fix: disable non-blocking tensor copies to MPS during model loading

When loading model weights with `device_map="mps"`, the `non_blocking=True`
parameter in `set_module_tensor_to_device` causes asynchronous CPU-to-MPS
copies. With mmap-backed safetensors the source CPU memory can be released
before the MPS copy completes, silently corrupting the destination weights.

The corruption is non-deterministic and dtype-dependent (float32 corrupts
weights but not biases; float16 corrupts biases but not weights), resulting
in extreme values (~1e37), LayerNorm overflow, and NaN outputs.

Force synchronous copies (`non_blocking=False`) when the target device is
MPS. Other devices (CUDA, CPU) continue to use non-blocking transfers.

Fixes #13227

Made-with: Cursor

Author: Prakhar Agarwal

src/diffusers/models/model_loading_utils.py

@@ -254,7 +254,14 @@ def load_model_dict_into_meta(
                 set_module_kwargs["dtype"] = dtype
 
         if is_accelerate_version(">", "1.8.1"):
-            set_module_kwargs["non_blocking"] = True
+            # MPS does not support truly asynchronous non-blocking transfers from CPU.
+            # When non_blocking=True the source tensor may be freed or recycled (especially
+            # with mmap-backed safetensors) before the MPS copy completes, silently corrupting
+            # the destination weights. Force synchronous copies on MPS to avoid this.
+            is_mps_target = str(param_device) == "mps" or (
+                isinstance(param_device, torch.device) and param_device.type == "mps"
+            )
+            set_module_kwargs["non_blocking"] = not is_mps_target
             set_module_kwargs["clear_cache"] = False
 
         # For compatibility with PyTorch load_state_dict which converts state dict dtype to existing dtype in model, and which