Patch Genesis from_torch to fix PyTorch 2.5 tensor subclass error

In PyTorch 2.5+, torch.Tensor.__new__(SubClass, existing_tensor) raises:
"raw Tensor object is already associated to a python object of type
Tensor which is not a subclass of the requested type"
when the existing TensorImpl's Python wrapper is a plain torch.Tensor
(not a subclass of genesis.grad.Tensor).

Genesis's from_torch() does exactly this: torch.zeros() → plain tensor,
then Tensor(plain_tensor) → fails because plain torch.Tensor is not a
subclass of genesis.grad.Tensor.

Fix: use torch.Tensor._make_subclass(Tensor, t) which is PyTorch's
official API for creating tensor subclass views, bypassing the
association check. Patch is applied before startup() runs, so all
genesis gs.zeros()/gs.ones() etc. calls use the fixed path.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app.py

@@ -27,6 +27,54 @@ try:
 except Exception:
     pass
 
+# Patch Genesis from_torch: in PyTorch 2.5+, Tensor(existing_plain_tensor) raises
+#   "raw Tensor object is already associated to a python object of type Tensor
+#    which is not a subclass of the requested type"
+# because torch.Tensor.__new__(SubClass, existing_tensor) checks that the existing
+# TensorImpl's Python wrapper is a subclass of SubClass. torch.Tensor is the parent,
+# not a subclass of genesis.grad.Tensor, so the check fails.
+# Fix: use torch.Tensor._make_subclass(cls, t) which is the proper PyTorch API for
+# creating a subclass view of an existing tensor regardless of the wrapper type.
+def _patch_genesis_from_torch():
+    try:
+        import genesis
+        import genesis.grad.creation_ops as _gc_ops
+        import genesis.grad.tensor as _gt_mod
+        _Tensor = _gt_mod.Tensor
+        _gs = genesis
+
+        def _patched_from_torch(torch_tensor, dtype=None, requires_grad=False, detach=True, scene=None):
+            if dtype is None:
+                dtype = torch_tensor.dtype
+            if dtype in (float, torch.float32, torch.float64):
+                dtype = _gs.tc_float
+            elif dtype in (int, torch.int32, torch.int64):
+                dtype = _gs.tc_int
+            elif dtype in (bool, torch.bool):
+                dtype = torch.bool
+            else:
+                _gs.raise_exception(f"Unsupported dtype: {dtype}")
+            if torch_tensor.requires_grad and (not detach) and (not requires_grad):
+                requires_grad = True
+            t = torch_tensor.to(device=_gs.device, dtype=dtype)
+            # _make_subclass creates a SubClass view without triggering the PyTorch
+            # "already associated" check that Tensor(existing_tensor) hits.
+            gs_tensor = torch.Tensor._make_subclass(_Tensor, t)
+            gs_tensor.scene = scene
+            gs_tensor.uid = _gs.UID()
+            gs_tensor.parents = []
+            gs_tensor = gs_tensor.clone()
+            if detach:
+                gs_tensor = gs_tensor.detach(sceneless=False)
+            if requires_grad:
+                gs_tensor = gs_tensor.requires_grad_()
+            return gs_tensor
+
+        _gc_ops.from_torch = _patched_from_torch
+        print("[patch] Genesis from_torch patched (_make_subclass fix for PyTorch 2.5+)")
+    except Exception as e:
+        print(f"[patch] Genesis from_torch patch skipped: {e}")
+
 import base64
 import io
 import threading
@@ -911,6 +959,7 @@ def build_demo():
 # files are already on disk so snapshot_download() is a fast no-op.  By doing
 # this here we avoid holding a ZeroGPU allocation while waiting on downloads.
 _ensure_models_downloaded()
+_patch_genesis_from_torch()  # Fix Genesis from_torch for PyTorch 2.5 compatibility
 startup()   # Load all models and scenes to CPU at module level
 demo = build_demo()