app.py

# app.py
import gradio as gr
from hunyuan_model import HunyuanVideoPipeline
from diffusers.utils import export_to_video
import huggingface_hub
import spaces

@spaces.GPU(duration=120)
def generate_video(image, video_length=85, infer_steps=30, seed=123):
    pipeline = HunyuanVideoPipeline.from_pretrained(
        "AeroScripts/leapfusion-hunyuan-image2video",
        variant="fp8",
        torch_dtype=torch.float16
    )

    generator = torch.Generator().manual_seed(seed)
    frames = pipeline(
        image,
        video_length=video_length,
        num_inference_steps=infer_steps,
        generator=generator
    ).frames

    video_path = export_to_video(frames)
    return video_path

interface = gr.Blocks(title="Hunyuan-ITV")

with interface:
    gr.Markdown("# Hunyuan Image-to-Video Converter")
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(label="Input Image", type="filepath")
            video_length = gr.Slider(16, 120, value=85, label="Frame Count")
            infer_steps = gr.Slider(10, 50, value=30, step=5, label="Inference Steps")
            seed = gr.Number(123, label="Random Seed")
            submit_btn = gr.Button("Generate Video")
        with gr.Column():
            output_video = gr.Video(label="Generated Video")

    submit_btn.click(
        generate_video,
        inputs=[input_image, video_length, infer_steps, seed],
        outputs=output_video
    )

if __name__ == "__main__":
    interface.launch(server_name="0.0.0.0", server_port=7860)