diff --git a/app.py b/app.py
index 2447929..2a7c788 100644
--- a/app.py
+++ b/app.py
@@ -8,55 +8,22 @@
 import tempfile
 import torch
 from diffusers import FluxTransformer2DModel, FluxFillPipeline
+import subprocess
 
-import shutil
+subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-def find_cuda():
-    # Check if CUDA_HOME or CUDA_PATH environment variables are set
-    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
-
-    if cuda_home and os.path.exists(cuda_home):
-        return cuda_home
-
-    # Search for the nvcc executable in the system's PATH
-    nvcc_path = shutil.which('nvcc')
-
-    if nvcc_path:
-        # Remove the 'bin/nvcc' part to get the CUDA installation path
-        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
-        return cuda_path
-
-    return None
-
-cuda_path = find_cuda()
-
-if cuda_path:
-    print(f"CUDA installation found at: {cuda_path}")
-else:
-    print("CUDA installation not found")
-
-device = torch.device('cuda')
-
-print("Start loading LoRA weights")
-state_dict, network_alphas = FluxFillPipeline.lora_state_dict(
-    pretrained_model_name_or_path_or_dict="xiaozaa/catvton-flux-lora-alpha",     ## The tryon Lora weights
-    weight_name="pytorch_lora_weights.safetensors",
-    return_alphas=True
-)
-is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
-if not is_correct_format:
-    raise ValueError("Invalid LoRA checkpoint.")
 print('Loading diffusion model ...')
+transformer = FluxTransformer2DModel.from_pretrained(
+    "xiaozaa/catvton-flux-alpha", 
+    torch_dtype=device
+)
 pipe = FluxFillPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-Fill-dev",
-    torch_dtype=torch.bfloat16
+    "black-forest-labs/FLUX.1-dev",
+    transformer=transformer,
+    torch_dtype=device
 ).to(device)
-FluxFillPipeline.load_lora_into_transformer(
-    state_dict=state_dict,
-    network_alphas=network_alphas,
-    transformer=pipe.transformer,
-)
-
 print('Loading Finished!')
 
 @spaces.GPU
@@ -109,7 +76,7 @@ def gradio_inference(
 
 with gr.Blocks() as demo:
     gr.Markdown("""
-    # CATVTON FLUX Virtual Try-On Demo (by using LoRA weights)
+    # CATVTON FLUX Virtual Try-On Demo
     Upload a model image, draw a mask, and a garment image to generate virtual try-on results.
     
     [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/xiaozaa/catvton-flux-alpha)
@@ -222,4 +189,4 @@ def gradio_inference(
     )
 
 
-demo.launch()
\ No newline at end of file
+demo.launch()