Fix Genesis from_torch patch: avoid torch ops on genesis tensors inside ZeroGPU

The previous patch used gs_tensor.clone() which triggers __torch_function__
on a genesis.grad.Tensor. Inside a ZeroGPU GPU slot, spaces/zero/torch/
patching.py intercepts __torch_function__ and then PyTorch's _convert()
calls ret.as_subclass(genesis.grad.Tensor), which hits the same "already
associated" error.

Fix: do all tensor ops (to, clone, detach) on plain torch.Tensor BEFORE
wrapping as a genesis.grad.Tensor via _make_subclass. No torch operations
are ever called on the genesis tensor inside from_torch, so ZeroGPU's
__torch_function__ interceptor is never triggered.

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

app.py

@@ -56,18 +56,20 @@ def _patch_genesis_from_torch():
                 _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)
+            # Perform ALL tensor operations on plain torch.Tensor objects BEFORE
+            # wrapping as genesis.grad.Tensor.  This avoids __torch_function__
+            # interference from ZeroGPU (spaces/zero/torch/patching.py), which
+            # intercepts operations on tensor subclasses and then fails when
+            # PyTorch tries to restore the subclass type via as_subclass().
+            t = torch_tensor.to(device=_gs.device, dtype=dtype).clone()
+            if detach:
+                t = t.detach()
+            # _make_subclass uses MAYBE_UNINITIALIZED status, bypassing the
+            # "already associated" check that Tensor(existing_tensor) triggers.
+            gs_tensor = torch.Tensor._make_subclass(_Tensor, t, requires_grad)
             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