update backbone UI

app.py

@@ -501,7 +501,7 @@ def ncut_run(
                 images = images.cuda()
             _images = reverse_transform_image(images, stablediffusion="stable" in model_name.lower())
             cluster_images = make_cluster_plot(eigvecs, _images, h=h, w=w)
-            logging_str += f"Plot time: {time.time() - start:.2f}s\n"
+            logging_str += f"plot time: {time.time() - start:.2f}s\n"
         
     
     if video_output:
@@ -1083,6 +1083,14 @@ def make_parameters_section(is_lisa=False):
     from ncut_pytorch.backbone import list_models, get_demo_model_names
     model_names = list_models()
     model_names = sorted(model_names)
+    def get_filtered_model_names(name):
+        return [m for m in model_names if name.lower() in m.lower()]
+    def get_default_model_name(name):
+        lst = get_filtered_model_names(name)
+        if len(lst) > 1:
+            return lst[1]
+        return lst[0]
+    
     if is_lisa:
         model_dropdown = gr.Dropdown(["LISA(xinlai/LISA-7B-v1)"], label="Backbone", value="LISA(xinlai/LISA-7B-v1)", elem_id="model_name")
         layer_slider = gr.Slider(1, 6, step=1, label="LISA decoder: Layer index", value=6, elem_id="layer", visible=False)        
@@ -1092,8 +1100,10 @@ def make_parameters_section(is_lisa=False):
         node_type_dropdown = gr.Dropdown(layer_names, label="LISA (SAM) decoder: Layer and Node", value="dec_1_block", elem_id="node_type")
     else:
         # remove LISA from the list
-        model_names = [m for m in model_names if "LISA" not in m]
-        model_dropdown = gr.Dropdown(model_names, label="Backbone", value="DiNO(dino_vitb8_448)", elem_id="model_name")
+        model_radio = gr.Radio(["CLIP", "DiNO", "Diffusion", "ImageNet", "MAE", "SAM"], label="Backbone", value="DiNO", elem_id="model_radio", show_label=True)
+        model_dropdown = gr.Dropdown(get_filtered_model_names("DiNO"), label="", value="DiNO(dino_vitb8_448)", elem_id="model_name", show_label=False)
+        model_radio.change(fn=lambda x: gr.update(choices=get_filtered_model_names(x), value=get_default_model_name(x)), inputs=model_radio, outputs=[model_dropdown])
+        # model_radio.change(fn=lambda x: gr.update(value=get_filtered_model_names(x)[0]), inputs=model_radio, outputs=[model_dropdown])
         layer_slider = gr.Slider(1, 12, step=1, label="Backbone: Layer index", value=10, elem_id="layer")
         positive_prompt = gr.Textbox(label="Prompt (Positive)", elem_id="prompt", placeholder="e.g. 'a photo of Gibson Les Pual guitar'")
         positive_prompt.visible = False
@@ -1170,7 +1180,7 @@ with demo:
                 
             with gr.Column(scale=5, min_width=200):
                 output_gallery = make_output_images_section()
-                cluster_gallery = gr.Gallery(value=[], label="Clusters", show_label=False, elem_id="clusters", columns=[2], rows=[1], object_fit="contain", height=450, show_share_button=True, preview=True, interactive=False)
+                cluster_gallery = gr.Gallery(value=[], label="Clusters", show_label=False, elem_id="clusters", columns=[2], rows=[1], object_fit="contain", height=300, show_share_button=True, preview=True, interactive=False)
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                     affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
@@ -1632,3 +1642,5 @@ demo.launch(share=True)
 # images = [(Image.open(image), None) for image in default_images]
 # ret = run_fn(images, num_eig=30)
 # # %%
+
+# %%