update parameters

app.py

@@ -592,9 +592,9 @@ def to_pil_images(images):
 def main_fn(
     images,
     model_name="SAM(sam_vit_b)",
-    node_type="block",
     layer=-1,
     num_eig=100,
+    node_type="block",
     affinity_focal_gamma=0.3,
     num_sample_ncut=10000,
     knn_ncut=10,
@@ -628,21 +628,21 @@ def main_fn(
     rgb = dont_use_too_much_green(rgb)
     return to_pil_images(rgb)
 
-default_images = ['./images/image_0.jpg', './images/image_1.jpg', './images/image_2.jpg', './images/image_3.jpg', './images/image_4.jpg', './images/image_5.jpg']
-default_outputs = ['./images/ncut_0.jpg', './images/ncut_1.jpg', './images/ncut_2.jpg', './images/ncut_3.jpg', './images/ncut_4.jpg', './images/ncut_5.jpg']
+default_images = ['./images/image_0.jpg', './images/image_1.jpg', './images/image_2.jpg', './images/image_3.jpg', './images/image_5.jpg']
+default_outputs = ['./images/ncut_0.jpg', './images/ncut_1.jpg', './images/ncut_2.jpg', './images/ncut_3.jpg', './images/ncut_5.jpg']
 
 demo = gr.Interface(
     main_fn,
     [
         gr.Gallery(value=default_images, label="Select images", show_label=False, elem_id="images", columns=[3], rows=[1], object_fit="contain", height="auto", type="pil"),
         gr.Dropdown(["MobileSAM", "SAM(sam_vit_b)", "DiNO(dinov2_vitb14_reg)", "CLIP(openai/clip-vit-base-patch16)"], label="Model", value="MobileSAM", elem_id="model_name"),
-        gr.Dropdown(["attn", "mlp", "block"], label="Node type", value="block", elem_id="node_type", info="attn: attention output, mlp: mlp output, block: sum of residual stream"),
         gr.Slider(0, 11, step=1, label="Layer", value=11, elem_id="layer", info="which layer of the image backbone features"),
         gr.Slider(1, 1000, step=1, label="Number of eigenvectors", value=100, elem_id="num_eig", info='increase for more object parts, decrease for whole object'),
-        gr.Slider(0.01, 1, step=0.01, label="Affinity focal gamma", value=0.3, elem_id="affinity_focal_gamma", info="decrease for more aggressive cleaning on the affinity matrix"),
     ],
     gr.Gallery(value=default_outputs, label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto"),
     additional_inputs=[
+        gr.Dropdown(["attn", "mlp", "block"], label="Node type", value="block", elem_id="node_type", info="attn: attention output, mlp: mlp output, block: sum of residual stream"),
+        gr.Slider(0.01, 1, step=0.01, label="Affinity focal gamma", value=0.3, elem_id="affinity_focal_gamma", info="decrease for more aggressive cleaning on the affinity matrix"),
         gr.Slider(100, 10000, step=100, label="num_sample (NCUT)", value=5000, elem_id="num_sample_ncut", info="for Nyström approximation"),
         gr.Slider(1, 100, step=1, label="KNN (NCUT)", value=10, elem_id="knn_ncut", info="for Nyström approximation"),
         gr.Slider(100, 1000, step=100, label="num_sample (t-SNE)", value=500, elem_id="num_sample_tsne", info="for Nyström approximation. Adding will slow down t-SNE quite a lot"),