update parameters

app.py

@@ -75,8 +75,10 @@ def compute_ncut(
     perplexity=150,
     n_neighbors=150,
     min_dist=0.1,
-    sampling_method="fps",
+    sampling_method="QuickFPS",
     metric="cosine",
+    indirect_connection=True,
+    make_orthogonal=False,
     progess_start=0.4,
 ):        
     progress = gr.Progress()
@@ -100,6 +102,8 @@ def compute_ncut(
         sample_method=sampling_method,
         distance=metric,
         normalize_features=False,
+        indirect_connection=indirect_connection,
+        make_orthogonal=make_orthogonal,
     ).fit_transform(features.reshape(-1, features.shape[-1]))
     # print(f"NCUT time: {time.time() - start:.2f}s")
     logging_str += f"NCUT time: {time.time() - start:.2f}s\n"
@@ -384,7 +388,10 @@ def ncut_run(
     perplexity=500,
     n_neighbors=500,
     min_dist=0.1,
-    sampling_method="fps",
+    sampling_method="QuickFPS",
+    ncut_metric="cosine",
+    indirect_connection=True,
+    make_orthogonal=False,
     old_school_ncut=False,
     recursion=False,
     recursion_l2_n_eigs=50,
@@ -475,7 +482,9 @@ def ncut_run(
                 n_neighbors=n_neighbors,
                 min_dist=min_dist,
                 sampling_method=sampling_method,
-                metric="cosine" if i == 0 else recursion_metric,
+                metric=ncut_metric if i == 0 else recursion_metric,
+                indirect_connection=indirect_connection,
+                make_orthogonal=make_orthogonal,
                 progess_start=progress_start,
             )
             logging_str += _logging_str
@@ -522,6 +531,9 @@ def ncut_run(
                 n_neighbors=n_neighbors,
                 min_dist=min_dist,
                 sampling_method=sampling_method,
+                metric=ncut_metric,
+                indirect_connection=indirect_connection,
+                make_orthogonal=make_orthogonal,
                 progess_start=progress_start+step_float*i_image,
             )
             logging_str += _logging_str
@@ -544,6 +556,9 @@ def ncut_run(
             n_neighbors=n_neighbors,
             min_dist=min_dist,
             sampling_method=sampling_method,
+            indirect_connection=indirect_connection,
+            make_orthogonal=make_orthogonal,
+            metric=ncut_metric,
         )
         logging_str += _logging_str
         
@@ -792,6 +807,8 @@ def run_fn(
     affinity_focal_gamma=0.5,
     num_sample_ncut=10000,
     knn_ncut=10,
+    ncut_indirect_connection=True,
+    ncut_make_orthogonal=False,
     embedding_method="tsne_3d",
     embedding_metric='euclidean',
     num_sample_tsne=300,
@@ -799,7 +816,8 @@ def run_fn(
     perplexity=150,
     n_neighbors=150,
     min_dist=0.1,
-    sampling_method="fps",
+    sampling_method="QuickFPS",
+    ncut_metric="cosine",
     old_school_ncut=False,
     max_frames=100,
     recursion=False,
@@ -826,7 +844,7 @@ def run_fn(
         images = extract_video_frames(images, max_frames=max_frames)
         video_output = True
     
-    if sampling_method == "fps":
+    if sampling_method == "QuickFPS":
         sampling_method = "farthest"
         
     # resize the images before acquiring GPU
@@ -923,6 +941,9 @@ def run_fn(
         "n_neighbors": n_neighbors,
         "min_dist": min_dist,
         "sampling_method": sampling_method,
+        "ncut_metric": ncut_metric,
+        "indirect_connection": ncut_indirect_connection,
+        "make_orthogonal": ncut_make_orthogonal,
         "old_school_ncut": old_school_ncut,
         "recursion": recursion,
         "recursion_l2_n_eigs": recursion_l2_n_eigs,
@@ -1001,7 +1022,8 @@ def make_dataset_images_section(advanced=False, is_random=False):
         dataset_classes = DATASET_CLASSES
         with gr.Row():
             dataset_dropdown = gr.Dropdown(dataset_names, label="Dataset name", value="mrm8488/ImageNet1K-val", elem_id="dataset", min_width=300)
-            num_images_slider = gr.Number(10, label="Number of images", elem_id="num_images")
+            # num_images_slider = gr.Number(10, label="Number of images", elem_id="num_images")
+            num_images_slider = gr.Slider(1, 1000, step=1, label="Number of images", value=10, elem_id="num_images")
             if not is_random:
                 filter_by_class_checkbox = gr.Checkbox(label="Filter by class", value=True, elem_id="filter_by_class_checkbox")
                 filter_by_class_text = gr.Textbox(label="Class to select", value="0,33,99", elem_id="filter_by_class_text", info=f"e.g. `0,1,2`. (1000 classes)", visible=True)
@@ -1241,24 +1263,32 @@ def make_parameters_section(is_lisa=False, model_ratio=True):
                 gr.Textbox(label="Prompt (Negative)", elem_id="prompt", placeholder="e.g. 'a photo from egocentric view'", visible=False))
     model_dropdown.change(fn=change_prompt_text, inputs=model_dropdown, outputs=[positive_prompt, negative_prompt])
     
-    with gr.Accordion("➡️ Click to expand: more parameters", open=False):
+    with gr.Accordion("Advanced Parameters: NCUT", open=False):
         gr.Markdown("<a href='https://ncut-pytorch.readthedocs.io/en/latest/how_to_get_better_segmentation/' target='_blank'>Docs: How to Get Better Segmentation</a>")
         affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="NCUT: Affinity focal gamma", value=0.5, elem_id="affinity_focal_gamma", info="decrease for shaper segmentation")
         num_sample_ncut_slider = gr.Slider(100, 50000, step=100, label="NCUT: num_sample", value=10000, elem_id="num_sample_ncut", info="Nyström approximation")
-        sampling_method_dropdown = gr.Dropdown(["fps", "random"], label="NCUT: Sampling method", value="fps", elem_id="sampling_method", info="Nyström approximation")
-        knn_ncut_slider = gr.Slider(1, 100, step=1, label="NCUT: KNN", value=10, elem_id="knn_ncut", info="Nyström approximation")
-        embedding_method_dropdown = gr.Dropdown(["tsne_3d", "umap_3d", "umap_shpere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method")
-        embedding_metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="t-SNE/UMAP metric", value="euclidean", elem_id="embedding_metric")
+        # sampling_method_dropdown = gr.Dropdown(["QuickFPS", "random"], label="NCUT: Sampling method", value="QuickFPS", elem_id="sampling_method", info="Nyström approximation")
+        sampling_method_dropdown = gr.Radio(["QuickFPS", "random"], label="NCUT: Sampling method", value="QuickFPS", elem_id="sampling_method")
+        # ncut_metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="NCUT: Distance metric", value="cosine", elem_id="ncut_metric")
+        ncut_metric_dropdown = gr.Radio(["euclidean", "cosine"], label="NCUT: Distance metric", value="cosine", elem_id="ncut_metric")
+        ncut_knn_slider = gr.Slider(1, 100, step=1, label="NCUT: KNN", value=10, elem_id="knn_ncut", info="Nyström approximation")
+        ncut_indirect_connection = gr.Checkbox(label="indirect_connection", value=True, elem_id="ncut_indirect_connection", info="Add indirect connection to the sub-sampled graph")
+        ncut_make_orthogonal = gr.Checkbox(label="make_orthogonal", value=False, elem_id="ncut_make_orthogonal", info="Apply post-hoc eigenvectors orthogonalization")
+    with gr.Accordion("Advanced Parameters: t-SNE/UMAP", open=False):
+        # embedding_method_dropdown = gr.Dropdown(["tsne_3d", "umap_3d", "umap_shpere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method")
+        embedding_method_dropdown = gr.Radio(["tsne_3d", "umap_3d", "umap_shpere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method")
+        # embedding_metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="t-SNE/UMAP metric", value="euclidean", elem_id="embedding_metric")
+        embedding_metric_dropdown = gr.Radio(["euclidean", "cosine"], label="t-SNE/UMAP: metric", value="euclidean", elem_id="embedding_metric")
         num_sample_tsne_slider = gr.Slider(100, 10000, step=100, label="t-SNE/UMAP: num_sample", value=300, elem_id="num_sample_tsne", info="Nyström approximation")
         knn_tsne_slider = gr.Slider(1, 100, step=1, label="t-SNE/UMAP: KNN", value=10, elem_id="knn_tsne", info="Nyström approximation")
         perplexity_slider = gr.Slider(10, 1000, step=10, label="t-SNE: perplexity", value=150, elem_id="perplexity")
         n_neighbors_slider = gr.Slider(10, 1000, step=10, label="UMAP: n_neighbors", value=150, elem_id="n_neighbors")
         min_dist_slider = gr.Slider(0.1, 1, step=0.1, label="UMAP: min_dist", value=0.1, elem_id="min_dist")
     return [model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
-            affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+            affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal,
             embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
             perplexity_slider, n_neighbors_slider, min_dist_slider, 
-            sampling_method_dropdown, positive_prompt, negative_prompt]
+            sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt]
 
