diff --git a/model_loading.py b/model_loading.py
index 7a08057..8308b68 100644
--- a/model_loading.py
+++ b/model_loading.py
@@ -241,36 +241,43 @@ def loadmodel(self, model, precision, quantization="disabled", compile="disabled
         
         #LoRAs
         if lora is not None:
-            try:
+                dimensionx_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
+                dimensionx_lora = False
                 adapter_list = []
                 adapter_weights = []
                 for l in lora:
+                    if any(item in l["path"].lower() for item in dimensionx_loras):
+                        dimensionx_lora = True
                     fuse = True if l["fuse_lora"] else False
-                    lora_sd = load_torch_file(l["path"])             
+                    lora_sd = load_torch_file(l["path"])
+                    lora_rank = None            
                     for key, val in lora_sd.items():
                         if "lora_B" in key:
                             lora_rank = val.shape[1]
                             break
-                    log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
-                    adapter_name = l['path'].split("/")[-1].split(".")[0]
-                    adapter_weight = l['strength']
-                    pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
+                    if lora_rank is not None:
+                        log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
+                        adapter_name = l['path'].split("/")[-1].split(".")[0]
+                        adapter_weight = l['strength']
+                        pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
+                        
+                        adapter_list.append(adapter_name)
+                        adapter_weights.append(adapter_weight)
+                    else:
+                        try: #Fun trainer LoRAs are loaded differently
+                            from .lora_utils import merge_lora
+                            log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
+                            transformer = merge_lora(transformer, l["path"], l["strength"])
+                        except:
+                            raise ValueError(f"Can't recognize LoRA {l['path']}")
                     
-                    adapter_list.append(adapter_name)
-                    adapter_weights.append(adapter_weight)
-                for l in lora:
-                    pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
+                pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
                 if fuse:
                     lora_scale = 1
-                    dimension_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
-                    if any(item in lora[-1]["path"].lower() for item in dimension_loras):
+                    if dimensionx_lora:
                         lora_scale = lora_scale / lora_rank
                     pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
-            except: #Fun trainer LoRAs are loaded differently
-                from .lora_utils import merge_lora
-                for l in lora:
-                    log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
-                    transformer = merge_lora(transformer, l["path"], l["strength"])
+           
 
         if "fused" in attention_mode:
             from diffusers.models.attention import Attention