app.py

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

# Use a smaller SD model variant that fits within free tier
MODEL_ID = "CompVis/ldm-super-resolution-4x-openimages"  # Only ~1.4GB

@gr.cache()
def load_model():
    pipe = StableDiffusionPipeline.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.float16,
        safety_checker=None,
        use_safetensors=True
    )
    pipe = pipe.to("cpu")
    pipe.enable_attention_slicing()  # Reduces memory usage
    return pipe

def generate_character(prompt, seed=42):
    try:
        pipe = load_model()
        generator = torch.Generator(device="cpu").manual_seed(seed)
        
        image = pipe(
            prompt=f"pixel art {prompt}, clean lines, vibrant colors",
            num_inference_steps=20,
            guidance_scale=7.0,
            width=256,
            height=256,
            generator=generator
        ).images[0]
        
        return image
    except Exception as e:
        return f"Error: {str(e)}\nTry a simpler prompt."

with gr.Blocks(theme=gr.themes.Default()) as demo:
    gr.Markdown("# 🎮 Lightweight Character Generator")
    
    with gr.Row():
        prompt = gr.Textbox(
            label="Describe your character",
            placeholder="e.g. 'robot pirate with laser eye'",
            max_lines=2
        )
    
    generate_btn = gr.Button("Generate", variant="primary")
    output = gr.Image(label="Your Character", type="pil")
    
    generate_btn.click(
        generate_character,
        inputs=prompt,
        outputs=output
    )

demo.launch(debug=False)