from dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.loaders import PeftAdapterMixin from diffusers.models.modeling_utils import ModelMixin from diffusers.models.attention_processor import AttentionProcessor from diffusers.utils import ( USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers, ) from diffusers.models.controlnet import BaseOutput, zero_module from diffusers.models.embeddings import ( CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, ) from diffusers.models.modeling_outputs import Transformer2DModelOutput from diffusers.models.embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed from diffusers.models.transformers.transformer_flux import FluxTransformerBlock, FluxSingleTransformerBlock logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class MultiLayerAdapterOutput(BaseOutput): adapter_block_samples: Tuple[torch.Tensor] adapter_single_block_samples: Tuple[torch.Tensor] class MultiLayerAdapter(ModelMixin, ConfigMixin, PeftAdapterMixin): _supports_gradient_checkpointing = True @register_to_config def __init__( self, patch_size: int = 1, in_channels: int = 64, num_layers: int = 19, num_single_layers: int = 38, attention_head_dim: int = 128, num_attention_heads: int = 24, joint_attention_dim: int = 4096, pooled_projection_dim: int = 768, guidance_embeds: bool = False, axes_dims_rope: List[int] = [16, 56, 56], extra_condition_channels: int = 1 * 4, ): super().__init__() self.out_channels = in_channels self.inner_dim = num_attention_heads * attention_head_dim self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope) text_time_guidance_cls = ( CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings ) self.time_text_embed = text_time_guidance_cls( embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim ) self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim) self.x_embedder = nn.Linear(in_channels, self.inner_dim) self.transformer_blocks = nn.ModuleList( [ FluxTransformerBlock( dim=self.inner_dim, num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim, ) for _ in range(num_layers) ] ) self.single_transformer_blocks = nn.ModuleList( [ FluxSingleTransformerBlock( dim=self.inner_dim, num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim, ) for _ in range(num_single_layers) ] ) self.controlnet_blocks = nn.ModuleList([]) for _ in range(len(self.transformer_blocks)): self.controlnet_blocks.append( zero_module(nn.Linear(self.inner_dim, self.inner_dim)) ) self.controlnet_single_blocks = nn.ModuleList([]) for _ in range(len(self.single_transformer_blocks)): self.controlnet_single_blocks.append( zero_module(nn.Linear(self.inner_dim, self.inner_dim)) ) self.controlnet_x_embedder = zero_module( torch.nn.Linear(in_channels + extra_condition_channels, self.inner_dim) ) self.gradient_checkpointing = False @property # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors def attn_processors(self): r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): if hasattr(module, "get_processor"): processors[f"{name}.processor"] = module.get_processor() for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor def set_attn_processor(self, processor): r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor for **all** `Attention` layers. If `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors. """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) else: module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) def _set_gradient_checkpointing(self, module, value=False): if hasattr(module, "gradient_checkpointing"): module.gradient_checkpointing = value @classmethod def from_transformer( cls, transformer, num_layers: int = 4, num_single_layers: int = 10, attention_head_dim: int = 128, num_attention_heads: int = 24, load_weights_from_transformer=True, ): config = transformer.config config["num_layers"] = num_layers config["num_single_layers"] = num_single_layers config["attention_head_dim"] = attention_head_dim config["num_attention_heads"] = num_attention_heads adapter = cls(**config) if load_weights_from_transformer: adapter.pos_embed.load_state_dict(transformer.pos_embed.state_dict()) adapter.time_text_embed.load_state_dict( transformer.time_text_embed.state_dict() ) adapter.context_embedder.load_state_dict( transformer.context_embedder.state_dict() ) adapter.x_embedder.load_state_dict(transformer.x_embedder.state_dict()) adapter.transformer_blocks.load_state_dict( transformer.transformer_blocks.state_dict(), strict=False ) adapter.single_transformer_blocks.load_state_dict( transformer.single_transformer_blocks.state_dict(), strict=False ) adapter.controlnet_x_embedder = zero_module( adapter.controlnet_x_embedder ) return adapter def crop_each_layer(self, hidden_states, list_layer_box): """ hidden_states: [1, n_layers, h, w, inner_dim] list_layer_box: List, length=n_layers, each element is a Tuple of 4 elements (x1, y1, x2, y2) """ token_list = [] for layer_idx in range(hidden_states.shape[1]): if list_layer_box[layer_idx] == None: continue else: x1, y1, x2, y2 = list_layer_box[layer_idx] x1, y1, x2, y2 = x1 // 16, y1 // 16, x2 // 16, y2 // 16 layer_token = hidden_states[:, layer_idx, y1:y2, x1:x2, :] bs, h, w, c = layer_token.shape layer_token = layer_token.reshape(bs, -1, c) token_list.append(layer_token) result = torch.cat(token_list, dim=1) return result def set_layerPE(self, layerPE, max_layer_num): self.layer_pe = layerPE self.max_layer_num = max_layer_num def forward( self, hidden_states: torch.Tensor, list_layer_box: List[Tuple] = None, adapter_cond: torch.Tensor = None, conditioning_scale: float = 1.0, encoder_hidden_states: torch.Tensor = None, pooled_projections: torch.Tensor = None, timestep: torch.LongTensor = None, img_ids: