app.py

import gradio as gr
import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
from PIL import Image
import time

# Load CPU-optimized model
model_id = "OFA-Sys/small-stable-diffusion-v0"  # Smaller model for CPU
pipe = StableDiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float32  # Force float32 for CPU
)

# Use DPMSolver for better CPU performance
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cpu")

def generate_image(text):
    if not text:
        return None, "Please enter some text first!"
    
    start_time = time.time()
    
    try:
        # Generate with reduced steps for faster processing
        image = pipe(
            text,
            num_inference_steps=20,  # Reduced from typical 50 steps
            guidance_scale=7.5
        ).images[0]
        
        if image.mode != "RGB":
            image = image.convert("RGB")
            
        gen_time = time.time() - start_time
        return image, f"Generated in {gen_time:.1f} seconds"
    
    except Exception as e:
        return None, f"Error: {str(e)}"

# Create Gradio interface with loading states
with gr.Blocks(title="CPU Poetry to Image") as demo:
    gr.Markdown("# 💖 CPU-Friendly Poetry to Image")
    gr.Markdown("Note: Generation may take 2-5 minutes on CPU")
    
    with gr.Row():
        with gr.Column():
            input_text = gr.Textbox(
                label="Your Romantic Text",
                placeholder="e.g., 'Your eyes sparkle like stars'",
                lines=3
            )
            generate_btn = gr.Button("Create Magic ✨")
            
        with gr.Column():
            output_image = gr.Image(label="Your Generated Art")
            time_info = gr.Textbox(label="Generation Time")
    
    examples = gr.Examples(
        examples=[
            ["A moonlit beach with heart-shaped waves"],
            ["Two roses intertwined with golden light"],
            ["A love letter floating in the clouds"]
        ],
        inputs=[input_text]
    )
    
    generate_btn.click(
        fn=generate_image,
        inputs=[input_text],
        outputs=[output_image, time_info],
        api_name="generate"
    )

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