Update app.py

app.py

@@ -1,22 +1,5 @@
 # MIT License
-# Copyright (c) Microsoft
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to do so, subject to the following conditions:
-# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
-# Copyright (c) [2025] [Microsoft]
-# Copyright (c) [2025] [Chongjie Ye]
-# SPDX-License-Identifier: MIT
-# This file has been modified by Chongjie Ye on 2025/04/10
-# Original file was released under MIT, with the full license text available at:
-# https://github.com/atong01/conditional-flow-matching/blob/1.0.7/LICENSE
-# This modified file is released under the same license.
+# (see original notice and terms)
 
 import gradio as gr
 import os
@@ -86,6 +69,7 @@ import trimesh
 
 # ---- Force CPU inside hi3dgen (avoid any CUDA paths) ----
 print("[PATCH] Applying CPU monkey-patches to hi3dgen")
+
 # 1) utils_cube.construct_dense_grid(..., device=...) -> force CPU
 uc = importlib.import_module("hi3dgen.representations.mesh.utils_cube")
 if not hasattr(uc, "_CPU_PATCHED"):
@@ -96,16 +80,21 @@ if not hasattr(uc, "_CPU_PATCHED"):
     uc._CPU_PATCHED = True
     print("[PATCH] utils_cube.construct_dense_grid -> CPU")
 
-# 2) cube2mesh.EnhancedMarchingCubes default device -> force CPU
+# 2) cube2mesh.EnhancedMarchingCubes default device -> force CPU (flexible)
 cm = importlib.import_module("hi3dgen.representations.mesh.cube2mesh")
 M = cm.EnhancedMarchingCubes
 if not hasattr(M, "_CPU_PATCHED"):
     _orig_init = M.__init__
-    def _init_cpu(self, res, device=None):
-        return _orig_init(self, res, device=torch.device("cpu"))
+    def _init_cpu(self, *args, **kwargs):
+        # Ensure device is CPU regardless of how it's passed
+        if "device" in kwargs:
+            kwargs["device"] = torch.device("cpu")
+        else:
+            kwargs.setdefault("device", torch.device("cpu"))
+        return _orig_init(self, *args, **kwargs)
     M.__init__ = _init_cpu
     M._CPU_PATCHED = True
-    print("[PATCH] cube2mesh.EnhancedMarchingCubes.__init__ -> CPU")
+    print("[PATCH] cube2mesh.EnhancedMarchingCubes.__init__ -> CPU (flex)")
 
 # 3) Belt & suspenders: coerce torch.arange(device='cuda') to CPU if any call slips through
 if not hasattr(torch, "_ARANGE_CPU_PATCHED"):
@@ -337,8 +326,8 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
     gr.Markdown(
         """
         **Acknowledgments**: Hi3DGen is built on the shoulders of giants. We would like to express our gratitude to the open-source research community and the developers of these pioneering projects:
-        - **3D Modeling:** Our 3D Model is finetuned from the SOTA open-source 3D foundation model [Trellis](https://github.com/microsoft/TRELLIS); inspired by [Rodin], [Tripo], and [Dora].
-        - **Normal Estimation:** Builds on [StableNormal] and [GenPercept].
+        - **3D Modeling:** Our 3D Model is finetuned from the SOTA open-source 3D foundation model [Trellis](https://github.com/microsoft/TRELLIS); inspired by Rodin, Tripo, and Dora.
+        - **Normal Estimation:** Builds on StableNormal and GenPercept.
         """
     )
 
@@ -374,7 +363,6 @@ if __name__ == "__main__":
             yoso_version='yoso-normal-v1-8-1',
             local_cache_dir='./weights'
         )
-
     try:
         normal_predictor.to("cpu")
     except Exception: