app.py

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

# Tiny model that fits in free tier memory
MODEL_NAME = "OFA-Sys/small-stable-diffusion-v0"

# Load model (will cache after first run)
@gr.cache()
def load_model():
    return StableDiffusionPipeline.from_pretrained(
        MODEL_NAME,
        torch_dtype=torch.float16,
        safety_checker=None
    ).to("cpu")

def generate_character(description, seed=42):
    try:
        pipe = load_model()
        
        # Reduce memory usage
        torch.manual_seed(seed)
        with torch.inference_mode():
            image = pipe(
                prompt=f"pixel art character, {description}",
                num_inference_steps=15,  # Fewer steps = less memory
                guidance_scale=7.0,
                width=256,  # Smaller resolution
                height=256
            ).images[0]
        
        return image
    except Exception as e:
        return f"Error: {str(e)}\nTry a simpler description or different words."

# Create simple animation effect by generating variations
def generate_animation(description, frames=3):
    images = []
    for i in range(frames):
        img = generate_character(description, seed=i)
        if isinstance(img, str):  # If error returned
            return img
        images.append(img)
    
    # Create simple animation (GIF)
    images[0].save(
        "animation.gif",
        save_all=True,
        append_images=images[1:],
        duration=500,
        loop=0
    )
    return "animation.gif"

# Minimal interface
with gr.Blocks(title="Tiny Character Animator") as demo:
    gr.Markdown("""
    # 🎮 Tiny Character Animator
    *Free-tier optimized for Hugging Face Spaces*
    """)
    
    with gr.Row():
        desc = gr.Textbox(
            label="Describe your character",
            placeholder="e.g., 'blue robot with antennae'",
            max_lines=2
        )
    
    with gr.Row():
        btn_still = gr.Button("Generate Still", variant="secondary")
        btn_animate = gr.Button("Generate Animation", variant="primary")
    
    with gr.Row():
        output_still = gr.Image(label="Character", shape=(256, 256))
        output_anim = gr.Image(label="Animation", format="gif", visible=False)
    
    # Button actions
    btn_still.click(
        generate_character,
        inputs=desc,
        outputs=output_still
    )
    
    btn_animate.click(
        lambda: (gr.Image(visible=False), gr.Image(visible=True)),  # Toggle visibility
        None,
        [output_still, output_anim]
    ).then(
        generate_animation,
        inputs=desc,
        outputs=output_anim
    )

demo.launch(debug=False)