import torch, os from safetensors import safe_open from .sd_text_encoder import SDTextEncoder from .sd_unet import SDUNet from .sd_vae_encoder import SDVAEEncoder from .sd_vae_decoder import SDVAEDecoder from .sd_lora import SDLoRA from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 from .sdxl_unet import SDXLUNet from .sdxl_vae_decoder import SDXLVAEDecoder from .sdxl_vae_encoder import SDXLVAEEncoder from .sd_controlnet import SDControlNet from .sd_motion import SDMotionModel class ModelManager: def __init__(self, torch_dtype=torch.float16, device="cuda"): self.torch_dtype = torch_dtype self.device = device self.model = {} self.model_path = {} self.textual_inversion_dict = {} def is_RIFE(self, state_dict): param_name = "block_tea.convblock3.0.1.weight" return param_name in state_dict or ("module." + param_name) in state_dict def is_beautiful_prompt(self, state_dict): param_name = "transformer.h.9.self_attention.query_key_value.weight" return param_name in state_dict def is_stabe_diffusion_xl(self, state_dict): param_name = "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight" return param_name in state_dict def is_stable_diffusion(self, state_dict): if self.is_stabe_diffusion_xl(state_dict): return False param_name = "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight" return param_name in state_dict def is_controlnet(self, state_dict): param_name = "control_model.time_embed.0.weight" return param_name in state_dict def is_animatediff(self, state_dict): param_name = "mid_block.motion_modules.0.temporal_transformer.proj_out.weight" return param_name in state_dict def is_sd_lora(self, state_dict): param_name = "lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.lora_up.weight" return param_name in state_dict def is_translator(self, state_dict): param_name = "model.encoder.layers.5.self_attn_layer_norm.weight" return param_name in state_dict and len(state_dict) == 254 def load_stable_diffusion(self, state_dict, components=None, file_path=""): component_dict = { "text_encoder": SDTextEncoder, "unet": SDUNet, "vae_decoder": SDVAEDecoder, "vae_encoder": SDVAEEncoder, "refiner": SDXLUNet, } if components is None: components = ["text_encoder", "unet", "vae_decoder", "vae_encoder"] for component in components: if component == "text_encoder": # Add additional token embeddings to text encoder token_embeddings = [state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]] for keyword in self.textual_inversion_dict: _, embeddings = self.textual_inversion_dict[keyword] token_embeddings.append(embeddings.to(dtype=token_embeddings[0].dtype)) token_embeddings = torch.concat(token_embeddings, dim=0) state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"] = token_embeddings self.model[component] = component_dict[component](vocab_size=token_embeddings.shape[0]) self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict)) self.model[component].to(self.torch_dtype).to(self.device) else: self.model[component] = component_dict[component]() self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict)) self.model[component].to(self.torch_dtype).to(self.device) self.model_path[component] = file_path def load_stable_diffusion_xl(self, state_dict, components=None, file_path=""): component_dict = { "text_encoder": SDXLTextEncoder, "text_encoder_2": SDXLTextEncoder2, "unet": SDXLUNet, "vae_decoder": SDXLVAEDecoder, "vae_encoder": SDXLVAEEncoder, } if components is None: components = ["text_encoder", "text_encoder_2", "unet", "vae_decoder", "vae_encoder"] for component in components: self.model[component] = component_dict[component]() self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict)) if component in ["vae_decoder", "vae_encoder"]: # These two model will output nan when float16 is enabled. # The precision problem happens in the last three resnet blocks. # I do not know how to solve this problem. self.model[component].to(torch.float32).to(self.device) else: self.model[component].to(self.torch_dtype).to(self.device) self.model_path[component] = file_path def load_controlnet(self, state_dict, file_path=""): component = "controlnet" if component not in self.model: self.model[component] = [] self.model_path[component] = [] model = SDControlNet() model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) model.to(self.torch_dtype).to(self.device) self.model[component].append(model) self.model_path[component].append(file_path) def load_animatediff(self, state_dict, file_path=""): component = "motion_modules" model = SDMotionModel() model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) model.to(self.torch_dtype).to(self.device) self.model[component] = model self.model_path[component] = file_path def load_beautiful_prompt(self, state_dict, file_path=""): component = "beautiful_prompt" from transformers import AutoModelForCausalLM model_folder = os.path.dirname(file_path) model = AutoModelForCausalLM.from_pretrained( model_folder, state_dict=state_dict, local_files_only=True, torch_dtype=self.torch_dtype ).to(self.device).eval() self.model[component] = model self.model_path[component] = file_path def load_RIFE(self, state_dict, file_path=""): component = "RIFE" from ..extensions.RIFE import IFNet model = IFNet().eval() model.load_state_dict(model.state_dict_converter().from_civitai(state_dict)) model.to(torch.float32).to(self.device) self.model[component] = model self.model_path[component] = file_path def load_sd_lora(self, state_dict, alpha): SDLoRA().add_lora_to_text_encoder(self.model["text_encoder"], state_dict, alpha=alpha, device=self.device) SDLoRA().add_lora_to_unet(self.model["unet"], state_dict, alpha=alpha, device=self.device) def load_translator(self, state_dict, file_path=""): # This model is lightweight, we do not place it on GPU. component = "translator" from transformers import AutoModelForSeq2SeqLM model_folder = os.path.dirname(file_path) model = AutoModelForSeq2SeqLM.from_pretrained(model_folder).eval() self.model[component] = model self.model_path[component] = file_path def search_for_embeddings(self, state_dict): embeddings = [] for k in state_dict: if isinstance(state_dict[k], torch.Tensor): embeddings.append(state_dict[k]) elif isinstance(state_dict[k], dict): embeddings += self.search_for_embeddings(state_dict[k]) return embeddings def load_textual_inversions(self, folder): # Store additional tokens here self.textual_inversion_dict = {} # Load every textual inversion file for file_name in os.listdir(folder): if file_name.endswith(".txt"): continue keyword = os.path.splitext(file_name)[0] state_dict = load_state_dict(os.path.join(folder, file_name)) # Search for embeddings for embeddings in self.search_for_embeddings(state_dict): if len(embeddings.shape) == 2 and embeddings.shape[1] == 768: tokens = [f"{keyword}_{i}" for i in range(embeddings.shape[0])] self.textual_inversion_dict[keyword] = (tokens, embeddings) break def load_model(self, file_path, components=None, lora_alphas=[]): state_dict = load_state_dict(file_path, torch_dtype=self.torch_dtype) if self.is_animatediff(state_dict): self.load_animatediff(state_dict, file_path=file_path) elif self.is_controlnet(state_dict): self.load_controlnet(state_dict, file_path=file_path) elif self.is_stabe_diffusion_xl(state_dict): self.load_stable_diffusion_xl(state_dict, components=components, file_path=file_path) elif self.is_stable_diffusion(state_dict): self.load_stable_diffusion(state_dict, components=components, file_path=file_path) elif self.is_sd_lora(state_dict): self.load_sd_lora(state_dict, alpha=lora_alphas.pop(0)) elif self.is_beautiful_prompt(state_dict): self.load_beautiful_prompt(state_dict, file_path=file_path) elif self.is_RIFE(state_dict): self.load_RIFE(state_dict, file_path=file_path) elif self.is_translator(state_dict): self.load_translator(state_dict, file_path=file_path) def load_models(self, file_path_list, lora_alphas=[]): for file_path in file_path_list: self.load_model(file_path, lora_alphas=lora_alphas) def to(self, device): for component in self.model: if isinstance(self.model[component], list): for model in self.model[component]: model.to(device) else: self.model[component].to(device) torch.cuda.empty_cache() def get_model_with_model_path(self, model_path): for component in self.model_path: if isinstance(self.model_path[component], str): if os.path.samefile(self.model_path[component], model_path): return self.model[component] elif isinstance(self.model_path[component], list): for i, model_path_ in enumerate(self.model_path[component]): if os.path.samefile(model_path_, model_path): return self.model[component][i] raise ValueError(f"Please load model {model_path} before you use it.") def __getattr__(self, __name): if __name in self.model: return self.model[__name] else: return super.__getattribute__(__name) def load_state_dict(file_path, torch_dtype=None): if file_path.endswith(".safetensors"): return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype) else: return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype) def load_state_dict_from_safetensors(file_path, torch_dtype=None): state_dict = {} with safe_open(file_path, framework="pt", device="cpu") as f: for k in f.keys(): state_dict[k] = f.get_tensor(k) if torch_dtype is not None: state_dict[k] = state_dict[k].to(torch_dtype) return state_dict def load_state_dict_from_bin(file_path, torch_dtype=None): state_dict = torch.load(file_path, map_location="cpu") if torch_dtype is not None: state_dict = {i: state_dict[i].to(torch_dtype) for i in state_dict} return state_dict def search_parameter(param, state_dict): for name, param_ in state_dict.items(): if param.numel() == param_.numel(): if param.shape == param_.shape: if torch.dist(param, param_) < 1e-6: return name else: if torch.dist(param.flatten(), param_.flatten()) < 1e-6: return name return None def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False): matched_keys = set() with torch.no_grad(): for name in source_state_dict: rename = search_parameter(source_state_dict[name], target_state_dict) if rename is not None: print(f'"{name}": "{rename}",') matched_keys.add(rename) elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0: length = source_state_dict[name].shape[0] // 3 rename = [] for i in range(3): rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict)) if None not in rename: print(f'"{name}": {rename},') for rename_ in rename: matched_keys.add(rename_) for name in target_state_dict: if name not in matched_keys: print("Cannot find", name, target_state_dict[name].shape)