Spaces:
Build error
Build error
File size: 6,808 Bytes
b6af722 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
# 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
import os
from pathlib import Path
from huggingface_hub import snapshot_download
from scripts.download_guardrail_checkpoints import download_guardrail_checkpoints
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="A script to download NVIDIA Cosmos-Tokenizer1 models from Hugging Face"
)
parser.add_argument(
"--tokenizer_types",
nargs="*",
default=[
"CV8x8x8-720p",
"DV8x16x16-720p",
"CI8x8-360p",
"CI16x16-360p",
"CV4x8x8-360p",
"DI8x8-360p",
"DI16x16-360p",
"DV4x8x8-360p",
], # Download all by default
choices=[
"CV8x8x8-720p",
"DV8x16x16-720p",
"CI8x8-360p",
"CI16x16-360p",
"CV4x8x8-360p",
"DI8x8-360p",
"DI16x16-360p",
"DV4x8x8-360p",
],
help="Which tokenizer model types to download. Possible values: CV8x8x8-720p, DV8x16x16-720p, CV4x8x8-360p, DV4x8x8-360p",
)
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-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-CI16x16-360p/autoencoder.jit": "98f8fdf2ada5537705d6d1bc22c63cf1",
"Cosmos-Tokenize1-CI16x16-360p/decoder.jit": "dd31a73a8c7062bab25492401d83b473",
"Cosmos-Tokenize1-CI16x16-360p/encoder.jit": "7be1dadea5a1c283996ca1ce5b1a95a9",
"Cosmos-Tokenize1-CI8x8-360p/autoencoder.jit": "b2ff9280b12a97202641bb2a41d7b271",
"Cosmos-Tokenize1-CI8x8-360p/decoder.jit": "57fb213cd88c0a991e9d400875164571",
"Cosmos-Tokenize1-CI8x8-360p/encoder.jit": "138fe257df41d7a43c17396c23086565",
"Cosmos-Tokenize1-CV4x8x8-360p/autoencoder.jit": "0690ff725700128424d082b44a1eda08",
"Cosmos-Tokenize1-CV4x8x8-360p/decoder.jit": "7573744ec14cb1b2abdf9c80318b7224",
"Cosmos-Tokenize1-CV4x8x8-360p/encoder.jit": "fe3a7193defcb2db0b849b6df480b5e6",
"Cosmos-Tokenize1-CV8x8x8-720p/autoencoder.jit": "7f658580d5cf617ee1a1da85b1f51f0d",
"Cosmos-Tokenize1-CV8x8x8-720p/decoder.jit": "ff21a63ed817ffdbe4b6841111ec79a8",
"Cosmos-Tokenize1-CV8x8x8-720p/encoder.jit": "f5834d03645c379bc0f8ad14b9bc0299",
"Cosmos-Tokenize1-DI16x16-360p/autoencoder.jit": "88195130b86c3434d3d4b0e0376def6b",
"Cosmos-Tokenize1-DI16x16-360p/decoder.jit": "bf27a567388902acbd8abcc3a5afd8dd",
"Cosmos-Tokenize1-DI16x16-360p/encoder.jit": "12bae3a56c79a7ca0beb774843ee8c58",
"Cosmos-Tokenize1-DI8x8-360p/autoencoder.jit": "1d638e6034fcd43619bc1cdb343ebe56",
"Cosmos-Tokenize1-DI8x8-360p/decoder.jit": "b9b5eccaa7ab9ffbccae3b05b3903311",
"Cosmos-Tokenize1-DI8x8-360p/encoder.jit": "2bfa3c189aacdf9dc8faf17bcc30dd82",
"Cosmos-Tokenize1-DV4x8x8-360p/autoencoder.jit": "ff8802dc4497be60dc24a8f692833eed",
"Cosmos-Tokenize1-DV4x8x8-360p/decoder.jit": "f9a7d4bd24e4d2ee210cfd5f21550ce8",
"Cosmos-Tokenize1-DV4x8x8-360p/encoder.jit": "7af30a0223b2984d9d27dd3054fcd7af",
"Cosmos-Tokenize1-DV8x16x16-720p/autoencoder.jit": "606b8585b637f06057725cbb67036ae6",
"Cosmos-Tokenize1-DV8x16x16-720p/decoder.jit": "f0c8a9d992614a43e7ce24ebfc901e26",
"Cosmos-Tokenize1-DV8x16x16-720p/encoder.jit": "95186b0410346a3f0cf250b76daec452",
}
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) -> None:
ORG_NAME = "nvidia"
# Mapping from size argument to Hugging Face repository name
model_map = {
"CV8x8x8-720p": "Cosmos-Tokenize1-CV8x8x8-720p",
"DV8x16x16-720p": "Cosmos-Tokenize1-DV8x16x16-720p",
"CI8x8-360p": "Cosmos-Tokenize1-CI8x8-360p",
"CI16x16-360p": "Cosmos-Tokenize1-CI16x16-360p",
"CV4x8x8-360p": "Cosmos-Tokenize1-CV4x8x8-360p",
"DI8x8-360p": "Cosmos-Tokenize1-DI8x8-360p",
"DI16x16-360p": "Cosmos-Tokenize1-DI16x16-360p",
"DV4x8x8-360p": "Cosmos-Tokenize1-DV4x8x8-360p",
}
# 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", "mean_std.pt", "config.json", "*.jit"])
# Download the requested Tokenizer models
for tokenizer_type in args.tokenizer_types:
model_name = model_map[tokenizer_type]
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_guardrail_checkpoints(args.checkpoint_dir)
if __name__ == "__main__":
args = parse_args()
main(args)
|