import gradio as gr
import torch
from diffusers import DiffusionPipeline, DDIMScheduler

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

scheduler = DDIMScheduler.from_pretrained('li-yan/diffusion-aurora-256')
scheduler.set_timesteps(num_inference_steps=20)

pipeline = DiffusionPipeline.from_pretrained(
    'li-yan/diffusion-aurora-256', scheduler=scheduler).to(device)

def image_gen(name):
	images = pipeline(num_inference_steps=20).images
	return images[0]

css = ".output-image, .input-image, .image-preview {height: 256px !important}"

demo = gr.Interface(fn=image_gen, inputs=None, outputs="image", css=css)
demo.launch()