app.py

#!/usr/bin/env python
"""
TransNormal - Hugging Face Spaces Zero GPU Version

Surface Normal Estimation for Transparent Objects
"""

import os
import spaces
import torch
import gradio as gr
from PIL import Image
from huggingface_hub import snapshot_download

from transnormal import TransNormalPipeline, create_dino_encoder

# ============== Model Paths ==============
TRANSNORMAL_REPO = "Longxiang-ai/TransNormal"
DINO_REPO = "facebook/dinov3-vith16plus-pretrain-lvd1689m"
# =========================================

# Global pipeline
pipe = None
weights_downloaded = False


def download_weights():
    """Download model weights from HuggingFace Hub."""
    global weights_downloaded
    
    if weights_downloaded:
        return "./weights/transnormal", "./weights/dinov3_vith16plus"
    
    print("[TransNormal] Downloading TransNormal weights...")
    transnormal_path = snapshot_download(
        TRANSNORMAL_REPO,
        local_dir="./weights/transnormal"
    )
    
    print("[TransNormal] Downloading DINOv3 weights...")
    dino_path = snapshot_download(
        DINO_REPO,
        local_dir="./weights/dinov3_vith16plus"
    )
    
    weights_downloaded = True
    print("[TransNormal] Weights downloaded successfully!")
    return transnormal_path, dino_path


def load_pipeline():
    """Load the TransNormal pipeline."""
    global pipe
    
    if pipe is not None:
        return pipe
    
    device = "cuda" if torch.cuda.is_available() else "cpu"
    dtype = torch.bfloat16 if device == "cuda" else torch.float32
    
    print(f"[TransNormal] Loading model on {device} with {dtype}...")
    
    # Download weights
    transnormal_path, dino_path = download_weights()
    projector_path = os.path.join(transnormal_path, "cross_attention_projector.pt")
    
    # Load DINO encoder
    dino_encoder = create_dino_encoder(
        model_name="dinov3_vith16plus",
        cross_attention_dim=1024,
        weights_path=dino_path,
        projector_path=projector_path,
        device=device,
        dtype=dtype,
        freeze_encoder=True,
    )
    
    # Load pipeline
    pipe = TransNormalPipeline.from_pretrained(
        transnormal_path,
        dino_encoder=dino_encoder,
        torch_dtype=dtype,
    )
    pipe = pipe.to(device)
    
    print("[TransNormal] Model loaded successfully!")
    return pipe


@spaces.GPU(duration=120)
def predict_normal(image: Image.Image, processing_res: int = 768) -> Image.Image:
    """
    Predict surface normal from input image using Zero GPU.
    
    Args:
        image: Input RGB image
        processing_res: Processing resolution
    
    Returns:
        Normal map as PIL Image
    """
    if image is None:
        return None
    
    # Load pipeline (will use GPU allocated by @spaces.GPU)
    pipeline = load_pipeline()
    
    # Run inference
    with torch.no_grad():
        normal_map = pipeline(
            image=image,
            processing_res=processing_res,
            output_type="pil",
        )
    
    return normal_map


# ============== Gradio Interface ==============

custom_css = """
.gradio-container {
    font-family: 'Segoe UI', 'Helvetica Neue', Arial, sans-serif !important;
}
h1 {
    font-weight: 600 !important;
}
"""

with gr.Blocks(
    title="TransNormal",
    theme=gr.themes.Soft(),
    css=custom_css,
) as demo:
    
    gr.Markdown(
        """
        # 🔮 TransNormal
        ### Surface Normal Estimation for Transparent Objects
        
        Upload an image to estimate surface normals. Particularly effective for **transparent objects** like glass and plastic.
        
        **Normal Convention:** Red=X (Left) | Green=Y (Up) | Blue=Z (Out)
        
        > ⏱️ First inference may take ~1-2 minutes to load model weights.
        """
    )
    
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(
                label="Input Image",
                type="pil",
                height=400,
            )
            
            processing_res = gr.Slider(
                minimum=256,
                maximum=1024,
                value=768,
                step=64,
                label="Processing Resolution (higher = better quality but slower)",
            )
            
            submit_btn = gr.Button("🚀 Estimate Normal", variant="primary", size="lg")
        
        with gr.Column():
            output_image = gr.Image(
                label="Normal Map",
                type="pil",
                height=400,
            )
    
    # Event handlers
    submit_btn.click(
        fn=predict_normal,
        inputs=[input_image, processing_res],
        outputs=output_image,
    )
    
    # Footer
    gr.Markdown(
        """
        ---
        
        **Paper:** [TransNormal: Dense Visual Semantics for Diffusion-based Transparent Object Normal Estimation](https://longxiang-ai.github.io/TransNormal/)
        
        **Authors:** Mingwei Li, Hehe Fan, Yi Yang (Zhejiang University)
        
        **Code:** [GitHub](https://github.com/longxiang-ai/TransNormal)
        """
    )

# Launch
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)