a d @sdZddlZddlZddlmZmZmZddlZddlm Z m Z ddl m Z m Z mZddlmZmZmZdd gZeed d dZdeeeeeeejfeed d d ZdS)z OpenAI pretrained model functions Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. N)ListOptionalUnion)OPENAI_DATASET_MEANOPENAI_DATASET_STD)"build_model_from_openai_state_dictconvert_weights_to_lpget_cast_dtype)get_pretrained_urllist_pretrained_models_by_tagdownload_pretrained_from_urllist_openai_modelsload_openai_model)returncCstdS)z*Returns the names of available CLIP modelsopenai)r rrY/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/open_clip/openai.pyrs)name precisiondevice cache_dirc CsV|durtjrdnd}|dur2|dkr.dnd}t|drPtt|d|d}n(tj|rb|}ntd|d t ztj j |dd  }d}Wn tytj |dd }Yn0t |}zt|p||d }Wn6ty d d |dD}t||d }Yn0||}|dkrB||dkrBt|tjdt|j_t|j_|S)aLoad a CLIP model Parameters ---------- name : str A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict precision: str Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. device : Union[str, torch.device] The device to put the loaded model cache_dir : Optional[str] The directory to cache the downloaded model weights Returns ------- model : torch.nn.Module The CLIP model preprocess : Callable[[PIL.Image], torch.Tensor] A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input NcudacpuZfp32Zfp16r)rzModel z not found; available models = )Z map_location) cast_dtypecSsi|]\}}|dd|qS)Nr).0kvrrr Kz%load_openai_model.. state_dictZbf16)Zdtype)torchrZ is_availabler r ospathisfile RuntimeErrorrZjitloadevalr rr!KeyErroritemstofloatr Zbfloat16rvisualZ image_meanrZ image_std) rrrrZ model_pathmodelr!rsdrrrrs8      )NNN)__doc__r#warningstypingrrrr" constantsrrr.rr r Z pretrainedr r r __all__strrrrrrrrs$