app.py

import torch
import gradio as gr
from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

print("Running on:", device)

# Load motion adapter (video motion)
adapter = MotionAdapter.from_pretrained(
    "guoyww/animatediff-motion-adapter-v1-5-2",
    torch_dtype=dtype
)

# Load base Stable Diffusion model
pipe = AnimateDiffPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    motion_adapter=adapter,
    torch_dtype=dtype
)

pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(device)

pipe.enable_vae_slicing()
pipe.enable_attention_slicing()

def generate(prompt, steps, guidance, frames, fps, seed):
    generator = torch.Generator(device=device).manual_seed(int(seed))

    video = pipe(
        prompt=prompt,
        num_inference_steps=int(steps),
        guidance_scale=float(guidance),
        num_frames=int(frames),
        generator=generator
    ).frames[0]

    return video

demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(label="Prompt"),
        gr.Slider(10, 40, value=20, step=1, label="Steps"),
        gr.Slider(1, 12, value=7.5, step=0.5, label="Guidance"),
        gr.Slider(8, 32, value=16, step=1, label="Frames"),
        gr.Slider(4, 24, value=8, step=1, label="FPS"),
        gr.Number(value=42, label="Seed"),
    ],
    outputs=gr.Video(),
    title="AnimateDiff Video Generator",
    description="Text to Video using Stable Diffusion + AnimateDiff"
)

demo.launch()