app.py

import gradio as gr
import torch
from diffusers import AnimateDiffPipeline, MotionAdapter, DDIMScheduler
from diffusers.utils import export_to_gif

# Load the motion adapter
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
# Load the base model
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter)
# Set up the scheduler
scheduler = DDIMScheduler.from_pretrained(
    model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", steps_offset=1
)
pipe.scheduler = scheduler

# Enable memory savings
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()

def generate_animation(prompt):
    output = pipe(
        prompt=prompt,
        negative_prompt="bad quality, worse quality",
        num_frames=16,
        guidance_scale=7.5,
        num_inference_steps=25,
        generator=torch.Generator("cpu").manual_seed(42),
    )
    frames = output.frames[0]
    export_to_gif(frames, "animation.gif")
    return "animation.gif"

# Create Gradio interface
demo = gr.Interface(fn=generate_animation, inputs="text", outputs="image")
demo.launch()