Update app.py

app.py

@@ -24,8 +24,7 @@ def get_lora_sd_pipeline(
     base_model_name_or_path=model_id_default, 
     dtype=torch_dtype, 
     device=device, 
-    adapter_name="default",
-    lora_scale=0.5
+    adapter_name="default"
 ):
     unet_sub_dir = os.path.join(ckpt_dir, "unet")
     text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
@@ -39,7 +38,6 @@ def get_lora_sd_pipeline(
     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.fuse_lora(lora_scale=lora_scale)
 
     if os.path.exists(text_encoder_sub_dir):
         pipe.text_encoder = PeftModel.from_pretrained(
@@ -73,7 +71,9 @@ def encode_prompt(prompt, tokenizer, text_encoder):
                 if len(part_of_text_inputs) < tokenizer.model_max_length:
                     part_of_text_inputs = torch.cat([part_of_text_inputs, torch.tensor([tokenizer.pad_token_id] * (tokenizer.model_max_length - len(part_of_text_inputs)))])
                 embeds.append(text_encoder(part_of_text_inputs.to(text_encoder.device).unsqueeze(0))[0])
-                start += int((8/11)*tokenizer.model_max_length)
+                start += int((8/
+                              
+                              11)*tokenizer.model_max_length)
             prompt_embeds = torch.mean(torch.stack(embeds, dim=0), dim=0)
     return prompt_embeds
 
@@ -93,11 +93,11 @@ def infer(
 ):  
     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",
-                                lora_scale=lora_scale)    
+                                adapter_name="sticker_of_funny_cat_Pusheen")    
     pipe = pipe.to(device)
     prompt_embeds = encode_prompt(prompt, pipe.tokenizer, pipe.text_encoder)
     negative_prompt_embeds = encode_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder)
+    pipe.fuse_lora(lora_scale=lora_scale)
     
     image = pipe(
         prompt_embeds=prompt_embeds,