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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 | |
| class AdaLayerNorm(nn.Module): | |
| def __init__(self, embedding_dim: int, time_embedding_dim: int = None): | |
| super().__init__() | |
| if time_embedding_dim is None: | |
| time_embedding_dim = embedding_dim | |
| self.silu = nn.SiLU() | |
| self.linear = nn.Linear(time_embedding_dim, 2 * embedding_dim, bias=True) | |
| nn.init.zeros_(self.linear.weight) | |
| nn.init.zeros_(self.linear.bias) | |
| self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6) | |
| def forward( | |
| self, x: torch.Tensor, timestep_embedding: torch.Tensor | |
| ): | |
| emb = self.linear(self.silu(timestep_embedding)) | |
| shift, scale = emb.view(len(x), 1, -1).chunk(2, dim=-1) | |
| x = self.norm(x) * (1 + scale) + shift | |
| return x | |
| class AttnProcessor(nn.Module): | |
| r""" | |
| Default processor for performing attention-related computations. | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size=None, | |
| cross_attention_dim=None, | |
| ): | |
| super().__init__() | |
| 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) | |
| 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 IPAttnProcessor(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, scale=1.0, num_tokens=4): | |
| super().__init__() | |
| 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) | |
| 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) | |
| 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 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) | |
| 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) | |
| # 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 TA_IPAttnProcessor(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, time_embedding_dim: int = None, scale=1.0, num_tokens=4): | |
| super().__init__() | |
| 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) | |
| self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim) | |
| self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim) | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." | |
| 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 | |
| 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) | |
| 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 ip-adapter | |
| ip_key = self.to_k_ip(ip_hidden_states) | |
| ip_value = self.to_v_ip(ip_hidden_states) | |
| # time-dependent adaLN | |
| ip_key = self.ln_k_ip(ip_key, temb) | |
| ip_value = self.ln_v_ip(ip_value, temb) | |
| 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) | |
| 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) | |
| # 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(torch.nn.Module): | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size=None, | |
| 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.") | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| external_kv=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 | |
| ) | |
| 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) | |
| if external_kv: | |
| key = torch.cat([key, external_kv.k], axis=1) | |
| value = torch.cat([value, external_kv.v], axis=1) | |
| 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) | |
| # 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 split_AttnProcessor2_0(torch.nn.Module): | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size=None, | |
| cross_attention_dim=None, | |
| time_embedding_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.") | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| external_kv=None, | |
| temb=None, | |
| cat_dim=-2, | |
| original_shape=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: | |
| # 2d to sequence. | |
| height, width = hidden_states.shape[-2:] | |
| if cat_dim==-2 or cat_dim==2: | |
| hidden_states_0 = hidden_states[:, :, :height//2, :] | |
| hidden_states_1 = hidden_states[:, :, -(height//2):, :] | |
| elif cat_dim==-1 or cat_dim==3: | |
| hidden_states_0 = hidden_states[:, :, :, :width//2] | |
| hidden_states_1 = hidden_states[:, :, :, -(width//2):] | |
| batch_size, channel, height, width = hidden_states_0.shape | |
| hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2) | |
| hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2) | |
| else: | |
| # directly split sqeuence according to concat dim. | |
| single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1] | |
| hidden_states_0 = hidden_states[:, :single_dim*single_dim,:] | |
| hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:] | |
| hidden_states = torch.cat([hidden_states_0, hidden_states_1], dim=1) | |
| 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) | |
| key = attn.to_k(hidden_states) | |
| value = attn.to_v(hidden_states) | |
| if external_kv: | |
| key = torch.cat([key, external_kv.k], dim=1) | |
| value = torch.cat([value, external_kv.v], dim=1) | |
| 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) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| # spatially split. | |
| hidden_states_0, hidden_states_1 = hidden_states.chunk(2, dim=1) | |
| if input_ndim == 4: | |
| hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if cat_dim==-2 or cat_dim==2: | |
