From 7a9d765627b3f3131bfcb089ce54a6582256e33d Mon Sep 17 00:00:00 2001
From: s572915912 <54531516+s572915912@users.noreply.github.com>
Date: Fri, 11 Jul 2025 16:53:19 +0800
Subject: [PATCH] Update run_inference_with_fix.py

---
 run_inference_with_fix.py | 55 ---------------------------------------
 1 file changed, 55 deletions(-)

diff --git a/run_inference_with_fix.py b/run_inference_with_fix.py
index a69f9a2..8b13789 100644
--- a/run_inference_with_fix.py
+++ b/run_inference_with_fix.py
@@ -1,56 +1 @@
-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}")