 demo = gr.Blocks(
     theme=gr.themes.Base(spacing_size='md', text_size='lg', primary_hue='blue', neutral_hue='slate', secondary_hue='pink'),
@@ -1280,10 +1310,10 @@ with demo:
                 cluster_gallery = gr.Gallery(value=[], label="Clusters", show_label=True, elem_id="clusters", columns=[5], rows=[2], object_fit="contain", height="auto", 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, 
+                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                     embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                     perplexity_slider, n_neighbors_slider, min_dist_slider, 
-                    sampling_method_dropdown, positive_prompt, negative_prompt
+                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                 ] = make_parameters_section()
                 num_eig_slider.value = 30
 
@@ -1298,9 +1328,9 @@ with demo:
                 input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                 positive_prompt, negative_prompt,
                 false_placeholder, no_prompt, no_prompt, no_prompt,
-                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                 embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
-                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown
+                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
             ],
             outputs=[output_gallery, cluster_gallery, logging_text],
             api_name="API_AlignedCut",
@@ -1341,14 +1371,14 @@ with demo:
                 output_gallery = make_output_images_section()
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
-                    affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                     embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                     perplexity_slider, n_neighbors_slider, min_dist_slider, 
-                    sampling_method_dropdown, positive_prompt, negative_prompt
+                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                 ] = make_parameters_section()
                 old_school_ncut_checkbox = gr.Checkbox(label="Old school NCut", value=True, elem_id="old_school_ncut")
-                invisible_list = [old_school_ncut_checkbox, num_sample_ncut_slider, knn_ncut_slider,
-                                    num_sample_tsne_slider, knn_tsne_slider, sampling_method_dropdown]
+                invisible_list = [old_school_ncut_checkbox, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal,
+                                    num_sample_tsne_slider, knn_tsne_slider, sampling_method_dropdown, ncut_metric_dropdown]
                 for item in invisible_list:
                     item.visible = False
                 # logging text box
@@ -1364,9 +1394,9 @@ with demo:
                 input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                 positive_prompt, negative_prompt,
                 false_placeholder, no_prompt, no_prompt, no_prompt,
-                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                 embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
-                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, 
+                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                 old_school_ncut_checkbox
             ],
             outputs=[output_gallery, logging_text],
@@ -1411,10 +1441,10 @@ with demo:
                     l3_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #3: Affinity focal gamma", value=0.5, elem_id="recursion_l3_gamma")
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
-                    affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                     embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                     perplexity_slider, n_neighbors_slider, min_dist_slider, 
-                    sampling_method_dropdown, positive_prompt, negative_prompt
+                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                 ] = make_parameters_section()
                 num_eig_slider.visible = False
                 affinity_focal_gamma_slider.visible = False
@@ -1433,9 +1463,9 @@ with demo:
                 input_gallery, model_dropdown, layer_slider, l1_num_eig_slider, node_type_dropdown, 
                 positive_prompt, negative_prompt,
                 false_placeholder, no_prompt, no_prompt, no_prompt,
-                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                 embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
-                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown,
+                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                 false_placeholder, number_placeholder, true_placeholder,
                 l2_num_eig_slider, l3_num_eig_slider, metric_dropdown, 
                 l1_affinity_focal_gamma_slider, l2_affinity_focal_gamma_slider, l3_affinity_focal_gamma_slider
@@ -1453,10 +1483,10 @@ with demo:
                 video_output_gallery = gr.Video(value=None, label="NCUT Embedding", elem_id="ncut", height="auto", show_share_button=False)
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
-                    affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                     embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                     perplexity_slider, n_neighbors_slider, min_dist_slider, 
-                    sampling_method_dropdown, positive_prompt, negative_prompt
+                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                 ] = make_parameters_section()
                 num_sample_tsne_slider.value = 1000
                 perplexity_slider.value = 500
@@ -1476,9 +1506,9 @@ with demo:
                 video_input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                 positive_prompt, negative_prompt,
                 false_placeholder, no_prompt, no_prompt, no_prompt,
-                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                 embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
-                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown,
+                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                 place_holder_false, max_frame_number
             ],
             outputs=[video_output_gallery, logging_text],
@@ -1522,10 +1552,10 @@ with demo:
             with gr.Column(scale=5, min_width=200):
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
-                    affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                     embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                     perplexity_slider, n_neighbors_slider, min_dist_slider, 
-                    sampling_method_dropdown, positive_prompt, negative_prompt
+                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                 ] = make_parameters_section(is_lisa=True)
                 logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
         
