from fastvideo import VideoGenerator

# from fastvideo.configs.sample import SamplingParam

OUTPUT_PATH = "video_samples"
def main():
    # FastVideo will automatically use the optimal default arguments for the
    # model.
    # If a local path is provided, FastVideo will make a best effort
    # attempt to identify the optimal arguments.
    generator = VideoGenerator.from_pretrained(
        "Wan-AI/Wan2.1-T2V-14B-Diffusers",
        # FastVideo will automatically handle distributed setup
        num_gpus=4,
        use_fsdp_inference=True,
        dit_cpu_offload=False,
        vae_cpu_offload=False,
        text_encoder_cpu_offload=True,
        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
        # image_encoder_cpu_offload=False,
    )

    # sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
    # sampling_param.num_frames = 45
    # sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
    # Generate videos with the same simple API, regardless of GPU count
    prompt = (
        "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open."
    )
    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True)
    # video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")

    # Generate another video with a different prompt, without reloading the
    # model!
    prompt2 = (
        "The video shows a green and orange object being flattened as if it were under a hydraulic press, with the press moving down and compressing the object")
    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)


if __name__ == "__main__":
    main()