Update app.py

app.py

@@ -31,16 +31,6 @@ def end_session(req: gr.Request):
     shutil.rmtree(user_dir)
 
 def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
-    """
-    Preprocess the input image.
-
-    Args:
-        image (Image.Image): The input image.
-
-    Returns:
-        str: uuid of the trial.
-        Image.Image: The preprocessed image.
-    """
     processed_image = pipeline.preprocess_image(image)
     return processed_image
 
@@ -84,9 +74,6 @@ def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
     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(
@@ -97,7 +84,7 @@ def image_to_3d(
     slat_guidance_strength: float,
     slat_sampling_steps: int,
     req: gr.Request,
-) -> Tuple[dict, str, str, str]:
+) -> Tuple[dict, str, str]:
     """
     Convert an image to a 3D model.
     """
@@ -116,49 +103,67 @@ def image_to_3d(
             "cfg_strength": slat_guidance_strength,
         },
     )
+
+    # Generate video preview
     video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
     video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
     video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
     trial_id = str(uuid.uuid4())
     video_path = os.path.join(user_dir, f"{trial_id}.mp4")
     imageio.mimsave(video_path, video, fps=15)
-    
+
     # Save full quality GLB
-    full_glb = postprocessing_utils.to_glb(
+    glb = postprocessing_utils.to_glb(
         outputs['gaussian'][0], 
         outputs['mesh'][0],
-        simplify=0.0,  # No simplification
-        texture_size=2048,  # Maximum texture resolution
+        simplify=0.0,  # No simplification for full quality
+        fill_holes=True,
+        fill_holes_max_size=0.04,
+        texture_size=2048,  # Maximum texture size
         verbose=False
     )
     full_glb_path = os.path.join(user_dir, f"{trial_id}_full.glb")
-    full_glb.export(full_glb_path)
+    glb.export(full_glb_path)
     
+    # Pack state for potential reduced version
     state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], trial_id)
-    return state, video_path, model_output, full_glb_path
+    
+    return state, video_path, full_glb_path
 
-def extract_glb(
+def extract_reduced_glb(
     state: dict,
     mesh_simplify: float,
     texture_size: int,
     req: gr.Request,
 ) -> Tuple[str, str]:
     """
-    Extract a GLB file from the 3D model.
+    Extract a reduced quality GLB file.
     """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     gs, mesh, trial_id = unpack_state(state)
-    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
-    glb_path = os.path.join(user_dir, f"{trial_id}.glb")
-    glb.export(glb_path)
-    return glb_path, glb_path
+    
+    # Create reduced quality GLB with user settings
+    glb = postprocessing_utils.to_glb(
+        gs, 
+        mesh,
+        simplify=mesh_simplify,
+        fill_holes=True,
+        fill_holes_max_size=0.04,
+        texture_size=texture_size,
+        verbose=False
+    )
+    reduced_glb_path = os.path.join(user_dir, f"{trial_id}_reduced.glb")
+    glb.export(reduced_glb_path)
+    
+    return reduced_glb_path, reduced_glb_path
 
 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 asset
-    * You can download the full quality GLB immediately after generation
-    * Or create a reduced size version using the GLB Extraction Settings
+    * After generation:
+        * Download the full quality GLB (no mesh simplification, maximum texture resolution)
+        * Or create a reduced size version with customizable settings
     """)
     
     with gr.Row():
@@ -179,15 +184,17 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
 
             generate_btn = gr.Button("Generate")
             
-            with gr.Accordion(label="GLB Extraction Settings", open=False):
-                mesh_simplify = gr.Slider(0.0, 0.98, label="Simplify", value=0.95, step=0.01)
+            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, 
+                                        info="Higher values = more reduction")
                 texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
             
-            extract_glb_btn = gr.Button("Extract Reduced GLB", interactive=False)
+            extract_reduced_btn = gr.Button("Extract Reduced GLB", interactive=False)
 
         with gr.Column():
             video_output = gr.Video(label="Generated 3D Asset", 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)
@@ -223,28 +230,4 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         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_glb_btn, download_reduced],
-    )
-
-    extract_glb_btn.click(
-        extract_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__":
-    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
-    pipeline.cuda()
-    try:
-        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
-    except:
-        pass
-    demo.launch()
+        image_to_3d,