Update app.py

app.py

@@ -69,13 +69,15 @@ def infer(
         controlnet_model = ControlNetModel.from_pretrained(CONTROLNET_MODES.get(control_mode))
         if model_id == "SD1.5 + lora Unet TextEncoder" or model_id == "SD1.5 + lora Unet":
             pipe=StableDiffusionControlNetPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5",controlnet=controlnet_model)
+            pipe.unet = PeftModel.from_pretrained(pipe.unet, "um235/vCat_v2", subfolder="unet")
+            pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, "um235/vCat_v2", subfolder="text_encoder")
         else:
             pipe=StableDiffusionControlNetPipeline.from_pretrained(model_id, controlnet=controlnet_model)
     else:
         if model_id == "SD1.5 + lora Unet TextEncoder": 
             pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch_dtype)
-            pipe.unet = PeftModel.from_pretrained(pipe.unet, "um235/VanillaCat", subfolder="unet")
-            pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, "um235/VanillaCat", subfolder="text_encoder")
+            pipe.unet = PeftModel.from_pretrained(pipe.unet, "um235/vCat_v2", subfolder="unet")
+            pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, "um235/vCat_v2", subfolder="text_encoder")
         elif model_id == "SD1.5 + lora Unet": 
             pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch_dtype)
             pipe.unet = PeftModel.from_pretrained(pipe.unet, "um235/cartoon_cat_stickers")