Update gradio_app.py

gradio_app.py

@@ -88,6 +88,8 @@ def generate_and_process_3d(prompt: str, seed: int = 42, width: int = 1024, heig
     try:
         # Generate image using FLUX
         generator = torch.Generator(device=device).manual_seed(seed)
+
+        print("[debug] generating the image using Flux")
         generated_image = flux_pipe(
             prompt=prompt,
             width=width,
@@ -98,19 +100,24 @@ def generate_and_process_3d(prompt: str, seed: int = 42, width: int = 1024, heig
         ).images[0]
         
         # Process the generated image
+        print("[debug] converting the image to rgb")
         rgb_image = generated_image.convert('RGB')
         
         # Remove background
+        print("[debug] removing the background by calling bg_remover.process(rgb_image)")
         no_bg_image = bg_remover.process(rgb_image)
         
         # Convert to numpy array to extract mask
+        print("[debug] converting to numpy array to extract the mask")
         no_bg_array = np.array(no_bg_image)
         mask = (no_bg_array.sum(axis=2) > 0).astype(np.float32)
         
         # Create RGBA image
+        print("[debug] creating the RGBA image using create_rgba_image(rgb_image, mask)")
         rgba_image = create_rgba_image(rgb_image, mask)
         
         # Auto crop with foreground
+        print(f"[debug] auto-cromming the rgba_image using spar3d_utils.foreground_crop(...). newsize=(COND_WIDTH, COND_HEIGHT) = ({COND_WIDTH}, {COND_HEIGHT})")
         processed_image = spar3d_utils.foreground_crop(
             rgba_image,
             crop_ratio=1.3,
@@ -118,12 +125,14 @@ def generate_and_process_3d(prompt: str, seed: int = 42, width: int = 1024, heig
             no_crop=False
         )
 
+        print("[debug] preparing the batch by calling create_batch(processed_image)")
         # Prepare batch for 3D generation
         batch = create_batch(processed_image)
         batch = {k: v.to(device) for k, v in batch.items()}
 
         # Generate mesh
         with torch.no_grad():
+            print("[debug] calling torch.autocast(....) to generate the mesh")
             with torch.autocast(device_type='cuda' if torch.cuda.is_available() else 'cpu', dtype=torch.bfloat16):
                 trimesh_mesh, _ = spar3d_model.generate_mesh(
                     batch,
@@ -135,8 +144,11 @@ def generate_and_process_3d(prompt: str, seed: int = 42, width: int = 1024, heig
                 trimesh_mesh = trimesh_mesh[0]
 
         # Export to GLB
+        print("[debug] creating tmp dir for the .glb output")
         temp_dir = tempfile.mkdtemp()
         output_path = os.path.join(temp_dir, 'output.glb')
+        
+        print("[debug] calling trimesh_mesh.export(...) to export to .glb")
         trimesh_mesh.export(output_path, file_type="glb", include_normals=True)
         
         return output_path, generated_image