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 def zero_module(module): for p in module.parameters(): nn.init.zeros_(p) return module class ControlNetFlux(nn.Module): """ Transformer model for flow matching on sequences. """ _supports_gradient_checkpointing = True def __init__(self, params: FluxParams, controlnet_depth=2): 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(controlnet_depth) ] ) # add ControlNet blocks self.controlnet_blocks = nn.ModuleList([]) for _ in range(controlnet_depth): controlnet_block = nn.Linear(self.hidden_size, self.hidden_size) controlnet_block = zero_module(controlnet_block) self.controlnet_blocks.append(controlnet_block) self.pos_embed_input = nn.Linear(self.in_channels, self.hidden_size, bias=True) self.gradient_checkpointing = False self.input_hint_block = nn.Sequential( nn.Conv2d(3, 16, 3, padding=1), nn.SiLU(), nn.Conv2d(16, 16, 3, padding=1), nn.SiLU(), nn.Conv2d(16, 16, 3, padding=1, stride=2), nn.SiLU(), nn.Conv2d(16, 16, 3, padding=1), nn.SiLU(), nn.Conv2d(16, 16, 3, padding=1, stride=2), nn.SiLU(), nn.Conv2d(16, 16, 3, padding=1), nn.SiLU(), nn.Conv2d(16, 16, 3, padding=1, stride=2), nn.SiLU(), zero_module(nn.Conv2d(16, 16, 3, padding=1)) ) 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, controlnet_cond: Tensor, txt: Tensor, txt_ids: Tensor, timesteps: Tensor, y: Tensor, guidance: Tensor | None = None, ) -> Tensor: 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) controlnet_cond = self.input_hint_block(controlnet_cond) controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2) controlnet_cond = self.pos_embed_input(controlnet_cond) img = img + controlnet_cond vec = self.time_in(timestep_embedding(timesteps, 256)) 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)) vec = vec + self.vector_in(y) txt = self.txt_in(txt) ids = torch.cat((txt_ids, img_ids), dim=1) pe = self.pe_embedder(ids) block_res_samples = () for block in 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 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), img, txt, vec, pe, ) else: img, txt = block(img=img, txt=txt, vec=vec, pe=pe) block_res_samples = block_res_samples + (img,) controlnet_block_res_samples = () for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks): block_res_sample = controlnet_block(block_res_sample) controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,) return controlnet_block_res_samples