var_3852_cast_fp16)[name = tensor("attn_51_cast_fp16")]; + tensor var_3856 = const()[name = tensor("op_3856"), val = tensor([2, 640, 1, -1])]; + tensor input_437_cast_fp16 = reshape(shape = var_3856, x = attn_51_cast_fp16)[name = tensor("input_437_cast_fp16")]; + tensor var_3861 = const()[name = tensor("op_3861"), val = tensor([1, 1])]; + tensor var_3863 = const()[name = tensor("op_3863"), val = tensor([1, 1])]; + tensor var_3865_pad_type_0 = const()[name = tensor("op_3865_pad_type_0"), val = tensor("custom")]; + tensor var_3865_pad_0 = const()[name = tensor("op_3865_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209859840))), lut = tensor([-0x1.5b4p-6, -0x1.884p-8, 0x1.93p-8, 0x1.60cp-6]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209962304)))]; + tensor var_3865_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3863, groups = var_3126, pad = var_3865_pad_0, pad_type = var_3865_pad_type_0, strides = var_3861, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_437_cast_fp16)[name = tensor("op_3865_cast_fp16")]; + tensor inputs_77_cast_fp16 = add(x = var_3865_cast_fp16, y = inputs_75_cast_fp16)[name = tensor("inputs_77_cast_fp16")]; + tensor var_3869 = const()[name = tensor("op_3869"), val = tensor([1])]; + tensor channels_mean_77_cast_fp16 = reduce_mean(axes = var_3869, keep_dims = var_3121, x = inputs_77_cast_fp16)[name = tensor("channels_mean_77_cast_fp16")]; + tensor zero_mean_77_cast_fp16 = sub(x = inputs_77_cast_fp16, y = channels_mean_77_cast_fp16)[name = tensor("zero_mean_77_cast_fp16")]; + tensor zero_mean_sq_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = zero_mean_77_cast_fp16)[name = tensor("zero_mean_sq_77_cast_fp16")]; + tensor var_3873 = const()[name = tensor("op_3873"), val = tensor([1])]; + tensor var_3874_cast_fp16 = reduce_mean(axes = var_3873, keep_dims = var_3121, x = zero_mean_sq_77_cast_fp16)[name = tensor("op_3874_cast_fp16")]; + tensor var_3875_to_fp16 = const()[name = tensor("op_3875_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3876_cast_fp16 = add(x = var_3874_cast_fp16, y = var_3875_to_fp16)[name = tensor("op_3876_cast_fp16")]; + tensor denom_77_epsilon_0_to_fp16 = const()[name = tensor("denom_77_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_77_cast_fp16 = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_3876_cast_fp16)[name = tensor("denom_77_cast_fp16")]; + tensor out_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = denom_77_cast_fp16)[name = tensor("out_77_cast_fp16")]; + tensor var_3880_to_fp16 = const()[name = tensor("op_3880_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209963648)))]; + tensor var_3881_cast_fp16 = add(x = out_77_cast_fp16, y = var_3880_to_fp16)[name = tensor("op_3881_cast_fp16")]; + tensor var_3883_to_fp16 = const()[name = tensor("op_3883_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209964992)))]; + tensor input_439_cast_fp16 = mul(x = var_3881_cast_fp16, y = var_3883_to_fp16)[name = tensor("input_439_cast_fp16")]; + tensor var_3891 = const()[name = tensor("op_3891"), val = tensor([1, 1])]; + tensor var_3893 = const()[name = tensor("op_3893"), val = tensor([1, 1])]; + tensor var_3895_pad_type_0 = const()[name = tensor("op_3895_pad_type_0"), val = tensor("custom")]; + tensor var_3895_pad_0 = const()[name = tensor("op_3895_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209966336))), lut = tensor([-0x1.8acp-4, -0x1.cacp-6, 0x1.c8p-6, 0x1.898p-4]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210785600))), lut = tensor([-0x1.79cp-4, 0x1.794p-5, -0x1.62p-7, -0x1.31p-2]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_3895_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3893, groups = var_3126, pad = var_3895_pad_0, pad_type = var_3895_pad_type_0, strides = var_3891, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_439_cast_fp16)[name = tensor("op_3895_cast_fp16")]; + tensor var_3896_split_sizes_0 = const()[name = tensor("op_3896_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3896_axis_0 = const()[name = tensor("op_3896_axis_0"), val = tensor(1)]; + tensor var_3896_cast_fp16_0, tensor var_3896_cast_fp16_1 = split(axis = var_3896_axis_0, split_sizes = var_3896_split_sizes_0, x = var_3895_cast_fp16)[name = tensor("op_3896_cast_fp16")]; + tensor var_3898_mode_0 = const()[name = tensor("op_3898_mode_0"), val = tensor("EXACT")]; + tensor var_3898_cast_fp16 = gelu(mode = var_3898_mode_0, x = var_3896_cast_fp16_1)[name = tensor("op_3898_cast_fp16")]; + tensor input_441_cast_fp16 = mul(x = var_3896_cast_fp16_0, y = var_3898_cast_fp16)[name = tensor("input_441_cast_fp16")]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([1, 1])]; + tensor var_3904 = const()[name = tensor("op_3904"), val = tensor([1, 1])]; + tensor var_3906_pad_type_0 = const()[name = tensor("op_3906_pad_type_0"), val = tensor("custom")]; + tensor var_3906_pad_0 = const()[name = tensor("op_3906_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210786944))), lut = tensor([-0x1.8a4p-4, -0x1.d0cp-6, 0x1.d5cp-6, 0x1.8b8p-4]), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211196608)))]; + tensor var_3906_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3904, groups = var_3126, pad = var_3906_pad_0, pad_type = var_3906_pad_type_0, strides = var_3902, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_441_cast_fp16)[name = tensor("op_3906_cast_fp16")]; + tensor hidden_states_273_cast_fp16 = add(x = var_3906_cast_fp16, y = inputs_77_cast_fp16)[name = tensor("hidden_states_273_cast_fp16")]; + tensor var_3908 = const()[name = tensor("op_3908"), val = tensor([2, 640, 32, 32])]; + tensor input_443_cast_fp16 = reshape(shape = var_3908, x = hidden_states_273_cast_fp16)[name = tensor("input_443_cast_fp16")]; + tensor var_3912 = const()[name = tensor("op_3912"), val = tensor([1, 1])]; + tensor var_3914 = const()[name = tensor("op_3914"), val = tensor([1, 1])]; + tensor hidden_states_275_pad_type_0 = const()[name = tensor("hidden_states_275_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_275_pad_0 = const()[name = tensor("hidden_states_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211197952))), lut = tensor([-0x1.a7p-4, -0x1.f9p-6, 0x1.f14p-6, 0x1.a4cp-4]), name = tensor("up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211300416)))]; + tensor hidden_states_275_cast_fp16 = conv(bias = up_blocks_2_attentions_2_proj_out_bias_to_fp16, dilations = var_3914, groups = var_3126, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_3912, weight = up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized, x = input_443_cast_fp16)[name = tensor("hidden_states_275_cast_fp16")]; + tensor input_445_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_263_cast_fp16)[name = tensor("input_445_cast_fp16")]; + tensor input_447_scale_factor_height_0 = const()[name = tensor("input_447_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_447_scale_factor_width_0 = const()[name = tensor("input_447_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_447_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_447_scale_factor_height_0, scale_factor_width = input_447_scale_factor_width_0, x = input_445_cast_fp16)[name = tensor("input_447_cast_fp16")]; + tensor var_3923 = const()[name = tensor("op_3923"), val = tensor([1, 1])]; + tensor var_3925 = const()[name = tensor("op_3925"), val = tensor([1, 1])]; + tensor hidden_states_277_pad_type_0 = const()[name = tensor("hidden_states_277_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_277_pad_0 = const()[name = tensor("hidden_states_277_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211301760))), lut = tensor([-0x1.9a4p-5, -0x1.ca8p-7, 0x1.c6cp-7, 0x1.99p-5]), name = tensor("up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212223424)))]; + tensor hidden_states_277_cast_fp16 = conv(bias = up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_3925, groups = var_3126, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = var_3923, weight = up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized, x = input_447_cast_fp16)[name = tensor("hidden_states_277_cast_fp16")]; + tensor var_3929 = const()[name = tensor("op_3929"), val = tensor(3)]; + tensor var_3940 = const()[name = tensor("op_3940"), val = tensor(true)]; + tensor var_3945 = const()[name = tensor("op_3945"), val = tensor(1)]; + tensor input_449_interleave_0 = const()[name = tensor("input_449_interleave_0"), val = tensor(false)]; + tensor input_449_cast_fp16 = concat(axis = var_3945, interleave = input_449_interleave_0, values = (hidden_states_277_cast_fp16, input_61_cast_fp16))[name = tensor("input_449_cast_fp16")]; + tensor reshape_204_shape_0 = const()[name = tensor("reshape_204_shape_0"), val = tensor([2, 32, 30, 64, 64])]; + tensor reshape_204_cast_fp16 = reshape(shape = reshape_204_shape_0, x = input_449_cast_fp16)[name = tensor("reshape_204_cast_fp16")]; + tensor reduce_mean_153_axes_0 = const()[name = tensor("reduce_mean_153_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_153_keep_dims_0 = const()[name = tensor("reduce_mean_153_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_153_cast_fp16 = reduce_mean(axes = reduce_mean_153_axes_0, keep_dims = reduce_mean_153_keep_dims_0, x = reshape_204_cast_fp16)[name = tensor("reduce_mean_153_cast_fp16")]; + tensor sub_102_cast_fp16 = sub(x = reshape_204_cast_fp16, y = reduce_mean_153_cast_fp16)[name = tensor("sub_102_cast_fp16")]; + tensor square_51_cast_fp16 = square(x = sub_102_cast_fp16)[name = tensor("square_51_cast_fp16")]; + tensor reduce_mean_155_axes_0 = const()[name = tensor("reduce_mean_155_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_155_keep_dims_0 = const()[name = tensor("reduce_mean_155_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_155_cast_fp16 = reduce_mean(axes = reduce_mean_155_axes_0, keep_dims = reduce_mean_155_keep_dims_0, x = square_51_cast_fp16)[name = tensor("reduce_mean_155_cast_fp16")]; + tensor add_102_y_0_to_fp16 = const()[name = tensor("add_102_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_102_cast_fp16 = add(x = reduce_mean_155_cast_fp16, y = add_102_y_0_to_fp16)[name = tensor("add_102_cast_fp16")]; + tensor sqrt_51_cast_fp16 = sqrt(x = add_102_cast_fp16)[name = tensor("sqrt_51_cast_fp16")]; + tensor real_div_51_cast_fp16 = real_div(x = sub_102_cast_fp16, y = sqrt_51_cast_fp16)[name = tensor("real_div_51_cast_fp16")]; + tensor reshape_205_shape_0 = const()[name = tensor("reshape_205_shape_0"), val = tensor([2, 960, 64, 64])]; + tensor reshape_205_cast_fp16 = reshape(shape = reshape_205_shape_0, x = real_div_51_cast_fp16)[name = tensor("reshape_205_cast_fp16")]; + tensor add_103_gamma_0_to_fp16 = const()[name = tensor("add_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212224768)))]; + tensor add_103_beta_0_to_fp16 = const()[name = tensor("add_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212226752)))]; + tensor add_103_epsilon_0_to_fp16 = const()[name = tensor("add_103_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_103_cast_fp16 = batch_norm(beta = add_103_beta_0_to_fp16, epsilon = add_103_epsilon_0_to_fp16, gamma = add_103_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_205_cast_fp16)[name = tensor("add_103_cast_fp16")]; + tensor input_453_cast_fp16 = silu(x = add_103_cast_fp16)[name = tensor("input_453_cast_fp16")]; + tensor var_3972 = const()[name = tensor("op_3972"), val = tensor([1, 1])]; + tensor var_3974 = const()[name = tensor("op_3974"), val = tensor([1, 1])]; + tensor hidden_states_279_pad_type_0 = const()[name = tensor("hidden_states_279_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_279_pad_0 = const()[name = tensor("hidden_states_279_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212228736))), lut = tensor([-0x1.f1p-4, -0x1.bd4p-6, 0x1.7d4p-6, 0x1.ccp-4]), name = tensor("up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([320, 960, 3, 3])]; + tensor up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212920000)))]; + tensor hidden_states_279_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_3974, groups = var_3945, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_3972, weight = up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized, x = input_453_cast_fp16)[name = tensor("hidden_states_279_cast_fp16")]; + tensor var_3980 = const()[name = tensor("op_3980"), val = tensor([1, 1])]; + tensor var_3982 = const()[name = tensor("op_3982"), val = tensor([1, 1])]; + tensor temb_39_pad_type_0 = const()[name = tensor("temb_39_pad_type_0"), val = tensor("custom")]; + tensor temb_39_pad_0 = const()[name = tensor("temb_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212920704))), lut = tensor([-0x1.8p-5, -0x1.06p-8, 0x1.048p-8, 0x1.cfp-5]), name = tensor("up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213023168)))]; + tensor temb_39_cast_fp16 = conv(bias = up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3982, groups = var_3945, pad = temb_39_pad_0, pad_type = temb_39_pad_type_0, strides = var_3980, weight = up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_39_cast_fp16")]; + tensor input_457_cast_fp16 = add(x = hidden_states_279_cast_fp16, y = temb_39_cast_fp16)[name = tensor("input_457_cast_fp16")]; + tensor reshape_208_shape_0 = const()[name = tensor("reshape_208_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_208_cast_fp16 = reshape(shape = reshape_208_shape_0, x = input_457_cast_fp16)[name = tensor("reshape_208_cast_fp16")]; + tensor reduce_mean_156_axes_0 = const()[name = tensor("reduce_mean_156_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_156_keep_dims_0 = const()[name = tensor("reduce_mean_156_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_156_cast_fp16 = reduce_mean(axes = reduce_mean_156_axes_0, keep_dims = reduce_mean_156_keep_dims_0, x = reshape_208_cast_fp16)[name = tensor("reduce_mean_156_cast_fp16")]; + tensor sub_104_cast_fp16 = sub(x = reshape_208_cast_fp16, y = reduce_mean_156_cast_fp16)[name = tensor("sub_104_cast_fp16")]; + tensor square_52_cast_fp16 = square(x = sub_104_cast_fp16)[name = tensor("square_52_cast_fp16")]; + tensor reduce_mean_158_axes_0 = const()[name = tensor("reduce_mean_158_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_158_keep_dims_0 = const()[name = tensor("reduce_mean_158_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_158_cast_fp16 = reduce_mean(axes = reduce_mean_158_axes_0, keep_dims = reduce_mean_158_keep_dims_0, x = square_52_cast_fp16)[name = tensor("reduce_mean_158_cast_fp16")]; + tensor add_104_y_0_to_fp16 = const()[name = tensor("add_104_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_104_cast_fp16 = add(x = reduce_mean_158_cast_fp16, y = add_104_y_0_to_fp16)[name = tensor("add_104_cast_fp16")]; + tensor sqrt_52_cast_fp16 = sqrt(x = add_104_cast_fp16)[name = tensor("sqrt_52_cast_fp16")]; + tensor real_div_52_cast_fp16 = real_div(x = sub_104_cast_fp16, y = sqrt_52_cast_fp16)[name = tensor("real_div_52_cast_fp16")]; + tensor reshape_209_shape_0 = const()[name = tensor("reshape_209_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_209_cast_fp16 = reshape(shape = reshape_209_shape_0, x = real_div_52_cast_fp16)[name = tensor("reshape_209_cast_fp16")]; + tensor add_105_gamma_0_to_fp16 = const()[name = tensor("add_105_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213023872)))]; + tensor add_105_beta_0_to_fp16 = const()[name = tensor("add_105_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213024576)))]; + tensor add_105_epsilon_0_to_fp16 = const()[name = tensor("add_105_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_105_cast_fp16 = batch_norm(beta = add_105_beta_0_to_fp16, epsilon = add_105_epsilon_0_to_fp16, gamma = add_105_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_209_cast_fp16)[name = tensor("add_105_cast_fp16")]; + tensor input_461_cast_fp16 = silu(x = add_105_cast_fp16)[name = tensor("input_461_cast_fp16")]; + tensor var_3992 = const()[name = tensor("op_3992"), val = tensor([1, 1])]; + tensor var_3994 = const()[name = tensor("op_3994"), val = tensor([1, 1])]; + tensor hidden_states_281_pad_type_0 = const()[name = tensor("hidden_states_281_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_281_pad_0 = const()[name = tensor("hidden_states_281_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213025280))), lut = tensor([-0x1.f8p-4, -0x1.cecp-6, 0x1.dbp-6, 0x1.004p-3]), name = tensor("up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213255744)))]; + tensor hidden_states_281_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_3994, groups = var_3945, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_3992, weight = up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized, x = input_461_cast_fp16)[name = tensor("hidden_states_281_cast_fp16")]; + tensor var_3999 = const()[name = tensor("op_3999"), val = tensor([1, 1])]; + tensor var_4001 = const()[name = tensor("op_4001"), val = tensor([1, 1])]; + tensor x_23_pad_type_0 = const()[name = tensor("x_23_pad_type_0"), val = tensor("custom")]; + tensor x_23_pad_0 = const()[name = tensor("x_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213256448))), lut = tensor([-0x1.e9p-5, -0x1.38cp-7, 0x1.5bp-7, 0x1.f68p-5]), name = tensor("up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 960, 1, 1])]; + tensor up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213333312)))]; + tensor x_23_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4001, groups = var_3945, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = var_3999, weight = up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_449_cast_fp16)[name = tensor("x_23_cast_fp16")]; + tensor hidden_states_283_cast_fp16 = add(x = x_23_cast_fp16, y = hidden_states_281_cast_fp16)[name = tensor("hidden_states_283_cast_fp16")]; + tensor reshape_212_shape_0 = const()[name = tensor("reshape_212_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_212_cast_fp16 = reshape(shape = reshape_212_shape_0, x = hidden_states_283_cast_fp16)[name = tensor("reshape_212_cast_fp16")]; + tensor reduce_mean_159_axes_0 = const()[name = tensor("reduce_mean_159_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_159_keep_dims_0 = const()[name = tensor("reduce_mean_159_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_159_cast_fp16 = reduce_mean(axes = reduce_mean_159_axes_0, keep_dims = reduce_mean_159_keep_dims_0, x = reshape_212_cast_fp16)[name = tensor("reduce_mean_159_cast_fp16")]; + tensor sub_106_cast_fp16 = sub(x = reshape_212_cast_fp16, y = reduce_mean_159_cast_fp16)[name = tensor("sub_106_cast_fp16")]; + tensor square_53_cast_fp16 = square(x = sub_106_cast_fp16)[name = tensor("square_53_cast_fp16")]; + tensor reduce_mean_161_axes_0 = const()[name = tensor("reduce_mean_161_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_161_keep_dims_0 = const()[name = tensor("reduce_mean_161_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_161_cast_fp16 = reduce_mean(axes = reduce_mean_161_axes_0, keep_dims = reduce_mean_161_keep_dims_0, x = square_53_cast_fp16)[name = tensor("reduce_mean_161_cast_fp16")]; + tensor add_106_y_0_to_fp16 = const()[name = tensor("add_106_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_106_cast_fp16 = add(x = reduce_mean_161_cast_fp16, y = add_106_y_0_to_fp16)[name = tensor("add_106_cast_fp16")]; + tensor sqrt_53_cast_fp16 = sqrt(x = add_106_cast_fp16)[name = tensor("sqrt_53_cast_fp16")]; + tensor real_div_53_cast_fp16 = real_div(x = sub_106_cast_fp16, y = sqrt_53_cast_fp16)[name = tensor("real_div_53_cast_fp16")]; + tensor reshape_213_shape_0 = const()[name = tensor("reshape_213_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_213_cast_fp16 = reshape(shape = reshape_213_shape_0, x = real_div_53_cast_fp16)[name = tensor("reshape_213_cast_fp16")]; + tensor add_107_gamma_0_to_fp16 = const()[name = tensor("add_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213334016)))]; + tensor add_107_beta_0_to_fp16 = const()[name = tensor("add_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213334720)))]; + tensor add_107_epsilon_0_to_fp16 = const()[name = tensor("add_107_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_107_cast_fp16 = batch_norm(beta = add_107_beta_0_to_fp16, epsilon = add_107_epsilon_0_to_fp16, gamma = add_107_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_213_cast_fp16)[name = tensor("add_107_cast_fp16")]; + tensor var_4021 = const()[name = tensor("op_4021"), val = tensor([1, 1])]; + tensor var_4023 = const()[name = tensor("op_4023"), val = tensor([1, 1])]; + tensor hidden_states_285_pad_type_0 = const()[name = tensor("hidden_states_285_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_285_pad_0 = const()[name = tensor("hidden_states_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213335424))), lut = tensor([-0x1.a38p-4, -0x1.e94p-6, 0x1.e5p-6, 0x1.9f8p-4]), name = tensor("up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213361088)))]; + tensor hidden_states_285_cast_fp16 = conv(bias = up_blocks_3_attentions_0_proj_in_bias_to_fp16, dilations = var_4023, groups = var_3945, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_4021, weight = up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized, x = add_107_cast_fp16)[name = tensor("hidden_states_285_cast_fp16")]; + tensor var_4028 = const()[name = tensor("op_4028"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_79_cast_fp16 = reshape(shape = var_4028, x = hidden_states_285_cast_fp16)[name = tensor("inputs_79_cast_fp16")]; + tensor var_4038 = const()[name = tensor("op_4038"), val = tensor([1])]; + tensor channels_mean_79_cast_fp16 = reduce_mean(axes = var_4038, keep_dims = var_3940, x = inputs_79_cast_fp16)[name = tensor("channels_mean_79_cast_fp16")]; + tensor zero_mean_79_cast_fp16 = sub(x = inputs_79_cast_fp16, y = channels_mean_79_cast_fp16)[name = tensor("zero_mean_79_cast_fp16")]; + tensor zero_mean_sq_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = zero_mean_79_cast_fp16)[name = tensor("zero_mean_sq_79_cast_fp16")]; + tensor var_4042 = const()[name = tensor("op_4042"), val = tensor([1])]; + tensor var_4043_cast_fp16 = reduce_mean(axes = var_4042, keep_dims = var_3940, x = zero_mean_sq_79_cast_fp16)[name = tensor("op_4043_cast_fp16")]; + tensor var_4044_to_fp16 = const()[name = tensor("op_4044_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4045_cast_fp16 = add(x = var_4043_cast_fp16, y = var_4044_to_fp16)[name = tensor("op_4045_cast_fp16")]; + tensor denom_79_epsilon_0_to_fp16 = const()[name = tensor("denom_79_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_79_cast_fp16 = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_4045_cast_fp16)[name = tensor("denom_79_cast_fp16")]; + tensor out_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = denom_79_cast_fp16)[name = tensor("out_79_cast_fp16")]; + tensor var_4049_to_fp16 = const()[name = tensor("op_4049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213361792)))]; + tensor var_4050_cast_fp16 = add(x = out_79_cast_fp16, y = var_4049_to_fp16)[name = tensor("op_4050_cast_fp16")]; + tensor var_4052_to_fp16 = const()[name = tensor("op_4052_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213362496)))]; + tensor hidden_states_287_cast_fp16 = mul(x = var_4050_cast_fp16, y = var_4052_to_fp16)[name = tensor("hidden_states_287_cast_fp16")]; + tensor var_4059 = const()[name = tensor("op_4059"), val = tensor([1, 1])]; + tensor var_4061 = const()[name = tensor("op_4061"), val = tensor([1, 1])]; + tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; + tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213363200))), lut = tensor([-0x1.27cp-3, -0x1.6bp-5, 0x1.52p-5, 0x1.23cp-3]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_53_cast_fp16 = conv(dilations = var_4061, groups = var_3945, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_4059, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_287_cast_fp16)[name = tensor("q_53_cast_fp16")]; + tensor var_4065 = const()[name = tensor("op_4065"), val = tensor([1, 1])]; + tensor var_4067 = const()[name = tensor("op_4067"), val = tensor([1, 1])]; + tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; + tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213388864))), lut = tensor([-0x1.1a4p-3, -0x1.484p-5, 0x1.52p-5, 0x1.1fp-3]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_53_cast_fp16 = conv(dilations = var_4067, groups = var_3945, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_4065, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_287_cast_fp16)[name = tensor("k_53_cast_fp16")]; + tensor var_4071 = const()[name = tensor("op_4071"), val = tensor([1, 1])]; + tensor var_4073 = const()[name = tensor("op_4073"), val = tensor([1, 1])]; + tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; + tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213414528))), lut = tensor([-0x1.c5cp-4, -0x1.02p-5, 0x1.018p-5, 0x1.c14p-4]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_53_cast_fp16 = conv(dilations = var_4073, groups = var_3945, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_4071, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_287_cast_fp16)[name = tensor("v_53_cast_fp16")]; + tensor var_4077 = const()[name = tensor("op_4077"), val = tensor([2, 5, 64, -1])]; + tensor var_4078_cast_fp16 = reshape(shape = var_4077, x = q_53_cast_fp16)[name = tensor("op_4078_cast_fp16")]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([2, 5, 64, -1])]; + tensor var_4080_cast_fp16 = reshape(shape = var_4079, x = k_53_cast_fp16)[name = tensor("op_4080_cast_fp16")]; + tensor var_4081 = const()[name = tensor("op_4081"), val = tensor([2, 5, 64, -1])]; + tensor var_4082_cast_fp16 = reshape(shape = var_4081, x = v_53_cast_fp16)[name = tensor("op_4082_cast_fp16")]; + tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_4078_cast_fp16, y = var_4080_cast_fp16)[name = tensor("attn_weights_105_cast_fp16")]; + tensor var_3936_to_fp16 = const()[name = tensor("op_3936_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_107_cast_fp16")]; + tensor