app.py

import gradio as gr
import torch
import spaces
import os
from diffusers import DiffusionPipeline

# --- Model Configuration and Loading ---
MODEL_ID = "Manojb/stable-diffusion-2-1-base"
DTYPE = torch.bfloat16

try:
    # Load pipeline
    pipe = DiffusionPipeline.from_pretrained(
        MODEL_ID, 
        torch_dtype=DTYPE, 
        use_safetensors=True
    )
    pipe.to('cuda')
    
    # --- Mandatory ZeroGPU AoT Compilation for Optimization ---
    
    @spaces.GPU(duration=1500)  # Extended duration for startup compilation
    def compile_unet():
        print("Starting AoT compilation for UNet...")
        
        # Dummy inputs for 512x512 generation (B=1, latents=64x64 for UNet)
        B, C, H, W = 1, 4, 64, 64
        sample = torch.randn(B, C, H, W, dtype=DTYPE, device='cuda')
        timestep = torch.tensor([999], dtype=torch.long, device='cuda')
        
        # Encoder Hidden States (text embeddings): (B, 77, 1024) for SD2.1
        EHS_DIM = 77
        EHS_HIDDEN = 1024
        encoder_hidden_states = torch.randn(B, EHS_DIM, EHS_HIDDEN, dtype=DTYPE, device='cuda')

        inputs = (sample, timestep, encoder_hidden_states)
        
        with spaces.aoti_capture(pipe.unet) as call:
            call(*inputs)
        
        exported = torch.export.export(pipe.unet, args=call.args, kwargs=call.kwargs)
        compiled_model = spaces.aoti_compile(exported)
        print("AoT compilation successful.")
        return compiled_model

    # Execute compilation during startup
    compiled_unet = compile_unet()
    spaces.aoti_apply(compiled_unet, pipe.unet)

except Exception as e:
    print(f"⚠️ Warning: Model initialization or AoT compilation failed ({e}). Running without optimization or skipping initialization if severe.")
    # Fallback to loading the model without AoT if compilation fails
    if 'pipe' not in locals():
        pipe = DiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=DTYPE, use_safetensors=True)
        pipe.to('cuda')
        print("Model loaded successfully without AoT.")

@spaces.GPU(duration=60) # Standard GPU allocation for inference
def generate(prompt: str, num_images: int):
    """Generates images using the Stable Diffusion pipeline."""
    
    if not prompt:
        raise gr.Error("Prompt cannot be empty.")

    # Prepare batch input
    prompt_list = [prompt] * num_images
    
    # Generate images
    output = pipe(
        prompt_list,
        num_inference_steps=25,
        guidance_scale=9.0,
    )
    
    return output.images

# --- Gradio Interface ---

with gr.Blocks(theme=gr.themes.Soft(), title="SD 2.1 Base Generator") as demo:
    gr.HTML(
        """
        <div style="text-align: center; margin-bottom: 20px;">
            <h1>Stable Diffusion 2.1 Base (512x512)</h1>
            <p>Model: Manojb/stable-diffusion-2-1-base | Optimized with ZeroGPU AoT</p>
            <p>Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank">anycoder</a></p>
        </div>
        """
    )

    with gr.Row():
        with gr.Column(scale=1):
            prompt = gr.Textbox(
                label="Prompt",
                placeholder="A detailed digital painting of a majestic dragon flying over a medieval castle, fantasy art",
                lines=3
            )
            num_images = gr.Slider(
                minimum=1,
                maximum=4,
                step=1,
                value=2,
                label="Number of Images to Generate (Max 4)",
                info="Generates multiple images in a single batch call."
            )
            generate_btn = gr.Button("Generate Images", variant="primary")
        
        with gr.Column(scale=2):
            output_gallery = gr.Gallery(
                label="Generated Images (512x512)",
                height=512,
                columns=2,
                rows=2,
                object_fit="contain"
            )

    generate_btn.click(
        fn=generate,
        inputs=[prompt, num_images],
        outputs=output_gallery
    )

    gr.Examples(
        examples=[
            ["A photorealistic portrait of a golden retriever wearing sunglasses on a beach, cinematic lighting", 2],
            ["Steampunk owl on a bookshelf, detailed brass gears, oil painting", 4],
            ["High contrast black and white photograph of an old lighthouse during a storm", 1]
        ],
        inputs=[prompt, num_images],
        outputs=output_gallery,
        fn=generate,
        cache_examples=True,
        cache_mode="eager"
    )

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