from dataclasses import dataclass import torch from torch import Tensor, nn from einops import rearrange from .modules.layers import (DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, SingleStreamBlock, timestep_embedding) @dataclass class FluxParams: in_channels: int vec_in_dim: int context_in_dim: int hidden_size: int mlp_ratio: float num_heads: int depth: int depth_single_blocks: int axes_dim: list[int] theta: int qkv_bias: bool guidance_embed: bool class Flux(nn.Module): """ Transformer model for flow matching on sequences. """ _supports_gradient_checkpointing = True def __init__(self, params: FluxParams): super().__init__() self.params = params self.in_channels = params.in_channels self.out_channels = self.in_channels if params.hidden_size % params.num_heads != 0: raise ValueError( f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" ) pe_dim = params.hidden_size // params.num_heads if sum(params.axes_dim) != pe_dim: raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") self.hidden_size = params.hidden_size self.num_heads = params.num_heads self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) self.guidance_in = ( MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity() ) self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, ) for _ in range(params.depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio) for _ in range(params.depth_single_blocks) ] ) self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) self.gradient_checkpointing = True # False def _set_gradient_checkpointing(self, module, value=False): if hasattr(module, "gradient_checkpointing"): module.gradient_checkpointing = value @property def attn_processors(self): # set recursively processors = {} def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors): if hasattr(module, "set_processor"): processors[f"{name}.processor"] = module.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 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 forward( self, img: Tensor, img_ids: Tensor, txt: Tensor, txt_ids: Tensor, timesteps: Tensor, y: Tensor, # clip block_controlnet_hidden_states=None, guidance: Tensor | None = None, image_proj: Tensor | None = None, ip_scale: Tensor | float = 1.0, use_share_weight_referencenet=False, single_img_ids: Tensor | None = None, single_block_refnet=False, double_block_refnet=False, ) -> Tensor: if single_block_refnet or double_block_refnet: assert use_share_weight_referencenet == True if img.ndim != 3 or txt.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") # running on sequences img img = self.img_in(img) vec = self.time_in(timestep_embedding(timesteps, 256)) # print("vec shape 1:", vec.shape) # print("y shape 1:", y.shape) if self.params.guidance_embed: if guidance is None: raise ValueError("Didn't get guidance strength for guidance distilled model.") vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) # print("vec shape 1.5:", vec.shape) vec = vec + self.vector_in(y) # print("vec shape 2:", vec.shape) txt = self.txt_in(txt) ids = torch.cat((txt_ids, img_ids), dim=1) pe = self.pe_embedder(ids) if use_share_weight_referencenet: # print("In img shape:", img.shape) img_latent_length = img.shape[1] single_ids = torch.cat((txt_ids, single_img_ids), dim=1) single_pe = self.pe_embedder(single_ids) if double_block_refnet and (not single_block_refnet): double_block_pe = pe double_block_img = img single_block_pe = single_pe elif single_block_refnet and (not double_block_refnet): double_block_pe = single_pe double_block_img = img[:, img_latent_length//2:, :] single_block_pe = pe ref_img_latent = img[:, :img_latent_length//2, :] else: print("RefNet only support either double blocks or single blocks. If you want to turn on all blocks for RefNet, please use Spatial Condition.") raise NotImplementedError if block_controlnet_hidden_states is not None: controlnet_depth = len(block_controlnet_hidden_states) for index_block, block in enumerate(self.double_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 if not use_share_weight_referencenet: img, txt = torch.utils.checkpoint.checkpoint( create_custom_forward(block), img, txt, vec, pe, image_proj, ip_scale, use_reentrant=True, ) else: double_block_img, txt = torch.utils.checkpoint.checkpoint( create_custom_forward(block), double_block_img, txt, vec, double_block_pe, image_proj, ip_scale, use_reentrant=True, ) else: if not use_share_weight_referencenet: img, txt = block( img=img, txt=txt, vec=vec, pe=pe, image_proj=image_proj, ip_scale=ip_scale, ) else: double_block_img, txt = block( img=double_block_img, txt=txt, vec=vec, pe=double_block_pe, image_proj=image_proj, ip_scale=ip_scale, ) # controlnet residual if block_controlnet_hidden_states is not None: if not use_share_weight_referencenet: img = img + block_controlnet_hidden_states[index_block % 2] else: double_block_img = double_block_img + block_controlnet_hidden_states[index_block % 2] if use_share_weight_referencenet: mid_img = double_block_img # print("After double blocks img shape:",mid_img.shape) if double_block_refnet and (not single_block_refnet): single_block_img = mid_img[:, img_latent_length//2:, :] elif single_block_refnet and (not double_block_refnet): single_block_img = torch.cat([ref_img_latent, mid_img], dim=1) single_block_img = torch.cat((txt, single_block_img), 1) else: img = torch.cat((txt, img), 1) # print("single block input img shape:", single_block_img.shape) for block in self.single_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 if not use_share_weight_referencenet: img = torch.utils.checkpoint.checkpoint( create_custom_forward(block), img, vec, pe, use_reentrant=True, ) else: single_block_img = torch.utils.checkpoint.checkpoint( create_custom_forward(block), single_block_img, vec, single_block_pe, use_reentrant=True, ) else: if not use_share_weight_referencenet: img = block( img, vec=vec, pe=pe, ) else: single_block_img = block( single_block_img, vec=vec, pe=single_block_pe, ) if use_share_weight_referencenet: out_img = single_block_img if double_block_refnet and (not single_block_refnet): out_img = out_img[:, txt.shape[1]:, ...] elif single_block_refnet and (not double_block_refnet): out_img = out_img[:, txt.shape[1]:, ...] out_img = out_img[:, img_latent_length//2:, :] img = out_img # print("output img shape:", img.shape) else: img = img[:, txt.shape[1] :, ...] img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) return img # In img shape: torch.Size([1, 2048, 3072]) # After double blocks img shape: torch.Size([1, 1024, 3072]) # single block input img shape: torch.Size([1, 2560, 3072]) # output img shape: torch.Size([1, 1024, 3072]) # # In img shape: torch.Size([1, 2048, 3072]) # After double blocks img shape: torch.Size([1, 2048, 3072]) [78/1966] # single block input img shape: torch.Size([1, 1536, 3072]) # output img shape: torch.Size([1, 1024, 3072])