fix: Handle un-inited meta-tensors in models (fixes a CPU TE crash) (CORE-67) (#13578)

comfy/ops.py

@@ -79,14 +79,21 @@ def cast_to_input(weight, input, non_blocking=False, copy=True):
     return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
 
-def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant):
+def materialize_meta_param(s, param_keys):
+    for param_key in param_keys:
+        param = getattr(s, param_key, None)
+        if param is not None and getattr(param, "is_meta", False):
+            setattr(s, param_key, torch.nn.Parameter(torch.zeros(param.shape, dtype=param.dtype), requires_grad=param.requires_grad))
 
+
+def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant):
     #vbar doesn't support CPU weights, but some custom nodes have weird paths
     #that might switch the layer to the CPU and expect it to work. We have to take
     #a clone conservatively as we are mmapped and some SFT files are packed misaligned
     #If you are a custom node author reading this, please move your layer to the GPU
     #or declare your ModelPatcher as CPU in the first place.
     if comfy.model_management.is_device_cpu(device):
+        materialize_meta_param(s, ["weight", "bias"])
         weight = s.weight.to(dtype=dtype, copy=True)
         if isinstance(weight, QuantizedTensor):
             weight = weight.dequantize()
@@ -108,6 +115,7 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
             xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device)
 
     if not resident:
+        materialize_meta_param(s, ["weight", "bias"])
         cast_geometry = comfy.memory_management.tensors_to_geometries([ s.weight, s.bias ])
         cast_dest = None
 
@@ -306,6 +314,12 @@ class CastWeightBiasOp:
     bias_function = []
 
 class disable_weight_init:
+    @staticmethod
+    def _zero_init_parameter(module, name):
+        param = getattr(module, name)
+        device = None if getattr(param, "is_meta", False) else param.device
+        setattr(module, name, torch.nn.Parameter(torch.zeros(param.shape, device=device, dtype=param.dtype), requires_grad=False))
+
     @staticmethod
     def _lazy_load_from_state_dict(module, state_dict, prefix, local_metadata,
                                    missing_keys, unexpected_keys, weight_shape,