diff --git a/run_inference_with_fix.py b/run_inference_with_fix.py
new file mode 100644
index 0000000..a69f9a2
--- /dev/null
+++ b/run_inference_with_fix.py
@@ -0,0 +1,56 @@
+import os
+import sys
+import torch
+from PIL import Image
+
+# Add the project's sub-directory to the path to allow direct imports
+sys.path.insert(0, os.path.join(os.path.abspath('.'), 'hy3dshape'))
+from hy3dshape.pipelines import Hunyuan3DDiTFlowMatchingPipeline
+
+# --- 1. Set up the paths for your trained model ---
+
+# The training configuration file you used
+CONFIG_PATH = "./hy3dshape/configs/hunyuandit-mini-overfitting-flowmatching-dinol518-bf16-lr1e4-4096.yaml"
+
+# !!! IMPORTANT: Change this to the actual checkpoint directory you generated !!!
+# This should be the path to the directory, e.g., 'ckpt-step=00002000.ckpt'
+CKPT_PATH = "./hy3dshape/output_folder/dit/overfitting_depth_16_token_4096_lr1e4/ckpt/ckpt-step=00002000.ckpt"
+
+# The input image for inference
+IMAGE_PATH = "./assets/demo.png"
+
+# The path where the final 3D model will be saved
+OUTPUT_PATH = "./my_model_output.glb"
+
+
+if __name__ == '__main__':
+    # Setup device and data type
+    if not torch.cuda.is_available():
+        print("Warning: CUDA not available, running on CPU. This will be very slow.")
+        device = torch.device('cpu')
+        # Use float32 on CPU as it does not support bfloat16
+        dtype = torch.float32
+    else:
+        device = torch.device('cuda')
+        # Use the same precision as in training for best results
+        dtype = torch.bfloat16
+
+    print("\n--- Attempting to load the model using the new from_lightning_checkpoint method ---")
+
+    # Load the pipeline using the new, elegant class method
+    pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_lightning_checkpoint(
+        ckpt_path=CKPT_PATH,
+        config_path=CONFIG_PATH,
+        device=str(device),
+        dtype=dtype,
+    )
+
+    print("\n Model loaded successfully! Starting inference...")
+    input_image = Image.open(IMAGE_PATH)
+
+    # Run inference
+    mesh_output = pipeline(image=input_image)[0]
+
+    # Save the result
+    mesh_output.export(OUTPUT_PATH)
+    print(f"\n Inference complete! The 3D model has been saved to: {OUTPUT_PATH}")