app.py

import gradio
import torch

from deps.AnimateDiff.animatediff.utils.util import save_videos_grid
from minimal_animatediff.animation_pipeline import create_animation_pipeline


if __name__ == "__main__":
    pipeline = create_animation_pipeline()

    def generate(prompt, neg_prompt, steps, width, height, frames, progress=gradio.Progress()):
        torch.manual_seed(16372571278361863751)

        def update(i, _, __):
            progress(i / float(steps + 1), desc="Sampling")

        progress(0, desc="Sampling")
        sample = pipeline(
            prompt,
            negative_prompt=neg_prompt,
            num_inference_steps=int(steps),
            guidance_scale=7.5,
            width=int(width),
            height=int(height),
            video_length=int(frames),
            callback=update,
        ).videos

        progress(steps / float(steps + 1), desc="Converting")
        save_videos_grid(sample, "samples/sample.mp4")

        progress(1)
        return "samples/sample.mp4"

    app = gradio.Interface(
        fn=generate,
        inputs=[
            gradio.Textbox(label="Prompt"),
            gradio.Textbox(label="Negative Prompt"),
            gradio.Number(label="Steps", value=25, minimum=1),
            gradio.Number(label="Width", value=512, minimum=1),
            gradio.Number(label="Height", value=512, minimum=1),
            gradio.Number(label="Frames", value=16, minimum=1),
        ],
        outputs=gradio.Video(label="Generated Animation"),
    )

    app.launch(share=True, enable_queue=True)