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| <img src="assets/qrcode/wechat.png" height=140> | <img src="assets/qrcode/xiaohongshu.png" height=140> | <img src="assets/qrcode/x.png" height=140> | <img src="assets/qrcode/discord.png" height=140> |
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| <img src="assets/qrcode/wechat.png" height=140> | <img src="assets/qrcode/xiaohongshu.png" height=140> | <img src="assets/qrcode/x.png" height=140> | <img src="assets/qrcode/discord.png" height=140> |
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## 🤗 Community Contribution Leaderboard
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1. By [@visualbruno](https://github.com/visualbruno)
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- ComfyUI-Hunyuan3d-2-1: https://github.com/visualbruno/ComfyUI-Hunyuan3d-2-1
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## ☯️ **Hunyuan3D 2.1**
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## ☯️ **Hunyuan3D 2.1**
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# Hunyuan3D-2.1-Shape
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# 训练
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我们会展示小数据集上DiT的训练全流程
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## 数据预处理
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渲染和水密化参考[链接](tools/README.md),最终得到如下结构
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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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我们提供了一个8个case(均来自Objaverse-XL)预处理后的结果在 tools/mini_trainset,可以直接用于过拟合训练
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## 启动训练
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我们提供了可供参考的训练配置文件和启动脚本(默认单机8卡deepspeed训练),用户根据需要自行修改。
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配置文件
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```
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configs/dit-from-scratch-overfitting-flowmatching-dinog518-bf16-lr1e4-1024.yaml
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```
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启动脚本
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```
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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/dit-from-scratch-overfitting-flowmatching-dinog518-bf16-lr1e4-1024.yaml'
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export output_dir='output_folder/dit/overfitting'
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bash scripts/train_deepspeed.sh $node_num $node_rank $master_ip $config $output_dir
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```
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Launch Script
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Launch Script
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```
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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_num=1
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export node_rank=0
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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 master_ip=0.0.0.0 # set your master_ip
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export config=configs/hunyuandit-mini-overfitting-flowmatching-dinog518-bf16-lr1e4-512.yaml
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export output_dir=output_folder/dit/overfitting
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# export config=configs/hunyuandit-finetuning-flowmatching-dinol518-bf16-lr1e5-4096.yaml
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bash scripts/train_deepspeed.sh $node_num $node_rank $master_ip $config $output_dir
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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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```
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name: "HunyuanDiT flowmatching; VAE: 4096 token length; ImageEncoder: DINO-v2 Large; ImageSize: 518"
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name: "HunyuanDiT flowmatching; VAE: 4096 token length; ImageEncoder: DINO-v2 Large; ImageSize: 518"
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# oversitting successfully cost 68G memory under current settings
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# Overfitting successfully cost 68G memory under current settings
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# you can adjust model arch or batch_size according to your GPU memory
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# You can adjust model arch or batch_size according to your GPU memory
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training:
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training:
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steps: 10_0000_0000
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steps: 10_0000_0000
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