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| # modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from diffusers.models.lora import LoRALinearLayer | |
| class LoRAAttnProcessor(nn.Module): | |
| r""" | |
| Default processor for performing attention-related computations. | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size=None, | |
| cross_attention_dim=None, | |
| rank=4, | |
| network_alpha=None, | |
| lora_scale=1.0, | |
| ): | |
| super().__init__() | |
| self.rank = rank | |
| self.lora_scale = lora_scale | |
| self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) | |
| self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) | |
| self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) | |
| self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) + self.lora_scale * self.to_q_lora(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| elif attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states) + self.lora_scale * self.to_k_lora(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) + self.lora_scale * self.to_v_lora(encoder_hidden_states) | |
| query = attn.head_to_batch_dim(query) | |
| key = attn.head_to_batch_dim(key) | |
| value = attn.head_to_batch_dim(value) | |
| attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
| hidden_states = torch.bmm(attention_probs, value) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) + self.lora_scale * self.to_out_lora(hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class LoRAIPAttnProcessor(nn.Module): | |
| r""" | |
| Attention processor for IP-Adapater. | |
| Args: | |
| hidden_size (`int`): | |
| The hidden size of the attention layer. | |
| cross_attention_dim (`int`): | |
| The number of channels in the `encoder_hidden_states`. | |
| scale (`float`, defaults to 1.0): | |
| the weight scale of image prompt. | |
| num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): | |
| The context length of the image features. | |
| """ | |
| def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None, lora_scale=1.0, scale=1.0, num_tokens=4): | |
| super().__init__() | |
| self.rank = rank | |
| self.lora_scale = lora_scale | |
| self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) | |
| self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) | |
| self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) | |
| self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) | |
| self.hidden_size = hidden_size | |
| self.cross_attention_dim = cross_attention_dim | |
| self.scale = scale | |
| self.num_tokens = num_tokens | |
| self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) + self.lora_scale * self.to_q_lora(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| else: | |
| # get encoder_hidden_states, ip_hidden_states | |
| end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
| encoder_hidden_states, ip_hidden_states = ( | |
| encoder_hidden_states[:, :end_pos, :], | |
| encoder_hidden_states[:, end_pos:, :], | |
| ) | |
| if attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states) + self.lora_scale * self.to_k_lora(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) + self.lora_scale * self.to_v_lora(encoder_hidden_states) | |
| query = attn.head_to_batch_dim(query) | |
| key = attn.head_to_batch_dim(key) | |
| value = attn.head_to_batch_dim(value) | |
| attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
| hidden_states = torch.bmm(attention_probs, value) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| # for ip-adapter | |
| ip_key = self.to_k_ip(ip_hidden_states) | |
| ip_value = self.to_v_ip(ip_hidden_states) | |
| ip_key = attn.head_to_batch_dim(ip_key) | |
| ip_value = attn.head_to_batch_dim(ip_value) | |
| ip_attention_probs = attn.get_attention_scores(query, ip_key, None) | |
| self.attn_map = ip_attention_probs | |
| ip_hidden_states = torch.bmm(ip_attention_probs, ip_value) | |
| ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states) | |
| hidden_states = hidden_states + self.scale * ip_hidden_states | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) + self.lora_scale * self.to_out_lora(hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class LoRAAttnProcessor2_0(nn.Module): | |
| r""" | |
| Default processor for performing attention-related computations. | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size=None, | |
| cross_attention_dim=None, | |
| rank=4, | |
| network_alpha=None, | |
| lora_scale=1.0, | |
| ): | |
| super().__init__() | |
| self.rank = rank | |
| self.lora_scale = lora_scale | |
| self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) | |
| self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) | |
| self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) | |
| self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) + self.lora_scale * self.to_q_lora(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| elif attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states) + self.lora_scale * self.to_k_lora(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) + self.lora_scale * self.to_v_lora(encoder_hidden_states) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| # TODO: add support for attn.scale when we move to Torch 2.1 | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) + self.lora_scale * self.to_out_lora(hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class LoRAIPAttnProcessor2_0(nn.Module): | |
| r""" | |
| Processor for implementing the LoRA attention mechanism. | |
| Args: | |
| hidden_size (`int`, *optional*): | |
| The hidden size of the attention layer. | |
| cross_attention_dim (`int`, *optional*): | |
| The number of channels in the `encoder_hidden_states`. | |
| rank (`int`, defaults to 4): | |
| The dimension of the LoRA update matrices. | |
| network_alpha (`int`, *optional*): | |
| Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs. | |
| """ | |
| def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None, lora_scale=1.0, scale=1.0, num_tokens=4): | |
| super().__init__() | |
| self.rank = rank | |
| self.lora_scale = lora_scale | |
| self.num_tokens = num_tokens | |
| self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) | |
| self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) | |
| self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) | |
| self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) | |
| self.hidden_size = hidden_size | |
| self.cross_attention_dim = cross_attention_dim | |
| self.scale = scale | |
| self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| def __call__( | |
| self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None | |
| ): | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) + self.lora_scale * self.to_q_lora(hidden_states) | |
| #query = attn.head_to_batch_dim(query) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| else: | |
| # get encoder_hidden_states, ip_hidden_states | |
| end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
| encoder_hidden_states, ip_hidden_states = ( | |
| encoder_hidden_states[:, :end_pos, :], | |
| encoder_hidden_states[:, end_pos:, :], | |
| ) | |
| if attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| # for text | |
| key = attn.to_k(encoder_hidden_states) + self.lora_scale * self.to_k_lora(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) + self.lora_scale * self.to_v_lora(encoder_hidden_states) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| # TODO: add support for attn.scale when we move to Torch 2.1 | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # for ip | |
| ip_key = self.to_k_ip(ip_hidden_states) | |
| ip_value = self.to_v_ip(ip_hidden_states) | |
| ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| # TODO: add support for attn.scale when we move to Torch 2.1 | |
| ip_hidden_states = F.scaled_dot_product_attention( | |
| query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False | |
| ) | |
| ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| ip_hidden_states = ip_hidden_states.to(query.dtype) | |
| hidden_states = hidden_states + self.scale * ip_hidden_states | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) + self.lora_scale * self.to_out_lora(hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |