[examples] Added longcat-video python api examples (#994)
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Shao Duan
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"""
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LongCat Image-to-Video (I2V) Example Script
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This script demonstrates LongCat I2V inference using the FastVideo Python API.
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LongCat I2V takes an input image and generates a video from it.
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It runs both basic generation (50 steps) and distill+refine generation
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(16 steps distill + 50 steps refinement to 720p with BSA).
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Usage:
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python examples/inference/basic/basic_longcat_i2v.py
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Note:
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Refinement uses 768x768 dimensions where latent (48x48) is divisible by 8,
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compatible with BSA chunks [4, 4, 8].
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"""
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import glob
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import os
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from fastvideo import VideoGenerator
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# Common prompts and settings matching the shell script examples
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PROMPT = (
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"A woman sits at a wooden table by the window in a cozy café. She reaches out "
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"with her right hand, picks up the white coffee cup from the saucer, and gently "
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"brings it to her lips to take a sip. After drinking, she places the cup back on "
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"the table and looks out the window, enjoying the peaceful atmosphere."
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)
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NEGATIVE_PROMPT = (
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"Bright tones, overexposed, static, blurred details, subtitles, style, works, "
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"paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
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"residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
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"deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
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"three legs, many people in the background, walking backwards"
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)
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# Input image path
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IMAGE_PATH = "assets/girl.png"
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SEED = 42
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def basic_generation():
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"""
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Run basic LongCat I2V generation (50 steps at 480p).
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This uses the full 50-step denoising process for highest quality.
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"""
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print("=" * 60)
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print("LongCat I2V: Basic Generation (50 steps, 480p)")
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print("=" * 60)
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generator = VideoGenerator.from_pretrained(
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"FastVideo/LongCat-Video-I2V-Diffusers",
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num_gpus=1,
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use_fsdp_inference=True,
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dit_cpu_offload=False,
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vae_cpu_offload=True,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=False,
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enable_bsa=False,
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)
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output_path = "outputs_video/longcat_i2v_basic"
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generator.generate_video(
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prompt=PROMPT,
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negative_prompt=NEGATIVE_PROMPT,
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image_path=IMAGE_PATH,
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output_path=output_path,
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save_video=True,
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height=480,
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width=480, # Square
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num_frames=93,
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num_inference_steps=50,
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fps=15,
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guidance_scale=4.0,
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seed=SEED,
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)
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print(f"\nBasic generation complete! Video saved to: {output_path}")
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generator.shutdown()
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def distill_refine_generation():
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"""
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Run LongCat I2V with distill+refine pipeline (16 steps + refinement to 768p).
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This uses the distilled LoRA for fast 480p generation (16 steps),
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then refines to 768p using the refinement LoRA with BSA enabled.
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"""
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print("\n" + "=" * 60)
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print("LongCat I2V: Distill + Refine Pipeline")
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print("=" * 60)
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# Stage 1: Distilled generation (16 steps at 480p)
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print("\n[Stage 1] Distilled generation (16 steps, 480p)")
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print("-" * 40)
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generator = VideoGenerator.from_pretrained(
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"FastVideo/LongCat-Video-I2V-Diffusers",
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num_gpus=1,
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use_fsdp_inference=True,
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dit_cpu_offload=False,
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vae_cpu_offload=True,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=False,
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enable_bsa=False,
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lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
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lora_nickname="distilled",
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)
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distill_output_path = "outputs_video/longcat_i2v_distill"
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generator.generate_video(
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prompt=PROMPT,
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negative_prompt=NEGATIVE_PROMPT,
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image_path=IMAGE_PATH,
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output_path=distill_output_path,
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save_video=True,
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height=480,
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width=480, # Square
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num_frames=93,
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num_inference_steps=16,
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fps=15,
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guidance_scale=1.0,
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seed=SEED,
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)
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print(f"Distilled generation complete! Video saved to: {distill_output_path}")
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generator.shutdown()
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# Stage 2: Refinement (480p -> 768p)
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print("\n[Stage 2] Refinement (480p -> 768p with BSA)")
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print("-" * 40)
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# Find the actual saved video file from stage 1
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video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
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if not video_files:
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raise FileNotFoundError(f"No video file found in {distill_output_path}")
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# Use the most recently created video file
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distill_video_path = max(video_files, key=os.path.getmtime)
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print(f"Using stage 1 video: {distill_video_path}")
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# Create a new generator with refinement LoRA and BSA enabled
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# Note: Refinement uses the T2V model (not I2V) since it's upscaling the generated video
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# For BSA [4, 4, 8]: latent must be divisible by 8
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# 768x768: latent 48x48, 48%8=0 ✓
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refine_generator = VideoGenerator.from_pretrained(
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"FastVideo/LongCat-Video-T2V-Diffusers",
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num_gpus=1,
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use_fsdp_inference=True,
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dit_cpu_offload=True,
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vae_cpu_offload=True,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=False,
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enable_bsa=True,
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bsa_sparsity=0.875,
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bsa_chunk_q=[4, 4, 4],
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bsa_chunk_k=[4, 4, 4],
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lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
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lora_nickname="refinement",
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)
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refine_output_path = "outputs_video/longcat_i2v_refine_720p"
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refine_generator.generate_video(
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prompt=PROMPT,
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negative_prompt=NEGATIVE_PROMPT,
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output_path=refine_output_path,
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save_video=True,
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refine_from=distill_video_path,
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t_thresh=0.5,
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spatial_refine_only=False,
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num_cond_frames=0,
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height=720,
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width=720,
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num_inference_steps=50,
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fps=30,
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guidance_scale=1.0,
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seed=SEED,
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)
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print(f"Refinement complete! Video saved to: {refine_output_path}")
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refine_generator.shutdown()
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def main():
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"""Run both basic and distill+refine generation pipelines."""
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print("\n" + "=" * 60)
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print("LongCat Image-to-Video Example")
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print("=" * 60 + "\n")
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# Run basic generation
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basic_generation()
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# Run distill+refine pipeline
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distill_refine_generation()
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print("\n" + "=" * 60)
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print("All generations complete!")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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