app.py

import torch
import gradio as gr
from diffusers import StableDiffusionPipeline
from PIL import Image
import io

# 🔧 Set device and dtype
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

# 🧠 Load SD Turbo pipeline
pipe = StableDiffusionPipeline.from_pretrained(
    "stabilityai/sd-turbo",
    torch_dtype=dtype,
)
pipe.to(device)
pipe.enable_attention_slicing()

# 🎯 Optimized image generation
def generate(prompt):
    with torch.autocast(device_type=device):
        # 🔻 Generate smaller image directly
        image = pipe(prompt, height=384, width=384).images[0]

    # 🖼️ Optional: compress image for faster Gradio display
    buffer = io.BytesIO()
    image.save(buffer, format="JPEG", quality=70)  # 🔻 reduce quality to 70
    buffer.seek(0)

    return Image.open(buffer)

# 🚀 Gradio UI
gr.Interface(
    fn=generate,
    inputs=gr.Textbox(label="Enter Prompt", placeholder="A futuristic cyberpunk city at night"),
    outputs=gr.Image(label="Generated Image"),
    title="🎨 Fast Text-to-Image Generator (SD Turbo)",
    description="Optimized for speed and light resource use. Generates lower-res compressed images from text.",
).launch()