app.py

# -*- coding: utf-8 -*-
"""
Gradio Space: Text → Image (Diffusers Pipeline)
UI designed by Mehak Mazhar
"""

import os
import torch
from diffusers import StableDiffusionPipeline
import gradio as gr

# --- Available models ---
MODEL_CHOICES = {
    "Dreamlike Diffusion 1.0": "dreamlike-art/dreamlike-diffusion-1.0",
    "Stable Diffusion XL Base": "stabilityai/stable-diffusion-xl-base-1.0"
}

# --- Cache pipelines to avoid reloading ---
loaded_pipelines = {}

def get_pipeline(model_id):
    """Load pipeline if not cached"""
    if model_id not in loaded_pipelines:
        pipe = StableDiffusionPipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16,
            use_safetensors=True
        )
        pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
        loaded_pipelines[model_id] = pipe
    return loaded_pipelines[model_id]

# --- Image generation function ---
def generate_image(prompt, model_choice, width, height, guidance_scale, steps):
    try:
        model_id = MODEL_CHOICES[model_choice]
        pipe = get_pipeline(model_id)

        image = pipe(
            prompt,
            width=int(width),
            height=int(height),
            guidance_scale=float(guidance_scale),
            num_inference_steps=int(steps)
        ).images[0]

        return image, f"✅ Generated with {model_choice}"

    except Exception as e:
        return None, f"⚠️ Error: {str(e)}"

# --- Gradio UI ---
css = """
body { background-color: #fff7e6; }
h1 { color: #a0522d; font-weight: bold; }
"""

with gr.Blocks(css=css, title="Stable Diffusion Text-to-Image") as demo:
    gr.HTML("<h1>Stable Diffusion — designed by Mehak Mazhar</h1>")

    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt", placeholder="A futuristic city at night", lines=3)
            model_choice = gr.Dropdown(
                list(MODEL_CHOICES.keys()),
                value="Dreamlike Diffusion 1.0",
                label="Choose Model"
            )
            width = gr.Dropdown([256, 384, 512, 768, 1024], value=512, label="Width")
            height = gr.Dropdown([256, 384, 512, 768, 1024], value=512, label="Height")
            guidance = gr.Slider(1.0, 15.0, value=7.5, step=0.1, label="Guidance Scale")
            steps = gr.Slider(10, 100, value=30, step=1, label="Steps")
            generate_btn = gr.Button("Generate Image", variant="primary")

        with gr.Column():
            output_image = gr.Image(label="Generated Image", type="pil")
            status = gr.Textbox(label="Status", interactive=False)

    generate_btn.click(
        fn=generate_image,
        inputs=[prompt, model_choice, width, height, guidance, steps],
        outputs=[output_image, status]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0")