app.py

import streamlit as st
from diffusers import StableDiffusionPipeline
import torch
import time

# Set up the page
st.set_page_config(page_title="Stable Diffusion Image Generator", layout="wide")
st.title("🚀 Stable Diffusion Image Generator")
st.write("Generate images using Stable Diffusion v1-5")

# Sidebar for settings
with st.sidebar:
    st.header("Settings")
    prompt = st.text_area(
        "Enter your prompt",
        value="a photo of an astronaut riding a horse on mars",
        height=100,
    )
    generate_button = st.button("Generate Image")

# Load the model (with caching to avoid reloading)
@st.cache_resource
def load_model():
    model_id = "runwayml/stable-diffusion-v1-5"
    try:
        # Try GPU first, fallback to CPU
        device = "cuda" if torch.cuda.is_available() else "cpu"
        pipe = StableDiffusionPipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16 if device == "cuda" else torch.float32,safety_checker=None
        )
        pipe = pipe.to(device)
        return pipe
    except Exception as e:
        st.error(f"Failed to load model: {e}")
        return None

pipe = load_model()

# Generate and display the image
if generate_button and prompt:
    if pipe is None:
        st.error("Model failed to load. Check logs for details.")
    else:
        with st.spinner("Generating image (this may take a while...) ⏳"):
            try:
                start_time = time.time()
                image = pipe(prompt).images[0]
                generation_time = time.time() - start_time
                
                st.image(image, caption=f"Generated in {generation_time:.2f} seconds")
                st.success("Image generated successfully! 🎉")
                
                # Option to download
                st.download_button(
                    label="Download Image",
                    data=image_to_bytes(image),
                    file_name="generated_image.png",
                    mime="image/png",
                )
            except Exception as e:
                st.error(f"Error during generation: {e}")

# Helper function to convert PIL image to bytes
def image_to_bytes(image):
    import io
    buf = io.BytesIO()
    image.save(buf, format="PNG")
    return buf.getvalue()