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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 cosmos_transfer1.auxiliary.upsampler.model.upsampler import PixtralPromptUpsampler
from cosmos_transfer1.utils.misc import extract_video_frames
def parse_args():
parser = argparse.ArgumentParser(description="Prompt upsampler pipeline")
parser.add_argument("--prompt", type=str, required=False, help="Prompt to upsample")
parser.add_argument("--input_video", type=str, required=True, help="Path to input video file")
parser.add_argument(
"--checkpoint_dir", type=str, default="checkpoints", help="Base directory containing model checkpoints"
)
parser.add_argument(
"--offload_prompt_upsampler", action="store_true", help="Offload prompt upsampler model after inference"
)
return parser.parse_args()
def main():
args = parse_args()
model = PixtralPromptUpsampler(args.checkpoint_dir, offload_prompt_upsampler=args.offload_prompt_upsampler)
# Upsample the prompt with the given video
frame_paths = extract_video_frames(args.input_video)
upsampled_prompt = model._prompt_upsample_with_offload(args.prompt, frame_paths)
print("Upsampled prompt:", upsampled_prompt)
if __name__ == "__main__":
import os
rank = int(os.environ["RANK"])
dist_keys = [
"RANK",
"LOCAL_RANK",
"WORLD_SIZE",
"LOCAL_WORLD_SIZE",
"GROUP_RANK",
"ROLE_RANK",
"ROLE_NAME",
"OMP_NUM_THREADS",
"MASTER_ADDR",
"MASTER_PORT",
"TORCHELASTIC_USE_AGENT_STORE",
"TORCHELASTIC_MAX_RESTARTS",
"TORCHELASTIC_RUN_ID",
"TORCH_NCCL_ASYNC_ERROR_HANDLING",
"TORCHELASTIC_ERROR_FILE",
]
for dist_key in dist_keys:
del os.environ[dist_key]
if rank == 0:
main()
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