6534f4ba15
Two root causes of CUDA OOM fixed:
1. onnxruntime-gpu CUDAExecutionProvider pre-allocated ~12GB VRAM arena
for bria-rmbg background removal, starving PyTorch models.
Fix: force CPUExecutionProvider in BackgroundRemover (rembg is
lightweight, runs fine on CPU, frees all VRAM for shape/tex).
2. Previous 'always delete' strategy was wasteful on high-RAM machines.
New adaptive strategy checks available system RAM at runtime:
- RAM >= 16GB free: offload i23d to CPU (.to('cpu')) — fast, ~1s
- RAM < 16GB free: full del + reload from disk — safe, ~20-30s
This gives instant model switching on 32GB+ machines while keeping
16GB machines safe from OOM Killer.
Helper functions:
- _prepare_for_tex(): adaptive offload/delete based on RAM check
- _ensure_i23d_worker(): restore from CPU (fast) or disk (slow)
- _get_available_ram_gb(): reads /proc/meminfo
- _can_offload_to_cpu(): threshold check with logging
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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.
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. After preprocessing, the dataset directory structure should look like the following:
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 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export num_gpu_per_node=8
export node_num=1
export node_rank=0
export master_ip=0.0.0.0 # set your master_ip
# export config=configs/hunyuandit-finetuning-flowmatching-dinol518-bf16-lr1e5-4096.yaml
# export output_dir=output_folder/dit/fintuning_lr1e5
export config=configs/hunyuandit-mini-overfitting-flowmatching-dinol518-bf16-lr1e4-4096.yaml
export output_dir=output_folder/dit/overfitting_depth_16_token_4096_lr1e4
bash scripts/train_deepspeed.sh $node_num $node_rank $num_gpu_per_node $master_ip $config $output_dir