@@ -1537,9 +1567,9 @@ with demo:
                 input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                 positive_prompt, negative_prompt,
                 true_placeholder, prompt1, prompt2, prompt3,
-                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                 embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
-                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown
+                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
             ],
             outputs=galleries + [logging_text],
         )
@@ -1569,10 +1599,10 @@ with demo:
                 gr.Markdown('---')
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
-                    affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                     embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                     perplexity_slider, n_neighbors_slider, min_dist_slider, 
-                    sampling_method_dropdown, positive_prompt, negative_prompt
+                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                 ] = make_parameters_section(model_ratio=False)
                 model_dropdown.value = "AlignedThreeModelAttnNodes"
                 model_dropdown.visible = False
@@ -1594,9 +1624,9 @@ with demo:
                 input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                 positive_prompt, negative_prompt,
                 false_placeholder, no_prompt, no_prompt, no_prompt,
-                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                 embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
-                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown
+                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
             ],
             # outputs=galleries + [logging_text],
             outputs=[output_gallery, logging_text],
@@ -1655,10 +1685,10 @@ with demo:
                 gr.Markdown('Layer type: attention output (attn), without sum of residual')
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
-                    affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                     embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                     perplexity_slider, n_neighbors_slider, min_dist_slider, 
-                    sampling_method_dropdown, positive_prompt, negative_prompt
+                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                 ] = make_parameters_section(model_ratio=False)
                 num_eig_slider.visible = False
                 affinity_focal_gamma_slider.visible = False
@@ -1687,9 +1717,9 @@ with demo:
                 input_gallery, model_dropdown, layer_slider, l1_num_eig_slider, node_type_dropdown, 
                 positive_prompt, negative_prompt,
                 false_placeholder, no_prompt, no_prompt, no_prompt,
-                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                 embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
-                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown,
+                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                 false_placeholder, number_placeholder, true_placeholder,
                 l2_num_eig_slider, l3_num_eig_slider, metric_dropdown, 
                 l1_affinity_focal_gamma_slider, l2_affinity_focal_gamma_slider, l3_affinity_focal_gamma_slider
@@ -1707,10 +1737,10 @@ with demo:
                 add_output_images_buttons(output_gallery)
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
-                    affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                     embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                     perplexity_slider, n_neighbors_slider, min_dist_slider, 
-                    sampling_method_dropdown, positive_prompt, negative_prompt
+                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                 ] = make_parameters_section()
                 # logging text box
                 logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
@@ -1723,9 +1753,9 @@ with demo:
                         input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                         positive_prompt, negative_prompt,
                         false_placeholder, no_prompt, no_prompt, no_prompt,
-                        affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
+                        affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                         embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
-                        perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown
+                        perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
                     ],
                     outputs=[output_gallery, logging_text]
                 )