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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
import hashlib
from pathlib import Path
from huggingface_hub import snapshot_download
from scripts.download_guardrail_checkpoints import download_guardrail_checkpoints
def parse_args():
parser = argparse.ArgumentParser(
description="Download NVIDIA Cosmos Predict1 autoregressive models from Hugging Face"
)
parser.add_argument(
"--model_sizes",
nargs="*",
default=[
"4B",
"5B",
"12B",
"13B",
], # Download all by default
choices=["4B", "5B", "12B", "13B"],
help="Which model sizes to download. Possible values: 4B, 5B, 12B, 13B.",
)
parser.add_argument(
"--checkpoint_dir", type=str, default="checkpoints", help="Directory to save the downloaded checkpoints."
)
args = parser.parse_args()
return args
MD5_CHECKSUM_LOOKUP = {
"Cosmos-Predict1-12B/guardrail/video_content_safety_filter/safety_filter.pt": "b46dc2ad821fc3b0d946549d7ade19cf",
"Cosmos-Predict1-12B/model.pt": "ed748fabcb544d30d35385a8c28efb4d",
"Cosmos-Predict1-13B-Video2World/guardrail/video_content_safety_filter/safety_filter.pt": "b46dc2ad821fc3b0d946549d7ade19cf",
"Cosmos-Predict1-13B-Video2World/model.pt": "21a9fb02c61fbebc96c1af1fcaa5893f",
"Cosmos-Predict1-4B/guardrail/video_content_safety_filter/safety_filter.pt": "b46dc2ad821fc3b0d946549d7ade19cf",
"Cosmos-Predict1-4B/model.pt": "5fdc62fc87fbf470dbcc2288589b7942",
"Cosmos-Predict1-5B-Video2World/guardrail/video_content_safety_filter/safety_filter.pt": "b46dc2ad821fc3b0d946549d7ade19cf",
"Cosmos-Predict1-5B-Video2World/model.pt": "2a48a854bb6e04abb6b7c72979f1a69b",
"Cosmos-Predict1-7B-Decoder-DV8x16x16ToCV8x8x8-720p/aux_vars.pt": "29e450d81839e82bb4bdbf12e43a74f1",
"Cosmos-Predict1-7B-Decoder-DV8x16x16ToCV8x8x8-720p/model.pt": "a30149cc3730f3142b01fd374b6076f8",
"Cosmos-Predict1-7B-Decoder-DV8x16x16ToCV8x8x8-720p/guardrail/video_content_safety_filter/safety_filter.pt": "b46dc2ad821fc3b0d946549d7ade19cf",
"Cosmos-Tokenize1-CV8x8x8-720p/autoencoder.jit": "7f658580d5cf617ee1a1da85b1f51f0d",
"Cosmos-Tokenize1-CV8x8x8-720p/decoder.jit": "ff21a63ed817ffdbe4b6841111ec79a8",
"Cosmos-Tokenize1-CV8x8x8-720p/encoder.jit": "f5834d03645c379bc0f8ad14b9bc0299",
"Cosmos-Tokenize1-CV8x8x8-720p/mean_std.pt": "f07680ad7eefae57d698778e2a0c7c96",
"Cosmos-Tokenize1-CV8x8x8-720p/image_mean_std.pt": "9f19fd3312fc1198e4905ada02e68bce",
"Cosmos-Tokenize1-DV8x16x16-720p/autoencoder.jit": "606b8585b637f06057725cbb67036ae6",
"Cosmos-Tokenize1-DV8x16x16-720p/decoder.jit": "f0c8a9d992614a43e7ce24ebfc901e26",
"Cosmos-Tokenize1-DV8x16x16-720p/encoder.jit": "95186b0410346a3f0cf250b76daec452",
"google-t5/t5-11b/pytorch_model.bin": "f890878d8a162e0045a25196e27089a3",
"google-t5/t5-11b/tf_model.h5": "e081fc8bd5de5a6a9540568241ab8973",
}
def get_md5_checksum(checkpoints_dir, model_name):
print("---------------------")
for key, value in MD5_CHECKSUM_LOOKUP.items():
if key.startswith(model_name + "/"):
print(f"Verifying checkpoint {key}...")
file_path = checkpoints_dir.joinpath(key)
# File must exist
if not Path(file_path).exists():
print(f"Checkpoint {key} does not exist.")
return False
# File must match give MD5 checksum
with open(file_path, "rb") as f:
file_md5 = hashlib.md5(f.read()).hexdigest()
if file_md5 != value:
print(f"MD5 checksum of checkpoint {key} does not match.")
return False
print(f"Model checkpoints for {model_name} exist with matched MD5 checksums.")
return True
def main(args):
ORG_NAME = "nvidia"
# Mapping from size argument to Hugging Face repository name
model_map = {
"4B": "Cosmos-Predict1-4B",
"5B": "Cosmos-Predict1-5B-Video2World",
"12B": "Cosmos-Predict1-12B",
"13B": "Cosmos-Predict1-13B-Video2World",
}
# Additional models that are always downloaded
extra_models = [
"Cosmos-Predict1-7B-Decoder-DV8x16x16ToCV8x8x8-720p",
"Cosmos-Tokenize1-CV8x8x8-720p",
"Cosmos-Tokenize1-DV8x16x16-720p",
"google-t5/t5-11b",
]
# 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", "image_mean_std.pt", "mean_std.pt", "config.json", "*.jit"]
)
# Download the requested Autoregressive models
for size in args.model_sizes:
model_name = model_map[size]
repo_id = f"{ORG_NAME}/{model_name}"
local_dir = checkpoints_dir.joinpath(model_name)
if not get_md5_checksum(checkpoints_dir, 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:
if model_name == "google-t5/t5-11b":
repo_id = model_name
else:
repo_id = f"{ORG_NAME}/{model_name}"
local_dir = checkpoints_dir.joinpath(model_name)
if not get_md5_checksum(checkpoints_dir, model_name):
local_dir.mkdir(parents=True, exist_ok=True)
print(f"Downloading {repo_id} to {local_dir}...")
# Download all files
snapshot_download(
repo_id=repo_id,
local_dir=str(local_dir),
local_dir_use_symlinks=False,
)
download_guardrail_checkpoints(args.checkpoint_dir)
if __name__ == "__main__":
args = parse_args()
main(args)
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