app.py

import spaces
import torch
# from diffusers import BitsAndBytesConfig
from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel

import os
import time
from datetime import datetime
import gradio as gr

@spaces.GPU
def generate_video(
    prompt,
    resolution,
    video_length,
    seed,
    num_inference_steps,
    guidance_scale,
    flow_shift,
    embedded_guidance_scale
):
    seed = None if seed == -1 else seed
    width, height = resolution.split("x")
    width, height = int(width), int(height)

    transformer_8bit = HunyuanVideoTransformer3DModel.from_pretrained(
        model,
        subfolder="transformer",
        # quantization_config=quant_config,
        torch_dtype=torch.bfloat16,
        device_map="balanced",
    )

    # Cargar el pipeline
    pipeline = HunyuanVideoPipeline.from_pretrained(
        model,
        transformer=transformer_8bit,
        torch_dtype=torch.float16,
        device_map="balanced",
    )
    
    # Generar el video usando el pipeline
    video = pipeline(
        prompt=prompt,
        height=height,
        width=width,
        num_frames=video_length,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
    ).frames[0]
    
    # Guardar el video
    save_path = os.path.join(os.getcwd(), "gradio_outputs")
    os.makedirs(save_path, exist_ok=True)
    
    time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%H:%M:%S")
    video_path = f"{save_path}/{time_flag}_seed{seed}_{prompt[:100].replace('/','')}.mp4"
    
    from diffusers.utils import export_to_video
    export_to_video(video, video_path, fps=24)
    print(f'Sample saved to: {video_path}')
    
    return video_path

def create_demo(model):
    with gr.Blocks() as demo:
        gr.Markdown("# Hunyuan Video Generation")
        
        with gr.Row():
            with gr.Column():
                prompt = gr.Textbox(label="Prompt", value="A cat walks on the grass, realistic style.")
                with gr.Row():
                    resolution = gr.Dropdown(
                        choices=[
                            # 720p
                            ("1280x720 (16:9, 720p)", "1280x720"),
                            ("720x1280 (9:16, 720p)", "720x1280"), 
                            ("1104x832 (4:3, 720p)", "1104x832"),
                            ("832x1104 (3:4, 720p)", "832x1104"),
                            ("960x960 (1:1, 720p)", "960x960"),
                            # 540p
                            ("960x544 (16:9, 540p)", "960x544"),
                            ("544x960 (9:16, 540p)", "544x960"),
                            ("832x624 (4:3, 540p)", "832x624"), 
                            ("624x832 (3:4, 540p)", "624x832"),
                            ("720x720 (1:1, 540p)", "720x720"),
                        ],
                        value="1280x720",
                        label="Resolution"
                    )
                    video_length = gr.Dropdown(
                        label="Video Length",
                        choices=[
                            ("2s(65f)", 65),
                            ("5s(129f)", 129),
                        ],
                        value=129,
                    )
                num_inference_steps = gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps")
                show_advanced = gr.Checkbox(label="Show Advanced Options", value=False)
                with gr.Row(visible=False) as advanced_row:
                    with gr.Column():
                        seed = gr.Number(value=-1, label="Seed (-1 for random)")
                        guidance_scale = gr.Slider(1.0, 20.0, value=1.0, step=0.5, label="Guidance Scale")
                        flow_shift = gr.Slider(0.0, 10.0, value=7.0, step=0.1, label="Flow Shift") 
                        embedded_guidance_scale = gr.Slider(1.0, 20.0, value=6.0, step=0.5, label="Embedded Guidance Scale")
                show_advanced.change(fn=lambda x: gr.Row(visible=x), inputs=[show_advanced], outputs=[advanced_row])
                generate_btn = gr.Button("Generate")
            
            with gr.Column():
                output = gr.Video(label="Generated Video")
        
        generate_btn.click(
            fn=lambda *inputs: generate_video(*inputs),
            inputs=[
                prompt,
                resolution,
                video_length,
                seed,
                num_inference_steps,
                guidance_scale,
                flow_shift,
                embedded_guidance_scale
            ],
            outputs=output
        )
    
    return demo

if __name__ == "__main__":
    print("Starting Gradio server...")
    os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
    server_name = os.getenv("SERVER_NAME", "0.0.0.0")
    server_port = int(os.getenv("SERVER_PORT", "8081"))
    model = "hunyuanvideo-community/HunyuanVideo"  # Actualizado el path del modelo
    demo = create_demo(model)
    demo.launch(server_name=server_name, server_port=server_port)