var_4086_cast_fp16 = softmax(axis = var_3929, x = attn_weights_107_cast_fp16)[name = tensor("op_4086_cast_fp16")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast_fp16 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_4082_cast_fp16, y = var_4086_cast_fp16)[name = tensor("attn_53_cast_fp16")]; + tensor var_4090 = const()[name = tensor("op_4090"), val = tensor([2, 320, 1, -1])]; + tensor input_465_cast_fp16 = reshape(shape = var_4090, x = attn_53_cast_fp16)[name = tensor("input_465_cast_fp16")]; + tensor var_4095 = const()[name = tensor("op_4095"), val = tensor([1, 1])]; + tensor var_4097 = const()[name = tensor("op_4097"), val = tensor([1, 1])]; + tensor var_4099_pad_type_0 = const()[name = tensor("op_4099_pad_type_0"), val = tensor("custom")]; + tensor var_4099_pad_0 = const()[name = tensor("op_4099_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213440192))), lut = tensor([-0x1.a28p-4, -0x1.eacp-6, 0x1.e24p-6, 0x1.a08p-4]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213465856)))]; + tensor var_4099_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4097, groups = var_3945, pad = var_4099_pad_0, pad_type = var_4099_pad_type_0, strides = var_4095, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_465_cast_fp16)[name = tensor("op_4099_cast_fp16")]; + tensor inputs_81_cast_fp16 = add(x = var_4099_cast_fp16, y = inputs_79_cast_fp16)[name = tensor("inputs_81_cast_fp16")]; + tensor var_4103 = const()[name = tensor("op_4103"), val = tensor([1])]; + tensor channels_mean_81_cast_fp16 = reduce_mean(axes = var_4103, keep_dims = var_3940, x = inputs_81_cast_fp16)[name = tensor("channels_mean_81_cast_fp16")]; + tensor zero_mean_81_cast_fp16 = sub(x = inputs_81_cast_fp16, y = channels_mean_81_cast_fp16)[name = tensor("zero_mean_81_cast_fp16")]; + tensor zero_mean_sq_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = zero_mean_81_cast_fp16)[name = tensor("zero_mean_sq_81_cast_fp16")]; + tensor var_4107 = const()[name = tensor("op_4107"), val = tensor([1])]; + tensor var_4108_cast_fp16 = reduce_mean(axes = var_4107, keep_dims = var_3940, x = zero_mean_sq_81_cast_fp16)[name = tensor("op_4108_cast_fp16")]; + tensor var_4109_to_fp16 = const()[name = tensor("op_4109_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4110_cast_fp16 = add(x = var_4108_cast_fp16, y = var_4109_to_fp16)[name = tensor("op_4110_cast_fp16")]; + tensor denom_81_epsilon_0_to_fp16 = const()[name = tensor("denom_81_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_81_cast_fp16 = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_4110_cast_fp16)[name = tensor("denom_81_cast_fp16")]; + tensor out_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = denom_81_cast_fp16)[name = tensor("out_81_cast_fp16")]; + tensor var_4114_to_fp16 = const()[name = tensor("op_4114_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213466560)))]; + tensor var_4115_cast_fp16 = add(x = out_81_cast_fp16, y = var_4114_to_fp16)[name = tensor("op_4115_cast_fp16")]; + tensor var_4117_to_fp16 = const()[name = tensor("op_4117_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213467264)))]; + tensor hidden_states_289_cast_fp16 = mul(x = var_4115_cast_fp16, y = var_4117_to_fp16)[name = tensor("hidden_states_289_cast_fp16")]; + tensor var_4124 = const()[name = tensor("op_4124"), val = tensor([1, 1])]; + tensor var_4126 = const()[name = tensor("op_4126"), val = tensor([1, 1])]; + tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; + tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213467968))), lut = tensor([-0x1.aa8p-4, -0x1.094p-5, 0x1.d8p-6, 0x1.9fcp-4]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_55_cast_fp16 = conv(dilations = var_4126, groups = var_3945, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_4124, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_289_cast_fp16)[name = tensor("q_55_cast_fp16")]; + tensor var_4130 = const()[name = tensor("op_4130"), val = tensor([1, 1])]; + tensor var_4132 = const()[name = tensor("op_4132"), val = tensor([1, 1])]; + tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; + tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213493632))), lut = tensor([-0x1.49p-4, -0x1.72p-6, 0x1.8c8p-6, 0x1.4f8p-4]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor k_55_cast_fp16 = conv(dilations = var_4132, groups = var_3945, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_4130, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_55_cast_fp16")]; + tensor var_4136 = const()[name = tensor("op_4136"), val = tensor([1, 1])]; + tensor var_4138 = const()[name = tensor("op_4138"), val = tensor([1, 1])]; + tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; + tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213575616))), lut = tensor([-0x1.fb4p-6, -0x1.1f8p-7, 0x1.2ap-7, 0x1.02p-5]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor v_55_cast_fp16 = conv(dilations = var_4138, groups = var_3945, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_4136, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_55_cast_fp16")]; + tensor var_4142 = const()[name = tensor("op_4142"), val = tensor([2, 5, 64, -1])]; + tensor var_4143_cast_fp16 = reshape(shape = var_4142, x = q_55_cast_fp16)[name = tensor("op_4143_cast_fp16")]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([2, 5, 64, -1])]; + tensor var_4145_cast_fp16 = reshape(shape = var_4144, x = k_55_cast_fp16)[name = tensor("op_4145_cast_fp16")]; + tensor var_4146 = const()[name = tensor("op_4146"), val = tensor([2, 5, 64, -1])]; + tensor var_4147_cast_fp16 = reshape(shape = var_4146, x = v_55_cast_fp16)[name = tensor("op_4147_cast_fp16")]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_109_cast_fp16 = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_4143_cast_fp16, y = var_4145_cast_fp16)[name = tensor("attn_weights_109_cast_fp16")]; + tensor attn_weights_111_cast_fp16 = mul(x = attn_weights_109_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_111_cast_fp16")]; + tensor var_4151_cast_fp16 = softmax(axis = var_3929, x = attn_weights_111_cast_fp16)[name = tensor("op_4151_cast_fp16")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast_fp16 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_4147_cast_fp16, y = var_4151_cast_fp16)[name = tensor("attn_55_cast_fp16")]; + tensor var_4155 = const()[name = tensor("op_4155"), val = tensor([2, 320, 1, -1])]; + tensor input_467_cast_fp16 = reshape(shape = var_4155, x = attn_55_cast_fp16)[name = tensor("input_467_cast_fp16")]; + tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([1, 1])]; + tensor var_4162 = const()[name = tensor("op_4162"), val = tensor([1, 1])]; + tensor var_4164_pad_type_0 = const()[name = tensor("op_4164_pad_type_0"), val = tensor("custom")]; + tensor var_4164_pad_0 = const()[name = tensor("op_4164_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213657600))), lut = tensor([-0x1.e3cp-6, -0x1.e08p-9, 0x1.f28p-9, 0x1.fp-6]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213683264)))]; + tensor var_4164_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4162, groups = var_3945, pad = var_4164_pad_0, pad_type = var_4164_pad_type_0, strides = var_4160, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_467_cast_fp16)[name = tensor("op_4164_cast_fp16")]; + tensor inputs_83_cast_fp16 = add(x = var_4164_cast_fp16, y = inputs_81_cast_fp16)[name = tensor("inputs_83_cast_fp16")]; + tensor var_4168 = const()[name = tensor("op_4168"), val = tensor([1])]; + tensor channels_mean_83_cast_fp16 = reduce_mean(axes = var_4168, keep_dims = var_3940, x = inputs_83_cast_fp16)[name = tensor("channels_mean_83_cast_fp16")]; + tensor zero_mean_83_cast_fp16 = sub(x = inputs_83_cast_fp16, y = channels_mean_83_cast_fp16)[name = tensor("zero_mean_83_cast_fp16")]; + tensor zero_mean_sq_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = zero_mean_83_cast_fp16)[name = tensor("zero_mean_sq_83_cast_fp16")]; + tensor var_4172 = const()[name = tensor("op_4172"), val = tensor([1])]; + tensor var_4173_cast_fp16 = reduce_mean(axes = var_4172, keep_dims = var_3940, x = zero_mean_sq_83_cast_fp16)[name = tensor("op_4173_cast_fp16")]; + tensor var_4174_to_fp16 = const()[name = tensor("op_4174_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4175_cast_fp16 = add(x = var_4173_cast_fp16, y = var_4174_to_fp16)[name = tensor("op_4175_cast_fp16")]; + tensor denom_83_epsilon_0_to_fp16 = const()[name = tensor("denom_83_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_83_cast_fp16 = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_4175_cast_fp16)[name = tensor("denom_83_cast_fp16")]; + tensor out_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = denom_83_cast_fp16)[name = tensor("out_83_cast_fp16")]; + tensor var_4179_to_fp16 = const()[name = tensor("op_4179_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213683968)))]; + tensor var_4180_cast_fp16 = add(x = out_83_cast_fp16, y = var_4179_to_fp16)[name = tensor("op_4180_cast_fp16")]; + tensor var_4182_to_fp16 = const()[name = tensor("op_4182_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213684672)))]; + tensor input_469_cast_fp16 = mul(x = var_4180_cast_fp16, y = var_4182_to_fp16)[name = tensor("input_469_cast_fp16")]; + tensor var_4190 = const()[name = tensor("op_4190"), val = tensor([1, 1])]; + tensor var_4192 = const()[name = tensor("op_4192"), val = tensor([1, 1])]; + tensor var_4194_pad_type_0 = const()[name = tensor("op_4194_pad_type_0"), val = tensor("custom")]; + tensor var_4194_pad_0 = const()[name = tensor("op_4194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213685376))), lut = tensor([-0x1.7ep-4, -0x1.c1cp-6, 0x1.c08p-6, 0x1.7e4p-4]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213890240))), lut = tensor([0x1.fecp-7, 0x1.18cp-4, -0x1.36cp-5, 0x1.03cp-3]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_4194_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4192, groups = var_3945, pad = var_4194_pad_0, pad_type = var_4194_pad_type_0, strides = var_4190, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_469_cast_fp16)[name = tensor("op_4194_cast_fp16")]; + tensor var_4195_split_sizes_0 = const()[name = tensor("op_4195_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4195_axis_0 = const()[name = tensor("op_4195_axis_0"), val = tensor(1)]; + tensor var_4195_cast_fp16_0, tensor var_4195_cast_fp16_1 = split(axis = var_4195_axis_0, split_sizes = var_4195_split_sizes_0, x = var_4194_cast_fp16)[name = tensor("op_4195_cast_fp16")]; + tensor var_4197_mode_0 = const()[name = tensor("op_4197_mode_0"), val = tensor("EXACT")]; + tensor var_4197_cast_fp16 = gelu(mode = var_4197_mode_0, x = var_4195_cast_fp16_1)[name = tensor("op_4197_cast_fp16")]; + tensor input_471_cast_fp16 = mul(x = var_4195_cast_fp16_0, y = var_4197_cast_fp16)[name = tensor("input_471_cast_fp16")]; + tensor var_4201 = const()[name = tensor("op_4201"), val = tensor([1, 1])]; + tensor var_4203 = const()[name = tensor("op_4203"), val = tensor([1, 1])]; + tensor var_4205_pad_type_0 = const()[name = tensor("op_4205_pad_type_0"), val = tensor("custom")]; + tensor var_4205_pad_0 = const()[name = tensor("op_4205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213890944))), lut = tensor([-0x1.9ep-4, -0x1.e84p-6, 0x1.ed8p-6, 0x1.9fcp-4]), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213993408)))]; + tensor var_4205_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4203, groups = var_3945, pad = var_4205_pad_0, pad_type = var_4205_pad_type_0, strides = var_4201, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_471_cast_fp16)[name = tensor("op_4205_cast_fp16")]; + tensor hidden_states_293_cast_fp16 = add(x = var_4205_cast_fp16, y = inputs_83_cast_fp16)[name = tensor("hidden_states_293_cast_fp16")]; + tensor var_4207 = const()[name = tensor("op_4207"), val = tensor([2, 320, 64, 64])]; + tensor input_473_cast_fp16 = reshape(shape = var_4207, x = hidden_states_293_cast_fp16)[name = tensor("input_473_cast_fp16")]; + tensor var_4211 = const()[name = tensor("op_4211"), val = tensor([1, 1])]; + tensor var_4213 = const()[name = tensor("op_4213"), val = tensor([1, 1])]; + tensor hidden_states_295_pad_type_0 = const()[name = tensor("hidden_states_295_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_295_pad_0 = const()[name = tensor("hidden_states_295_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213994112))), lut = tensor([-0x1.378p-3, -0x1.6cp-5, 0x1.79p-5, 0x1.3ccp-3]), name = tensor("up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214019776)))]; + tensor hidden_states_295_cast_fp16 = conv(bias = up_blocks_3_attentions_0_proj_out_bias_to_fp16, dilations = var_4213, groups = var_3945, pad = hidden_states_295_pad_0, pad_type = hidden_states_295_pad_type_0, strides = var_4211, weight = up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized, x = input_473_cast_fp16)[name = tensor("hidden_states_295_cast_fp16")]; + tensor hidden_states_297_cast_fp16 = add(x = hidden_states_295_cast_fp16, y = hidden_states_283_cast_fp16)[name = tensor("hidden_states_297_cast_fp16")]; + tensor input_475_interleave_0 = const()[name = tensor("input_475_interleave_0"), val = tensor(false)]; + tensor input_475_cast_fp16 = concat(axis = var_3945, interleave = input_475_interleave_0, values = (hidden_states_297_cast_fp16, input_35_cast_fp16))[name = tensor("input_475_cast_fp16")]; + tensor reshape_216_shape_0 = const()[name = tensor("reshape_216_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_216_cast_fp16 = reshape(shape = reshape_216_shape_0, x = input_475_cast_fp16)[name = tensor("reshape_216_cast_fp16")]; + tensor reduce_mean_162_axes_0 = const()[name = tensor("reduce_mean_162_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_162_keep_dims_0 = const()[name = tensor("reduce_mean_162_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_162_cast_fp16 = reduce_mean(axes = reduce_mean_162_axes_0, keep_dims = reduce_mean_162_keep_dims_0, x = reshape_216_cast_fp16)[name = tensor("reduce_mean_162_cast_fp16")]; + tensor sub_108_cast_fp16 = sub(x = reshape_216_cast_fp16, y = reduce_mean_162_cast_fp16)[name = tensor("sub_108_cast_fp16")]; + tensor square_54_cast_fp16 = square(x = sub_108_cast_fp16)[name = tensor("square_54_cast_fp16")]; + tensor reduce_mean_164_axes_0 = const()[name = tensor("reduce_mean_164_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_164_keep_dims_0 = const()[name = tensor("reduce_mean_164_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_164_cast_fp16 = reduce_mean(axes = reduce_mean_164_axes_0, keep_dims = reduce_mean_164_keep_dims_0, x = square_54_cast_fp16)[name = tensor("reduce_mean_164_cast_fp16")]; + tensor add_108_y_0_to_fp16 = const()[name = tensor("add_108_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_108_cast_fp16 = add(x = reduce_mean_164_cast_fp16, y = add_108_y_0_to_fp16)[name = tensor("add_108_cast_fp16")]; + tensor sqrt_54_cast_fp16 = sqrt(x = add_108_cast_fp16)[name = tensor("sqrt_54_cast_fp16")]; + tensor real_div_54_cast_fp16 = real_div(x = sub_108_cast_fp16, y = sqrt_54_cast_fp16)[name = tensor("real_div_54_cast_fp16")]; + tensor reshape_217_shape_0 = const()[name = tensor("reshape_217_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_217_cast_fp16 = reshape(shape = reshape_217_shape_0, x = real_div_54_cast_fp16)[name = tensor("reshape_217_cast_fp16")]; + tensor add_109_gamma_0_to_fp16 = const()[name = tensor("add_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214020480)))]; + tensor add_109_beta_0_to_fp16 = const()[name = tensor("add_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214021824)))]; + tensor add_109_epsilon_0_to_fp16 = const()[name = tensor("add_109_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_109_cast_fp16 = batch_norm(beta = add_109_beta_0_to_fp16, epsilon = add_109_epsilon_0_to_fp16, gamma = add_109_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_217_cast_fp16)[name = tensor("add_109_cast_fp16")]; + tensor input_479_cast_fp16 = silu(x = add_109_cast_fp16)[name = tensor("input_479_cast_fp16")]; + tensor var_4231 = const()[name = tensor("op_4231"), val = tensor([1, 1])]; + tensor var_4233 = const()[name = tensor("op_4233"), val = tensor([1, 1])]; + tensor hidden_states_299_pad_type_0 = const()[name = tensor("hidden_states_299_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_299_pad_0 = const()[name = tensor("hidden_states_299_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214023168))), lut = tensor([-0x1.a3cp-4, -0x1.7ccp-6, 0x1.b74p-6, 0x1.bdp-4]), name = tensor("up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 3, 3])]; + tensor up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214484032)))]; + tensor hidden_states_299_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_4233, groups = var_3945, pad = hidden_states_299_pad_0, pad_type = hidden_states_299_pad_type_0, strides = var_4231, weight = up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized, x = input_479_cast_fp16)[name = tensor("hidden_states_299_cast_fp16")]; + tensor var_4239 = const()[name = tensor("op_4239"), val = tensor([1, 1])]; + tensor var_4241 = const()[name = tensor("op_4241"), val = tensor([1, 1])]; + tensor temb_41_pad_type_0 = const()[name = tensor("temb_41_pad_type_0"), val = tensor("custom")]; + tensor temb_41_pad_0 = const()[name = tensor("temb_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214484736))), lut = tensor([-0x1.5b4p-5, -0x1.7b4p-9, 0x1.69cp-9, 0x1.3a8p-5]), name = tensor("up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214587200)))]; + tensor temb_41_cast_fp16 = conv(bias = up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4241, groups = var_3945, pad = temb_41_pad_0, pad_type = temb_41_pad_type_0, strides = var_4239, weight = up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_41_cast_fp16")]; + tensor input_483_cast_fp16 = add(x = hidden_states_299_cast_fp16, y = temb_41_cast_fp16)[name = tensor("input_483_cast_fp16")]; + tensor reshape_220_shape_0 = const()[name = tensor("reshape_220_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_220_cast_fp16 = reshape(shape = reshape_220_shape_0, x = input_483_cast_fp16)[name = tensor("reshape_220_cast_fp16")]; + tensor reduce_mean_165_axes_0 = const()[name = tensor("reduce_mean_165_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_165_keep_dims_0 = const()[name = tensor("reduce_mean_165_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_165_cast_fp16 = reduce_mean(axes = reduce_mean_165_axes_0, keep_dims = reduce_mean_165_keep_dims_0, x = reshape_220_cast_fp16)[name = tensor("reduce_mean_165_cast_fp16")]; + tensor sub_110_cast_fp16 = sub(x = reshape_220_cast_fp16, y = reduce_mean_165_cast_fp16)[name = tensor("sub_110_cast_fp16")]; + tensor square_55_cast_fp16 = square(x = sub_110_cast_fp16)[name = tensor("square_55_cast_fp16")]; + tensor reduce_mean_167_axes_0 = const()[name = tensor("reduce_mean_167_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_167_keep_dims_0 = const()[name = tensor("reduce_mean_167_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_167_cast_fp16 = reduce_mean(axes = reduce_mean_167_axes_0, keep_dims = reduce_mean_167_keep_dims_0, x = square_55_cast_fp16)[name = tensor("reduce_mean_167_cast_fp16")]; + tensor add_110_y_0_to_fp16 = const()[name = tensor("add_110_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_110_cast_fp16 = add(x = reduce_mean_167_cast_fp16, y = add_110_y_0_to_fp16)[name = tensor("add_110_cast_fp16")]; + tensor sqrt_55_cast_fp16 = sqrt(x = add_110_cast_fp16)[name = tensor("sqrt_55_cast_fp16")]; + tensor real_div_55_cast_fp16 = real_div(x = sub_110_cast_fp16, y = sqrt_55_cast_fp16)[name = tensor("real_div_55_cast_fp16")]; + tensor reshape_221_shape_0 = const()[name = tensor("reshape_221_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_221_cast_fp16 = reshape(shape = reshape_221_shape_0, x = real_div_55_cast_fp16)[name = tensor("reshape_221_cast_fp16")]; + tensor add_111_gamma_0_to_fp16 = const()[name = tensor("add_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214587904)))]; + tensor add_111_beta_0_to_fp16 = const()[name = tensor("add_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214588608)))]; + tensor add_111_epsilon_0_to_fp16 = const()[name = tensor("add_111_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_111_cast_fp16 = batch_norm(beta = add_111_beta_0_to_fp16, epsilon = add_111_epsilon_0_to_fp16, gamma = add_111_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_221_cast_fp16)[name = tensor("add_111_cast_fp16")]; + tensor input_487_cast_fp16 = silu(x = add_111_cast_fp16)[name = tensor("input_487_cast_fp16")]; + tensor var_4251 = const()[name = tensor("op_4251"), val = tensor([1, 1])]; + tensor var_4253 = const()[name = tensor("op_4253"), val = tensor([1, 1])]; + tensor hidden_states_301_pad_type_0 = const()[name = tensor("hidden_states_301_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_301_pad_0 = const()[name = tensor("hidden_states_301_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214589312))), lut = tensor([-0x1.b64p-4, -0x1.92p-6, 0x1.7f4p-6, 0x1.adp-4]), name = tensor("up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214819776)))]; + tensor hidden_states_301_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_4253, groups = var_3945, pad = hidden_states_301_pad_0, pad_type = hidden_states_301_pad_type_0, strides = var_4251, weight = up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized, x = input_487_cast_fp16)[name = tensor("hidden_states_301_cast_fp16")]; + tensor var_4258 = const()[name = tensor("op_4258"), val = tensor([1, 1])]; + tensor var_4260 = const()[name = tensor("op_4260"), val = tensor([1, 1])]; + tensor x_25_pad_type_0 = const()[name = tensor("x_25_pad_type_0"), val = tensor("custom")]; + tensor x_25_pad_0 = const()[name = tensor("x_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214820480))), lut = tensor([-0x1.a48p-5, -0x1.8fp-7, 0x1.cf8p-7, 0x1.bf4p-5]), name = tensor("up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 1, 1])]; + tensor up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214871744)))]; + tensor x_25_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_4260, groups = var_3945, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = var_4258, weight = up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_475_cast_fp16)[name = tensor("x_25_cast_fp16")]; + tensor hidden_states_303_cast_fp16 = add(x = x_25_cast_fp16, y = hidden_states_301_cast_fp16)[name = tensor("hidden_states_303_cast_fp16")]; + tensor reshape_224_shape_0 = const()[name = tensor("reshape_224_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_224_cast_fp16 = reshape(shape = reshape_224_shape_0, x = hidden_states_303_cast_fp16)[name = tensor("reshape_224_cast_fp16")]; + tensor reduce_mean_168_axes_0 = const()[name = tensor("reduce_mean_168_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_168_keep_dims_0 = const()[name = tensor("reduce_mean_168_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_168_cast_fp16 = reduce_mean(axes = reduce_mean_168_axes_0, keep_dims = reduce_mean_168_keep_dims_0, x = reshape_224_cast_fp16)[name = tensor("reduce_mean_168_cast_fp16")]; + tensor sub_112_cast_fp16 = sub(x = reshape_224_cast_fp16, y = reduce_mean_168_cast_fp16)[name = tensor("sub_112_cast_fp16")]; + tensor square_56_cast_fp16 = square(x = sub_112_cast_fp16)[name = tensor("square_56_cast_fp16")]; + tensor reduce_mean_170_axes_0 = const()[name = tensor("reduce_mean_170_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_170_keep_dims_0 = const()[name = tensor("reduce_mean_170_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_170_cast_fp16 = reduce_mean(axes = reduce_mean_170_axes_0, keep_dims = reduce_mean_170_keep_dims_0, x = square_56_cast_fp16)[name = tensor("reduce_mean_170_cast_fp16")]; + tensor add_112_y_0_to_fp16 = const()[name = tensor("add_112_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_112_cast_fp16 = add(x = reduce_mean_170_cast_fp16, y = add_112_y_0_to_fp16)[name = tensor("add_112_cast_fp16")]; + tensor sqrt_56_cast_fp16 = sqrt(x = add_112_cast_fp16)[name = tensor("sqrt_56_cast_fp16")]; + tensor real_div_56_cast_fp16 = real_div(x = sub_112_cast_fp16, y = sqrt_56_cast_fp16)[name = tensor("real_div_56_cast_fp16")]; + tensor reshape_225_shape_0 = const()[name = tensor("reshape_225_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_225_cast_fp16 = reshape(shape = reshape_225_shape_0, x = real_div_56_cast_fp16)[name = tensor("reshape_225_cast_fp16")]; + tensor add_113_gamma_0_to_fp16 = const()[name = tensor("add_113_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214872448)))]; + tensor add_113_beta_0_to_fp16 = const()[name = tensor("add_113_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214873152)))]; + tensor add_113_epsilon_0_to_fp16 = const()[name = tensor("add_113_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_113_cast_fp16 = batch_norm(beta = add_113_beta_0_to_fp16, epsilon = add_113_epsilon_0_to_fp16, gamma = add_113_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_225_cast_fp16)[name = tensor("add_113_cast_fp16")]; + tensor var_4280 = const()[name = tensor("op_4280"), val = tensor([1, 1])]; + tensor var_4282 = const()[name = tensor("op_4282"), val = tensor([1, 1])]; + tensor hidden_states_305_pad_type_0 = const()[name = tensor("hidden_states_305_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_305_pad_0 = const()[name = tensor("hidden_states_305_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214873856))), lut = tensor([-0x1.9c8p-4, -0x1.d58p-6, 0x1.f2p-6, 0x1.a48p-4]), name = tensor("up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214899520)))]; + tensor hidden_states_305_cast_fp16 = conv(bias = up_blocks_3_attentions_1_proj_in_bias_to_fp16, dilations = var_4282, groups = var_3945, pad = hidden_states_305_pad_0, pad_type = hidden_states_305_pad_type_0, strides = var_4280, weight = up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized, x = add_113_cast_fp16)[name = tensor("hidden_states_305_cast_fp16")]; + tensor var_4287 = const()[name = tensor("op_4287"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_85_cast_fp16 = reshape(shape = var_4287, x = hidden_states_305_cast_fp16)[name = tensor("inputs_85_cast_fp16")]; + tensor var_4297 = const()[name = tensor("op_4297"), val = tensor([1])]; + tensor channels_mean_85_cast_fp16 = reduce_mean(axes = var_4297, keep_dims = var_3940, x = inputs_85_cast_fp16)[name = tensor("channels_mean_85_cast_fp16")]; + tensor zero_mean_85_cast_fp16 = sub(x = inputs_85_cast_fp16, y = channels_mean_85_cast_fp16)[name = tensor("zero_mean_85_cast_fp16")]; + tensor zero_mean_sq_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = zero_mean_85_cast_fp16)[name = tensor("zero_mean_sq_85_cast_fp16")]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([1])]; + tensor var_4302_cast_fp16 = reduce_mean(axes = var_4301, keep_dims = var_3940, x = zero_mean_sq_85_cast_fp16)[name = tensor("op_4302_cast_fp16")]; + tensor var_4303_to_fp16 = const()[name = tensor("op_4303_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4304_cast_fp16 = add(x = var_4302_cast_fp16, y = var_4303_to_fp16)[name = tensor("op_4304_cast_fp16")]; + tensor denom_85_epsilon_0_to_fp16 = const()[name = tensor("denom_85_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_85_cast_fp16 = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_4304_cast_fp16)[name = tensor("denom_85_cast_fp16")]; + tensor out_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = denom_85_cast_fp16)[name = tensor("out_85_cast_fp16")]; + tensor var_4308_to_fp16 = const()[name = tensor("op_4308_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214900224)))]; + tensor var_4309_cast_fp16 = add(x = out_85_cast_fp16, y = var_4308_to_fp16)[name = tensor("op_4309_cast_fp16")]; + tensor var_4311_to_fp16 = const()[name = tensor("op_4311_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214900928)))]; + tensor hidden_states_307_cast_fp16 = mul(x = var_4309_cast_fp16, y = var_4311_to_fp16)[name = tensor("hidden_states_307_cast_fp16")]; + tensor var_4318 = const()[name = tensor("op_4318"), val = tensor([1, 1])]; + tensor var_4320 = const()[name = tensor("op_4320"), val = tensor([1, 1])]; + tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; + tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214901632))), lut = tensor([-0x1.958p-3, -0x1.d9cp-5, 0x1.edp-5, 0x1.9cp-3]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_57_cast_fp16 = conv(dilations = var_4320, groups = var_3945, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_4318, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_307_cast_fp16)[name = tensor("q_57_cast_fp16")]; + tensor var_4324 = const()[name = tensor("op_4324"), val = tensor([1, 1])]; + tensor var_4326 = const()[name = tensor("op_4326"), val = tensor([1, 1])]; + tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; + tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214927296))), lut = tensor([-0x1.908p-3, -0x1.b84p-5, 0x1.d8cp-5, 0x1.978p-3]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_57_cast_fp16 = conv(dilations = var_4326, groups = var_3945, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_4324, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_307_cast_fp16)[name = tensor("k_57_cast_fp16")]; + tensor var_4330 = const()[name = tensor("op_4330"), val = tensor([1, 1])]; + tensor var_4332 = const()[name = tensor("op_4332"), val = tensor([1, 1])]; + tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; + tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214952960))), lut = tensor([-0x1.81cp-4, -0x1.a7p-6, 0x1.cdp-6, 0x1.8b4p-4]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_57_cast_fp16 = conv(dilations = var_4332, groups = var_3945, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_4330, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_307_cast_fp16)[name = tensor("v_57_cast_fp16")]; + tensor var_4336 = const()[name = tensor("op_4336"), val = tensor([2, 5, 64, -1])]; + tensor var_4337_cast_fp16 = reshape(shape = var_4336, x = q_57_cast_fp16)[name = tensor("op_4337_cast_fp16")]; + tensor var_4338 = const()[name = tensor("op_4338"), val = tensor([2, 5, 64, -1])]; + tensor var_4339_cast_fp16 = reshape(shape = var_4338, x = k_57_cast_fp16)[name = tensor("op_4339_cast_fp16")]; + tensor var_4340 = const()[name = tensor("op_4340"), val = tensor([2, 5, 64, -1])]; + tensor var_4341_cast_fp16 = reshape(shape = var_4340, x = v_57_cast_fp16)[name = tensor("op_4341_cast_fp16")]; + tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_4337_cast_fp16, y = var_4339_cast_fp16)[name = tensor("attn_weights_113_cast_fp16")]; + tensor attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_115_cast_fp16")]; + tensor var_4345_cast_fp16 = softmax(axis = var_3929, x = attn_weights_115_cast_fp16)[name = tensor("op_4345_cast_fp16")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast_fp16 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_4341_cast_fp16, y = var_4345_cast_fp16)[name = tensor("attn_57_cast_fp16")]; + tensor var_4349 = const()[name = tensor("op_4349"), val = tensor([2, 320, 1, -1])]; + tensor input_491_cast_fp16 = reshape(shape = var_4349, x = attn_57_cast_fp16)[name = tensor("input_491_cast_fp16")]; + tensor var_4354 = const()[name = tensor("op_4354"), val = tensor([1, 1])]; + tensor var_4356 = const()[name = tensor("op_4356"), val = tensor([1, 1])]; + tensor var_4358_pad_type_0 = const()[name = tensor("op_4358_pad_type_0"), val = tensor("custom")]; + tensor var_4358_pad_0 = const()[name = tensor("op_4358_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214978624))), lut = tensor([-0x1.7c8p-4, -0x1.bp-6, 0x1.b2cp-6, 0x1.7bcp-4]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215004288)))]; + tensor var_4358_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4356, groups = var_3945, pad = var_4358_pad_0, pad_type = var_4358_pad_type_0, strides = var_4354, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_491_cast_fp16)[name = tensor("op_4358_cast_fp16")]; + tensor inputs_87_cast_fp16 = add(x = var_4358_cast_fp16, y = inputs_85_cast_fp16)[name = tensor("inputs_87_cast_fp16")]; + tensor var_4362 = const()[name = tensor("op_4362"), val = tensor([1])]; + tensor channels_mean_87_cast_fp16 = reduce_mean(axes = var_4362, keep_dims = var_3940, x = inputs_87_cast_fp16)[name = tensor("channels_mean_87_cast_fp16")]; + tensor zero_mean_87_cast_fp16 = sub(x = inputs_87_cast_fp16, y = channels_mean_87_cast_fp16)[name = tensor("zero_mean_87_cast_fp16")]; + tensor zero_mean_sq_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = zero_mean_87_cast_fp16)[name = tensor("zero_mean_sq_87_cast_fp16")]; + tensor var_4366 = const()[name = tensor("op_4366"), val = tensor([1])]; + tensor var_4367_cast_fp16 = reduce_mean(axes = var_4366, keep_dims = var_3940, x = zero_mean_sq_87_cast_fp16)[name = tensor("op_4367_cast_fp16")]; + tensor var_4368_to_fp16 = const()[name = tensor("op_4368_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4369_cast_fp16 = add(x = var_4367_cast_fp16, y = var_4368_to_fp16)[name = tensor("op_4369_cast_fp16")]; + tensor denom_87_epsilon_0_to_fp16 = const()[name = tensor("denom_87_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_87_cast_fp16 = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_4369_cast_fp16)[name = tensor("denom_87_cast_fp16")]; + tensor out_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = denom_87_cast_fp16)[name = tensor("out_87_cast_fp16")]; + tensor var_4373_to_fp16 = const()[name = tensor("op_4373_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215004992)))]; + tensor var_4374_cast_fp16 = add(x = out_87_cast_fp16, y = var_4373_to_fp16)[name = tensor("op_4374_cast_fp16")]; + tensor var_4376_to_fp16 = const()[name = tensor("op_4376_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215005696)))]; + tensor hidden_states_309_cast_fp16 = mul(x = var_4374_cast_fp16, y = var_4376_to_fp16)[name = tensor("hidden_states_309_cast_fp16")]; + tensor var_4383 = const()[name = tensor("op_4383"), val = tensor([1, 1])]; + tensor var_4385 = const()[name = tensor("op_4385"), val = tensor([1, 1])]; + tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; + tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215006400))), lut = tensor([-0x1.afcp-4, -0x1.02cp-5, 0x1.facp-6, 0x1.b18p-4]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_59_cast_fp16 = conv(dilations = var_4385, groups = var_3945, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_4383, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_309_cast_fp16)[name = tensor("q_59_cast_fp16")]; + tensor var_4389 = const()[name = tensor("op_4389"), val = tensor([1, 1])]; + tensor var_4391 = const()[name = tensor("op_4391"), val = tensor([1, 1])]; + tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; + tensor k_59_pad_0 = const()[name = tensor("k_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215032064))), lut = tensor([-0x1.53p-4, -0x1.7ap-6, 0x1.844p-6, 0x1.558p-4]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor k_59_cast_fp16 = conv(dilations = var_4391, groups = var_3945, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_4389, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_59_cast_fp16")]; + tensor var_4395 = const()[name = tensor("op_4395"), val = tensor([1, 1])]; + tensor var_4397 = const()[name = tensor("op_4397"), val = tensor([1, 1])]; + tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; + tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215114048))), lut = tensor([-0x1.a6cp-6, -0x1.d58p-8, 0x1.decp-8, 0x1.ad8p-6]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor v_59_cast_fp16 = conv(dilations = var_4397, groups = var_3945, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_4395, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_59_cast_fp16")]; + tensor var_4401 = const()[name = tensor("op_4401"), val = tensor([2, 5, 64, -1])]; + tensor var_4402_cast_fp16 = reshape(shape = var_4401, x = q_59_cast_fp16)[name = tensor("op_4402_cast_fp16")]; + tensor var_4403 = const()[name = tensor("op_4403"), val = tensor([2, 5, 64, -1])]; + tensor var_4404_cast_fp16 = reshape(shape = var_4403, x = k_59_cast_fp16)[name = tensor("op_4404_cast_fp16")]; + tensor var_4405 = const()[name = tensor("op_4405"), val = tensor([2, 5, 64, -1])]; + tensor var_4406_cast_fp16 = reshape(shape = var_4405, x = v_59_cast_fp16)[name = tensor("op_4406_cast_fp16")]; + tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_117_cast_fp16 = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_4402_cast_fp16, y = var_4404_cast_fp16)[name = tensor("attn_weights_117_cast_fp16")]; + tensor attn_weights_119_cast_fp16 = mul(x = attn_weights_117_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_119_cast_fp16")]; + tensor var_4410_cast_fp16 = softmax(axis = var_3929, x = attn_weights_119_cast_fp16)[name = tensor("op_4410_cast_fp16")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast_fp16 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_4406_cast_fp16, y = var_4410_cast_fp16)[name = tensor("attn_59_cast_fp16")]; + tensor var_4414 = const()[name = tensor("op_4414"), val = tensor([2, 320, 1, -1])]; + tensor input_493_cast_fp16 = reshape(shape = var_4414, x = attn_59_cast_fp16)[name = tensor("input_493_cast_fp16")]; + tensor var_4419 = const()[name = tensor("op_4419"), val = tensor([1, 1])]; + tensor var_4421 = const()[name = tensor("op_4421"), val = tensor([1, 1])]; + tensor var_4423_pad_type_0 = const()[name = tensor("op_4423_pad_type_0"), val = tensor("custom")]; + tensor var_4423_pad_0 = const()[name = tensor("op_4423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215196032))), lut = tensor([-0x1.8cp-6, -0x1.4dp-9, 0x1.474p-9, 0x1.798p-6]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215221696)))]; + tensor var_4423_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4421, groups = var_3945, pad = var_4423_pad_0, pad_type = var_4423_pad_type_0, strides = var_4419, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_493_cast_fp16)[name = tensor("op_4423_cast_fp16")]; + tensor inputs_89_cast_fp16 = add(x = var_4423_cast_fp16, y = inputs_87_cast_fp16)[name = tensor("inputs_89_cast_fp16")]; + tensor var_4427 = const()[name = tensor("op_4427"), val = tensor([1])]; + tensor channels_mean_89_cast_fp16 = reduce_mean(axes = var_4427, keep_dims = var_3940, x = inputs_89_cast_fp16)[name = tensor("channels_mean_89_cast_fp16")]; + tensor zero_mean_89_cast_fp16 = sub(x = inputs_89_cast_fp16, y = channels_mean_89_cast_fp16)[name = tensor("zero_mean_89_cast_fp16")]; + tensor zero_mean_sq_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = zero_mean_89_cast_fp16)[name = tensor("zero_mean_sq_89_cast_fp16")]; + tensor var_4431 = const()[name = tensor("op_4431"), val = tensor([1])]; + tensor var_4432_cast_fp16 = reduce_mean(axes = var_4431, keep_dims = var_3940, x = zero_mean_sq_89_cast_fp16)[name = tensor("op_4432_cast_fp16")]; + tensor var_4433_to_fp16 = const()[name = tensor("op_4433_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4434_cast_fp16 = add(x = var_4432_cast_fp16, y = var_4433_to_fp16)[name = tensor("op_4434_cast_fp16")]; + tensor denom_89_epsilon_0_to_fp16 = const()[name = tensor("denom_89_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_89_cast_fp16 = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_4434_cast_fp16)[name = tensor("denom_89_cast_fp16")]; + tensor out_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = denom_89_cast_fp16)[name = tensor("out_89_cast_fp16")]; + tensor var_4438_to_fp16 = const()[name = tensor("op_4438_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215222400)))]; + tensor var_4439_cast_fp16 = add(x = out_89_cast_fp16, y = var_4438_to_fp16)[name = tensor("op_4439_cast_fp16")]; + tensor var_4441_to_fp16 = const()[name = tensor("op_4441_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215223104)))]; + tensor input_495_cast_fp16 = mul(x = var_4439_cast_fp16, y = var_4441_to_fp16)[name = tensor("input_495_cast_fp16")]; + tensor var_4449 = const()[name = tensor("op_4449"), val = tensor([1, 1])]; + tensor var_4451 = const()[name = tensor("op_4451"), val = tensor([1, 1])]; + tensor var_4453_pad_type_0 = const()[name = tensor("op_4453_pad_type_0"), val = tensor("custom")]; + tensor var_4453_pad_0 = const()[name = tensor("op_4453_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215223808))), lut = tensor([-0x1.7c4p-4, -0x1.bd4p-6, 0x1.b8cp-6, 0x1.788p-4]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215428672))), lut = tensor([0x1.40cp-4, 0x1.b1cp-7, -0x1.45cp-2, -0x1.5b4p-5]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_4453_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4451, groups = var_3945, pad = var_4453_pad_0, pad_type = var_4453_pad_type_0, strides = var_4449, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_495_cast_fp16)[name = tensor("op_4453_cast_fp16")]; + tensor var_4454_split_sizes_0 = const()[name = tensor("op_4454_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4454_axis_0 = const()[name = tensor("op_4454_axis_0"), val = tensor(1)]; + tensor var_4454_cast_fp16_0, tensor var_4454_cast_fp16_1 = split(axis = var_4454_axis_0, split_sizes = var_4454_split_sizes_0, x = var_4453_cast_fp16)[name = tensor("op_4454_cast_fp16")]; + tensor var_4456_mode_0 = const()[name = tensor("op_4456_mode_0"), val = tensor("EXACT")]; + tensor var_4456_cast_fp16 = gelu(mode = var_4456_mode_0, x = var_4454_cast_fp16_1)[name = tensor("op_4456_cast_fp16")]; + tensor input_497_cast_fp16 = mul(x = var_4454_cast_fp16_0, y = var_4456_cast_fp16)[name = tensor("input_497_cast_fp16")]; + tensor var_4460 = const()[name = tensor("op_4460"), val = tensor([1, 1])]; + tensor var_4462 = const()[name = tensor("op_4462"), val = tensor([1, 1])]; + tensor var_4464_pad_type_0 = const()[name = tensor("op_4464_pad_type_0"), val = tensor("custom")]; + tensor var_4464_pad_0 = const()[name = tensor("op_4464_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215429376))), lut = tensor([-0x1.978p-4, -0x1.e24p-6, 0x1.e4p-6, 0x1.98p-4]), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215531840)))]; + tensor var_4464_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4462, groups = var_3945, pad = var_4464_pad_0, pad_type = var_4464_pad_type_0, strides = var_4460, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_497_cast_fp16)[name = tensor("op_4464_cast_fp16")]; + tensor hidden_states_313_cast_fp16 = add(x = var_4464_cast_fp16, y = inputs_89_cast_fp16)[name = tensor("hidden_states_313_cast_fp16")]; + tensor var_4466 = const()[name = tensor("op_4466"), val = tensor([2, 320, 64, 64])]; + tensor input_499_cast_fp16 = reshape(shape = var_4466, x = hidden_states_313_cast_fp16)[name = tensor("input_499_cast_fp16")]; + tensor var_4470 = const()[name = tensor("op_4470"), val = tensor([1, 1])]; + tensor var_4472 = const()[name = tensor("op_4472"), val = tensor([1, 1])]; + tensor hidden_states_315_pad_type_0 = const()[name = tensor("hidden_states_315_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_315_pad_0 = const()[name = tensor("hidden_states_315_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215532544))), lut = tensor([-0x1.0f8p-3, -0x1.4p-5, 0x1.46p-5, 0x1.104p-3]), name = tensor("up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215558208)))]; + tensor hidden_states_315_cast_fp16 = conv(bias = up_blocks_3_attentions_1_proj_out_bias_to_fp16, dilations = var_4472, groups = var_3945, pad = hidden_states_315_pad_0, pad_type = hidden_states_315_pad_type_0, strides = var_4470, weight = up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized, x = input_499_cast_fp16)[name = tensor("hidden_states_315_cast_fp16")]; + tensor hidden_states_317_cast_fp16 = add(x = hidden_states_315_cast_fp16, y = hidden_states_303_cast_fp16)[name = tensor("hidden_states_317_cast_fp16")]; + tensor input_501_interleave_0 = const()[name = tensor("input_501_interleave_0"), val = tensor(false)]; + tensor input_501_cast_fp16 = concat(axis = var_3945, interleave = input_501_interleave_0, values = (hidden_states_317_cast_fp16, input_7_cast_fp16))[name = tensor("input_501_cast_fp16")]; + tensor reshape_228_shape_0 = const()[name = tensor("reshape_228_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_228_cast_fp16 = reshape(shape = reshape_228_shape_0, x = input_501_cast_fp16)[name = tensor("reshape_228_cast_fp16")]; + tensor reduce_mean_171_axes_0 = const()[name = tensor("reduce_mean_171_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_171_keep_dims_0 = const()[name = tensor("reduce_mean_171_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_171_cast_fp16 = reduce_mean(axes = reduce_mean_171_axes_0, keep_dims = reduce_mean_171_keep_dims_0, x = reshape_228_cast_fp16)[name = tensor("reduce_mean_171_cast_fp16")]; + tensor sub_114_cast_fp16 = sub(x = reshape_228_cast_fp16, y = reduce_mean_171_cast_fp16)[name = tensor("sub_114_cast_fp16")]; + tensor square_57_cast_fp16 = square(x = sub_114_cast_fp16)[name = tensor("square_57_cast_fp16")]; + tensor reduce_mean_173_axes_0 = const()[name = tensor("reduce_mean_173_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_173_keep_dims_0 = const()[name = tensor("reduce_mean_173_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_173_cast_fp16 = reduce_mean(axes = reduce_mean_173_axes_0, keep_dims = reduce_mean_173_keep_dims_0, x = square_57_cast_fp16)[name = tensor("reduce_mean_173_cast_fp16")]; + tensor add_114_y_0_to_fp16 = const()[name = tensor("add_114_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_114_cast_fp16 = add(x = reduce_mean_173_cast_fp16, y = add_114_y_0_to_fp16)[name = tensor("add_114_cast_fp16")]; + tensor sqrt_57_cast_fp16 = sqrt(x = add_114_cast_fp16)[name = tensor("sqrt_57_cast_fp16")]; + tensor real_div_57_cast_fp16 = real_div(x = sub_114_cast_fp16, y = sqrt_57_cast_fp16)[name = tensor("real_div_57_cast_fp16")]; + tensor reshape_229_shape_0 = const()[name = tensor("reshape_229_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_229_cast_fp16 = reshape(shape = reshape_229_shape_0, x = real_div_57_cast_fp16)[name = tensor("reshape_229_cast_fp16")]; + tensor add_115_gamma_0_to_fp16 = const()[name = tensor("add_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215558912)))]; + tensor add_115_beta_0_to_fp16 = const()[name = tensor("add_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215560256)))]; + tensor add_115_epsilon_0_to_fp16 = const()[name = tensor("add_115_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_115_cast_fp16 = batch_norm(beta = add_115_beta_0_to_fp16, epsilon = add_115_epsilon_0_to_fp16, gamma = add_115_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_229_cast_fp16)[name = tensor("add_115_cast_fp16")]; + tensor input_505_cast_fp16 = silu(x = add_115_cast_fp16)[name = tensor("input_505_cast_fp16")]; + tensor var_4490 = const()[name = tensor("op_4490"), val = tensor([1, 1])]; + tensor var_4492 = const()[name = tensor("op_4492"), val = tensor([1, 1])]; + tensor hidden_states_319_pad_type_0 = const()[name = tensor("hidden_states_319_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_319_pad_0 = const()[name = tensor("hidden_states_319_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215561600))), lut = tensor([-0x1.93cp-4, -0x1.774p-6, 0x1.918p-6, 0x1.9c8p-4]), name = tensor("up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 3, 3])]; + tensor up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216022464)))]; + tensor hidden_states_319_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_4492, groups = var_3945, pad = hidden_states_319_pad_0, pad_type = hidden_states_319_pad_type_0, strides = var_4490, weight = up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized, x = input_505_cast_fp16)[name = tensor("hidden_states_319_cast_fp16")]; + tensor var_4498 = const()[name = tensor("op_4498"), val = tensor([1, 1])]; + tensor var_4500 = const()[name = tensor("op_4500"), val = tensor([1, 1])]; + tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; + tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216023168))), lut = tensor([-0x1.f9cp-5, -0x1.96p-9, 0x1.724p-9, 0x1.9p-5]), name = tensor("up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216125632)))]; + tensor temb_cast_fp16 = conv(bias = up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_4500, groups = var_3945, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_4498, weight = up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor("temb_cast_fp16")]; + tensor input_509_cast_fp16 = add(x = hidden_states_319_cast_fp16, y = temb_cast_fp16)[name = tensor("input_509_cast_fp16")]; + tensor reshape_232_shape_0 = const()[name = tensor("reshape_232_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_232_cast_fp16 = reshape(shape = reshape_232_shape_0, x = input_509_cast_fp16)[name = tensor("reshape_232_cast_fp16")]; + tensor reduce_mean_174_axes_0 = const()[name = tensor("reduce_mean_174_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_174_keep_dims_0 = const()[name = tensor("reduce_mean_174_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_174_cast_fp16 = reduce_mean(axes = reduce_mean_174_axes_0, keep_dims = reduce_mean_174_keep_dims_0, x = reshape_232_cast_fp16)[name = tensor("reduce_mean_174_cast_fp16")]; + tensor sub_116_cast_fp16 = sub(x = reshape_232_cast_fp16, y = reduce_mean_174_cast_fp16)[name = tensor("sub_116_cast_fp16")]; + tensor square_58_cast_fp16 = square(x = sub_116_cast_fp16)[name = tensor("square_58_cast_fp16")]; + tensor reduce_mean_176_axes_0 = const()[name = tensor("reduce_mean_176_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_176_keep_dims_0 = const()[name = tensor("reduce_mean_176_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_176_cast_fp16 = reduce_mean(axes = reduce_mean_176_axes_0, keep_dims = reduce_mean_176_keep_dims_0, x = square_58_cast_fp16)[name = tensor("reduce_mean_176_cast_fp16")]; + tensor add_116_y_0_to_fp16 = const()[name = tensor("add_116_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_116_cast_fp16 = add(x = reduce_mean_176_cast_fp16, y = add_116_y_0_to_fp16)[name = tensor("add_116_cast_fp16")]; + tensor sqrt_58_cast_fp16 = sqrt(x = add_116_cast_fp16)[name = tensor("sqrt_58_cast_fp16")]; + tensor real_div_58_cast_fp16 = real_div(x = sub_116_cast_fp16, y = sqrt_58_cast_fp16)[name = tensor("real_div_58_cast_fp16")]; + tensor reshape_233_shape_0 = const()[name = tensor("reshape_233_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_233_cast_fp16 = reshape(shape = reshape_233_shape_0, x = real_div_58_cast_fp16)[name = tensor("reshape_233_cast_fp16")]; + tensor add_117_gamma_0_to_fp16 = const()[name = tensor("add_117_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216126336)))]; + tensor add_117_beta_0_to_fp16 = const()[name = tensor("add_117_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216127040)))]; + tensor add_117_epsilon_0_to_fp16 = const()[name = tensor("add_117_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_117_cast_fp16 = batch_norm(beta = add_117_beta_0_to_fp16, epsilon = add_117_epsilon_0_to_fp16, gamma = add_117_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_233_cast_fp16)[name = tensor("add_117_cast_fp16")]; + tensor input_513_cast_fp16 = silu(x = add_117_cast_fp16)[name = tensor("input_513_cast_fp16")]; + tensor var_4510 = const()[name = tensor("op_4510"), val = tensor([1, 1])]; + tensor var_4512 = const()[name = tensor("op_4512"), val = tensor([1, 1])]; + tensor hidden_states_321_pad_type_0 = const()[name = tensor("hidden_states_321_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_321_pad_0 = const()[name = tensor("hidden_states_321_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216127744))), lut = tensor([-0x1.89cp-4, -0x1.974p-6, 0x1.87cp-6, 0x1.828p-4]), name = tensor("up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216358208)))]; + tensor hidden_states_321_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_4512, groups = var_3945, pad = hidden_states_321_pad_0, pad_type = hidden_states_321_pad_type_0, strides = var_4510, weight = up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized, x = input_513_cast_fp16)[name = tensor("hidden_states_321_cast_fp16")]; + tensor var_4517 = const()[name = tensor("op_4517"), val = tensor([1, 1])]; + tensor var_4519 = const()[name = tensor("op_4519"), val = tensor([1, 1])]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216358912))), lut = tensor([-0x1.2d4p-4, -0x1.20cp-6, 0x1.31cp-6, 0x1.34p-4]), name = tensor("up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 1, 1])]; + tensor up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216410176)))]; + tensor x_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_4519, groups = var_3945, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_4517, weight = up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_501_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor hidden_states_323_cast_fp16 = add(x = x_cast_fp16, y = hidden_states_321_cast_fp16)[name = tensor("hidden_states_323_cast_fp16")]; + tensor reshape_236_shape_0 = const()[name = tensor("reshape_236_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_236_cast_fp16 = reshape(shape = reshape_236_shape_0, x = hidden_states_323_cast_fp16)[name = tensor("reshape_236_cast_fp16")]; + tensor reduce_mean_177_axes_0 = const()[name = tensor("reduce_mean_177_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_177_keep_dims_0 = const()[name = tensor("reduce_mean_177_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_177_cast_fp16 = reduce_mean(axes = reduce_mean_177_axes_0, keep_dims = reduce_mean_177_keep_dims_0, x = reshape_236_cast_fp16)[name = tensor("reduce_mean_177_cast_fp16")]; + tensor sub_118_cast_fp16 = sub(x = reshape_236_cast_fp16, y = reduce_mean_177_cast_fp16)[name = tensor("sub_118_cast_fp16")]; + tensor square_59_cast_fp16 = square(x = sub_118_cast_fp16)[name = tensor("square_59_cast_fp16")]; + tensor reduce_mean_179_axes_0 = const()[name = tensor("reduce_mean_179_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_179_keep_dims_0 = const()[name = tensor("reduce_mean_179_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_179_cast_fp16 = reduce_mean(axes = reduce_mean_179_axes_0, keep_dims = reduce_mean_179_keep_dims_0, x = square_59_cast_fp16)[name = tensor("reduce_mean_179_cast_fp16")]; + tensor add_118_y_0_to_fp16 = const()[name = tensor("add_118_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_118_cast_fp16 = add(x = reduce_mean_179_cast_fp16, y = add_118_y_0_to_fp16)[name = tensor("add_118_cast_fp16")]; + tensor sqrt_59_cast_fp16 = sqrt(x = add_118_cast_fp16)[name = tensor("sqrt_59_cast_fp16")]; + tensor real_div_59_cast_fp16 = real_div(x = sub_118_cast_fp16, y = sqrt_59_cast_fp16)[name = tensor("real_div_59_cast_fp16")]; + tensor reshape_237_shape_0 = const()[name = tensor("reshape_237_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_237_cast_fp16 = reshape(shape = reshape_237_shape_0, x = real_div_59_cast_fp16)[name = tensor("reshape_237_cast_fp16")]; + tensor add_119_gamma_0_to_fp16 = const()[name = tensor("add_119_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216410880)))]; + tensor add_119_beta_0_to_fp16 = const()[name = tensor("add_119_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216411584)))]; + tensor add_119_epsilon_0_to_fp16 = const()[name = tensor("add_119_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_119_cast_fp16 = batch_norm(beta = add_119_beta_0_to_fp16, epsilon = add_119_epsilon_0_to_fp16, gamma = add_119_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_237_cast_fp16)[name = tensor("add_119_cast_fp16")]; + tensor var_4539 = const()[name = tensor("op_4539"), val = tensor([1, 1])]; + tensor var_4541 = const()[name = tensor("op_4541"), val = tensor([1, 1])]; + tensor hidden_states_325_pad_type_0 = const()[name = tensor("hidden_states_325_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_325_pad_0 = const()[name = tensor("hidden_states_325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216412288))), lut = tensor([-0x1.8ccp-4, -0x1.d94p-6, 0x1.dap-6, 0x1.89p-4]), name = tensor("up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216437952)))]; + tensor hidden_states_325_cast_fp16 = conv(bias = up_blocks_3_attentions_2_proj_in_bias_to_fp16, dilations = var_4541, groups = var_3945, pad = hidden_states_325_pad_0, pad_type = hidden_states_325_pad_type_0, strides = var_4539, weight = up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized, x = add_119_cast_fp16)[name = tensor("hidden_states_325_cast_fp16")]; + tensor var_4546 = const()[name = tensor("op_4546"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_91_cast_fp16 = reshape(shape = var_4546, x = hidden_states_325_cast_fp16)[name = tensor("inputs_91_cast_fp16")]; + tensor var_4556 = const()[name = tensor("op_4556"), val = tensor([1])]; + tensor channels_mean_91_cast_fp16 = reduce_mean(axes = var_4556, keep_dims = var_3940, x = inputs_91_cast_fp16)[name = tensor("channels_mean_91_cast_fp16")]; + tensor zero_mean_91_cast_fp16 = sub(x = inputs_91_cast_fp16, y = channels_mean_91_cast_fp16)[name = tensor("zero_mean_91_cast_fp16")]; + tensor zero_mean_sq_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = zero_mean_91_cast_fp16)[name = tensor("zero_mean_sq_91_cast_fp16")]; + tensor var_4560 = const()[name = tensor("op_4560"), val = tensor([1])]; + tensor var_4561_cast_fp16 = reduce_mean(axes = var_4560, keep_dims = var_3940, x = zero_mean_sq_91_cast_fp16)[name = tensor("op_4561_cast_fp16")]; + tensor var_4562_to_fp16 = const()[name = tensor("op_4562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4563_cast_fp16 = add(x = var_4561_cast_fp16, y = var_4562_to_fp16)[name = tensor("op_4563_cast_fp16")]; + tensor denom_91_epsilon_0_to_fp16 = const()[name = tensor("denom_91_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_91_cast_fp16 = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_4563_cast_fp16)[name = tensor("denom_91_cast_fp16")]; + tensor out_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = denom_91_cast_fp16)[name = tensor("out_91_cast_fp16")]; + tensor var_4567_to_fp16 = const()[name = tensor("op_4567_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216438656)))]; + tensor var_4568_cast_fp16 = add(x = out_91_cast_fp16, y = var_4567_to_fp16)[name = tensor("op_4568_cast_fp16")]; + tensor var_4570_to_fp16 = const()[name = tensor("op_4570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216439360)))]; + tensor hidden_states_327_cast_fp16 = mul(x = var_4568_cast_fp16, y = var_4570_to_fp16)[name = tensor("hidden_states_327_cast_fp16")]; + tensor var_4577 = const()[name = tensor("op_4577"), val = tensor([1, 1])]; + tensor var_4579 = const()[name = tensor("op_4579"), val = tensor([1, 1])]; + tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; + tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216440064))), lut = tensor([-0x1.75p-3, -0x1.b2p-5, 0x1.a64p-5, 0x1.744p-3]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_61_cast_fp16 = conv(dilations = var_4579, groups = var_3945, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_4577, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_327_cast_fp16)[name = tensor("q_61_cast_fp16")]; + tensor var_4583 = const()[name = tensor("op_4583"), val = tensor([1, 1])]; + tensor var_4585 = const()[name = tensor("op_4585"), val = tensor([1, 1])]; + tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; + tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216465728))), lut = tensor([-0x1.654p-3, -0x1.7ccp-5, 0x1.88p-5, 0x1.698p-3]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_61_cast_fp16 = conv(dilations = var_4585, groups = var_3945, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_4583, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_327_cast_fp16)[name = tensor("k_61_cast_fp16")]; + tensor var_4589 = const()[name = tensor("op_4589"), val = tensor([1, 1])]; + tensor var_4591 = const()[name = tensor("op_4591"), val = tensor([1, 1])]; + tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; + tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216491392))), lut = tensor([-0x1.708p-4, -0x1.9b8p-6, 0x1.9c8p-6, 0x1.6f4p-4]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_61_cast_fp16 = conv(dilations = var_4591, groups = var_3945, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_4589, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_327_cast_fp16)[name = tensor("v_61_cast_fp16")]; + tensor var_4595 = const()[name = tensor("op_4595"), val = tensor([2, 5, 64, -1])]; + tensor var_4596_cast_fp16 = reshape(shape = var_4595, x = q_61_cast_fp16)[name = tensor("op_4596_cast_fp16")]; + tensor var_4597 = const()[name = tensor("op_4597"), val = tensor([2, 5, 64, -1])]; + tensor var_4598_cast_fp16 = reshape(shape = var_4597, x = k_61_cast_fp16)[name = tensor("op_4598_cast_fp16")]; + tensor var_4599 = const()[name = tensor("op_4599"), val = tensor([2, 5, 64, -1])]; + tensor var_4600_cast_fp16 = reshape(shape = var_4599, x = v_61_cast_fp16)[name = tensor("op_4600_cast_fp16")]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_4596_cast_fp16, y = var_4598_cast_fp16)[name = tensor("attn_weights_121_cast_fp16")]; + tensor attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_123_cast_fp16")]; + tensor var_4604_cast_fp16 = softmax(axis = var_3929, x = attn_weights_123_cast_fp16)[name = tensor("op_4604_cast_fp16")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast_fp16 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_4600_cast_fp16, y = var_4604_cast_fp16)[name = tensor("attn_61_cast_fp16")]; + tensor var_4608 = const()[name = tensor("op_4608"), val = tensor([2, 320, 1, -1])]; + tensor input_517_cast_fp16 = reshape(shape = var_4608, x = attn_61_cast_fp16)[name = tensor("input_517_cast_fp16")]; + tensor var_4613 = const()[name = tensor("op_4613"), val = tensor([1, 1])]; + tensor var_4615 = const()[name = tensor("op_4615"), val = tensor([1, 1])]; + tensor var_4617_pad_type_0 = const()[name = tensor("op_4617_pad_type_0"), val = tensor("custom")]; + tensor var_4617_pad_0 = const()[name = tensor("op_4617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216517056))), lut = tensor([-0x1.614p-4, -0x1.94cp-6, 0x1.8f4p-6, 0x1.628p-4]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216542720)))]; + tensor var_4617_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4615, groups = var_3945, pad = var_4617_pad_0, pad_type = var_4617_pad_type_0, strides = var_4613, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_517_cast_fp16)[name = tensor("op_4617_cast_fp16")]; + tensor inputs_93_cast_fp16 = add(x = var_4617_cast_fp16, y = inputs_91_cast_fp16)[name = tensor("inputs_93_cast_fp16")]; + tensor var_4621 = const()[name = tensor("op_4621"), val = tensor([1])]; + tensor channels_mean_93_cast_fp16 = reduce_mean(axes = var_4621, keep_dims = var_3940, x = inputs_93_cast_fp16)[name = tensor("channels_mean_93_cast_fp16")]; + tensor zero_mean_93_cast_fp16 = sub(x = inputs_93_cast_fp16, y = channels_mean_93_cast_fp16)[name = tensor("zero_mean_93_cast_fp16")]; + tensor zero_mean_sq_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = zero_mean_93_cast_fp16)[name = tensor("zero_mean_sq_93_cast_fp16")]; + tensor var_4625 = const()[name = tensor("op_4625"), val = tensor([1])]; + tensor var_4626_cast_fp16 = reduce_mean(axes = var_4625, keep_dims = var_3940, x = zero_mean_sq_93_cast_fp16)[name = tensor("op_4626_cast_fp16")]; + tensor var_4627_to_fp16 = const()[name = tensor("op_4627_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4628_cast_fp16 = add(x = var_4626_cast_fp16, y = var_4627_to_fp16)[name = tensor("op_4628_cast_fp16")]; + tensor denom_93_epsilon_0_to_fp16 = const()[name = tensor("denom_93_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_93_cast_fp16 = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_4628_cast_fp16)[name = tensor("denom_93_cast_fp16")]; + tensor out_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = denom_93_cast_fp16)[name = tensor("out_93_cast_fp16")]; + tensor var_4632_to_fp16 = const()[name = tensor("op_4632_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216543424)))]; + tensor var_4633_cast_fp16 = add(x = out_93_cast_fp16, y = var_4632_to_fp16)[name = tensor("op_4633_cast_fp16")]; + tensor var_4635_to_fp16 = const()[name = tensor("op_4635_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216544128)))]; + tensor hidden_states_329_cast_fp16 = mul(x = var_4633_cast_fp16, y = var_4635_to_fp16)[name = tensor("hidden_states_329_cast_fp16")]; + tensor var_4642 = const()[name = tensor("op_4642"), val = tensor([1, 1])]; + tensor var_4644 = const()[name = tensor("op_4644"), val = tensor([1, 1])]; + tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; + tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216544832))), lut = tensor([-0x1.a1p-4, -0x1.e8p-6, 0x1.f3cp-6, 0x1.a5cp-4]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_cast_fp16 = conv(dilations = var_4644, groups = var_3945, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_4642, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_329_cast_fp16)[name = tensor("q_cast_fp16")]; + tensor var_4648 = const()[name = tensor("op_4648"), val = tensor([1, 1])]; + tensor var_4650 = const()[name = tensor("op_4650"), val = tensor([1, 1])]; + tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; + tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216570496))), lut = tensor([-0x1.43cp-4, -0x1.728p-6, 0x1.5e8p-6, 0x1.3fcp-4]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor k_cast_fp16 = conv(dilations = var_4650, groups = var_3945, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_4648, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_cast_fp16")]; + tensor var_4654 = const()[name = tensor("op_4654"), val = tensor([1, 1])]; + tensor var_4656 = const()[name = tensor("op_4656"), val = tensor([1, 1])]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216652480))), lut = tensor([-0x1.83cp-6, -0x1.7bp-8, 0x1.9d8p-8, 0x1.93p-6]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 1024, 1, 1])]; + tensor v_cast_fp16 = conv(dilations = var_4656, groups = var_3945, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_4654, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_cast_fp16")]; + tensor var_4660 = const()[name = tensor("op_4660"), val = tensor([2, 5, 64, -1])]; + tensor var_4661_cast_fp16 = reshape(shape = var_4660, x = q_cast_fp16)[name = tensor("op_4661_cast_fp16")]; + tensor var_4662 = const()[name = tensor("op_4662"), val = tensor([2, 5, 64, -1])]; + tensor var_4663_cast_fp16 = reshape(shape = var_4662, x = k_cast_fp16)[name = tensor("op_4663_cast_fp16")]; + tensor var_4664 = const()[name = tensor("op_4664"), val = tensor([2, 5, 64, -1])]; + tensor var_4665_cast_fp16 = reshape(shape = var_4664, x = v_cast_fp16)[name = tensor("op_4665_cast_fp16")]; + tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_125_cast_fp16 = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_4661_cast_fp16, y = var_4663_cast_fp16)[name = tensor("attn_weights_125_cast_fp16")]; + tensor attn_weights_cast_fp16 = mul(x = attn_weights_125_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_cast_fp16")]; + tensor var_4669_cast_fp16 = softmax(axis = var_3929, x = attn_weights_cast_fp16)[name = tensor("op_4669_cast_fp16")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_4665_cast_fp16, y = var_4669_cast_fp16)[name = tensor("attn_cast_fp16")]; + tensor var_4673 = const()[name = tensor("op_4673"), val = tensor([2, 320, 1, -1])]; + tensor input_519_cast_fp16 = reshape(shape = var_4673, x = attn_cast_fp16)[name = tensor("input_519_cast_fp16")]; + tensor var_4678 = const()[name = tensor("op_4678"), val = tensor([1, 1])]; + tensor var_4680 = const()[name = tensor("op_4680"), val = tensor([1, 1])]; + tensor var_4682_pad_type_0 = const()[name = tensor("op_4682_pad_type_0"), val = tensor("custom")]; + tensor var_4682_pad_0 = const()[name = tensor("op_4682_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216734464))), lut = tensor([-0x1.818p-6, -0x1.308p-9, 0x1.258p-9, 0x1.6acp-6]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216760128)))]; + tensor var_4682_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4680, groups = var_3945, pad = var_4682_pad_0, pad_type = var_4682_pad_type_0, strides = var_4678, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_519_cast_fp16)[name = tensor("op_4682_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = var_4682_cast_fp16, y = inputs_93_cast_fp16)[name = tensor("inputs_cast_fp16")]; + tensor var_4686 = const()[name = tensor("op_4686"), val = tensor([1])]; + tensor channels_mean_cast_fp16 = reduce_mean(axes = var_4686, keep_dims = var_3940, x = inputs_cast_fp16)[name = tensor("channels_mean_cast_fp16")]; + tensor zero_mean_cast_fp16 = sub(x = inputs_cast_fp16, y = channels_mean_cast_fp16)[name = tensor("zero_mean_cast_fp16")]; + tensor zero_mean_sq_cast_fp16 = mul(x = zero_mean_cast_fp16, y = zero_mean_cast_fp16)[name = tensor("zero_mean_sq_cast_fp16")]; + tensor var_4690 = const()[name = tensor("op_4690"), val = tensor([1])]; + tensor var_4691_cast_fp16 = reduce_mean(axes = var_4690, keep_dims = var_3940, x = zero_mean_sq_cast_fp16)[name = tensor("op_4691_cast_fp16")]; + tensor var_4692_to_fp16 = const()[name = tensor("op_4692_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4693_cast_fp16 = add(x = var_4691_cast_fp16, y = var_4692_to_fp16)[name = tensor("op_4693_cast_fp16")]; + tensor denom_epsilon_0_to_fp16 = const()[name = tensor("denom_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_cast_fp16 = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_4693_cast_fp16)[name = tensor("denom_cast_fp16")]; + tensor out_cast_fp16 = mul(x = zero_mean_cast_fp16, y = denom_cast_fp16)[name = tensor("out_cast_fp16")]; + tensor var_4697_to_fp16 = const()[name = tensor("op_4697_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216760832)))]; + tensor var_4698_cast_fp16 = add(x = out_cast_fp16, y = var_4697_to_fp16)[name = tensor("op_4698_cast_fp16")]; + tensor var_4700_to_fp16 = const()[name = tensor("op_4700_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216761536)))]; + tensor input_521_cast_fp16 = mul(x = var_4698_cast_fp16, y = var_4700_to_fp16)[name = tensor("input_521_cast_fp16")]; + tensor var_4708 = const()[name = tensor("op_4708"), val = tensor([1, 1])]; + tensor var_4710 = const()[name = tensor("op_4710"), val = tensor([1, 1])]; + tensor var_4712_pad_type_0 = const()[name = tensor("op_4712_pad_type_0"), val = tensor("custom")]; + tensor var_4712_pad_0 = const()[name = tensor("op_4712_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216762240))), lut = tensor([-0x1.82p-4, -0x1.b64p-6, 0x1.de4p-6, 0x1.8ecp-4]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216967104))), lut = tensor([-0x1.32p-4, -0x1.22cp-7, 0x1.54p-4, 0x1.1d8p-5]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_4712_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4710, groups = var_3945, pad = var_4712_pad_0, pad_type = var_4712_pad_type_0, strides = var_4708, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_521_cast_fp16)[name = tensor("op_4712_cast_fp16")]; + tensor var_4713_split_sizes_0 = const()[name = tensor("op_4713_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4713_axis_0 = const()[name = tensor("op_4713_axis_0"), val = tensor(1)]; + tensor var_4713_cast_fp16_0, tensor var_4713_cast_fp16_1 = split(axis = var_4713_axis_0, split_sizes = var_4713_split_sizes_0, x = var_4712_cast_fp16)[name = tensor("op_4713_cast_fp16")]; + tensor var_4715_mode_0 = const()[name = tensor("op_4715_mode_0"), val = tensor("EXACT")]; + tensor var_4715_cast_fp16 = gelu(mode = var_4715_mode_0, x = var_4713_cast_fp16_1)[name = tensor("op_4715_cast_fp16")]; + tensor input_523_cast_fp16 = mul(x = var_4713_cast_fp16_0, y = var_4715_cast_fp16)[name = tensor("input_523_cast_fp16")]; + tensor var_4719 = const()[name = tensor("op_4719"), val = tensor([1, 1])]; + tensor var_4721 = const()[name = tensor("op_4721"), val = tensor([1, 1])]; + tensor var_4723_pad_type_0 = const()[name = tensor("op_4723_pad_type_0"), val = tensor("custom")]; + tensor var_4723_pad_0 = const()[name = tensor("op_4723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216967808))), lut = tensor([-0x1.bdcp-4, -0x1.024p-5, 0x1.04cp-5, 0x1.bep-4]), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217070272)))]; + tensor var_4723_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4721, groups = var_3945, pad = var_4723_pad_0, pad_type = var_4723_pad_type_0, strides = var_4719, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_523_cast_fp16)[name = tensor("op_4723_cast_fp16")]; + tensor hidden_states_333_cast_fp16 = add(x = var_4723_cast_fp16, y = inputs_cast_fp16)[name = tensor("hidden_states_333_cast_fp16")]; + tensor var_4725 = const()[name = tensor("op_4725"), val = tensor([2, 320, 64, 64])]; + tensor input_525_cast_fp16 = reshape(shape = var_4725, x = hidden_states_333_cast_fp16)[name = tensor("input_525_cast_fp16")]; + tensor var_4729 = const()[name = tensor("op_4729"), val = tensor([1, 1])]; + tensor var_4731 = const()[name = tensor("op_4731"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217070976))), lut = tensor([-0x1.0ep-3, -0x1.49p-5, 0x1.11p-5, 0x1.fc8p-4]), name = tensor("up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217096640)))]; + tensor hidden_states_cast_fp16 = conv(bias = up_blocks_3_attentions_2_proj_out_bias_to_fp16, dilations = var_4731, groups = var_3945, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_4729, weight = up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized, x = input_525_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor input_527_cast_fp16 = add(x = hidden_states_cast_fp16, y = hidden_states_323_cast_fp16)[name = tensor("input_527_cast_fp16")]; + tensor reshape_240_shape_0 = const()[name = tensor("reshape_240_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_240_cast_fp16 = reshape(shape = reshape_240_shape_0, x = input_527_cast_fp16)[name = tensor("reshape_240_cast_fp16")]; + tensor reduce_mean_180_axes_0 = const()[name = tensor("reduce_mean_180_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_180_keep_dims_0 = const()[name = tensor("reduce_mean_180_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_180_cast_fp16 = reduce_mean(axes = reduce_mean_180_axes_0, keep_dims = reduce_mean_180_keep_dims_0, x = reshape_240_cast_fp16)[name = tensor("reduce_mean_180_cast_fp16")]; + tensor sub_120_cast_fp16 = sub(x = reshape_240_cast_fp16, y = reduce_mean_180_cast_fp16)[name = tensor("sub_120_cast_fp16")]; + tensor square_60_cast_fp16 = square(x = sub_120_cast_fp16)[name = tensor("square_60_cast_fp16")]; + tensor reduce_mean_182_axes_0 = const()[name = tensor("reduce_mean_182_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_182_keep_dims_0 = const()[name = tensor("reduce_mean_182_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_182_cast_fp16 = reduce_mean(axes = reduce_mean_182_axes_0, keep_dims = reduce_mean_182_keep_dims_0, x = square_60_cast_fp16)[name = tensor("reduce_mean_182_cast_fp16")]; + tensor add_120_y_0_to_fp16 = const()[name = tensor("add_120_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_120_cast_fp16 = add(x = reduce_mean_182_cast_fp16, y = add_120_y_0_to_fp16)[name = tensor("add_120_cast_fp16")]; + tensor sqrt_60_cast_fp16 = sqrt(x = add_120_cast_fp16)[name = tensor("sqrt_60_cast_fp16")]; + tensor real_div_60_cast_fp16 = real_div(x = sub_120_cast_fp16, y = sqrt_60_cast_fp16)[name = tensor("real_div_60_cast_fp16")]; + tensor reshape_241_shape_0 = const()[name = tensor("reshape_241_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_241_cast_fp16 = reshape(shape = reshape_241_shape_0, x = real_div_60_cast_fp16)[name = tensor("reshape_241_cast_fp16")]; + tensor add_121_gamma_0_to_fp16 = const()[name = tensor("add_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217097344)))]; + tensor add_121_beta_0_to_fp16 = const()[name = tensor("add_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217098048)))]; + tensor add_121_epsilon_0_to_fp16 = const()[name = tensor("add_121_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_121_cast_fp16 = batch_norm(beta = add_121_beta_0_to_fp16, epsilon = add_121_epsilon_0_to_fp16, gamma = add_121_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_241_cast_fp16)[name = tensor("add_121_cast_fp16")]; + tensor input_cast_fp16 = silu(x = add_121_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor var_4745 = const()[name = tensor("op_4745"), val = tensor(1)]; + tensor var_4748 = const()[name = tensor("op_4748"), val = tensor([1, 1])]; + tensor var_4750 = const()[name = tensor("op_4750"), val = tensor([1, 1])]; + tensor var_4752_pad_type_0 = const()[name = tensor("op_4752_pad_type_0"), val = tensor("custom")]; + tensor var_4752_pad_0 = const()[name = tensor("op_4752_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217098752))), lut = tensor([-0x1.7bcp-4, -0x1.544p-7, 0x1.8b4p-6, 0x1.be8p-4]), name = tensor("conv_out_weight_to_fp16_palettized"), shape = tensor([4, 320, 3, 3])]; + tensor conv_out_bias_to_fp16 = const()[name = tensor("conv_out_bias_to_fp16"), val = tensor([-0x1.4b4p-9, 0x1.6f4p-9, 0x1.9ap-12, 0x1.04p-9])]; + tensor var_4752_cast_fp16 = conv(bias = conv_out_bias_to_fp16, dilations = var_4750, groups = var_4745, pad = var_4752_pad_0, pad_type = var_4752_pad_type_0, strides = var_4748, weight = conv_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = tensor("op_4752_cast_fp16")]; + tensor var_4752_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_4752_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_4752_cast_fp16_to_fp32_dtype_0, x = var_4752_cast_fp16)[name = tensor("cast_0")]; + } -> (noise_pred); +} \ No newline at end of file