add caching

app.py

@@ -191,6 +191,11 @@ def make_editing_canvas(canvas_color, brush, _reset_state, generation_mode, pain
 
 
     elif generation_mode == "Re-generate objects":
+        st.write("This mode allows you to choose which objects you want to re-generate in the image. "
+                 "Use the selection dropdown to add or remove objects. If you are ready, press the generate button"
+                 " to generate the image, which can take up to 30 seconds. If you want to improve the generated image, click"
+                 " the 'move image to input' button."
+                 )
         canvas = st_canvas(
             **canvas_dict,
         )
@@ -207,7 +212,7 @@ def make_editing_canvas(canvas_color, brush, _reset_state, generation_mode, pain
             st.session_state['unique_colors'] = unique_colors
 
         chosen_colors = st.multiselect(
-            label="Choose colors",
+            label="Choose which concepts you want to regenerate in the image",
             options=st.session_state['unique_colors'],
             key='chosen_colors',
             default=st.session_state['unique_colors'],

models.py

@@ -76,6 +76,7 @@ def postprocess_image_masking(inpainted: Image, image: Image, mask: Image) -> Im
 
 
 @st.experimental_singleton(max_entries=1)
+@st.cache_resource
 def get_controlnet() -> ControlNetModel:
     """Method to load the controlnet model
     Returns:
@@ -100,6 +101,7 @@ def get_controlnet() -> ControlNetModel:
 
 
 @st.experimental_singleton(max_entries=1)
+@st.cache_resource
 def get_segmentation_pipeline() -> Tuple[AutoImageProcessor, UperNetForSemanticSegmentation]:
     """Method to load the segmentation pipeline
     Returns:
@@ -112,6 +114,7 @@ def get_segmentation_pipeline() -> Tuple[AutoImageProcessor, UperNetForSemanticS
 
 
 @st.experimental_singleton(max_entries=1)
+@st.cache_resource
 def get_inpainting_pipeline() -> StableDiffusionInpaintPipeline:
     """Method to load the inpainting pipeline
     Returns: