diff --git a/ltx_video/ltxv.py b/ltx_video/ltxv.py
index e9b7d8f..7be215b 100644
--- a/ltx_video/ltxv.py
+++ b/ltx_video/ltxv.py
@@ -148,7 +148,6 @@ class LTXV:
     def __init__(
         self,
         model_filepath: str,
-        loras_filepath: str,
         text_encoder_filepath: str,
         dtype = torch.bfloat16,
         VAE_dtype = torch.bfloat16, 
@@ -156,8 +155,8 @@ class LTXV:
     ):
 
         self.mixed_precision_transformer = mixed_precision_transformer
-        self.distilled = loras_filepath != None
-        
+        self.distilled = any("lora" in name for name in model_filepath)
+        model_filepath = [name for name in model_filepath if not "lora" in name ]
         # with safe_open(ckpt_path, framework="pt") as f:
         #     metadata = f.metadata()
         #     config_str = metadata.get("config")
diff --git a/wgp.py b/wgp.py
index ac8d4ca..1a03a92 100644
--- a/wgp.py
+++ b/wgp.py
@@ -2066,14 +2066,13 @@ def load_wan_model(model_filename, quantizeTransformer = False, dtype = torch.bf
         pipe["text_encoder_2"] = wan_model.clip.model
     return wan_model, pipe
 
-def load_ltxv_model(model_filename, loras_filenames, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
+def load_ltxv_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
     filename = model_filename[-1]
     print(f"Loading '{filename}' model...")
     from ltx_video.ltxv import LTXV
 
     ltxv_model = LTXV(
         model_filepath = model_filename,
-        loras_filepath = loras_filenames,
         text_encoder_filepath = get_ltxv_text_encoder_filename(text_encoder_quantization),
         dtype = dtype,
         # quantizeTransformer = quantizeTransformer,
@@ -2126,11 +2125,8 @@ def load_models(model_filename):
     perc_reserved_mem_max = args.perc_reserved_mem_max
     new_transformer_loras_filenames = None
     dependent_models = get_dependent_models(model_filename, quantization= transformer_quantization, dtype_policy =  transformer_dtype_policy) 
-    if "_lora" in model_filename:
-        new_transformer_loras_filenames = [model_filename]
-        model_filelist = dependent_models
-    else:
-        model_filelist = dependent_models + [model_filename]
+    new_transformer_loras_filenames = [model_filename]  if "_lora" in model_filename else None
+    model_filelist = dependent_models + [model_filename]
     for filename in model_filelist: 
         download_models(filename)
     transformer_dtype = get_transformer_dtype(model_family, transformer_dtype_policy)
@@ -2142,7 +2138,7 @@ def load_models(model_filename):
     if model_family == "wan" :
         wan_model, pipe = load_wan_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
     elif model_family == "ltxv":
-        wan_model, pipe = load_ltxv_model(model_filelist, new_transformer_loras_filenames, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
+        wan_model, pipe = load_ltxv_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
     elif model_family == "hunyuan":
         wan_model, pipe = load_hunyuan_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
     else:
@@ -2159,8 +2155,6 @@ def load_models(model_filename):
     global prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer
     if server_config.get("enhancer_enabled", 0) == 1:
         from transformers import ( AutoModelForCausalLM, AutoProcessor, AutoTokenizer, LlamaForCausalLM )
-
- 
         prompt_enhancer_image_caption_model = AutoModelForCausalLM.from_pretrained( "ckpts/Florence2", trust_remote_code=True)
         prompt_enhancer_image_caption_processor = AutoProcessor.from_pretrained( "ckpts/Florence2", trust_remote_code=True)
         prompt_enhancer_llm_model = offload.fast_load_transformers_model("ckpts/Llama3_2/Llama3_2_quanto_bf16_int8.safetensors")