hao-ai-lab-FastVideo/examples/inference/basic/basic_longcat_vc.py

"""
LongCat Video Continuation (VC) Example Script

This script demonstrates LongCat VC inference using the FastVideo Python API.
LongCat VC takes an input video and generates a continuation of it.

It runs both basic generation (50 steps) and distill+refine generation
(16 steps distill + 50 steps refinement to 720p).

Usage:
    python examples/inference/basic/basic_longcat_vc.py

Prerequisites:
    - Ensure the input video exists at assets/motorcycle.mp4
      (or provide your own video)
"""

import glob
import os

from fastvideo import VideoGenerator

# Common prompts and settings matching the shell script examples
PROMPT = ("A person rides a motorcycle along a long, straight road that stretches between "
          "a body of water and a forested hillside. The rider steadily accelerates, keeping "
          "the motorcycle centered between the guardrails, while the scenery passes by on "
          "both sides. The video captures the journey from the rider's perspective, emphasizing "
          "the sense of motion and adventure.")

NEGATIVE_PROMPT = ("Bright tones, overexposed, static, blurred details, subtitles, style, works, "
                   "paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
                   "residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
                   "deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
                   "three legs, many people in the background, walking backwards")

# Input video path
VIDEO_PATH = "assets/motorcycle.mp4"

# Number of conditioning frames from the input video
NUM_COND_FRAMES = 13

SEED = 42


def basic_generation():
    """
    Run basic LongCat VC generation (50 steps at 480p).
    
    This uses the full 50-step denoising process for highest quality.
    """
    print("=" * 60)
    print("LongCat VC: Basic Generation (50 steps, 480p)")
    print("=" * 60)

    # Check if video exists
    if not os.path.exists(VIDEO_PATH):
        raise FileNotFoundError(f"Video not found at {VIDEO_PATH}. "
                                "Please provide a valid video path.")

    generator = VideoGenerator.from_config({
        "model_path": "FastVideo/LongCat-Video-VC-Diffusers",
        "engine": {
            "num_gpus": 1,
            "use_fsdp_inference": False,  # set to True if GPU is out of memory
            "offload": {
                "dit": False,
                "vae": True,
                "text_encoder": True,
                "pin_cpu_memory": False,
            },
        },
        "pipeline": {"model": {"longcat": {"enable_bsa": False}}},
    })

    output_path = "outputs_video/longcat_vc_basic"

    generator.generate({
        "prompt": PROMPT,
        "negative_prompt": NEGATIVE_PROMPT,
        "inputs": {"video_path": VIDEO_PATH},
        "sampling": {
            "height": 480,
            "width": 832,
            "num_frames": 93,
            "num_inference_steps": 50,
            "fps": 15,
            "guidance_scale": 4.0,
            "seed": SEED,
        },
        "output": {"output_path": output_path, "save_video": True},
        "extensions": {"num_cond_frames": NUM_COND_FRAMES},
    })

    print(f"\nBasic generation complete! Video saved to: {output_path}")
    generator.shutdown()


def distill_refine_generation():
    """
    Run LongCat VC with distill+refine pipeline (16 steps + refinement to 720p).
    
    This uses the distilled LoRA for fast 480p generation (16 steps),
    then refines to 720p using the refinement LoRA with BSA enabled.
    """
    print("\n" + "=" * 60)
    print("LongCat VC: Distill + Refine Pipeline")
    print("=" * 60)

    # Check if video exists
    if not os.path.exists(VIDEO_PATH):
        raise FileNotFoundError(f"Video not found at {VIDEO_PATH}. "
                                "Please provide a valid video path.")

    # Stage 1: Distilled generation (16 steps at 480p)
    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
    print("-" * 40)

    generator = VideoGenerator.from_config({
        "model_path": "FastVideo/LongCat-Video-VC-Diffusers",
        "engine": {
            "num_gpus": 1,
            "use_fsdp_inference": True,
            "offload": {
                "dit": False,
                "vae": True,
                "text_encoder": True,
                "pin_cpu_memory": False,
            },
        },
        "pipeline": {
            "model": {"longcat": {"enable_bsa": False}},
            "components": {
                "lora_path": "FastVideo/LongCat-Video-T2V-Distilled-LoRA",
                "lora_nickname": "distilled",
            },
        },
    })

    distill_output_path = "outputs_video/longcat_vc_distill"

    generator.generate({
        "prompt": PROMPT,
        "negative_prompt": NEGATIVE_PROMPT,
        "inputs": {"video_path": VIDEO_PATH},
        "sampling": {
            "height": 480,
            "width": 832,
            "num_frames": 93,
            "num_inference_steps": 16,
            "fps": 15,
            "guidance_scale": 1.0,
            "seed": SEED,
        },
        "output": {"output_path": distill_output_path, "save_video": True},
        "extensions": {"num_cond_frames": NUM_COND_FRAMES},
    })

    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
    generator.shutdown()

    # Stage 2: Refinement (480p -> 720p)
    print("\n[Stage 2] Refinement (480p -> 720p with BSA)")
    print("-" * 40)

    # Find the actual saved video file from stage 1
    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
    if not video_files:
        raise FileNotFoundError(f"No video file found in {distill_output_path}")
    # Use the most recently created video file
    distill_video_path = max(video_files, key=os.path.getmtime)
    print(f"Using stage 1 video: {distill_video_path}")

    # Create a new generator with refinement LoRA and BSA enabled
    # Note: Refinement uses the T2V model (not VC) since it's upscaling the generated video
    refine_generator = VideoGenerator.from_config({
        "model_path": "FastVideo/LongCat-Video-T2V-Diffusers",
        "engine": {
            "num_gpus": 1,
            "use_fsdp_inference": True,
            "offload": {
                "dit": True,
                "vae": True,
                "text_encoder": True,
                "pin_cpu_memory": False,
            },
        },
        "pipeline": {
            "model": {
                "longcat": {
                    "enable_bsa": True,
                    "bsa_sparsity": 0.875,
                    "bsa_chunk_q": [4, 4, 8],
                    "bsa_chunk_k": [4, 4, 8],
                },
            },
            "components": {
                "lora_path": "FastVideo/LongCat-Video-T2V-Refinement-LoRA",
                "lora_nickname": "refinement",
            },
        },
    })

    refine_output_path = "outputs_video/longcat_vc_refine_720p"

    refine_generator.generate({
        "prompt": PROMPT,
        "negative_prompt": NEGATIVE_PROMPT,
        "inputs": {"refine_from": distill_video_path},
        "sampling": {
            "height": 720,
            "width": 1280,
            "num_inference_steps": 50,
            "fps": 30,
            "guidance_scale": 1.0,
            "seed": SEED,
        },
        "output": {"output_path": refine_output_path, "save_video": True},
        "extensions": {
            "t_thresh": 0.5,
            "spatial_refine_only": False,
            "num_cond_frames": 0,  # For refinement, no conditioning frames
        },
    })

    print(f"Refinement complete! Video saved to: {refine_output_path}")
    refine_generator.shutdown()


def main():
    """Run both basic and distill+refine generation pipelines."""
    print("\n" + "=" * 60)
    print("LongCat Video Continuation Example")
    print("=" * 60 + "\n")

    # Run basic generation
    basic_generation()

    # Run distill+refine pipeline
    distill_refine_generation()

    print("\n" + "=" * 60)
    print("All generations complete!")
    print("=" * 60)


if __name__ == "__main__":
    main()