.

.gitignore

@@ -0,0 +1,2 @@
+.idea/*
+__pycache__

app.py

@@ -4,7 +4,7 @@ from gradio_litmodel3d import LitModel3D
 
 import os
 import shutil
-os.environ['TOKENIZERS_PARALLELISM'] = 'true'
+# os.environ['TOKENIZERS_PARALLELISM'] = 'true'
 os.environ['SPCONV_ALGO'] = 'native'
 from typing import *
 import torch
@@ -106,32 +106,28 @@ def text_to_3d(
         dict: The information of the generated 3D model.
         str: The path to the video of the 3D model.
     """
-    try:
-        user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-        outputs = pipeline.run(
-            prompt,
-            seed=seed,
-            formats=["gaussian", "mesh"],
-            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(user_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:
-        et, ev, tb = sys.exc_info()
-        traceback.print_tb(tb)
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    outputs = pipeline.run(
+        prompt,
+        seed=seed,
+        formats=["gaussian", "mesh"],
+        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(user_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
 
 
 @spaces.GPU(duration=90)