Update app.py

app.py

@@ -20,8 +20,63 @@ MAX_SEED = np.iinfo(np.int32).max
 TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
 os.makedirs(TMP_DIR, exist_ok=True)
 
-# Rest of the utility functions remain the same...
-[Previous utility functions: start_session, end_session, preprocess_image, pack_state, unpack_state, get_seed]
+
+def start_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    print(f'Creating user directory: {user_dir}')
+    os.makedirs(user_dir, exist_ok=True)
+    
+def end_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    print(f'Removing user directory: {user_dir}')
+    shutil.rmtree(user_dir)
+
+def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
+    processed_image = pipeline.preprocess_image(image)
+    return processed_image
+
+def pack_state(gs: Gaussian, mesh: MeshExtractResult, trial_id: str) -> dict:
+    return {
+        'gaussian': {
+            **gs.init_params,
+            '_xyz': gs._xyz.cpu().numpy(),
+            '_features_dc': gs._features_dc.cpu().numpy(),
+            '_scaling': gs._scaling.cpu().numpy(),
+            '_rotation': gs._rotation.cpu().numpy(),
+            '_opacity': gs._opacity.cpu().numpy(),
+        },
+        'mesh': {
+            'vertices': mesh.vertices.cpu().numpy(),
+            'faces': mesh.faces.cpu().numpy(),
+        },
+        'trial_id': trial_id,
+    }
+    
+def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
+    gs = Gaussian(
+        aabb=state['gaussian']['aabb'],
+        sh_degree=state['gaussian']['sh_degree'],
+        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
+        scaling_bias=state['gaussian']['scaling_bias'],
+        opacity_bias=state['gaussian']['opacity_bias'],
+        scaling_activation=state['gaussian']['scaling_activation'],
+    )
+    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
+    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
+    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
+    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
+    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
+    
+    mesh = edict(
+        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
+        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
+    )
+    
+    return gs, mesh, state['trial_id']
+
+def get_seed(randomize_seed: bool, seed: int) -> int:
+    """Get the random seed."""
+    return np.random.randint(0, MAX_SEED) if randomize_seed else seed
 
 def image_to_3d(
     image: Image.Image,
@@ -167,8 +222,94 @@ def extract_reduced_glb(
         torch.cuda.empty_cache()
         raise gr.Error(f"GLB reduction failed: {str(e)}")
 
-# Rest of the UI code and demo definition remains the same...
-[Previous UI code with Blocks, event handlers, etc.]
+with gr.Blocks(delete_cache=(600, 600)) as demo:
+    gr.Markdown("""
+    ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
+    * Upload an image and click "Generate" to create a 3D model
+    * You can download either:
+        * The full-quality GLB file (larger size, highest quality)
+        * A reduced version with customizable quality settings (smaller size)
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
+            
+            with gr.Accordion(label="Generation Settings", open=False):
+                seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+                gr.Markdown("Stage 1: Sparse Structure Generation")
+                with gr.Row():
+                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
+                    ss_sampling_steps = gr.Slider(1, 500, label="Sampling Steps", value=12, step=1)
+                gr.Markdown("Stage 2: Structured Latent Generation")
+                with gr.Row():
+                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
+                    slat_sampling_steps = gr.Slider(1, 500, label="Sampling Steps", value=12, step=1)
+
+            generate_btn = gr.Button("Generate")
+            
+            with gr.Accordion(label="Reduced GLB Settings", open=False):
+                mesh_simplify = gr.Slider(0.0, 0.98, label="Mesh Simplification", value=0.95, step=0.01)
+                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
+            
+            extract_reduced_btn = gr.Button("Extract Reduced GLB", interactive=False)
+
+        with gr.Column():
+            video_output = gr.Video(label="Generated 3D Asset Preview", autoplay=True, loop=True, height=300)
+            model_output = LitModel3D(label="3D Model Preview", exposure=20.0, height=300)
+            gr.Markdown("### Download Options")
+            with gr.Row():
+                download_full = gr.DownloadButton(label="Download Full-Quality GLB", interactive=False)
+                download_reduced = gr.DownloadButton(label="Download Reduced GLB", interactive=False)
+            
+    output_buf = gr.State()
+
+    # Example images
+    with gr.Row():
+        examples = gr.Examples(
+            examples=[
+                f'assets/example_image/{image}'
+                for image in os.listdir("assets/example_image")
+            ],
+            inputs=[image_prompt],
+            fn=preprocess_image,
+            outputs=[image_prompt],
+            run_on_click=True,
+            examples_per_page=64,
+        )
+
+    # Event handlers
+    demo.load(start_session)
+    demo.unload(end_session)
+    
+    image_prompt.upload(
+        preprocess_image,
+        inputs=[image_prompt],
+        outputs=[image_prompt],
+    )
+
+    generate_btn.click(
+        get_seed,
+        inputs=[randomize_seed, seed],
+        outputs=[seed],
+    ).then(
+        image_to_3d,
+        inputs=[image_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
+        outputs=[output_buf, video_output, model_output, download_full],
+    ).then(
+        lambda: (gr.Button(interactive=True), gr.Button(interactive=True), gr.Button(interactive=False)),
+        outputs=[download_full, extract_reduced_btn, download_reduced],
+    )
+
+    extract_reduced_btn.click(
+        extract_reduced_glb,
+        inputs=[output_buf, mesh_simplify, texture_size],
+        outputs=[model_output, download_reduced],
+    ).then(
+        lambda: gr.Button(interactive=True),
+        outputs=[download_reduced],
+    )
 
 if __name__ == "__main__":
     # Set some CUDA memory management options