| hidden_states_pad = torch.zeros(batch_size, channel, 1, width) | |
| elif cat_dim==-1 or cat_dim==3: | |
| hidden_states_pad = torch.zeros(batch_size, channel, height, 1) | |
| hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) | |
| hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim) | |
| assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" | |
| else: | |
| batch_size, sequence_length, inner_dim = hidden_states.shape | |
| hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim) | |
| hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) | |
| hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1) | |
| assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class sep_split_AttnProcessor2_0(torch.nn.Module): | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size=None, | |
| cross_attention_dim=None, | |
| time_embedding_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.ln_k_ref = AdaLayerNorm(hidden_size, time_embedding_dim) | |
| self.ln_v_ref = AdaLayerNorm(hidden_size, time_embedding_dim) | |
| # self.hidden_size = hidden_size | |
| # self.cross_attention_dim = cross_attention_dim | |
| # self.scale = scale | |
| # self.num_tokens = num_tokens | |
| # self.to_q_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| # self.to_k_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| # self.to_v_ref = 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, | |
| external_kv=None, | |
| temb=None, | |
| cat_dim=-2, | |
| original_shape=None, | |
| ref_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: | |
| # 2d to sequence. | |
| height, width = hidden_states.shape[-2:] | |
| if cat_dim==-2 or cat_dim==2: | |
| hidden_states_0 = hidden_states[:, :, :height//2, :] | |
| hidden_states_1 = hidden_states[:, :, -(height//2):, :] | |
| elif cat_dim==-1 or cat_dim==3: | |
| hidden_states_0 = hidden_states[:, :, :, :width//2] | |
| hidden_states_1 = hidden_states[:, :, :, -(width//2):] | |
| batch_size, channel, height, width = hidden_states_0.shape | |
| hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2) | |
| hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2) | |
| else: | |
| # directly split sqeuence according to concat dim. | |
| single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1] | |
| hidden_states_0 = hidden_states[:, :single_dim*single_dim,:] | |
| hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:] | |
| batch_size, sequence_length, _ = ( | |
| hidden_states_0.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_0 = attn.group_norm(hidden_states_0.transpose(1, 2)).transpose(1, 2) | |
| hidden_states_1 = attn.group_norm(hidden_states_1.transpose(1, 2)).transpose(1, 2) | |
| query_0 = attn.to_q(hidden_states_0) | |
| query_1 = attn.to_q(hidden_states_1) | |
| key_0 = attn.to_k(hidden_states_0) | |
| key_1 = attn.to_k(hidden_states_1) | |
| value_0 = attn.to_v(hidden_states_0) | |
| value_1 = attn.to_v(hidden_states_1) | |
| # time-dependent adaLN | |
| key_1 = self.ln_k_ref(key_1, temb) | |
| value_1 = self.ln_v_ref(value_1, temb) | |
| if external_kv: | |
| key_1 = torch.cat([key_1, external_kv.k], dim=1) | |
| value_1 = torch.cat([value_1, external_kv.v], dim=1) | |
| inner_dim = key_0.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| query_0 = query_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| query_1 = query_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key_0 = key_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key_1 = key_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value_0 = value_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value_1 = value_1.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_0 = F.scaled_dot_product_attention( | |
| query_0, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| hidden_states_1 = F.scaled_dot_product_attention( | |
| query_1, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| # cross-attn | |
| _hidden_states_0 = F.scaled_dot_product_attention( | |
| query_0, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| hidden_states_0 = hidden_states_0 + ref_scale * _hidden_states_0 * 10 | |
| # TODO: drop this cross-attn | |
| _hidden_states_1 = F.scaled_dot_product_attention( | |
| query_1, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| hidden_states_1 = hidden_states_1 + ref_scale * _hidden_states_1 | |
| hidden_states_0 = hidden_states_0.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| hidden_states_1 = hidden_states_1.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| hidden_states_0 = hidden_states_0.to(query_0.dtype) | |
| hidden_states_1 = hidden_states_1.to(query_1.dtype) | |
| # linear proj | |
| hidden_states_0 = attn.to_out[0](hidden_states_0) | |
| hidden_states_1 = attn.to_out[0](hidden_states_1) | |
| # dropout | |
| hidden_states_0 = attn.to_out[1](hidden_states_0) | |
| hidden_states_1 = attn.to_out[1](hidden_states_1) | |
| if input_ndim == 4: | |
| hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if cat_dim==-2 or cat_dim==2: | |
| hidden_states_pad = torch.zeros(batch_size, channel, 1, width) | |
| elif cat_dim==-1 or cat_dim==3: | |
| hidden_states_pad = torch.zeros(batch_size, channel, height, 1) | |
| hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) | |
| hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim) | |
| assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" | |
| else: | |
| batch_size, sequence_length, inner_dim = hidden_states.shape | |
| hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim) | |
| hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) | |
| hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1) | |
| assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class AdditiveKV_AttnProcessor2_0(torch.nn.Module): | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size: int = None, | |
| cross_attention_dim: int = None, | |
| time_embedding_dim: int = None, | |
| additive_scale: float = 1.0, | |
| ): | |
| 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.additive_scale = additive_scale | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| external_kv=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." | |
| 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) | |
| # 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) | |
| if external_kv: | |
| key = external_kv.k | |
| value = external_kv.v | |
| 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) | |
| external_attn_output = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| hidden_states = hidden_states + self.additive_scale * external_attn_output | |
| 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 TA_AdditiveKV_AttnProcessor2_0(torch.nn.Module): | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size: int = None, | |
| cross_attention_dim: int = None, | |
| time_embedding_dim: int = None, | |
| additive_scale: float = 1.0, | |
| ): | |
| 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.ln_k = AdaLayerNorm(hidden_size, time_embedding_dim) | |
| self.ln_v = AdaLayerNorm(hidden_size, time_embedding_dim) | |
| self.additive_scale = additive_scale | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| external_kv=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." | |
| 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) | |
| # 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) | |
| if external_kv: | |
| key = external_kv.k | |
| value = external_kv.v | |
| # time-dependent adaLN | |
| key = self.ln_k(key, temb) | |
| value = self.ln_v(value, temb) | |
| 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) | |
| external_attn_output = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| hidden_states = hidden_states + self.additive_scale * external_attn_output | |
| 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 IPAttnProcessor2_0(torch.nn.Module): | |
| r""" | |
| Attention processor for IP-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`. | |
| 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, scale=1.0, num_tokens=4): | |
| 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.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) | |
| if isinstance(encoder_hidden_states, tuple): | |
| # FIXME: now hard coded to single image prompt. | |
| batch_size, _, hid_dim = encoder_hidden_states[0].shape | |
| ip_tokens = encoder_hidden_states[1][0] | |
| encoder_hidden_states = torch.cat([encoder_hidden_states[0], ip_tokens], dim=1) | |
| 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 | |
| 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) | |
| 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) | |
| # 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-adapter | |
| 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) | |
| # 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 TA_IPAttnProcessor2_0(torch.nn.Module): | |
| r""" | |
| Attention processor for IP-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`. | |
| 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, time_embedding_dim: int = None, scale=1.0, num_tokens=4): | |
| 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.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) | |
| self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim) | |
| self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim) | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| external_kv=None, | |
| temb=None, | |
| ): | |
| assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." | |
| 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) | |
| if not isinstance(encoder_hidden_states, tuple): | |
| # 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:, :], | |
| ) | |
| else: | |
| # FIXME: now hard coded to single image prompt. | |
| batch_size, _, hid_dim = encoder_hidden_states[0].shape | |
| ip_hidden_states = encoder_hidden_states[1][0] | |
| encoder_hidden_states = encoder_hidden_states[0] | |
| 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 | |
| else: | |
| if 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) | |
| if external_kv: | |
| key = torch.cat([key, external_kv.k], axis=1) | |
| value = torch.cat([value, external_kv.v], axis=1) | |
| 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-adapter | |
| ip_key = self.to_k_ip(ip_hidden_states) | |
| ip_value = self.to_v_ip(ip_hidden_states) | |
| # time-dependent adaLN | |
| ip_key = self.ln_k_ip(ip_key, temb) | |
