app.py

import gradio as gr
import cv2
import torch
import numpy as np
from PIL import Image
import re
from datasets import load_dataset
from diffusers import DiffusionPipeline, EulerDiscreteScheduler

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

scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2", subfolder="scheduler", prediction_type="v_prediction")
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler)
pipe = pipe.to(device)

def genie (prompt, scale, steps, seed):
     generator = torch.Generator(device=device).manual_seed(seed)
     images = pipe(prompt, width=768, height=768, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=1, generator=generator).images
     return images[0]
gr.Interface(fn=genie, inputs=['text', gr.Slider(1, 10, 20), gr.Slider(), gr.Slider(maximum=987654321)], outputs='image').launch(debug=True)