torch.Tensor = None, txt_ids: torch.Tensor = None, guidance: torch.Tensor = None, joint_attention_kwargs: Optional[Dict[str, Any]] = None, return_dict: bool = True, ) -> Union[torch.FloatTensor, Transformer2DModelOutput]: """ The [`FluxTransformer2DModel`] forward method. Args: hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input `hidden_states`. encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`): Conditional embeddings (embeddings computed from the input conditions such as prompts) to use. pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected from the embeddings of input conditions. timestep ( `torch.LongTensor`): Used to indicate denoising step. joint_attention_kwargs (`dict`, *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). return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] 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. """ if joint_attention_kwargs is not None: joint_attention_kwargs = joint_attention_kwargs.copy() lora_scale = joint_attention_kwargs.pop("scale", 1.0) else: lora_scale = 1.0 if USE_PEFT_BACKEND: # weight the lora layers by setting `lora_scale` for each PEFT layer scale_lora_layers(self, lora_scale) else: if ( joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None ): logger.warning( "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective." ) bs, n_layers, channel_latent, height, width = hidden_states.shape # [bs, n_layers, c_latent, h, w] hidden_states = hidden_states.view(bs, n_layers, channel_latent, height // 2, 2, width // 2, 2) # [bs, n_layers, c_latent, h/2, 2, w/2, 2] hidden_states = hidden_states.permute(0, 1, 3, 5, 2, 4, 6) # [bs, n_layers, h/2, w/2, c_latent, 2, 2] hidden_states = hidden_states.reshape(bs, n_layers, height // 2, width // 2, channel_latent * 4) # [bs, n_layers, h/2, w/2, c_latent*4] hidden_states = self.x_embedder(hidden_states) adapter_cond = adapter_cond.view(1, height // 2, width // 2, channel_latent * 4 + 4) adapter_cond = adapter_cond.unsqueeze(1).expand(-1, n_layers, -1, -1, -1) # [1, n_layer, 32, 32, 68] # add condition hidden_states = hidden_states + self.controlnet_x_embedder(adapter_cond) full_hidden_states = torch.zeros_like(hidden_states) # [bs, n_layers, h/2, w/2, inner_dim] layer_pe = self.layer_pe.view(1, self.max_layer_num, 1, 1, self.inner_dim) # [1, max_n_layers, 1, 1, inner_dim] hidden_states = hidden_states + layer_pe[:, :n_layers] # [bs, n_layers, h/2, w/2, inner_dim] + [1, n_layers, 1, 1, inner_dim] --> [bs, f, h/2, w/2, inner_dim] hidden_states = self.crop_each_layer(hidden_states, list_layer_box) # [bs, token_len, inner_dim] timestep = timestep.to(hidden_states.dtype) * 1000 if guidance is not None: guidance = guidance.to(hidden_states.dtype) * 1000 else: guidance = None temb = ( self.time_text_embed(timestep, pooled_projections) if guidance is None else self.time_text_embed(timestep, guidance, pooled_projections) ) encoder_hidden_states = self.context_embedder(encoder_hidden_states) if txt_ids.ndim == 3: logger.warning( "Passing `txt_ids` 3d torch.Tensor is deprecated." "Please remove the batch dimension and pass it as a 2d torch Tensor" ) txt_ids = txt_ids[0] if img_ids.ndim == 3: logger.warning( "Passing `img_ids` 3d torch.Tensor is deprecated." "Please remove the batch dimension and pass it as a 2d torch Tensor" ) img_ids = img_ids[0] ids = torch.cat((txt_ids, img_ids), dim=0) image_rotary_emb = self.pos_embed(ids) block_samples = () for _, block in enumerate(self.transformer_blocks): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = ( {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} ) ( encoder_hidden_states, hidden_states, ) = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, encoder_hidden_states, temb, image_rotary_emb, **ckpt_kwargs, ) else: encoder_hidden_states, hidden_states = block( hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb, image_rotary_emb=image_rotary_emb, ) block_samples = block_samples + (hidden_states,) hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1) single_block_samples = () for _, block in enumerate(self.single_transformer_blocks): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = ( {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, temb, image_rotary_emb, **ckpt_kwargs, ) else: hidden_states = block( hidden_states=hidden_states, temb=temb, image_rotary_emb=image_rotary_emb, ) single_block_samples = single_block_samples + ( hidden_states[:, encoder_hidden_states.shape[1] :], ) adapter_block_samples = () for block_sample, adapter_block in zip( block_samples, self.controlnet_blocks ): block_sample = adapter_block(block_sample) adapter_block_samples = adapter_block_samples + (block_sample,) adapter_single_block_samples = () for single_block_sample, adapter_block in zip( single_block_samples, self.controlnet_single_blocks ): single_block_sample = adapter_block(single_block_sample) adapter_single_block_samples = adapter_single_block_samples + ( single_block_sample, ) # scaling adapter_block_samples = [ sample * conditioning_scale for sample in adapter_block_samples ] adapter_single_block_samples = [ sample * conditioning_scale for sample in adapter_single_block_samples ] # adapter_block_samples = ( None if len(adapter_block_samples) == 0 else adapter_block_samples ) adapter_single_block_samples = ( None if len(adapter_single_block_samples) == 0 else adapter_single_block_samples ) if USE_PEFT_BACKEND: # remove `lora_scale` from each PEFT layer unscale_lora_layers(self, lora_scale) if not return_dict: return (adapter_block_samples, adapter_single_block_samples) return MultiLayerAdapterOutput( adapter_block_samples=adapter_block_samples, adapter_single_block_samples=adapter_single_block_samples, )