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hy3dshape/README.md

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+# Hunyuan3D-2.1-Shape
+
+## Quick Inference
+
+Given a reference image `image.png`, you can run inference using the following code. The result will be saved as `demo.glb`.
+
+```bash
+python3 minimal_demo.py
+```
+
+**Memory Recommendation:** For we recommend using a GPU with at least **10GB VRAM**.
+
+# Training
+
+Here we demonstrate the complete training workflow of DiT on a small dataset.
+
+## Data Preprocessing
+
+The rendering and watertight mesh generation process is described in detail in [this document](tools/README.md). After preprocessing, the dataset directory structure should look like the following:
+
+```yaml
+dataset/preprocessed/{uid}
+├── geo_data
+│   ├── {uid}_sdf.npz
+│   ├── {uid}_surface.npz
+│   └── {uid}_watertight.obj
+└── render_cond
+    ├── 000.png
+    ├── ...
+    ├── 023.png
+    ├── mesh.ply
+    └── transforms.json
+```
+
+We provide a preprocessed mini_dataset containing 8 cases (all sourced from Objaverse-XL) as `tools/mini_trainset`, which can be used directly for DiT overfitting training experiments.
+
+## Launching Training
+
+We provide example configuration files and launch scripts for reference. By default, the training runs on a single node with 8 GPUs using DeepSpeed. Users can modify the configurations and scripts as needed to suit their environment.
+
+Configuration File
+```
+configs/hunyuandit-mini-overfitting-flowmatching-dinog518-bf16-lr1e4-512.yaml
+```
+Launch Script
+
+```
+export node_num=1
+export node_rank=0
+export master_ip=0.0.0.0 # set your master_ip
+export config=configs/hunyuandit-mini-overfitting-flowmatching-dinog518-bf16-lr1e4-512.yaml
+export output_dir=output_folder/dit/overfitting
+bash scripts/train_deepspeed.sh $node_num $node_rank $master_ip $config $output_dir
+```