app.py

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

MODEL_ID = "CompVis/stable-diffusion-v1-4"

pipe = None
device = "cuda" if torch.cuda.is_available() else "cpu"

print(f"🚀 Device: {device}")

def load_model():
    global pipe

    if pipe is not None:
        return "✅ Model sudah siap!"

    gc.collect()
    if device == "cuda":
        torch.cuda.empty_cache()

    print("📦 Loading model...")

    pipe = StableDiffusionPipeline.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.float16 if device == "cuda" else torch.float32,
        safety_checker=None
    )

    # 🔥 OPTIMASI WAJIB
    pipe.enable_attention_slicing()

    if device == "cuda":
        pipe.to("cuda")
    else:
        pipe.enable_vae_slicing()

    print("✅ Model ready")
    return "✅ Model siap digunakan!"

def generate(prompt, negative_prompt, steps, guidance, width, height, seed):
    global pipe

    if pipe is None:
        return None, "⚠️ Model belum siap"

    try:
        # Limit ukuran (biar ga OOM di free tier)
        width = min(width, 512)
        height = min(height, 512)

        generator = torch.manual_seed(int(seed)) if seed != -1 else None

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

        gc.collect()
        if device == "cuda":
            torch.cuda.empty_cache()

        return image, "✅ Done"

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

# UI
with gr.Blocks() as demo:
    gr.Markdown("# 🎨 AI Image Generator (HF Free Tier Safe)")

    status = gr.Markdown("⏳ Loading model...")

    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt")
            negative = gr.Textbox(label="Negative Prompt", value="blurry, low quality")

            steps = gr.Slider(10, 25, value=18)
            guidance = gr.Slider(1, 10, value=7)

            width = gr.Dropdown([256, 384, 512], value=512)
            height = gr.Dropdown([256, 384, 512], value=512)

            seed = gr.Number(value=-1)

            btn = gr.Button("Generate")

        with gr.Column():
            output = gr.Image()
            result = gr.Markdown()

    demo.load(load_model, outputs=status)

    btn.click(
        generate,
        inputs=[prompt, negative, steps, guidance, width, height, seed],
        outputs=[output, result]
    )

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