# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations from dataclasses import dataclass from typing import Any, Dict, List, Literal, Optional import logging from einops import rearrange import torch import torch.nn.functional as F from torch import nn from diffusers.models.transformer_2d import ( Transformer2DModelOutput, Transformer2DModel as DiffusersTransformer2DModel, ) from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.embeddings import ImagePositionalEmbeddings from diffusers.utils import BaseOutput, deprecate from diffusers.models.attention import ( BasicTransformerBlock as DiffusersBasicTransformerBlock, ) from diffusers.models.embeddings import PatchEmbed from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear from diffusers.models.modeling_utils import ModelMixin from diffusers.utils.constants import USE_PEFT_BACKEND from .attention import BasicTransformerBlock logger = logging.getLogger(__name__) # 本部分 与 diffusers/models/transformer_2d.py 几乎一样 # 更新部分 # 1. 替换自定义 BasicTransformerBlock 类 # 2. 在forward 里增加了 self_attn_block_embs 用于 提取 self_attn 中的emb # this module is same as diffusers/models/transformer_2d.py. The update part is # 1 redefine BasicTransformerBlock # 2. add self_attn_block_embs in forward to extract emb from self_attn class Transformer2DModel(DiffusersTransformer2DModel): """ A 2D Transformer model for image-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): The number of channels in the input and output (specify if the input is **continuous**). num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). This is fixed during training since it is used to learn a number of position embeddings. num_vector_embeds (`int`, *optional*): The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). Includes the class for the masked latent pixel. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. num_embeds_ada_norm ( `int`, *optional*): The number of diffusion steps used during training. Pass if at least one of the norm_layers is `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. attention_bias (`bool`, *optional*): Configure if the `TransformerBlocks` attention should contain a bias parameter. """ @register_to_config def __init__( self, num_attention_heads: int = 16, attention_head_dim: int = 88, in_channels: int | None = None, out_channels: int | None = None, num_layers: int = 1, dropout: float = 0, norm_num_groups: int = 32, cross_attention_dim: int | None = None, attention_bias: bool = False, sample_size: int | None = None, num_vector_embeds: int | None = None, patch_size: int | None = None, activation_fn: str = "geglu", num_embeds_ada_norm: int | None = None, use_linear_projection: bool = False, only_cross_attention: bool = False, double_self_attention: bool = False, upcast_attention: bool = False, norm_type: str = "layer_norm", norm_elementwise_affine: bool = True, attention_type: str = "default", cross_attn_temporal_cond: bool = False, ip_adapter_cross_attn: bool = False, need_t2i_facein: bool = False, need_t2i_ip_adapter_face: bool = False, image_scale: float = 1.0, ): super().__init__( num_attention_heads, attention_head_dim, in_channels, out_channels, num_layers, dropout, norm_num_groups, cross_attention_dim, attention_bias, sample_size, num_vector_embeds, patch_size, activation_fn, num_embeds_ada_norm, use_linear_projection, only_cross_attention, double_self_attention, upcast_attention, norm_type, norm_elementwise_affine, attention_type, ) inner_dim = num_attention_heads * attention_head_dim self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, cross_attention_dim=cross_attention_dim, activation_fn=activation_fn, num_embeds_ada_norm=num_embeds_ada_norm, attention_bias=attention_bias, only_cross_attention=only_cross_attention, double_self_attention=double_self_attention, upcast_attention=upcast_attention, norm_type=norm_type, norm_elementwise_affine=norm_elementwise_affine, attention_type=attention_type, cross_attn_temporal_cond=cross_attn_temporal_cond, ip_adapter_cross_attn=ip_adapter_cross_attn, need_t2i_facein=need_t2i_facein, need_t2i_ip_adapter_face=need_t2i_ip_adapter_face, image_scale=image_scale, ) for d in range(num_layers) ] ) self.num_layers = num_layers self.cross_attn_temporal_cond = cross_attn_temporal_cond self.ip_adapter_cross_attn = ip_adapter_cross_attn self.need_t2i_facein = need_t2i_facein self.need_t2i_ip_adapter_face = need_t2i_ip_adapter_face self.image_scale = image_scale self.print_idx = 0 def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, timestep: Optional[torch.LongTensor] = None, added_cond_kwargs: Dict[str, torch.Tensor] = None, class_labels: Optional[torch.LongTensor] = None, cross_attention_kwargs: Dict[str, Any] = None, attention_mask: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, self_attn_block_embs: Optional[List[torch.Tensor]] = None, self_attn_block_embs_mode: Literal["read", "write"] = "write", return_dict: bool = True, ): """ The [`Transformer2DModel`] forward method. Args: hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): Input `hidden_states`. encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. timestep ( `torch.LongTensor`, *optional*): Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in `AdaLayerZeroNorm`. cross_attention_kwargs ( `Dict[str, Any]`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). attention_mask ( `torch.Tensor`, *optional*): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. encoder_attention_mask ( `torch.Tensor`, *optional*): Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: * Mask `(batch, sequence_length)` True = keep, False = discard. * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format above. This bias will be added to the cross-attention scores. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a `tuple` where the first element is the sample tensor. """ # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. # expects mask of shape: # [batch, key_tokens] # adds singleton query_tokens dimension: # [batch, 1, key_tokens] # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) if attention_mask is not None and attention_mask.ndim == 2: # assume that mask is expressed as: # (1 = keep, 0 = discard) # convert mask into a bias that can be added to attention scores: # (keep = +0, discard = -10000.0) attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # convert encoder_attention_mask to a bias the same way we do for attention_mask if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: encoder_attention_mask = ( 1 - encoder_attention_mask.to(hidden_states.dtype) ) * -10000.0 encoder_attention_mask = encoder_attention_mask.unsqueeze(1) # Retrieve lora scale. lora_scale = ( cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 ) # 1. Input if self.is_input_continuous: batch, _, height, width = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) if not self.use_linear_projection: hidden_states = ( self.proj_in(hidden_states, scale=lora_scale) if not USE_PEFT_BACKEND else self.proj_in(hidden_states) ) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( batch, height * width, inner_dim ) else: inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( batch, height * width, inner_dim ) hidden_states = ( self.proj_in(hidden_states, scale=lora_scale) if not USE_PEFT_BACKEND else self.proj_in(hidden_states) ) elif self.is_input_vectorized: hidden_states = self.latent_image_embedding(hidden_states) elif self.is_input_patches: height, width = ( hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size, ) hidden_states = self.pos_embed(hidden_states) if self.adaln_single is not None: if self.use_additional_conditions and added_cond_kwargs is None: raise ValueError( "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`." ) batch_size = hidden_states.shape[0] timestep, embedded_timestep = self.adaln_single( timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype, ) # 2. Blocks if self.caption_projection is not None: batch_size = hidden_states.shape[0] encoder_hidden_states = self.caption_projection(encoder_hidden_states) encoder_hidden_states = encoder_hidden_states.view( batch_size, -1, hidden_states.shape[-1] ) for block in self.transformer_blocks: if self.training and self.gradient_checkpointing: hidden_states = torch.utils.checkpoint.checkpoint( block, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, timestep, cross_attention_kwargs, class_labels, self_attn_block_embs, self_attn_block_embs_mode, use_reentrant=False, ) else: hidden_states = block( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, timestep=timestep, cross_attention_kwargs=cross_attention_kwargs, class_labels=class_labels, self_attn_block_embs=self_attn_block_embs, self_attn_block_embs_mode=self_attn_block_embs_mode, ) # 将 转换 self_attn_emb的尺寸 if ( self_attn_block_embs is not None and self_attn_block_embs_mode.lower() == "write" ): self_attn_idx = block.spatial_self_attn_idx if self.print_idx == 0: logger.debug( f"self_attn_block_embs, num={len(self_attn_block_embs)}, before, shape={self_attn_block_embs[self_attn_idx].shape}, height={height}, width={width}" ) self_attn_block_embs[self_attn_idx] = rearrange( self_attn_block_embs[self_attn_idx], "bt (h w) c->bt c h w", h=height, w=width, ) if self.print_idx == 0: logger.debug( f"self_attn_block_embs, num={len(self_attn_block_embs)}, after ,shape={self_attn_block_embs[self_attn_idx].shape}, height={height}, width={width}" ) if self.proj_out is None: return hidden_states # 3. Output if self.is_input_continuous: if not self.use_linear_projection: hidden_states = ( hidden_states.reshape(batch, height, width, inner_dim) .permute(0, 3, 1, 2) .contiguous() ) hidden_states = ( self.proj_out(hidden_states, scale=lora_scale) if not USE_PEFT_BACKEND else self.proj_out(hidden_states) ) else: hidden_states = ( self.proj_out(hidden_states, scale=lora_scale) if not USE_PEFT_BACKEND else self.proj_out(hidden_states) ) hidden_states = ( hidden_states.reshape(batch, height, width, inner_dim) .permute(0, 3, 1, 2) .contiguous() ) output = hidden_states + residual elif self.is_input_vectorized: hidden_states = self.norm_out(hidden_states) logits = self.out(hidden_states) # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) logits = logits.permute(0, 2, 1) # log(p(x_0)) output = F.log_softmax(logits.double(), dim=1).float() if self.is_input_patches: if self.config.norm_type != "ada_norm_single": conditioning = self.transformer_blocks[0].norm1.emb( timestep, class_labels, hidden_dtype=hidden_states.dtype ) shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) hidden_states = ( self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] ) hidden_states = self.proj_out_2(hidden_states) elif self.config.norm_type == "ada_norm_single": shift, scale = ( self.scale_shift_table[None] + embedded_timestep[:, None] ).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) # Modulation hidden_states = hidden_states * (1 + scale) + shift hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states.squeeze(1) # unpatchify if self.adaln_single is None: height = width = int(hidden_states.shape[1] ** 0.5) hidden_states = hidden_states.reshape( shape=( -1, height, width, self.patch_size, self.patch_size, self.out_channels, ) ) hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( shape=( -1, self.out_channels, height * self.patch_size, width * self.patch_size, ) ) self.print_idx += 1 if not return_dict: return (output,) return Transformer2DModelOutput(sample=output)