from dataclasses import dataclass import torch from torch import Tensor, nn from .modules.layers import ( DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, SingleStreamBlock, timestep_embedding, DistilledGuidance, ChromaModulationOut, SigLIPMultiFeatProjModel, ) from .modules.lora import LinearLora, replace_linear_with_lora @dataclass class FluxParams: in_channels: int out_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 chroma: bool = False eso: bool = False class Flux(nn.Module): """ Transformer model for flow matching on sequences. """ 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 __init__(self, params: FluxParams): super().__init__() self.params = params self.in_channels = params.in_channels self.out_channels = params.out_channels self.chroma = params.chroma 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.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) if self.chroma: self.distilled_guidance_layer = DistilledGuidance( in_dim=64, hidden_dim=5120, out_dim=3072, n_layers=5, ) else: self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) self.vector_in = MLPEmbedder(params.vec_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, chroma_modulation = self.chroma, ) for _ in range(params.depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, chroma_modulation = self.chroma) for _ in range(params.depth_single_blocks) ] ) self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, chroma_modulation = self.chroma) def preprocess_loras(self, model_type, sd): new_sd = {} if len(sd) == 0: return sd def swap_scale_shift(weight): shift, scale = weight.chunk(2, dim=0) new_weight = torch.cat([scale, shift], dim=0) return new_weight first_key= next(iter(sd)) if first_key.startswith("lora_unet_"): new_sd = {} print("Converting Lora Safetensors format to Lora Diffusers format") repl_list = ["linear1", "linear2", "modulation", "img_attn", "txt_attn", "img_mlp", "txt_mlp", "img_mod", "txt_mod"] src_list = ["_" + k + "." for k in repl_list] src_list2 = ["_" + k + "_" for k in repl_list] tgt_list = ["." + k + "." for k in repl_list] for k,v in sd.items(): k = k.replace("lora_unet_blocks_","diffusion_model.blocks.") k = k.replace("lora_unet__blocks_","diffusion_model.blocks.") k = k.replace("lora_unet_single_blocks_","diffusion_model.single_blocks.") k = k.replace("lora_unet_double_blocks_","diffusion_model.double_blocks.") for s,s2, t in zip(src_list, src_list2, tgt_list): k = k.replace(s,t) k = k.replace(s2,t) k = k.replace("lora_up","lora_B") k = k.replace("lora_down","lora_A") new_sd[k] = v elif first_key.startswith("transformer."): root_src = ["time_text_embed.timestep_embedder.linear_1", "time_text_embed.timestep_embedder.linear_2", "time_text_embed.text_embedder.linear_1", "time_text_embed.text_embedder.linear_2", "time_text_embed.guidance_embedder.linear_1", "time_text_embed.guidance_embedder.linear_2", "x_embedder", "context_embedder", "proj_out" ] root_tgt = ["time_in.in_layer", "time_in.out_layer", "vector_in.in_layer", "vector_in.out_layer", "guidance_in.in_layer", "guidance_in.out_layer", "img_in", "txt_in", "final_layer.linear" ] double_src = ["norm1.linear", "norm1_context.linear", "attn.norm_q", "attn.norm_k", "ff.net.0.proj", "ff.net.2", "ff_context.net.0.proj", "ff_context.net.2", "attn.to_out.0" ,"attn.to_add_out", "attn.to_out", ".attn.to_", ".attn.add_q_proj.", ".attn.add_k_proj.", ".attn.add_v_proj.", ] double_tgt = ["img_mod.lin", "txt_mod.lin", "img_attn.norm.query_norm", "img_attn.norm.key_norm", "img_mlp.0", "img_mlp.2", "txt_mlp.0", "txt_mlp.2", "img_attn.proj", "txt_attn.proj", "img_attn.proj", ".img_attn.", ".txt_attn.q.", ".txt_attn.k.", ".txt_attn.v."] single_src = ["norm.linear", "attn.norm_q", "attn.norm_k", "proj_out",".attn.to_q.", ".attn.to_k.", ".attn.to_v.", ".proj_mlp."] single_tgt = ["modulation.lin","norm.query_norm", "norm.key_norm", "linear2", ".linear1_attn_q.", ".linear1_attn_k.", ".linear1_attn_v.", ".linear1_mlp."] for