diff --git a/invokeai/backend/lora/lora_patcher.py b/invokeai/backend/lora/lora_patcher.py
index 6640dcfdd8..1e32053f5b 100644
--- a/invokeai/backend/lora/lora_patcher.py
+++ b/invokeai/backend/lora/lora_patcher.py
@@ -6,13 +6,11 @@ import torch
 from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
 from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
 from invokeai.backend.lora.layers.lora_layer import LoRALayer
-from invokeai.backend.lora.layers.full_layer import FullLayer
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.sidecar_layers.concatenated_lora.concatenated_lora_linear_sidecar_layer import (
     ConcatenatedLoRALinearSidecarLayer,
 )
 from invokeai.backend.lora.sidecar_layers.lora.lora_linear_sidecar_layer import LoRALinearSidecarLayer
-from invokeai.backend.lora.sidecar_layers.lora.lora_full_linear_sidecar_layer import LoRAFullLinearSidecarLayer
 from invokeai.backend.lora.sidecar_layers.lora_sidecar_module import LoRASidecarModule
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
@@ -54,7 +52,8 @@ class LoRAPatcher:
             yield
         finally:
             for param_key, weight in original_weights.get_changed_weights():
-                model.get_parameter(param_key).copy_(weight)
+                cur_param = model.get_parameter(param_key)
+                cur_param.data = weight.to(dtype=cur_param.dtype, device=cur_param.device, copy=True)
 
     @staticmethod
     @torch.no_grad()
@@ -120,10 +119,20 @@ class LoRAPatcher:
                     if module_param.nelement() == lora_param_weight.nelement():
                         lora_param_weight = lora_param_weight.reshape(module_param.shape)
                     else:
-                        expanded_weight = torch.zeros_like(lora_param_weight, device=module_param.device)
+                        # This condition was added to handle layers in FLUX control LoRAs.
+                        # TODO(ryand): Move the weight update into the LoRA layer so that the LoRAPatcher doesn't need
+                        # to worry about this?
+                        expanded_weight = torch.zeros_like(
+                            lora_param_weight, dtype=module_param.dtype, device=module_param.device
+                        )
                         slices = tuple(slice(0, dim) for dim in module_param.shape)
                         expanded_weight[slices] = module_param
-                        setattr(module, param_name, expanded_weight)
+                        setattr(
+                            module,
+                            param_name,
+                            torch.nn.Parameter(expanded_weight, requires_grad=module_param.requires_grad),
+                        )
+                        module_param = expanded_weight
                 lora_param_weight *= patch_weight * layer_scale
                 module_param += lora_param_weight.to(dtype=dtype)
 
@@ -253,8 +262,6 @@ class LoRAPatcher:
                 return LoRALinearSidecarLayer(lora_layer=lora_layer, weight=patch_weight)
             elif isinstance(lora_layer, ConcatenatedLoRALayer):
                 return ConcatenatedLoRALinearSidecarLayer(concatenated_lora_layer=lora_layer, weight=patch_weight)
-            elif isinstance(lora_layer, FullLayer):
-                return LoRAFullLinearSidecarLayer(lora_layer=lora_layer, weight=patch_weight)
             else:
                 raise ValueError(f"Unsupported Linear LoRA layer type: {type(lora_layer)}")
         else: