Update app.py

app.py

@@ -148,7 +148,7 @@ for model in ahx_model_list:
   #if token cannot be loaded, skip it
   try:
     learned_token = load_learned_embed_in_clip(f"{model_id}/learned_embeds.bin", pipe.text_encoder, pipe.tokenizer, token_name)
-    _learned_token_controlnet = load_learned_embed_in_clip(f"{model_id}/learned_embeds.bin", controlnet_pipe.text_encoder, controlnet_pipe.tokenizer, token_name)
+    # _learned_token_controlnet = load_learned_embed_in_clip(f"{model_id}/learned_embeds.bin", controlnet_pipe.text_encoder, controlnet_pipe.tokenizer, token_name)
   except: 
     continue
   model_content["token"] = learned_token
@@ -482,11 +482,17 @@ def controlnet_edges(canny_input_prompt, input_image, input_low_threshold, input
     prompt = canny_input_prompt
     generator = torch.Generator(device="cpu").manual_seed(2)
 
+    # output_image = controlnet_pipe(
+    #     prompt,
+    #     canny_2,
+    #     negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
+    #     generator=generator,
+    #     num_inference_steps=20,
+    # )
     output_image = controlnet_pipe(
         prompt,
         canny_2,
         negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
-        generator=generator,
         num_inference_steps=20,
     )