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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from pathlib import Path
from huggingface_hub import snapshot_download
from cosmos1.scripts.convert_pixtral_ckpt import convert_pixtral_checkpoint
def parse_args():
parser = argparse.ArgumentParser(description="Download NVIDIA Cosmos-1.0 Diffusion models from Hugging Face")
parser.add_argument(
"--model_sizes",
nargs="*",
default=[
"7B",
"14B",
], # Download all by default
choices=["7B", "14B"],
help="Which model sizes to download. Possible values: 7B, 14B",
)
parser.add_argument(
"--model_types",
nargs="*",
default=[
"Text2World",
"Video2World",
], # Download all by default
choices=["Text2World", "Video2World"],
help="Which model types to download. Possible values: Text2World, Video2World",
)
parser.add_argument(
"--cosmos_version",
type=str,
default="1.0",
choices=["1.0"],
help="Which version of Cosmos to download. Only 1.0 is available at the moment.",
)
parser.add_argument(
"--checkpoint_dir", type=str, default="checkpoints", help="Directory to save the downloaded checkpoints."
)
args = parser.parse_args()
return args
def main(args):
ORG_NAME = "nvidia"
# Mapping from size argument to Hugging Face repository name
model_map = {
"7B": "Cosmos-1.0-Diffusion-7B",
"14B": "Cosmos-1.0-Diffusion-14B",
}
# Additional models that are always downloaded
extra_models = [
"Cosmos-1.0-Guardrail",
"Cosmos-1.0-Tokenizer-CV8x8x8",
]
if "Text2World" in args.model_types:
extra_models.append("Cosmos-1.0-Prompt-Upsampler-12B-Text2World")
# Create local checkpoints folder
checkpoints_dir = Path(args.checkpoint_dir)
checkpoints_dir.mkdir(parents=True, exist_ok=True)
download_kwargs = dict(allow_patterns=["README.md", "model.pt", "config.json", "*.jit"])
# Download the requested Autoregressive models
for size in args.model_sizes:
for model_type in args.model_types:
suffix = f"-{model_type}"
model_name = model_map[size] + suffix
repo_id = f"{ORG_NAME}/{model_name}"
local_dir = checkpoints_dir.joinpath(model_name)
local_dir.mkdir(parents=True, exist_ok=True)
print(f"Downloading {repo_id} to {local_dir}...")
snapshot_download(
repo_id=repo_id, local_dir=str(local_dir), local_dir_use_symlinks=False, **download_kwargs
)
# Download the always-included models
for model_name in extra_models:
repo_id = f"{ORG_NAME}/{model_name}"
local_dir = checkpoints_dir.joinpath(model_name)
local_dir.mkdir(parents=True, exist_ok=True)
print(f"Downloading {repo_id} to {local_dir}...")
# Download all files for Guardrail
snapshot_download(
repo_id=repo_id,
local_dir=str(local_dir),
local_dir_use_symlinks=False,
)
if "Video2World" in args.model_types:
# Prompt Upsampler for Cosmos-1.0-Diffusion-Video2World models
convert_pixtral_checkpoint(
checkpoint_dir=args.checkpoint_dir,
checkpoint_name="Pixtral-12B",
vit_type="pixtral-12b-vit",
)
if __name__ == "__main__":
args = parse_args()
main(args)
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