app.py

import torch
from diffusers import StableDiffusionPipeline
import gradio as gr

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

    pipe = StableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=dtype
    )
    return pipe.to(device)

pipe = load_pipeline()

def generate(prompt: str, steps: int, scale: float):
    """Run the pipeline and return a PIL image."""
    out = pipe(prompt, num_inference_steps=steps, guidance_scale=scale)
    return out.images[0]

demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(lines=1, placeholder="a steampunk robot in a lush jungle", label="Prompt"),
        gr.Slider(1, 100, value=50, step=1, label="Inference Steps"),
        gr.Slider(1.0, 15.0, value=7.5, step=0.1, label="Guidance Scale"),
    ],
    outputs=gr.Image(type="pil"),
    title="Stable Diffusion image generator",
    description="Generates images using Stable Diffusion."
)

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