fix sd3 bsz

app.py

@@ -11,7 +11,7 @@ if USE_HUGGINGFACE_ZEROGPU:  # huggingface ZeroGPU, dynamic GPU allocation
         USE_HUGGINGFACE_ZEROGPU = False
         
 if USE_HUGGINGFACE_ZEROGPU:
-    BATCH_SIZE = 4
+    BATCH_SIZE = 1
 else:  # run on local machine
     BATCH_SIZE = 1
 
@@ -621,7 +621,7 @@ def make_parameters_section():
             from ncut_pytorch.backbone import SD_KEY_DICT
             default_layer = 'up_2_resnets_1_block' if 'diffusion-3' not in model_name else 'block_23'
             return (gr.Slider(1, 49, step=1, label="Diffusion: Timestep (Noise)", value=5, elem_id="layer", visible=True, info="Noise level, 50 is max noise"),
-                    gr.Dropdown(SD_KEY_DICT[model_name], label="Diffusion: Layer and Node", value=default_layer, elem_id="node_type", info="From the SD U-Net"))
+                    gr.Dropdown(SD_KEY_DICT[model_name], label="Diffusion: Layer and Node", value=default_layer, elem_id="node_type", info="U-Net (v1, v2) or DiT (v3)"))
         
         layer_dict = LAYER_DICT
         if model_name in layer_dict:
@@ -643,7 +643,7 @@ def make_parameters_section():
         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")
         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")
+        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, 
@@ -935,7 +935,7 @@ with demo:
         
     with gr.Row():
         with gr.Column():
-            gr.Markdown("##### Powered by [ncut-pytorch](https://ncut-pytorch.readthedocs.io/) ")
+            gr.Markdown("##### POWERED BY [ncut-pytorch](https://ncut-pytorch.readthedocs.io/) ")
         with gr.Column():
             gr.Markdown("###### Running out of GPU? Try [Demo](https://ncut-pytorch.readthedocs.io/en/latest/demo/) hosted at UPenn")
 

requirements.txt

@@ -1,6 +1,5 @@
 torch
 torchvision 
-ncut-pytorch>=1.3.4
 opencv-python
 decord
 transformers
@@ -14,4 +13,5 @@ SAM-2 @ git+https://github.com/huzeyann/segment-anything-2.git
 segment-anything @ git+https://github.com/facebookresearch/segment-anything.git@6fdee8f
 mobile-sam @ git+https://github.com/ChaoningZhang/MobileSAM.git@c12dd83
 timm
-open-clip-torch==2.20.0
+open-clip-torch==2.20.0
+ncut-pytorch>=1.3.8