#Original code can be found on: https://github.com/black-forest-labs/flux from dataclasses import dataclass import torch from torch import Tensor, nn from einops import rearrange, repeat import comfy.patcher_extension import comfy.ldm.common_dit from comfy.ldm.flux.layers import ( EmbedND, timestep_embedding, DoubleStreamBlock, SingleStreamBlock, ) from .layers import ( LastLayer, Approximator, ChromaModulationOut, ) @dataclass class ChromaParams: in_channels: int out_channels: int context_in_dim: int hidden_size: int mlp_ratio: float num_heads: int depth: int depth_single_blocks: int axes_dim: list theta: int patch_size: int qkv_bias: bool in_dim: int out_dim: int hidden_dim: int n_layers: int txt_ids_dims: list vec_in_dim: int class Chroma(nn.Module): """ Transformer model for flow matching on sequences. """ def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): super().__init__() self.dtype = dtype params = ChromaParams(**kwargs) self.params = params self.patch_size = params.patch_size self.in_channels = params.in_channels self.out_channels = params.out_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.in_dim = params.in_dim self.out_dim = params.out_dim self.hidden_dim = params.hidden_dim self.n_layers = params.n_layers self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) # set as nn identity for now, will overwrite it later. self.distilled_guidance_layer = Approximator( in_dim=self.in_dim, hidden_dim=self.hidden_dim, out_dim=self.out_dim, n_layers=self.n_layers, dtype=dtype, device=device, operations=operations ) self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, modulation=False, dtype=dtype, device=device, operations=operations ) for _ in range(params.depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=False, dtype=dtype, device=device, operations=operations) for _ in range(params.depth_single_blocks) ] ) if final_layer: self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) self.skip_mmdit = [] self.skip_dit = [] self.lite = False def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0): # This function slices up the modulations tensor which has the following layout: # single : num_single_blocks * 3 elements # double_img : num_double_blocks * 6 elements # double_txt : num_double_blocks * 6 elements # final : 2 elements if block_type == "final": return (tensor[:, -2:-1, :], tensor[:, -1:, :]) single_block_count = self.params.depth_single_blocks double_block_count = self.params.depth offset = 3 * idx if block_type == "single": return ChromaModulationOut.from_offset(tensor, offset) # Double block modulations are 6 elements so we double 3 * idx. offset *= 2 if block_type in {"double_img", "double_txt"}: # Advance past the single block modulations. offset += 3 * single_block_count if block_type == "double_txt": # Advance past the double block img modulations. offset += 6 * double_block_count return ( ChromaModulationOut.from_offset(tensor, offset), ChromaModulationOut.from_offset(tensor, offset + 3), ) raise ValueError("Bad block_type") def forward_orig( self, img: Tensor, img_ids: Tensor, txt: Tensor, txt_ids: Tensor, timesteps: Tensor, guidance: Tensor = None, control = None, transformer_options={}, attn_mask: Tensor = None, ) -> Tensor: transformer_options = transformer_options.copy() patches_replace = transformer_options.get("patches_replace", {}) # running on sequences img img = self.img_in(img) # distilled vector guidance mod_index_length = 344 distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype) # guidance = guidance * distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype) # get all modulation index modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype) # we need to broadcast the modulation index here so each batch has all of the index modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype) # and we need to broadcast timestep and guidance along too timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype) # then and only then we could concatenate it together input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype) mod_vectors = self.distilled_guidance_layer(input_vec) txt = self.txt_in(txt) ids = torch.cat((txt_ids, img_ids), dim=1) pe = self.pe_embedder(ids) blocks_replace = patches_replace.get("dit", {}) transformer_options["total_blocks"] = len(self.double_blocks) transformer_options["block_type"] = "double" for i, block in enumerate(self.double_blocks): transformer_options["block_index"] = i if i not in self.skip_mmdit: double_mod = ( self.get_modulations(mod_vectors, "double_img", idx=i), self.get_modulations(mod_vectors, "double_txt", idx=i), ) if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask"), transformer_options=args.get("transformer_options")) return out out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": double_mod, "pe": pe, "attn_mask": attn_mask, "transformer_options": transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] else: img, txt = block(img=img, txt=txt, vec=double_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) if control is not None: # Controlnet control_i = control.get("input") if i < len(control_i): add = control_i[i] if add is not None: img += add img = torch.cat((txt, img), 1) transformer_options["total_blocks"] = len(self.single_blocks) transformer_options["block_type"] = "single" transformer_options["img_slice"] = [txt.shape[1], img.shape[1]] for i, block in enumerate(self.single_blocks): transformer_options["block_index"] = i if i not in self.skip_dit: single_mod = self.get_modulations(mod_vectors, "single", idx=i) if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args.get("attn_mask"), transformer_options=args.get("transformer_options")) return out out = blocks_replace[("single_block", i)]({"img": img, "vec": single_mod, "pe": pe, "attn_mask": attn_mask, "transformer_options": transformer_options}, {"original_block": block_wrap}) img = out["img"] else: img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) if control is not None: # Controlnet control_o = control.get("output") if i < len(control_o): add = control_o[i] if add is not None: img[:, txt.shape[1] :, ...] += add img = img[:, txt.shape[1] :, ...] if hasattr(self, "final_layer"): final_mod = self.get_modulations(mod_vectors, "final") img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels) return img def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( self._forward, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) ).execute(x, timestep, context, guidance, control, transformer_options, **kwargs) def _forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): bs, c, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size) if img.ndim != 3 or context.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") h_len = ((h + (self.patch_size // 2)) // self.patch_size) w_len = ((w + (self.patch_size // 2)) // self.patch_size) img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h,:w]