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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 DiT models
"""
from torchvision.datasets.utils import download_url
import torch
import os
pretrained_models = {'DiT-XL-2-512x512.pt', 'DiT-XL-2-256x256.pt'}
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 DiT checkpoint:
assert os.path.isfile(model_name), f'Could not find DiT checkpoint at {model_name}'
return torch.load(model_name, map_location=lambda storage, loc: storage)
def download_model(model_name):
"""
Downloads a pre-trained DiT model from the web.
"""
assert model_name in pretrained_models
local_path = f'pretrained_models/{model_name}'
if not os.path.isfile(local_path):
os.makedirs('pretrained_models', exist_ok=True)
web_path = f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
download_url(web_path, 'pretrained_models')
model = torch.load(local_path, map_location=lambda storage, loc: storage)
return model
if __name__ == "__main__":
# Download all DiT checkpoints
for model in pretrained_models:
download_model(model)
print('Done.')
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