Update app.py

app.py

@@ -24,7 +24,8 @@ def get_lora_sd_pipeline(
     base_model_name_or_path=model_id_default, 
     dtype=torch_dtype, 
     device=device, 
-    adapter_name="default"
+    adapter_name="default",
+    lora_scale=0.8
 ):
     unet_sub_dir = os.path.join(ckpt_dir, "unet")
     text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
@@ -38,6 +39,9 @@ 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.unet.set_peft_model_state_dict(
+        {k: v*lora_scale for k, v in pipe.unet.state_dict().items()}
+    )
 
     if os.path.exists(text_encoder_sub_dir):
         pipe.text_encoder = PeftModel.from_pretrained(
@@ -93,11 +97,12 @@ 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")    
+                                adapter_name="sticker_of_funny_cat_Pusheen",
+                                lora_scale=lora_scale)    
     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)
+    # pipe.fuse_lora(lora_scale=lora_scale)
     
     image = pipe(
         prompt_embeds=prompt_embeds,