import inspect import diffusers import transformers import safetensors.torch from modules import shared, devices, model_quant def get_safetensor_keys(filename): keys = [] try: with safetensors.torch.safe_open(filename, framework="pt", device="cpu") as f: keys = f.keys() except Exception as e: shared.log.error(f'Load dict: path="{filename}" {e}') return keys def get_modules(model: callable): signature = inspect.signature(model.__init__, follow_wrapped=True) params = {param.name: param.annotation for param in signature.parameters.values() if param.annotation != inspect._empty and hasattr(param.annotation, 'from_pretrained')} # pylint: disable=protected-access for name, cls in params.items(): shared.log.debug(f'Analyze: model={model} module={name} class={cls.__name__} loadable={getattr(cls, "from_pretrained", None)}') return params def load_modules(repo_id: str, params: dict): cache_dir = shared.opts.hfcache_dir modules = {} for name, cls in params.items(): subfolder = None kwargs = {} if cls == diffusers.AutoencoderKL: subfolder = 'vae' if cls == transformers.CLIPTextModel: # clip-vit-l subfolder = 'text_encoder' if cls == transformers.CLIPTextModelWithProjection: # clip-vit-g subfolder = 'text_encoder_2' if cls == transformers.T5EncoderModel: # t5-xxl subfolder = 'text_encoder_3' kwargs['quantization_config'] = model_quant.create_bnb_config() kwargs['variant'] = 'fp16' if cls == diffusers.SD3Transformer2DModel: subfolder = 'transformer' kwargs['quantization_config'] = model_quant.create_bnb_config() if subfolder is None: continue shared.log.debug(f'Load: module={name} class={cls.__name__} repo={repo_id} location={subfolder}') modules[name] = cls.from_pretrained(repo_id, subfolder=subfolder, cache_dir=cache_dir, torch_dtype=devices.dtype, **kwargs) return modules