2023-01-13 12:46:14 -07:00
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import logging
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2023-01-12 14:32:37 -07:00
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import os
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2023-01-23 11:19:22 -07:00
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from typing import Optional, Tuple
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import huggingface_hub
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2023-01-23 11:19:22 -07:00
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from utils.patch_unet import patch_unet
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def try_download_model_from_hf(repo_id: str,
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subfolder: Optional[str]=None) -> Tuple[Optional[str], Optional[bool], Optional[str]]:
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"""
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Attempts to download files from the following subfolders under the given repo id:
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"text_encoder", "vae", "unet", "scheduler", "tokenizer".
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:param repo_id The repository id of the model on huggingface, such as 'stabilityai/stable-diffusion-2-1' which
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corresponds to `https://huggingface.co/stabilityai/stable-diffusion-2-1`.
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:param access_token Access token to use when fetching. If None, uses environment-saved token.
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:return: Root folder on disk to the downloaded files, or None if download failed.
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"""
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try:
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access_token = os.environ['HF_API_TOKEN']
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if access_token is not None:
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huggingface_hub.login(access_token)
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except:
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logging.info("no HF_API_TOKEN env var found, will attempt to download without authenticating")
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# check if the model exists
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model_info = huggingface_hub.model_info(repo_id)
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if model_info is None:
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return None, None, None
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model_subfolders = ["text_encoder", "vae", "unet", "scheduler", "tokenizer"]
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allow_patterns = [os.path.join(subfolder or '', f, "*") for f in model_subfolders]
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downloaded_folder = huggingface_hub.snapshot_download(repo_id=repo_id, allow_patterns=allow_patterns)
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is_sd1_attn, yaml_path = patch_unet(downloaded_folder)
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return downloaded_folder, is_sd1_attn, yaml_path
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