Update app.py

app.py

@@ -67,7 +67,7 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
 )
 pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 pipe.to(device)
-
+pipe.load_lora_weights(lcm_lora_id, weight_name="lcm_sdxl_lora.safetensors", adapter_name="lcm_lora", use_auth_token=os.getenv('HF_TOKEN'))
 last_lora = ""
 last_merged = False
 last_fused = False
@@ -166,14 +166,11 @@ def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras, sdxl_lora
     loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"])
     cross_attention_kwargs = None
     if last_lora != repo_name:
-        if(last_fused):
-            pipe.unfuse_lora()
-        pipe.load_lora_weights(loaded_state_dict, adapter_name="loaded_lora")
-        pipe.load_lora_weights(lcm_lora_id, weight_name="lcm_sdxl_lora.safetensors", adapter_name="lcm_lora", use_auth_token=os.getenv('HF_TOKEN'))
-        pipe.set_adapters(["loaded_lora", "lcm_lora"], adapter_weights=[0.8, 1.0])
-        pipe.fuse_lora()
-        pipe.unload_lora_weights()
-        last_fused = True
+        #if(last_fused):
+            #pipe.unfuse_lora()
+        pipe.load_lora_weights(loaded_state_dict, adapter_name=state_dicts[repo_name]["saved_name"])
+        pipe.set_adapters([state_dicts[repo_name]["saved_name"], "lcm_lora"], adapter_weights=[0.8, 1.0])
+        #last_fused = True
         is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
         if(is_pivotal):
             #Add the textual inversion embeddings from pivotal tuning models