from dataclasses import dataclass import math import torch from torch import Tensor, nn import torch.nn.functional as F from .modules.layers import ( DoubleStreamBlock, EmbedND, EmbedNDFlux2, LastLayer, MLPEmbedder, Modulation, SingleStreamBlock, timestep_embedding, DistilledGuidance, ChromaModulationOut, SigLIPMultiFeatProjModel, ) from .modules.lora import LinearLora, replace_linear_with_lora from .radiance import apply_radiance_head, inject_radiance_modules @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 radiance: bool = False radiance_patch_size: int = 16 radiance_hidden_size: int = 64 radiance_mlp_ratio: int = 4 radiance_depth: int = 4 radiance_max_freqs: int = 8 radiance_tile_size: int = 0 radiance_final_head_type: str = "conv" single_linear1_mlp_ratio: float | None = None single_mlp_hidden_ratio: float | None = None double_mlp_ratio: float | None = None double_linear1_mlp_ratio: float | None = None flux2: bool = False piflow: 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 self.is_flux2 = getattr(params, "flux2", False) self.radiance = getattr(params, "radiance", False) self.piflow = getattr(params, "piflow", False) 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 if self.is_flux2: self.pe_embedder = EmbedNDFlux2(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) else: self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) self.img_in = ( nn.Identity() if self.radiance else nn.Linear(self.in_channels, self.hidden_size, bias=not self.is_flux2) ) self.guidance_in = ( MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=not self.is_flux2) if params.guidance_embed else None ) self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size, bias=not self.is_flux2) if self.is_flux2: self.double_stream_modulation_img = Modulation(self.hidden_size, double=True, bias=False) self.double_stream_modulation_txt = Modulation(self.hidden_size, double=True, bias=False) self.single_stream_modulation = Modulation(self.hidden_size, double=False, bias=False) 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, bias=not self.is_flux2) self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, bias=not self.is_flux2) if not self.is_flux2 else nn.Identity() self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, double_mlp_ratio=getattr(params, "double_mlp_ratio", None), double_linear1_mlp_ratio=getattr(params, "double_linear1_mlp_ratio", None), mod_bias=not self.is_flux2, mlp_bias=not self.is_flux2, proj_bias=not self.is_flux2, qkv_bias=params.qkv_bias, shared_modulation = self.chroma or self.is_flux2, ) for _ in range(params.depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, shared_modulation=self.chroma or self.is_flux2, single_linear1_mlp_ratio=getattr(params, "single_linear1_mlp_ratio", None), single_mlp_hidden_ratio=getattr(params, "single_mlp_hidden_ratio", None), qk_scale=None, linear_bias=not self.is_flux2, modulation_bias=not self.is_flux2, ) for _ in range(params.depth_single_blocks) ] ) if self.radiance: inject_radiance_modules(self, params) self.final_layer = None else: self.final_layer = LastLayer( self.hidden_size, 1, self.out_channels, chroma_modulation=self.chroma, use_linear=True, linear_bias=not self.is_flux2, modulation_bias=not self.is_flux2, ) if self.piflow: # Pi-Flow prediction heads for Gaussian mixture velocity field. self.proj_out_means = nn.Linear(self.hidden_size, 1024, bias=True) self.proj_out_logweights = nn.Linear(self.hidden_size, 32, bias=True) self.proj_out_logstds = nn.Sequential( nn.Identity(), nn.Linear(self.hidden_size, 1024, bias=True), nn.SiLU(), nn.Linear(1024, 1, bias=True), ) self.num_gaussians = self.proj_out_means.out_features // self.out_channels self.logweights_channels = self.proj_out_logweights.out_features // self.num_gaussians self.piflow_patch_size = int(math.sqrt(self.logweights_channels)) def _apply_final_layer(self, tokens: Tensor, vec): final_layer = self.final_layer normed = final_layer.norm_final(tokens) if self.chroma: shift, scale = vec shift = shift.squeeze(1) scale = scale.squeeze(1) else: shift, scale = final_layer.adaLN_modulation(vec).chunk(2, dim=1) modulated = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], normed) if final_layer.linear is None: raise RuntimeError("Final layer projection is not available.") base_tokens = final_layer.linear(modulated) return modulated, base_tokens def _apply_piflow_final_layer(self, tokens: Tensor, vec: Tensor, img_ids: Tensor, img_len: int): if img_ids is None: raise RuntimeError("pi-Flow requires image ids to reshape outputs.") final_layer = self.final_layer normed = final_layer.norm_final(tokens) shift, scale = final_layer.adaLN_modulation(vec).chunk(2, dim=1) modulated = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], normed) proj_dtype = self.proj_out_means.weight.dtype modulated = modulated.to(proj_dtype) means = self.proj_out_means(modulated) logweights = self.proj_out_logweights(modulated) logstds = self.proj_out_logstds(vec.detach().to(self.proj_out_logstds[-1].weight.dtype)) base_img_ids = img_ids[:, :img_len] h_len = int(base_img_ids[..., 1].max().item() + 1) w_len = int(base_img_ids[..., 2].max().item() + 1) if h_len * w_len != img_len: raise RuntimeError("pi-Flow token length does not match latent grid.") patch_size = self.piflow_patch_size if patch_size * patch_size != self.logweights_channels: raise RuntimeError("pi-Flow logweights channels mismatch.") bsz = means.shape[0] k = self.num_gaussians c = self.out_channels c_unpacked = c // (patch_size * patch_size) means = means.view( bsz, h_len, w_len, k, c_unpacked, patch_size, patch_size ).permute( 0, 3, 4, 1, 5, 2, 6 ).reshape( bsz, k, c_unpacked, h_len * patch_size, w_len * patch_size ) logweights = logweights.view( bsz, h_len, w_len, k, 1, patch_size, patch_size ).permute( 0, 3, 4, 1, 5, 2, 6 ).reshape( bsz, k, 1, h_len * patch_size, w_len * patch_size ).log_softmax(dim=1) logstds = logstds.reshape(bsz, 1, 1, 1, 1) return modulated, {"means": means, "logweights": logweights, "logstds": logstds} 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 lora_unet = False diffusers = False for k in sd.keys(): if "lora_unet_" in k: lora_unet = True break elif "single_transformer_blocks" in k or "transformer_blocks" in k: diffusers = True break first_key= next(iter(sd)) if 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 diffusers: 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", "time_guidance_embed.timestep_embedder.linear_1", "time_guidance_embed.timestep_embedder.linear_2" ] 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", "time_in.in_layer", "time_in.out_layer" ] 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.", ".ff_context.linear_out.", ".ff_context.linear_in.", ".ff.linear_out.", ".ff.linear_in." ] 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.", ".txt_mlp.2.", ".txt_mlp.0.", ".img_mlp.2.", ".img_mlp.0." ] single_src = ["norm.linear", "attn.norm_q", "attn.norm_k", "proj_out", ".attn.to_qkv_mlp_proj.", ".attn.to_q.", ".attn.to_k.", ".attn.to_v.", ".proj_mlp.", ".attn.to_out."] single_tgt = ["modulation.lin","norm.query_norm", "norm.key_norm", "linear2", ".linear1.", ".linear1_attn_q.", ".linear1_attn_k.", ".linear1_attn_v.", ".linear1_mlp.", ".linear2."] for k,v in sd.items(): if k.startswith("transformer."): k = k.replace("transformer.", "") if k.startswith("single_transformer_blocks"): k = k.replace("single_transformer_blocks", "single_blocks") for src, tgt in zip(single_src, single_tgt): k = k.replace(src, tgt) elif k.startswith("transformer_blocks"): k = k.replace("transformer_blocks", "double_blocks") for src, tgt in zip(double_src, double_tgt): k = k.replace(src, tgt) else: 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") k = k.replace("double_stream_modulation_img.linear.", "double_stream_modulation_img.lin.") k = k.replace("double_stream_modulation_txt.linear.", "double_stream_modulation_txt.lin.") k = k.replace("single_stream_modulation.linear.", "single_stream_modulation.lin.") k = k.replace(".lora.down.weight", ".lora_down.weight") k = k.replace(".lora.up.weight", ".lora_up.weight") if not k.startswith("diffusion_model."): k = "diffusion_model." + k new_sd[k] = v # elif not first_key.startswith("diffusion_model.") and not first_key.startswith("transformer."): # for k,v in sd.items(): # if "double" in k: # k = k.replace(".processor.proj_lora1.", ".img_attn.proj.lora_") # k = k.replace(".processor.proj_lora2.", ".txt_attn.proj.lora_") # k = k.replace(".processor.qkv_lora1.", ".img_attn.qkv.lora_") # k = k.replace(".processor.qkv_lora2.", ".txt_attn.qkv.lora_") # else: # k = k.replace(".processor.qkv_lora.", ".linear1_qkv.lora_") # k = k.replace(".processor.proj_lora.", ".linear2.lora_") # k = "diffusion_model." + k # new_sd[k] = v # from mmgp import safetensors2 # safetensors2.torch_write_file(new_sd, "fff.safetensors") 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, NAG: dict | None = None, ) -> Tensor: sz = len(txt_list) height = width = None base_image_list = None if self.radiance: patch_size = self.patch_size # Determine spatial dimensions from image ids. base_ids = img_ids[:, :img_len, :] height = int(base_ids[..., 1].max().item() + 1) * patch_size width = int(base_ids[..., 2].max().item() + 1) * patch_size # Convert tokens back to image space. tokens = img[:, :img_len, :].transpose(1, 2) image = F.fold( tokens, output_size=(height, width), kernel_size=patch_size, stride=patch_size, ) # Project image patches into hidden space. hidden = self.img_in_patch(image).flatten(2).transpose(1, 2) img = hidden base_image_list = [image] if sz == 1 else [image, image.clone()] else: 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 and self.guidance_in is not None: if guidance is None: raise ValueError("Didn't get guidance strength for guidance distilled model.") vec += self.guidance_in(timestep_embedding(guidance, 256)) base_vec_list = [vec + self.vector_in(y) for y in y_list] vec_list = base_vec_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] txt_len = txt_list[0].shape[1] if len(txt_list) > 0 else 0 if self.is_flux2: double_vec_list = [ ( self.double_stream_modulation_img(base_vec_list[i]), self.double_stream_modulation_txt(base_vec_list[i]), ) for i in range(sz) ] 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 elif self.is_flux2: vec_list = double_vec_list 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, NAG=NAG) img = txt = pe = vec= None img_list = [torch.cat((txt, img), 1) for txt, img in zip(txt_list, img_list)] if self.is_flux2: single_vec_list = [self.single_stream_modulation(base_vec_list[i])[0] for i in range(sz)] for i, block in enumerate(self.single_blocks): if self.chroma: vec_list= [self.get_modulations(mod_vectors, "single", idx=i)] * sz elif self.is_flux2: vec_list = single_vec_list 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, txt_len=txt_len, NAG=NAG) 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.radiance: final_vecs = None elif self.chroma: final_vecs = [self.get_modulations(mod_vectors, "final")] * sz else: final_vecs = base_vec_list out_list = [] for i in range(sz): hidden_seq = img_list[i] if self.radiance: base_image = base_image_list[i] pred_tokens = apply_radiance_head( module=self, hidden_seq=hidden_seq, base_image=base_image, height=height, width=width, ) base_image_list[i] = base_image = None img_list[i] = hidden_seq = None else: vec = final_vecs[i] if self.piflow: modulated, pred_tokens = self._apply_piflow_final_layer( hidden_seq, vec, img_ids, img_len ) else: modulated, pred_tokens = self._apply_final_layer(hidden_seq, vec) img_list[i] = hidden_seq = vec = modulated = None out_list.append(pred_tokens) 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)