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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 | |
| NUM_ZERO = 0 | |
| ORTHO = False | |
| ORTHO_v2 = False | |
| class AttnProcessor(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| id_embedding=None, | |
| id_scale=1.0, | |
| ): | |
| 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) | |
| 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) | |
| value = attn.to_v(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) | |
| # 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 IDAttnProcessor(nn.Module): | |
| r""" | |
| Attention processor for ID-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. | |
| """ | |
| def __init__(self, hidden_size, cross_attention_dim=None): | |
| super().__init__() | |
| self.id_to_k = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| self.id_to_v = 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, | |
| id_embedding=None, | |
| id_scale=1.0, | |
| ): | |
| 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) | |
| 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) | |
| value = attn.to_v(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 id-adapter | |
| if id_embedding is not None: | |
| if NUM_ZERO == 0: | |
| id_key = self.id_to_k(id_embedding) | |
| id_value = self.id_to_v(id_embedding) | |
| else: | |
| zero_tensor = torch.zeros( | |
| (id_embedding.size(0), NUM_ZERO, id_embedding.size(-1)), | |
| dtype=id_embedding.dtype, | |
| device=id_embedding.device, | |
| ) | |
| id_key = self.id_to_k(torch.cat((id_embedding, zero_tensor), dim=1)) | |
| id_value = self.id_to_v(torch.cat((id_embedding, zero_tensor), dim=1)) | |
| id_key = attn.head_to_batch_dim(id_key).to(query.dtype) | |
| id_value = attn.head_to_batch_dim(id_value).to(query.dtype) | |
| id_attention_probs = attn.get_attention_scores(query, id_key, None) | |
| id_hidden_states = torch.bmm(id_attention_probs, id_value) | |
| id_hidden_states = attn.batch_to_head_dim(id_hidden_states) | |
| if not ORTHO: | |
| hidden_states = hidden_states + id_scale * id_hidden_states | |
| else: | |
| projection = ( | |
| torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True) | |
| / torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True) | |
| * hidden_states | |
| ) | |
| orthogonal = id_hidden_states - projection | |
| hidden_states = hidden_states + id_scale * orthogonal | |
| # linear proj | |
| hidden_states = attn.to_out[0](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 AttnProcessor2_0(nn.Module): | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__(self): | |
| super().__init__() | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| id_embedding=None, | |
| id_scale=1.0, | |
| ): | |
| 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 | |
| ) | |
| if attention_mask is not None: | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| # scaled_dot_product_attention expects attention_mask shape to be | |
| # (batch, heads, source_length, target_length) | |
| attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) | |
| 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) | |
| 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) | |
| value = attn.to_v(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) | |
| 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) | |
| # 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 IDAttnProcessor2_0(torch.nn.Module): | |
| r""" | |
| Attention processor for ID-Adapater for PyTorch 2.0. | |
| Args: | |
| hidden_size (`int`): | |
| The hidden size of the attention layer. | |
| cross_attention_dim (`int`): | |
| The number of channels in the `encoder_hidden_states`. | |
| """ | |
| def __init__(self, hidden_size, cross_attention_dim=None): | |
| super().__init__() | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") | |
| self.id_to_k = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| self.id_to_v = 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, | |
| id_embedding=None, | |
| id_scale=1.0, | |
| ): | |
| 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 | |
| ) | |
| if attention_mask is not None: | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| # scaled_dot_product_attention expects attention_mask shape to be | |
| # (batch, heads, source_length, target_length) | |
| attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) | |
| 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) | |
| 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) | |
| value = attn.to_v(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) | |
| 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 id embedding | |
| if id_embedding is not None: | |
| if NUM_ZERO == 0: | |
| id_key = self.id_to_k(id_embedding).to(query.dtype) | |
| id_value = self.id_to_v(id_embedding).to(query.dtype) | |
| else: | |
| zero_tensor = torch.zeros( | |
| (id_embedding.size(0), NUM_ZERO, id_embedding.size(-1)), | |
| dtype=id_embedding.dtype, | |
| device=id_embedding.device, | |
| ) | |
| id_key = self.id_to_k(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype) | |
| id_value = self.id_to_v(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype) | |
| id_key = id_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| id_value = id_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| id_hidden_states = F.scaled_dot_product_attention( | |
| query, id_key, id_value, attn_mask=None, dropout_p=0.0, is_causal=False | |
| ) | |
| id_hidden_states = id_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| id_hidden_states = id_hidden_states.to(query.dtype) | |
| if not ORTHO and not ORTHO_v2: | |
| hidden_states = hidden_states + id_scale * id_hidden_states | |
| elif ORTHO_v2: | |
| orig_dtype = hidden_states.dtype | |
| hidden_states = hidden_states.to(torch.float32) | |
| id_hidden_states = id_hidden_states.to(torch.float32) | |
| attn_map = query @ id_key.transpose(-2, -1) | |
| attn_mean = attn_map.softmax(dim=-1).mean(dim=1) | |
| attn_mean = attn_mean[:, :, :5].sum(dim=-1, keepdim=True) | |
| projection = ( | |
| torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True) | |
| / torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True) | |
| * hidden_states | |
| ) | |
| orthogonal = id_hidden_states + (attn_mean - 1) * projection | |
| hidden_states = hidden_states + id_scale * orthogonal | |
| hidden_states = hidden_states.to(orig_dtype) | |
| else: | |
| orig_dtype = hidden_states.dtype | |
| hidden_states = hidden_states.to(torch.float32) | |
| id_hidden_states = id_hidden_states.to(torch.float32) | |
| projection = ( | |
| torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True) | |
| / torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True) | |
| * hidden_states | |
| ) | |
| orthogonal = id_hidden_states - projection | |
| hidden_states = hidden_states + id_scale * orthogonal | |
| hidden_states = hidden_states.to(orig_dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](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 | |