diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 5a13bca3f..46d423284 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -294,7 +294,6 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
     if network_layer_name is None:
         return
     t0 = time.time()
-    weight = self.weight # calculate quant weights once
     current_names = getattr(self, "network_current_names", ())
     wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
     weights_backup = getattr(self, "network_weights_backup", None)
@@ -304,7 +303,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
         if isinstance(self, torch.nn.MultiheadAttention):
             weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
         else:
-            weights_backup = weight.to(devices.cpu, copy=True)
+            weights_backup = self.weight.to(devices.cpu, copy=True)
         self.network_weights_backup = weights_backup
     bias_backup = getattr(self, "network_bias_backup", None)
     if bias_backup is None:
@@ -324,6 +323,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
             if module is not None and hasattr(self, 'weight'):
                 try:
                     with devices.inference_context():
+                        weight = self.weight # calculate quant weights once
                         updown, ex_bias = module.calc_updown(weight)
                         if len(weight.shape) == 4 and weight.shape[1] == 9:
                             # inpainting model. zero pad updown to make channel[1]  4 to 9
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 205cbd6b2..91a43ec9b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1766,6 +1766,7 @@ def disable_offload(sd_model):
         if not isinstance(model, torch.nn.Module):
             continue
         remove_hook_from_module(model, recurse=True)
+    sd_model.has_accelerate = False
 
 
 def unload_model_weights(op='model'):
diff --git a/modules/shared.py b/modules/shared.py
index deff549a4..bdc6a7916 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -374,6 +374,10 @@ if not (cmd_opts.lowvram or cmd_opts.medvram):
         else:
             offload_mode_default = "none"
             log.info(f"VRAM: Detected={gpu_memory} GB Optimization=none")
+elif cmd_opts.medvram:
+    offload_mode_default = "cpu"
+elif cmd_opts.lowvram:
+    offload_mode_default = "sequential"
 
 
 if devices.backend == "directml": # Force BMM for DirectML instead of SDP