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Update model.py
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model.py
CHANGED
@@ -1138,7 +1138,6 @@ class StreamMultiDiffusion(nn.Module):
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bootstrap_mask_ = torch.concat([bootstrap_mask, bootstrap_mask], dim=0)
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else:
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bootstrap_mask_ = bootstrap_mask
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print('2222222222222222222222222222222222222', model_pred.shape, bootstrap_mask_)
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model_pred = shift_to_mask_bbox_center(model_pred, bootstrap_mask_)
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x_t_latent = shift_to_mask_bbox_center(x_t_latent, bootstrap_mask)
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@@ -1223,7 +1222,6 @@ class StreamMultiDiffusion(nn.Module):
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self.stock_noise_ = self.stock_noise.repeat_interleave(self.num_layers, dim=0) # (T * p, 77, 768)
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x_0_pred_batch = self.unet_step(latent)
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print('111111111111111111111111111111111')
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latent = x_0_pred_batch[-1:]
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self.x_t_latent_buffer = (
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self.alpha_prod_t_sqrt[1:] * x_0_pred_batch[:-1]
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bootstrap_mask_ = torch.concat([bootstrap_mask, bootstrap_mask], dim=0)
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else:
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bootstrap_mask_ = bootstrap_mask
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model_pred = shift_to_mask_bbox_center(model_pred, bootstrap_mask_)
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x_t_latent = shift_to_mask_bbox_center(x_t_latent, bootstrap_mask)
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self.stock_noise_ = self.stock_noise.repeat_interleave(self.num_layers, dim=0) # (T * p, 77, 768)
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x_0_pred_batch = self.unet_step(latent)
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latent = x_0_pred_batch[-1:]
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self.x_t_latent_buffer = (
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self.alpha_prod_t_sqrt[1:] * x_0_pred_batch[:-1]
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