k,v in sd.items(): if k.startswith("transformer.single_transformer_blocks"): k = k.replace("transformer.single_transformer_blocks", "diffusion_model.single_blocks") for src, tgt in zip(single_src, single_tgt): k = k.replace(src, tgt) elif k.startswith("transformer.transformer_blocks"): k = k.replace("transformer.transformer_blocks", "diffusion_model.double_blocks") for src, tgt in zip(double_src, double_tgt): k = k.replace(src, tgt) else: k = k.replace("transformer.", "diffusion_model.") for src, tgt in zip(root_src, root_tgt): k = k.replace(src, tgt) if "norm_out.linear" in k: if "lora_B" in k: v = swap_scale_shift(v) k = k.replace("norm_out.linear", "final_layer.adaLN_modulation.1") new_sd[k] = v else: new_sd = sd return new_sd def forward( self, img: Tensor, img_ids: Tensor, txt_list, txt_ids_list, timesteps: Tensor, y_list, img_len = 0, guidance: Tensor | None = None, callback= None, pipeline =None, siglip_embedding = None, siglip_embedding_ids = None, ) -> Tensor: sz = len(txt_list) # running on sequences img img = self.img_in(img) img_list = [img] if sz==1 else [img, img.clone()] if self.chroma: mod_index_length = 344 distill_timestep = timestep_embedding(timesteps, 16).to(img.device, img.dtype) guidance = torch.tensor([0.]* distill_timestep.shape[0]) distil_guidance = timestep_embedding(guidance, 16).to(img.device, img.dtype) modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype) modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype) 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) input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype) mod_vectors = self.distilled_guidance_layer(input_vec) else: 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 += self.guidance_in(timestep_embedding(guidance, 256)) vec_list = [ vec + self.vector_in(y) for y in y_list] img = None txt_list = [self.txt_in(txt) for txt in txt_list ] if siglip_embedding is not None: txt_list = [torch.cat((siglip_embedding, txt) , dim=1) for txt in txt_list] txt_ids_list = [torch.cat((siglip_embedding_ids, txt_id) , dim=1) for txt_id in txt_ids_list] pe_list = [self.pe_embedder(torch.cat((txt_ids, img_ids), dim=1)) for txt_ids in txt_ids_list] for i, block in enumerate(self.double_blocks): if self.chroma: vec_list = [( self.get_modulations(mod_vectors, "double_img", idx=i), self.get_modulations(mod_vectors, "double_txt", idx=i))] * sz if callback != None: callback(-1, None, False, True) if pipeline._interrupt: return [None] * sz for img, txt, pe, vec in zip(img_list, txt_list, pe_list, vec_list): img[...], txt[...] = block(img=img, txt=txt, vec=vec, pe=pe) img = txt = pe = vec= None img_list = [torch.cat((txt, img), 1) for txt, img in zip(txt_list, img_list)] for i, block in enumerate(self.single_blocks): if self.chroma: vec_list= [self.get_modulations(mod_vectors, "single", idx=i)] * sz if callback != None: callback(-1, None, False, True) if pipeline._interrupt: return [None] * sz for img, pe, vec in zip(img_list, pe_list, vec_list): img[...]= block(x=img, vec=vec, pe=pe) img = pe = vec = None img_list = [ img[:, txt.shape[1] : txt.shape[1] + img_len, ...] for img, txt in zip(img_list, txt_list)] if self.chroma: vec_list = [self.get_modulations(mod_vectors, "final")] * sz out_list = [] for i, (img, vec) in enumerate(zip(img_list, vec_list)): out_list.append( self.final_layer(img, vec)) # (N, T, patch_size ** 2 * out_channels) img_list[i] = img = vec = None return out_list class FluxLoraWrapper(Flux): def __init__( self, lora_rank: int = 128, lora_scale: float = 1.0, *args, **kwargs, ) -> None: super().__init__(*args, **kwargs) self.lora_rank = lora_rank replace_linear_with_lora( self, max_rank=lora_rank, scale=lora_scale, ) def set_lora_scale(self, scale: float) -> None: for module in self.modules(): if isinstance(module, LinearLora): module.set_scale(scale=scale)