import torch 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 GeneralLoRAFromPeft: def __init__(self): self.supported_model_classes = [ 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 WanLoRAConverter: def __init__(self): pass @staticmethod def align_to_opensource_format(state_dict, **kwargs): state_dict = {"diffusion_model." + name.replace(".default.", "."): param for name, param in state_dict.items()} return state_dict @staticmethod def align_to_dkt_format(state_dict, **kwargs): state_dict = {name.replace("diffusion_model.", "").replace(".lora_A.weight", ".lora_A.default.weight").replace(".lora_B.weight", ".lora_B.default.weight"): param for name, param in state_dict.items()} return state_dict def get_lora_loaders(): return [GeneralLoRAFromPeft()]