Update app.py

app.py

@@ -2,7 +2,6 @@ import spaces
 import gradio as gr
 import numpy as np
 import random
-from lora import LoRAModel
 
 import torch
 from diffusers import StableDiffusion3Pipeline
@@ -74,16 +73,13 @@ pipe = StableDiffusion3Pipeline.from_pretrained(
     #tokenizer=CLIPTokenizer.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", add_prefix_space=True, subfolder="tokenizer", token=True),
     #tokenizer_2=CLIPTokenizer.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", add_prefix_space=True, subfolder="tokenizer_2", token=True),
     tokenizer_3=T5TokenizerFast.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", add_prefix_space=False, use_fast=True, subfolder="tokenizer_3", token=True),
-    torch_dtype=torch.bfloat16,
+    #torch_dtype=torch.bfloat16,
     #use_safetensors=False,
 )
 
-
-
-lora_model = LoRAModel.from_pretrained("https://huggingface.co/ford442/sdxl-vae-bf16/LoRA/UltraReal.safetensors")
-pipe.apply_lora(lora_model, scaling_factor=0.75)
-#pipe.to(device=device, dtype=torch.bfloat16)
-pipe.to(device)
+pipe.load_lora_weights("https://huggingface.co/ford442/sdxl-vae-bf16/LoRA/UltraReal.safetensors")
+pipe.to(device=device, dtype=torch.bfloat16)
+#pipe.to(device)
 
 upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device('cpu'))