62 lines
2.0 KiB
Markdown
62 lines
2.0 KiB
Markdown
# Hunyuan3D-2.1-Shape
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## Quick Inference
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Given a reference image `image.png`, you can run inference using the following code. The result will be saved as `demo.glb`.
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```bash
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python3 minimal_demo.py
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```
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**Memory Recommendation:** For we recommend using a GPU with at least **10GB VRAM**.
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# Training
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Here we demonstrate the complete training workflow of DiT on a small dataset.
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## Data Preprocessing
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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:
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```yaml
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dataset/preprocessed/{uid}
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├── geo_data
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│ ├── {uid}_sdf.npz
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│ ├── {uid}_surface.npz
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│ └── {uid}_watertight.obj
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└── render_cond
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├── 000.png
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├── ...
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├── 023.png
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├── mesh.ply
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└── transforms.json
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```
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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.
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## Launching Training
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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.
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Configuration File
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```
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configs/hunyuandit-mini-overfitting-flowmatching-dinog518-bf16-lr1e4-512.yaml
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```
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Launch Script
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```
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export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
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export num_gpu_per_node=8
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export node_num=1
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export node_rank=0
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export master_ip=0.0.0.0 # set your master_ip
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# export config=configs/hunyuandit-finetuning-flowmatching-dinol518-bf16-lr1e5-4096.yaml
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# export output_dir=output_folder/dit/fintuning_lr1e5
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export config=configs/hunyuandit-mini-overfitting-flowmatching-dinol518-bf16-lr1e4-4096.yaml
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export output_dir=output_folder/dit/overfitting_depth_16_token_4096_lr1e4
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bash scripts/train_deepspeed.sh $node_num $node_rank $num_gpu_per_node $master_ip $config $output_dir
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```
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