Update app.py

app.py

@@ -23,8 +23,7 @@ def get_lora_sd_pipeline(
     ckpt_dir='./model_output', 
     base_model_name_or_path=model_id_default, 
     dtype=torch_dtype, 
-    device=device, 
-    adapter_name="pusheen"
+    device=device
 ):
     unet_sub_dir = os.path.join(ckpt_dir, "unet")
     text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
@@ -36,11 +35,11 @@ def get_lora_sd_pipeline(
         raise ValueError("Please specify the base model name or path")
 
     pipe = StableDiffusionPipeline.from_pretrained(base_model_name_or_path, torch_dtype=dtype).to(device)
-    pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
+    pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir)
 
     if os.path.exists(text_encoder_sub_dir):
         pipe.text_encoder = PeftModel.from_pretrained(
-            pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name
+            pipe.text_encoder, text_encoder_sub_dir
         )
 
     if dtype in (torch.float16, torch.bfloat16):
@@ -93,7 +92,7 @@ def infer(
     generator = torch.Generator(device).manual_seed(seed)
     pipe = get_lora_sd_pipeline(base_model_name_or_path=model_id)    
     pipe = pipe.to(device)
-    pipe.fuse_lora(lora_scale=lora_scale)
+    # pipe.fuse_lora(lora_scale=lora_scale)
     pipe.safety_checker = None
     # prompt_embeds = encode_prompt(prompt, pipe.tokenizer, pipe.text_encoder)
     # negative_prompt_embeds = encode_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder)