Update app.py: override CUDA defaults for CPU environment

app.py

@@ -88,6 +88,8 @@ _ensure_hi3dgen_available()
 # ---------------------------------------------------------------------------
 
 def _ensure_xformers_stub():
+
+    # ---------------------------------------------------------------------------
     import sys
     # If xformers is already available, do nothing.
     if 'xformers.ops' in sys.modules:
@@ -116,9 +118,40 @@ def _ensure_xformers_stub():
     sys.modules['xformers'] = xformers_mod
     sys.modules['xformers.ops'] = ops_mod
 
-# Ensure the xformers stub is registered before importing Hi3DGen
+    # Ensure the xformers stub is registered before importing Hi3DGen
 _ensure_xformers_stub()
 
+# ---------------------------------------------------------------------------
+# Monkey-patch Hi3DGen to run on CPU
+#
+# Some utility functions and classes in the Hi3DGen codebase assume the
+# presence of a CUDA device by default.  Specifically, the function
+# `construct_dense_grid` in `hi3dgen.representations.mesh.utils_cube` uses
+# `device='cuda'` as its default argument, and the class
+# `EnhancedMarchingCubes` in `hi3dgen.representations.mesh.cube2mesh` has
+# a constructor that defaults to `device="cuda"`.  On CPU-only Spaces, these
+# defaults cause runtime errors when PyTorch attempts to allocate tensors on
+# a non-existent GPU.  To avoid this, we override the default arguments for
+# these functions to use the CPU instead.  If the patch cannot be applied
+# (for example, if the module structure changes in a future version), we
+# catch any exceptions and log a warning without stopping execution.
+try:
+    from hi3dgen.representations.mesh import utils_cube
+    if hasattr(utils_cube.construct_dense_grid, '__defaults__'):
+        _defaults = list(utils_cube.construct_dense_grid.__defaults__ or ())
+        if _defaults and _defaults[-1] == 'cuda':
+            _defaults[-1] = 'cpu'
+            utils_cube.construct_dense_grid.__defaults__ = tuple(_defaults)
+
+    from hi3dgen.representations.mesh.cube2mesh import EnhancedMarchingCubes
+    if hasattr(EnhancedMarchingCubes.__init__, '__defaults__'):
+        _mc_defaults = list(EnhancedMarchingCubes.__init__.__defaults__ or ())
+        if _mc_defaults and _mc_defaults[-1] == 'cuda':
+            _mc_defaults[-1] = 'cpu'
+            EnhancedMarchingCubes.__init__.__defaults__ = tuple(_mc_defaults)
+except Exception as _e:
+    print(f"Warning: failed to apply CPU device overrides: {_e}")
+
 from hi3dgen.pipelines import Hi3DGenPipeline
 import trimesh
 MAX_SEED = np.iinfo(np.int32).max
@@ -357,4 +390,4 @@ if __name__ == "__main__":
         normal_predictor = torch.hub.load("hugoycj/StableNormal", "StableNormal_turbo", trust_repo=True, yoso_version='yoso-normal-v1-8-1', local_cache_dir='./weights')    
 
     # Launch the app
-    demo.launch(share=False, server_name="0.0.0.0")
+    demo.launch(share=False, server_name="0.0.0.0")