uncomment 2

app.py

@@ -291,77 +291,77 @@ def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
     return gs, mesh
 
 
-# @spaces.GPU
-# def image_to_3d(
-#     image: Image.Image,
-#     seed: int,
-#     ss_guidance_strength: float,
-#     ss_sampling_steps: int,
-#     slat_guidance_strength: float,
-#     slat_sampling_steps: int,
-# ) -> Tuple[dict, str]:
-#     try:
-#         # Use a fixed temp directory instead of user-specific
-#         temp_dir = os.path.join(TMP_DIR, "temp_output")
-#         os.makedirs(temp_dir, exist_ok=True)
+@spaces.GPU
+def image_to_3d(
+    image: Image.Image,
+    seed: int,
+    ss_guidance_strength: float,
+    ss_sampling_steps: int,
+    slat_guidance_strength: float,
+    slat_sampling_steps: int,
+) -> Tuple[dict, str]:
+    try:
+        # Use a fixed temp directory instead of user-specific
+        temp_dir = os.path.join(TMP_DIR, "temp_output")
+        os.makedirs(temp_dir, exist_ok=True)
         
-#         # Get the pipeline using the getter function
-#         pipeline = get_trellis_pipeline()
-#         if pipeline is None:
-#             return None, "Trellis pipeline is unavailable."
+        # Get the pipeline using the getter function
+        pipeline = get_trellis_pipeline()
+        if pipeline is None:
+            return None, "Trellis pipeline is unavailable."
         
-#         outputs = pipeline.run(
-#             image,
-#             seed=seed,
-#             formats=["gaussian", "mesh"],
-#             preprocess_image=False,
-#             sparse_structure_sampler_params={
-#                 "steps": ss_sampling_steps,
-#                 "cfg_strength": ss_guidance_strength,
-#             },
-#             slat_sampler_params={
-#                 "steps": slat_sampling_steps,
-#                 "cfg_strength": slat_guidance_strength,
-#             },
-#         )
-
-#         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))]
-#         video_path = os.path.join(temp_dir, 'sample.mp4')
-#         imageio.mimsave(video_path, video, fps=15)
-#         state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
-#         torch.cuda.empty_cache()
-#         return state, video_path
-#     except Exception as e:
-#         print(f"Error in image_to_3d: {str(e)}")
-#         return None, f"Error generating 3D model: {str(e)}"
-
-
-# @spaces.GPU(duration=90)
-# def extract_glb(
-#     state: dict,
-#     mesh_simplify: float,
-#     texture_size: int,
-# ) -> Tuple[str, str]:
-#     """
-#     Extract a GLB file from the 3D model.
-
-#     Args:
-#         state (dict): The state of the generated 3D model.
-#         mesh_simplify (float): The mesh simplification factor.
-#         texture_size (int): The texture resolution.
+        outputs = pipeline.run(
+            image,
+            seed=seed,
+            formats=["gaussian", "mesh"],
+            preprocess_image=False,
+            sparse_structure_sampler_params={
+                "steps": ss_sampling_steps,
+                "cfg_strength": ss_guidance_strength,
+            },
+            slat_sampler_params={
+                "steps": slat_sampling_steps,
+                "cfg_strength": slat_guidance_strength,
+            },
+        )
 
-#     Returns:
-#         str: The path to the extracted GLB file.
-#     """
-#     temp_dir = os.path.join(TMP_DIR, "temp_output")
-#     gs, mesh = unpack_state(state)
-#     glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
-#     glb_path = os.path.join(temp_dir, 'sample.glb')
-#     glb.export(glb_path)
-#     torch.cuda.empty_cache()
-#     return glb_path, glb_path
+        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))]
+        video_path = os.path.join(temp_dir, 'sample.mp4')
+        imageio.mimsave(video_path, video, fps=15)
+        state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
+        torch.cuda.empty_cache()
+        return state, video_path
+    except Exception as e:
+        print(f"Error in image_to_3d: {str(e)}")
+        return None, f"Error generating 3D model: {str(e)}"
+
+
+@spaces.GPU(duration=90)
+def extract_glb(
+    state: dict,
+    mesh_simplify: float,
+    texture_size: int,
+) -> Tuple[str, str]:
+    """
+    Extract a GLB file from the 3D model.
+
+    Args:
+        state (dict): The state of the generated 3D model.
+        mesh_simplify (float): The mesh simplification factor.
+        texture_size (int): The texture resolution.
+
+    Returns:
+        str: The path to the extracted GLB file.
+    """
+    temp_dir = os.path.join(TMP_DIR, "temp_output")
+    gs, mesh = unpack_state(state)
+    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
+    glb_path = os.path.join(temp_dir, 'sample.glb')
+    glb.export(glb_path)
+    torch.cuda.empty_cache()
+    return glb_path, glb_path
 
 
 # @spaces.GPU