Allow --lora to use an absolute path

modules/LoRA.py

@@ -17,6 +17,14 @@ def add_lora_to_model(lora_names):
         add_lora_transformers(lora_names)
 
 
+def get_lora_path(lora_name):
+    p = Path(lora_name)
+    if p.exists():
+        lora_name = p.parts[-1]
+
+    return Path(f"{shared.args.lora_dir}/{lora_name}")
+
+
 def add_lora_exllama(lora_names):
 
     try:
@@ -40,7 +48,7 @@ def add_lora_exllama(lora_names):
         if len(lora_names) > 1:
             logger.warning('ExLlama can only work with 1 LoRA at the moment. Only the first one in the list will be loaded.')
 
-        lora_path = Path(f"{shared.args.lora_dir}/{lora_names[0]}")
+        lora_path = get_lora_path(lora_names[0])
         lora_config_path = lora_path / "adapter_config.json"
         lora_adapter_path = lora_path / "adapter_model.bin"
 
@@ -81,7 +89,7 @@ def add_lora_autogptq(lora_names):
             inference_mode=True,
         )
 
-        lora_path = Path(f"{shared.args.lora_dir}/{lora_names[0]}")
+        lora_path = get_lora_path(lora_names[0])
         logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]])))
         shared.model = get_gptq_peft_model(shared.model, peft_config, lora_path)
         shared.lora_names = [lora_names[0]]
@@ -101,7 +109,7 @@ def add_lora_transformers(lora_names):
     if len(removed_set) == 0 and len(prior_set) > 0:
         logger.info(f"Adding the LoRA(s) named {added_set} to the model...")
         for lora in added_set:
-            shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
+            shared.model.load_adapter(get_lora_path(lora), lora)
 
         return
 
@@ -123,9 +131,9 @@ def add_lora_transformers(lora_names):
                     params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()}
 
         logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
-        shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), adapter_name=lora_names[0], **params)
+        shared.model = PeftModel.from_pretrained(shared.model, get_lora_path(lora_names[0]), adapter_name=lora_names[0], **params)
         for lora in lora_names[1:]:
-            shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
+            shared.model.load_adapter(get_lora_path(lora), lora)
 
         shared.lora_names = lora_names