add rand model

app.py

@@ -2061,7 +2061,7 @@ def make_parameters_section(is_lisa=False, model_ratio=True):
         negative_prompt = gr.Textbox(label="Prompt (Negative)", elem_id="prompt", placeholder="e.g. 'a photo from egocentric view'", visible=False)
         node_type_dropdown = gr.Dropdown(layer_names, label="LISA (SAM) decoder: Layer and Node", value="dec_1_block", elem_id="node_type")
     else:
-        model_radio = gr.Radio(["CLIP", "DiNO", "Diffusion", "ImageNet", "MAE", "SAM"], label="Backbone", value="DiNO", elem_id="model_radio", show_label=True, visible=model_ratio)
+        model_radio = gr.Radio(["CLIP", "DiNO", "Diffusion", "ImageNet", "MAE", "SAM", "Rand"], label="Backbone", value="DiNO", elem_id="model_radio", show_label=True, visible=model_ratio)
         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])
         layer_slider = gr.Slider(1, 12, step=1, label="Backbone: Layer index", value=10, elem_id="layer")

requirements.txt

@@ -21,4 +21,4 @@ timm==0.9.2
 open-clip-torch==2.20.0
 pytorch_lightning==1.9.4
 gradio_image_prompter==0.1.0
-ncut-pytorch>=1.5.2
+ncut-pytorch>=1.5.4