cosmos_predict1/tokenizer/inference/video_cli.py

# 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.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""A CLI to run CausalVideoTokenizer on plain videos based on torch.jit.

Usage:
    python3 -m cosmos_predict1.tokenizer.inference.video_cli \
        --video_pattern 'path/to/video/samples/*.mp4' \
        --output_dir ./reconstructions \
        --checkpoint_enc ./checkpoints/<model-name>/encoder.jit \
        --checkpoint_dec ./checkpoints/<model-name>/decoder.jit

    Optionally, you can run the model in pure PyTorch mode:
    python3 -m cosmos_predict1.tokenizer.inference.video_cli \
        --video_pattern 'path/to/video/samples/*.mp4' \
        --mode=torch \
        --tokenizer_type=CV \
        --temporal_compression=4 \
        --spatial_compression=8 \
        --checkpoint_enc ./checkpoints/<model-name>/encoder.jit \
        --checkpoint_dec ./checkpoints/<model-name>/decoder.jit
"""

import os
import sys
from argparse import ArgumentParser, Namespace
from typing import Any

import numpy as np
from loguru import logger as logging

from cosmos_predict1.tokenizer.inference.utils import (
    get_filepaths,
    get_output_filepath,
    read_video,
    resize_video,
    write_video,
)
from cosmos_predict1.tokenizer.inference.video_lib import CausalVideoTokenizer
from cosmos_predict1.tokenizer.networks import TokenizerConfigs


def _parse_args() -> tuple[Namespace, dict[str, Any]]:
    parser = ArgumentParser(description="A CLI for CausalVideoTokenizer.")
    parser.add_argument(
        "--video_pattern",
        type=str,
        default="path/to/videos/*.mp4",
        help="Glob pattern.",
    )
    parser.add_argument(
        "--checkpoint",
        type=str,
        default=None,
        help="JIT full Autoencoder model filepath.",
    )
    parser.add_argument(
        "--checkpoint_enc",
        type=str,
        default=None,
        help="JIT Encoder model filepath.",
    )
    parser.add_argument(
        "--checkpoint_dec",
        type=str,
        default=None,
        help="JIT Decoder model filepath.",
    )
    parser.add_argument(
        "--tokenizer_type",
        type=str,
        default=None,
        choices=[
            "CV8x8x8-720p",
            "DV8x16x16-720p",
            "CV4x8x8-360p",
            "DV4x8x8-360p",
        ],
        help="Specifies the tokenizer type.",
    )
    parser.add_argument(
        "--mode",
        type=str,
        choices=["torch", "jit"],
        default="jit",
        help="Specify the backend: native 'torch' or 'jit' (default: 'jit')",
    )
    parser.add_argument(
        "--short_size",
        type=int,
        default=None,
        help="The size to resample inputs. None, by default.",
    )
    parser.add_argument(
        "--temporal_window",
        type=int,
        default=17,
        help="The temporal window to operate at a time.",
    )
    parser.add_argument(
        "--dtype",
        type=str,
        default="bfloat16",
        help="Sets the precision, default bfloat16.",
    )
    parser.add_argument(
        "--device",
        type=str,
        default="cuda",
        help="Device for invoking the model.",
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Output directory.")
    parser.add_argument(
        "--output_fps",
        type=float,
        default=24.0,
        help="Output frames-per-second (FPS).",
    )
    parser.add_argument(
        "--save_input",
        action="store_true",
        help="If on, the input video will be be outputted too.",
    )
    args = parser.parse_args()
    return args


logging.info("Initializes args ...")
args = _parse_args()
if args.mode == "torch" and args.tokenizer_type is None:
    logging.error("`torch` backend requires `--tokenizer_type` to be specified.")
    sys.exit(1)


def _run_eval() -> None:
    """Invokes JIT-compiled CausalVideoTokenizer on an input video."""

    if args.checkpoint_enc is None and args.checkpoint_dec is None and args.checkpoint is None:
        logging.warning("Aborting. Both encoder or decoder JIT required. Or provide the full autoencoder JIT model.")
        return

    if args.mode == "torch":
        _type = args.tokenizer_type.replace("-", "_")
        _config = TokenizerConfigs[_type].value
    else:
        _config = None

    logging.info(
        f"Loading a torch.jit model `{os.path.dirname(args.checkpoint or args.checkpoint_enc or args.checkpoint_dec)}` ..."
    )
    autoencoder = CausalVideoTokenizer(
        checkpoint=args.checkpoint,
        checkpoint_enc=args.checkpoint_enc,
        checkpoint_dec=args.checkpoint_dec,
        tokenizer_config=_config,
        device=args.device,
        dtype=args.dtype,
    )

    logging.info(f"Looking for files matching video_pattern={args.video_pattern} ...")
    filepaths = get_filepaths(args.video_pattern)
    logging.info(f"Found {len(filepaths)} videos from {args.video_pattern}.")

    for filepath in filepaths:
        logging.info(f"Reading video {filepath} ...")
        video = read_video(filepath)
        video = resize_video(video, short_size=args.short_size)

        logging.info("Invoking the autoencoder model in ... ")
        batch_video = video[np.newaxis, ...]
        output_video = autoencoder(batch_video, temporal_window=args.temporal_window)[0]
        logging.info("Constructing output filepath ...")
        output_filepath = get_output_filepath(filepath, output_dir=args.output_dir)
        logging.info(f"Outputing {output_filepath} ...")
        write_video(output_filepath, output_video, fps=args.output_fps)
        if args.save_input:
            ext = os.path.splitext(output_filepath)[-1]
            input_filepath = output_filepath.replace(ext, "_input" + ext)
            write_video(input_filepath, video, fps=args.output_fps)


@logging.catch(reraise=True)
def main() -> None:
    _run_eval()


if __name__ == "__main__":
    main()