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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Functions for downloading pre-trained Sana models
"""
import argparse
import os
import torch
from torchvision.datasets.utils import download_url
pretrained_models = {}
def find_model(model_name):
"""
Finds a pre-trained G.pt model, downloading it if necessary. Alternatively, loads a model from a local path.
"""
if model_name in pretrained_models: # Find/download our pre-trained G.pt checkpoints
return download_model(model_name)
else: # Load a custom Sana checkpoint:
assert os.path.isfile(model_name), f"Could not find Sana checkpoint at {model_name}"
return torch.load(model_name, map_location=lambda storage, loc: storage)
def download_model(model_name):
"""
Downloads a pre-trained Sana model from the web.
"""
assert model_name in pretrained_models
local_path = f"output/pretrained_models/{model_name}"
if not os.path.isfile(local_path):
hf_endpoint = os.environ.get("HF_ENDPOINT")
if hf_endpoint is None:
hf_endpoint = "https://huggingface.co"
os.makedirs("output/pretrained_models", exist_ok=True)
web_path = f"{hf_endpoint}/xxx/resolve/main/{model_name}"
download_url(web_path, "output/pretrained_models/")
model = torch.load(local_path, map_location=lambda storage, loc: storage)
return model
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_names", nargs="+", type=str, default=pretrained_models)
args = parser.parse_args()
model_names = args.model_names
model_names = set(model_names)
# Download Sana checkpoints
for model in model_names:
download_model(model)
print("Done.")
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