app.py

import gradio as gr
from diffusers import StableDiffusionPipeline
import torch

model_id = "stabilityai/stable-diffusion-2-1"

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

pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype)
pipe = pipe.to(device)

def generate(prompt):
    image = pipe(prompt).images[0]
    return image

demo = gr.Interface(fn=generate, inputs="text", outputs="image")

if __name__ == "__main__":
    demo.launch()