diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py
index a737fec37..d4345ada6 100644
--- a/extensions-builtin/Lora/lora.py
+++ b/extensions-builtin/Lora/lora.py
@@ -14,7 +14,7 @@ re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)
 re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
 
 
-def convert_diffusers_name_to_compvis(key):
+def convert_diffusers_name_to_compvis(key, is_sd2):
     def match(match_list, regex):
         r = re.match(regex, key)
         if not r:
@@ -36,6 +36,14 @@ def convert_diffusers_name_to_compvis(key):
         return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
 
     if match(m, re_text_block):
+        if is_sd2:
+            if 'mlp_fc1' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+            elif 'mlp_fc2' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+            else:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
+
         return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
 
     return key
@@ -102,9 +110,10 @@ def load_lora(name, filename):
     sd = sd_models.read_state_dict(filename)
 
     keys_failed_to_match = []
+    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
 
     for key_diffusers, weight in sd.items():
-        fullkey = convert_diffusers_name_to_compvis(key_diffusers)
+        fullkey = convert_diffusers_name_to_compvis(key_diffusers, is_sd2)
         key, lora_key = fullkey.split(".", 1)
 
         sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
@@ -123,9 +132,13 @@ def load_lora(name, filename):
 
         if type(sd_module) == torch.nn.Linear:
             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+        elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
+            module = torch.nn.modules.linear.NonDynamicallyQuantizableLinear(weight.shape[1], weight.shape[0], bias=False)
         elif type(sd_module) == torch.nn.Conv2d:
             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
         else:
+            print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
+            continue
             assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
 
         with torch.no_grad():
@@ -242,6 +255,10 @@ def lora_Conv2d_load_state_dict(self: torch.nn.Conv2d, *args, **kwargs):
     return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
 
 
+def lora_NonDynamicallyQuantizableLinear_forward(self, input):
+    return lora_forward(self, input, torch.nn.NonDynamicallyQuantizableLinear_forward_before_lora(self, input))
+
+
 def list_available_loras():
     available_loras.clear()