Update app.py

app.py

@@ -75,8 +75,6 @@ for item in sdxl_loras_raw:
         "state_dict": state_dict
     }
 
-sdxl_loras_raw_new = [item for item in sdxl_loras_raw if item.get("new") == True]
-
 sdxl_loras_raw = [item for item in sdxl_loras_raw if item.get("new") != True]
     
 # download models
@@ -125,7 +123,7 @@ pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config,
 pipe.load_ip_adapter_instantid(face_adapter)
 pipe.set_ip_adapter_scale(0.8)
 zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
-zoe.to("cuda")
+zoe.to(device)
 
 original_pipe = copy.deepcopy(pipe)
 pipe.to(device)
@@ -256,7 +254,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     weight_name = sdxl_loras[selected_state.index]["weights"]
     
     full_path_lora = state_dicts[repo_name]["saved_name"]
-    loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"])
+    #loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"])
     cross_attention_kwargs = None
     print("Last LoRA: ", last_lora)
     print("Current LoRA: ", repo_name)
@@ -265,7 +263,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
         if(last_fused):
             pipe.unfuse_lora()
         pipe.unload_lora_weights()
-        pipe.load_lora_weights(loaded_state_dict)
+        pipe.load_lora_weights(full_path_lora)
         pipe.fuse_lora(lora_scale)
         last_fused = True
         is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
@@ -325,7 +323,6 @@ def deselect():
 
 with gr.Blocks(css="custom.css") as demo:
     gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
-    gr_sdxl_loras_new = gr.State(value=sdxl_loras_raw_new)
     title = gr.HTML(
         """<h1>Face to All</h1>""",
         elem_id="title",