Removed the suppress_st_warning argument from st.experimental_memo as it was causing some issues on the latest version. (#1788)

scripts/sd_utils/__init__.py

@@ -99,7 +99,7 @@ shutup.please()
 if "defaults" in st.session_state:
     if st.session_state["defaults"].general.use_cudnn:
         torch.backends.cudnn.benchmark = True
-        torch.backends.cudnn.enabled = True 
+        torch.backends.cudnn.enabled = True
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -294,7 +294,7 @@ def merge(file1, file2, out, weight):
         file2 += ".ckpt"
     if not(out.endswith(".ckpt")):
         out += ".ckpt"
-    try: 
+    try:
         #Load Models
         model_0 = torch.load(file1)
         model_1 = torch.load(file2)
@@ -310,7 +310,7 @@ def merge(file1, file2, out, weight):
                 theta_0[key] = theta_1[key]
         torch.save(model_0, out)
     except:
-        logger.error("Error in merging") 
+        logger.error("Error in merging")
 
 
 def human_readable_size(size, decimal_places=3):
@@ -1325,7 +1325,7 @@ def torch_gc():
     torch.cuda.ipc_collect()
 
 @retry(tries=5)
-#@st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
+#@st.experimental_memo(persist="disk", show_spinner=False)
 def load_GFPGAN(model_name='GFPGANv1.4'):
     #model_name = 'GFPGANv1.3'
 
@@ -1629,10 +1629,10 @@ def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='Re
 #
 @retry(tries=5)
 def generation_callback(img, i=0):
-    
+
     # try to do garbage collection before decoding the image
     torch_gc()
-    
+
     if "update_preview_frequency" not in st.session_state:
         raise StopException
 
@@ -1757,7 +1757,7 @@ def slerp(device, t, v0:torch.Tensor, v1:torch.Tensor, DOT_THRESHOLD=0.9995):
     return v2
 
 #
-@st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
+@st.experimental_memo(persist="disk", show_spinner=False)
 def optimize_update_preview_frequency(current_chunk_speed, previous_chunk_speed_list, update_preview_frequency, update_preview_frequency_list):
     """Find the optimal update_preview_frequency value maximizing
     performance while minimizing the time between updates."""
@@ -2420,7 +2420,7 @@ def process_images(
             else: # just behave like usual
                 c = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]).get_learned_conditioning(prompts)
 
-                
+
             shape = [opt_C, height // opt_f, width // opt_f]
 
             if st.session_state['defaults'].general.optimized: