add paper

app.py

@@ -491,7 +491,7 @@ def run_fn(
     # resize the images before acquiring GPU
     if "AlignedThreeModelAttnNodes" == model_name:
         # dirty patch for the alignedcut paper
-        resolution = (672, 672)
+        resolution = (224, 224)
     else:
         resolution = RES_DICT[model_name]
     images = [tup[0] for tup in images]
@@ -1021,7 +1021,7 @@ with demo:
         gr.Markdown('**Features are aligned across models and layers.** A linear alignment transform is trained for each model/layer, learning signal comes from 1) fMRI brain activation and 2) segmentation preserving eigen-constraints.')
         gr.Markdown('NCUT is computed on the concatenated graph of all models, layers, and images. Color is **aligned** across all models and layers.')
         gr.Markdown('')
-        gr.Markdown("To see a good pattern, you will need to load 100 images. Running out of HuggingFace GPU Quota? Try [Demo](https://ncut-pytorch.readthedocs.io/en/latest/demo/) hosted at UPenn")
+        gr.Markdown("To see a good pattern, you will need to load 100~1000 images. 100 images need 10sec for RTX4090. Running out of HuggingFace GPU Quota? Try [Demo](https://ncut-pytorch.readthedocs.io/en/latest/demo/) hosted at UPenn")
         gr.Markdown('---')
         with gr.Row():
             with gr.Column(scale=5, min_width=200):