diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 67f9abe2a..e58e1fb56 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -377,6 +377,8 @@ def store_weights_backup(weight):
     if weight is None:
         return None
 
+    if shared.opts.lora_without_backup_weight:
+        return True
     return weight.to(devices.cpu, copy=True)
 
 
@@ -395,6 +397,9 @@ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Li
     if weights_backup is None and bias_backup is None:
         return
 
+    if weights_backup is True or weights_backup == (True, True): # fake backup
+        return
+
     if weights_backup is not None:
         if isinstance(self, torch.nn.MultiheadAttention):
             restore_weights_backup(self, 'in_proj_weight', weights_backup[0])
@@ -539,7 +544,12 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
             logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
             extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
 
-        self.network_current_names = wanted_names
+
+        if weights_backup is True or weights_backup == (True, True): # fake backup
+            self.network_weights_backup = None
+            self.network_bias_backup = None
+        else:
+            self.network_current_names = wanted_names
 
 
 def network_forward(org_module, input, original_forward):
diff --git a/modules/shared_options.py b/modules/shared_options.py
index 78089cbec..6b6faf332 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -242,6 +242,7 @@ options_templates.update(options_section(('optimizations', "Optimizations", "sd"
     "batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond commandline argument"),
     "fp8_storage": OptionInfo("Disable", "FP8 weight", gr.Radio, {"choices": ["Disable", "Enable for SDXL", "Enable"]}).info("Use FP8 to store Linear/Conv layers' weight. Require pytorch>=2.1.0."),
     "cache_fp16_weight": OptionInfo(False, "Cache FP16 weight for LoRA").info("Cache fp16 weight when enabling FP8, will increase the quality of LoRA. Use more system ram."),
+    "lora_without_backup_weight": OptionInfo(False, "LoRA without backup weights").info("LoRA without backup weights to save RAM."),
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility", "sd"), {