fix aligned advanced plot

app.py

@@ -536,6 +536,9 @@ def ncut_run(
                 
         if not advanced:
             return rgbs[0], rgbs[1], rgbs[2], logging_str
+        if "AlignedThreeModelAttnNodes" == model_name:
+            return rgbs[0], rgbs[1], rgbs[2], logging_str
+        
         if advanced:
             cluster_plots, norm_plots = [], []
             for i in range(3):
@@ -690,26 +693,26 @@ def ncut_run(
 
 def _ncut_run(*args, **kwargs):
     n_ret = kwargs.pop("n_ret", 1)
-    try:
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
+    # try:
+    #     if torch.cuda.is_available():
+    #         torch.cuda.empty_cache()
             
-        ret = ncut_run(*args, **kwargs)
+    #     ret = ncut_run(*args, **kwargs)
         
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
+    #     if torch.cuda.is_available():
+    #         torch.cuda.empty_cache()
         
-        ret = list(ret)[:n_ret] + [ret[-1]]
-        return ret
-    except Exception as e:
-        gr.Error(str(e))
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return *(None for _ in range(n_ret)), "Error: " + str(e)
-
-    # ret = ncut_run(*args, **kwargs)
-    # ret = list(ret)[:n_ret] + [ret[-1]]
-    # return ret
+    #     ret = list(ret)[:n_ret] + [ret[-1]]
+    #     return ret
+    # except Exception as e:
+    #     gr.Error(str(e))
+    #     if torch.cuda.is_available():
+    #         torch.cuda.empty_cache()
+    #     return *(None for _ in range(n_ret)), "Error: " + str(e)
+
+    ret = ncut_run(*args, **kwargs)
+    ret = list(ret)[:n_ret] + [ret[-1]]
+    return ret
 
 if USE_HUGGINGFACE_ZEROGPU:
     @spaces.GPU(duration=30)
@@ -871,6 +874,8 @@ def load_alignedthreemodel():
     model = ThreeAttnNodes(align_weights)
     
     return model
+# pre-load the alignedthree model in case it fails to load
+load_alignedthreemodel()
 
 promptable_diffusion_models = ["Diffusion(stabilityai/stable-diffusion-2)", "Diffusion(CompVis/stable-diffusion-v1-4)"]
 promptable_segmentation_models = ["LISA(xinlai/LISA-7B-v1)"]