| ip_value = self.ln_v_ip(ip_value, temb) | |
| 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) | |
| # 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 | |
| ## for controlnet | |
| class CNAttnProcessor: | |
| r""" | |
| Default processor for performing attention-related computations. | |
| """ | |
| def __init__(self, num_tokens=4): | |
| self.num_tokens = num_tokens | |
| 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) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| else: | |
| end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
| encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text | |
| if 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 CNAttnProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__(self, num_tokens=4): | |
| 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.num_tokens = num_tokens | |
| 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 | |
| ) | |
| 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 | |
| else: | |
| end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
| encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text | |
| if 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) | |
| # 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) | |
| # 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 | |
| def init_attn_proc(unet, ip_adapter_tokens=16, use_lcm=True, use_adaln=True, use_external_kv=False): | |
| attn_procs = {} | |
| unet_sd = unet.state_dict() | |
| for name in unet.attn_processors.keys(): | |
| cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim | |
| if name.startswith("mid_block"): | |
| hidden_size = unet.config.block_out_channels[-1] | |
| elif name.startswith("up_blocks"): | |
| block_id = int(name[len("up_blocks.")]) | |
| hidden_size = list(reversed(unet.config.block_out_channels))[block_id] | |
| elif name.startswith("down_blocks"): | |
| block_id = int(name[len("down_blocks.")]) | |
| hidden_size = unet.config.block_out_channels[block_id] | |
| if cross_attention_dim is None: | |
| if use_external_kv: | |
| attn_procs[name] = AdditiveKV_AttnProcessor2_0( | |
| hidden_size=hidden_size, | |
| cross_attention_dim=cross_attention_dim, | |
| time_embedding_dim=1280, | |
| ) if hasattr(F, "scaled_dot_product_attention") else AdditiveKV_AttnProcessor() | |
| else: | |
| attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor() | |
| else: | |
| if use_adaln: | |
| layer_name = name.split(".processor")[0] | |
| if use_lcm: | |
| weights = { | |
| "to_k_ip.weight": unet_sd[layer_name + ".to_k.base_layer.weight"], | |
| "to_v_ip.weight": unet_sd[layer_name + ".to_v.base_layer.weight"], | |
| } | |
| else: | |
| weights = { | |
| "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"], | |
| "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"], | |
| } | |
| attn_procs[name] = TA_IPAttnProcessor2_0( | |
| hidden_size=hidden_size, | |
| cross_attention_dim=cross_attention_dim, | |
| num_tokens=ip_adapter_tokens, | |
| time_embedding_dim=1280, | |
| ) if hasattr(F, "scaled_dot_product_attention") else \ | |
| TA_IPAttnProcessor( | |
| hidden_size=hidden_size, | |
| cross_attention_dim=cross_attention_dim, | |
| num_tokens=ip_adapter_tokens, | |
| time_embedding_dim=1280, | |
| ) | |
| attn_procs[name].load_state_dict(weights, strict=False) | |
| else: | |
| attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor() | |
| return attn_procs | |
| def init_aggregator_attn_proc(unet, use_adaln=False, split_attn=False): | |
| attn_procs = {} | |
| unet_sd = unet.state_dict() | |
| for name in unet.attn_processors.keys(): | |
| # get layer name and hidden dim | |
| cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim | |
| if name.startswith("mid_block"): | |
| hidden_size = unet.config.block_out_channels[-1] | |
| elif name.startswith("up_blocks"): | |
| block_id = int(name[len("up_blocks.")]) | |
| hidden_size = list(reversed(unet.config.block_out_channels))[block_id] | |
| elif name.startswith("down_blocks"): | |
| block_id = int(name[len("down_blocks.")]) | |
| hidden_size = unet.config.block_out_channels[block_id] | |
| # init attn proc | |
| if split_attn: | |
| # layer_name = name.split(".processor")[0] | |
| # weights = { | |
| # "to_q_ref.weight": unet_sd[layer_name + ".to_q.weight"], | |
| # "to_k_ref.weight": unet_sd[layer_name + ".to_k.weight"], | |
| # "to_v_ref.weight": unet_sd[layer_name + ".to_v.weight"], | |
| # } | |
| attn_procs[name] = ( | |
| sep_split_AttnProcessor2_0( | |
| hidden_size=hidden_size, | |
| cross_attention_dim=hidden_size, | |
| time_embedding_dim=1280, | |
| ) | |
| if use_adaln | |
| else split_AttnProcessor2_0( | |
| hidden_size=hidden_size, | |
| cross_attention_dim=cross_attention_dim, | |
| time_embedding_dim=1280, | |
| ) | |
| ) | |
| # attn_procs[name].load_state_dict(weights, strict=False) | |
| else: | |
| attn_procs[name] = ( | |
| AttnProcessor2_0( | |
| hidden_size=hidden_size, | |
| cross_attention_dim=hidden_size, | |
| ) | |
| if hasattr(F, "scaled_dot_product_attention") | |
| else AttnProcessor( | |
| hidden_size=hidden_size, | |
| cross_attention_dim=hidden_size, | |
| ) | |
| ) | |
| return attn_procs | |