Update app.py

app.py

@@ -27,9 +27,6 @@ def get_lora_sd_pipeline(
 
     pipe = StableDiffusionPipeline.from_pretrained(base_model_name_or_path, torch_dtype=dtype)
     pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
-    print(os.path.exists(unet_sub_dir))
-    print(unet_sub_dir)
-    print(dtype)
 
     if os.path.exists(text_encoder_sub_dir):
         pipe.text_encoder = PeftModel.from_pretrained(
@@ -42,6 +39,8 @@ def get_lora_sd_pipeline(
     return pipe
 
 def split_prompt(prompt, tokenizer, max_length=77):
+    print(prompt)
+    print(type(prompt))
     tokens = tokenizer(prompt, truncation=False)["input_ids"]
     chunks = [tokens[i:i + max_length] for i in range(0, len(tokens), max_length)]
     return chunks
@@ -136,10 +135,6 @@ def infer(
         pipe = pipe.to(device)
         image = pipe(**params).images[0]
     else:
-        print('----')
-        print(lora_scale)
-        print(prompt)
-        print(negative_prompt)
         prompt_embeds, negative_prompt_embeds = prompts_embeddings(
             prompt, 
             negative_prompt,