vllm-omni/examples/offline_inference/ltx2/text_to_video.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import argparse
from pathlib import Path

import numpy as np
import torch

from vllm_omni.entrypoints.omni import Omni
from vllm_omni.outputs import OmniRequestOutput
from vllm_omni.utils.platform_utils import detect_device_type


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Generate a video with LTX-2 T2V.")
    parser.add_argument(
        "--model",
        default="Lightricks/LTX-2",
        help="LTX-2 model ID or local path.",
    )
    parser.add_argument(
        "--prompt",
        default="A serene lakeside sunrise with mist over the water.",
        help="Text prompt for video generation.",
    )
    parser.add_argument("--negative_prompt", default="", help="Negative prompt.")
    parser.add_argument("--seed", type=int, default=42, help="Random seed.")
    parser.add_argument("--guidance_scale", type=float, default=4.0, help="CFG scale.")
    parser.add_argument("--height", type=int, default=512, help="Video height (must be divisible by 32).")
    parser.add_argument("--width", type=int, default=768, help="Video width (must be divisible by 32).")
    parser.add_argument(
        "--num_frames", type=int, default=121, help="Number of frames (should be 8*n+1, e.g., 25, 81, 121)."
    )
    parser.add_argument("--num_inference_steps", type=int, default=40, help="Sampling steps.")
    parser.add_argument("--output", type=str, default="ltx2_output.mp4", help="Path to save the video (mp4).")
    parser.add_argument("--fps", type=int, default=24, help="Frames per second for the output video.")
    return parser.parse_args()


def main():
    args = parse_args()
    device = detect_device_type()
    generator = torch.Generator(device=device).manual_seed(args.seed)

    omni = Omni(
        model=args.model,
    )

    frames = omni.generate(
        args.prompt,
        negative_prompt=args.negative_prompt,
        height=args.height,
        width=args.width,
        generator=generator,
        guidance_scale=args.guidance_scale,
        num_inference_steps=args.num_inference_steps,
        num_frames=args.num_frames,
    )

    # Extract video frames from OmniRequestOutput
    if isinstance(frames, list) and len(frames) > 0:
        first_item = frames[0]

        # Check if it's an OmniRequestOutput
        if hasattr(first_item, "final_output_type"):
            if first_item.final_output_type != "image":
                raise ValueError(
                    f"Unexpected output type '{first_item.final_output_type}', "
                    f"expected 'image' for video frames output."
                )

            # Pipeline mode: extract from nested request_output
            if hasattr(first_item, "is_pipeline_output") and first_item.is_pipeline_output:
                if isinstance(first_item.request_output, list) and len(first_item.request_output) > 0:
                    inner_output = first_item.request_output[0]
                    if isinstance(inner_output, OmniRequestOutput) and hasattr(inner_output, "images"):
                        frames = inner_output.images[0] if inner_output.images else None
                        if frames is None:
                            raise ValueError("No video frames found in output.")
            # Diffusion mode: use direct images field
            elif hasattr(first_item, "images") and first_item.images:
                frames = first_item.images
            else:
                raise ValueError("No video frames found in OmniRequestOutput.")

    output_path = Path(args.output)
    output_path.parent.mkdir(parents=True, exist_ok=True)
    try:
        from diffusers.utils import export_to_video
    except ImportError:
        raise ImportError("diffusers is required for export_to_video.")

    # frames may be np.ndarray (preferred) or torch.Tensor
    # export_to_video expects a list of frames with values in [0, 1]
    if isinstance(frames, torch.Tensor):
        video_tensor = frames.detach().cpu()
        if video_tensor.dim() == 5:
            # [B, C, F, H, W] or [B, F, H, W, C]
            if video_tensor.shape[1] in (3, 4):
                video_tensor = video_tensor[0].permute(1, 2, 3, 0)
            else:
                video_tensor = video_tensor[0]
        elif video_tensor.dim() == 4 and video_tensor.shape[0] in (3, 4):
            video_tensor = video_tensor.permute(1, 2, 3, 0)
        # If float, assume [-1,1] and normalize to [0,1]
        if video_tensor.is_floating_point():
            video_tensor = video_tensor.clamp(-1, 1) * 0.5 + 0.5
        video_array = video_tensor.float().numpy()
    else:
        video_array = frames
        if hasattr(video_array, "shape") and video_array.ndim == 5:
            video_array = video_array[0]

    # Convert 4D array (frames, H, W, C) to list of frames for export_to_video
    if isinstance(video_array, np.ndarray) and video_array.ndim == 4:
        video_array = list(video_array)

    export_to_video(video_array, str(output_path), fps=args.fps)
    print(f"Saved generated video to {output_path}")


if __name__ == "__main__":
    main()