Fixed issue with rescaled loras only saving af fp32

jobs/process/ModRescaleLoraProcess.py

@@ -84,7 +84,8 @@ class ModRescaleLoraProcess(BaseProcess):
             if self.scale_target == 'up_down' and key.endswith('.lora_up.weight') or key.endswith('.lora_down.weight'):
                 # would it be better to adjust the up weights for fp16 precision? Doing both should reduce chance of NaN
                 v = v * up_down_scale
-            new_state_dict[key] = v.to(get_torch_dtype(self.save_dtype))
+            v = v.detach().clone().to("cpu").to(self.save_dtype)
+            new_state_dict[key] = v
 
         save_meta = add_model_hash_to_meta(new_state_dict, save_meta)
         save_file(new_state_dict, self.output_path, save_meta)