import torch from .sd_unet import SDUNet from .sdxl_unet import SDXLUNet from .sd_text_encoder import SDTextEncoder from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 from .sd3_dit import SD3DiT from .flux_dit import FluxDiT from .hunyuan_dit import HunyuanDiT from .cog_dit import CogDiT from .hunyuan_video_dit import HunyuanVideoDiT from .wan_video_dit import WanModel class LoRAFromCivitai: def __init__(self): self.supported_model_classes = [] self.lora_prefix = [] self.renamed_lora_prefix = {} self.special_keys = {} def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alpha=1.0): for key in state_dict: if ".lora_up" in key: return self.convert_state_dict_up_down(state_dict, lora_prefix, alpha) return self.convert_state_dict_AB(state_dict, lora_prefix, alpha) def convert_state_dict_up_down(self, state_dict, lora_prefix="lora_unet_", alpha=1.0): renamed_lora_prefix = self.renamed_lora_prefix.get(lora_prefix, "") state_dict_ = {} for key in state_dict: if ".lora_up" not in key: continue if not key.startswith(lora_prefix): continue weight_up = state_dict[key].to(device="cuda", dtype=torch.float16) weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16) if len(weight_up.shape) == 4: weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32) weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32) lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) else: lora_weight = alpha * torch.mm(weight_up, weight_down) target_name = key.split(".")[0].replace(lora_prefix, renamed_lora_prefix).replace("_", ".") + ".weight" for special_key in self.special_keys: target_name = target_name.replace(special_key, self.special_keys[special_key]) state_dict_[target_name] = lora_weight.cpu() return state_dict_ def convert_state_dict_AB(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16): state_dict_ = {} for key in state_dict: if ".lora_B." not in key: continue if not key.startswith(lora_prefix): continue weight_up = state_dict[key].to(device=device, dtype=torch_dtype) weight_down = state_dict[key.replace(".lora_B.", ".lora_A.")].to(device=device, dtype=torch_dtype) if len(weight_up.shape) == 4: weight_up = weight_up.squeeze(3).squeeze(2) weight_down = weight_down.squeeze(3).squeeze(2) lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) else: lora_weight = alpha * torch.mm(weight_up, weight_down) keys = key.split(".") keys.pop(keys.index("lora_B")) target_name = ".".join(keys) target_name = target_name[len(lora_prefix):] state_dict_[target_name] = lora_weight.cpu() return state_dict_ def load(self, model, state_dict_lora, lora_prefix, alpha=1.0, model_resource=None): state_dict_model = model.state_dict() state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=alpha) if model_resource == "diffusers": state_dict_lora = model.__class__.state_dict_converter().from_diffusers(state_dict_lora) elif model_resource == "civitai": state_dict_lora = model.__class__.state_dict_converter().from_civitai(state_dict_lora) if isinstance(state_dict_lora, tuple): state_dict_lora = state_dict_lora[0] if len(state_dict_lora) > 0: print(f" {len(state_dict_lora)} tensors are updated.") for name in state_dict_lora: fp8=False if state_dict_model[name].dtype == torch.float8_e4m3fn: state_dict_model[name]= state_dict_model[name].to(state_dict_lora[name].dtype) fp8=True state_dict_model[name] += state_dict_lora[name].to( dtype=state_dict_model[name].dtype, device=state_dict_model[name].device) if fp8: state_dict_model[name] = state_dict_model[name].to(torch.float8_e4m3fn) model.load_state_dict(state_dict_model) def match(self, model, state_dict_lora): for lora_prefix, model_class in zip(self.lora_prefix, self.supported_model_classes): if not isinstance(model, model_class): continue state_dict_model = model.state_dict() for model_resource in ["diffusers", "civitai"]: try: state_dict_lora_ = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=1.0) converter_fn = model.__class__.state_dict_converter().from_diffusers if model_resource == "diffusers" \ else model.__class__.state_dict_converter().from_civitai state_dict_lora_ = converter_fn(state_dict_lora_) if isinstance(state_dict_lora_, tuple): state_dict_lora_ = state_dict_lora_[0] if len(state_dict_lora_) == 0: continue for name in state_dict_lora_: if name not in state_dict_model: break else: return lora_prefix, model_resource except: pass return None class SDLoRAFromCivitai(LoRAFromCivitai): def __init__(self): super().__init__() self.supported_model_classes = [SDUNet, SDTextEncoder] self.lora_prefix = ["lora_unet_", "lora_te_"] self.special_keys = { "down.blocks": "down_blocks", "up.blocks": "up_blocks", "mid.block": "mid_block", "proj.in": "proj_in", "proj.out": "proj_out", "transformer.blocks": "transformer_blocks", "to.q": "to_q", "to.k": "to_k", "to.v": "to_v", "to.out": "to_out", "text.model": "text_model", "self.attn.q.proj": "self_attn.q_proj", "self.attn.k.proj": "self_attn.k_proj", "self.attn.v.proj": "self_attn.v_proj", "self.attn.out.proj": "self_attn.out_proj", "input.blocks": "model.diffusion_model.input_blocks", "middle.block": "model.diffusion_model.middle_block", "output.blocks": "model.diffusion_model.output_blocks", } class SDXLLoRAFromCivitai(LoRAFromCivitai): def __init__(self): super().__init__() self.supported_model_classes = [SDXLUNet, SDXLTextEncoder, SDXLTextEncoder2] self.lora_prefix = ["lora_unet_", "lora_te1_", "lora_te2_"] self.renamed_lora_prefix = {"lora_te2_": "2"} self.special_keys = { "down.blocks": "down_blocks", "up.blocks": "up_blocks", "mid.block": "mid_block", "proj.in": "proj_in", "proj.out": "proj_out", "transformer.blocks": "transformer_blocks", "to.q": "to_q", "to.k": "to_k", "to.v": "to_v", "to.out": "to_out", "text.model": "conditioner.embedders.0.transformer.text_model", "self.attn.q.proj": "self_attn.q_proj", "self.attn.k.proj": "self_attn.k_proj", "self.attn.v.proj": "self_attn.v_proj", "self.attn.out.proj": "self_attn.out_proj", "input.blocks": "model.diffusion_model.input_blocks", "middle.block": "model.diffusion_model.middle_block", "output.blocks": "model.diffusion_model.output_blocks", "2conditioner.embedders.0.transformer.text_model.encoder.layers": "text_model.encoder.layers" } class FluxLoRAFromCivitai(LoRAFromCivitai): def __init__(self): super().__init__() self.supported_model_classes = [FluxDiT, FluxDiT] self.lora_prefix = ["lora_unet_", "transformer."] self.renamed_lora_prefix = {} self.special_keys = { "single.blocks": "single_blocks", "double.blocks": "double_blocks", "img.attn": "img_attn", "img.mlp": "img_mlp", "img.mod": "img_mod", "txt.attn": "txt_attn", "txt.mlp": "txt_mlp", "txt.mod": "txt_mod", } class GeneralLoRAFromPeft: def __init__(self): self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT, FluxDiT, CogDiT, WanModel] def get_name_dict(self, lora_state_dict): lora_name_dict = {} for key in lora_state_dict: if ".lora_B." not in key: continue keys = key.split(".") if len(keys) > keys.index("lora_B") + 2: keys.pop(keys.index("lora_B") + 1) keys.pop(keys.index("lora_B")) if keys[0] == "diffusion_model": keys.pop(0) target_name = ".".join(keys) lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A.")) return lora_name_dict def match(self, model: torch.nn.Module, state_dict_lora): lora_name_dict = self.get_name_dict(state_dict_lora) model_name_dict = {name: None for name, _ in model.named_parameters()} matched_num = sum([i in model_name_dict for i in lora_name_dict]) if matched_num == len(lora_name_dict): return "", "" else: return None def fetch_device_and_dtype(self, state_dict): device, dtype = None, None for name, param in state_dict.items(): device, dtype = param.device, param.dtype break computation_device = device computation_dtype = dtype if computation_device == torch.device("cpu"): if torch.cuda.is_available(): computation_device = torch.device("cuda") if computation_dtype == torch.float8_e4m3fn: computation_dtype = torch.float32 return device, dtype, computation_device, computation_dtype def load(self, model, state_dict_lora, lora_prefix="", alpha=1.0, model_resource=""): state_dict_model = model.state_dict() device, dtype, computation_device, computation_dtype = self.fetch_device_and_dtype(state_dict_model) lora_name_dict = self.get_name_dict(state_dict_lora) for name in lora_name_dict: weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=computation_device, dtype=computation_dtype) weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=computation_device, dtype=computation_dtype) if len(weight_up.shape) == 4: weight_up = weight_up.squeeze(3).squeeze(2) weight_down = weight_down.squeeze(3).squeeze(2) weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) else: weight_lora = alpha * torch.mm(weight_up, weight_down) weight_model = state_dict_model[name].to(device=computation_device, dtype=computation_dtype) weight_patched = weight_model + weight_lora state_dict_model[name] = weight_patched.to(device=device, dtype=dtype) print(f" {len(lora_name_dict)} tensors are updated.") model.load_state_dict(state_dict_model) class HunyuanVideoLoRAFromCivitai(LoRAFromCivitai): def __init__(self): super().__init__() self.supported_model_classes = [HunyuanVideoDiT, HunyuanVideoDiT] self.lora_prefix = ["diffusion_model.", "transformer."] self.special_keys = {} class FluxLoRAConverter: def __init__(self): pass @staticmethod def align_to_opensource_format(state_dict, alpha=1.0): prefix_rename_dict = { "single_blocks": "lora_unet_single_blocks", "blocks": "lora_unet_double_blocks", } middle_rename_dict = { "norm.linear": "modulation_lin", "to_qkv_mlp": "linear1", "proj_out": "linear2", "norm1_a.linear": "img_mod_lin", "norm1_b.linear": "txt_mod_lin", "attn.a_to_qkv": "img_attn_qkv", "attn.b_to_qkv": "txt_attn_qkv", "attn.a_to_out": "img_attn_proj", "attn.b_to_out": "txt_attn_proj", "ff_a.0": "img_mlp_0", "ff_a.2": "img_mlp_2", "ff_b.0": "txt_mlp_0", "ff_b.2": "txt_mlp_2", } suffix_rename_dict = { "lora_B.weight": "lora_up.weight", "lora_A.weight": "lora_down.weight", } state_dict_ = {} for name, param in state_dict.items(): names = name.split(".") if names[-2] != "lora_A" and names[-2] != "lora_B": names.pop(-2) prefix = names[0] middle = ".".join(names[2:-2]) suffix = ".".join(names[-2:]) block_id = names[1] if middle not in middle_rename_dict: continue rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix] state_dict_[rename] = param if rename.endswith("lora_up.weight"): state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((alpha,))[0] return state_dict_ @staticmethod def align_to_diffsynth_format(state_dict): rename_dict = { "lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight", "lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight", "lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight", "lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight", "lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight", "lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight", "lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight", "lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight", "lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight", "lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight", "lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight", "lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight", "lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight", "lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight", } def guess_block_id(name): names = name.split("_") for i in names: if i.isdigit(): return i, name.replace(f"_{i}_", "_blockid_") return None, None state_dict_ = {} for name, param in state_dict.items(): block_id, source_name = guess_block_id(name) if source_name in rename_dict: target_name = rename_dict[source_name] target_name = target_name.replace(".blockid.", f".{block_id}.") state_dict_[target_name] = param else: state_dict_[name] = param return state_dict_ def get_lora_loaders(): return [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), FluxLoRAFromCivitai(), HunyuanVideoLoRAFromCivitai(), GeneralLoRAFromPeft()]