Update app.py

app.py

@@ -10,10 +10,11 @@ from diffusers import DiffusionPipeline
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_id_default = "stable-diffusion-v1-5/stable-diffusion-v1-5"  # Replace to the model you would like to use
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+# if torch.cuda.is_available():
+#     torch_dtype = torch.float16
+# else:
+#     torch_dtype = torch.float32
+torch_dtype = torch.float32
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -23,8 +24,7 @@ def get_lora_sd_pipeline(
     ckpt_dir='./output', 
     base_model_name_or_path=model_id_default, 
     dtype=torch_dtype, 
-    device=device, 
-    adapter_name="default"
+    device=device
 ):
     unet_sub_dir = os.path.join(ckpt_dir, "unet")
     text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
@@ -36,8 +36,7 @@ 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.set_adapter(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(
@@ -92,8 +91,7 @@ def infer(
     progress=gr.Progress(track_tqdm=True),
 ):  
     generator = torch.Generator(device).manual_seed(seed)
-    pipe = get_lora_sd_pipeline(base_model_name_or_path=model_id,
-                                adapter_name="sticker_of_funny_cat_Pusheen")    
+    pipe = get_lora_sd_pipeline(base_model_name_or_path=model_id)    
     pipe = pipe.to(device)
     # pipe.fuse_lora(lora_scale=lora_scale)
     # prompt_embeds = encode_prompt(prompt, pipe.tokenizer, pipe.text_encoder)