3DTopia-XL

3DTopia-XL: High-Quality 3D PBR Asset Generation via Primitive Diffusion

CVPR 2025 Highlight 
TL;DR
3DTopia-XL is a 3D diffusion transformer (DiT) operating on primitive-based representation. 
It can generate 3D asset with smooth geometry and PBR materials from single image or text.

Paper | Project Page | Video | Weights | Hugging Face :hugs: | WiseModel

News
[02/2025] 3DTopia-XL is accepted to CVPR 2025 :fire:

[02/2025] Training code released!

[10/2024] WiseModel demo released! 

[09/2024] Technical report released! 

[09/2024] Hugging Face demo released! 

[09/2024] Inference code released!

Citation
If you find our work useful for your research, please consider citing this paper:

@inproceedings{chen2025primx,
  title={3DTopia-XL: High-Quality 3D PBR Asset Generation via Primitive Diffusion},
  author={Chen, Zhaoxi and Tang, Jiaxiang and Dong, Yuhao and Cao, Ziang and Hong, Fangzhou and Lan, Yushi and Wang, Tengfei and Xie, Haozhe and Wu, Tong and Saito, Shunsuke and Pan, Liang and Lin, Dahua and Liu, Ziwei},
  booktitle={CVPR},
  year={2025}
}

:gear: Installation
We highly recommend using Anaconda to manage your python environment. You can setup the required environment by the following commands:
bash
install dependencies
conda create -n primx python=3.9
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia
requires xformer for efficient attention
conda install xformers::xformers
install other dependencies
pip install -r requirements.txt
compile third party libraries
bash install.sh
Now, all done!

For proper glTF texture and material packing, fix a bug in Trimesh (trimesh.visual.gloss.specular_to_pbr #L361) if you are using old version:
python
    result["metallicRoughnessTexture"] = toPIL(
        np.concatenate(
            [np.zeros_like(metallic), 1.0 - glossiness, metallic], axis=-1
        ),
        mode="RGB",
    )

:muscle: Pretrained Weights

Our pretrained weight can be downloaded from huggingface

For example, to download the singleview-conditioned model in fp16 precision for inference:
bash
mkdir pretrained && cd pretrained
download DiT
wget https://huggingface.co/FrozenBurning/3DTopia-XL/resolve/main/model_sview_dit_fp16.pt
download VAE
wget https://huggingface.co/FrozenBurning/3DTopia-XL/resolve/main/model_vae_fp16.pt
cd ..

For text-conditioned model, please download from this Google Drive
We put out text conditioner in models/conditioner/text.py. Please download the specific version of text conditioner as follows:
bash
cd pretrained
wget https://huggingface.co/laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K/resolve/main/open_clip_pytorch_model.bin\?download\=true
cd ..

:rocket: Inference

Gradio Demo
The gradio demo will automatically download pretrained weights using huggingface_hub.

You could locally launch our demo with Gradio UI by:
bash
python app.py

Alternatively, you can run the demo online 

CLI Test
Run the following command for inference:
bash
python inference.py ./configs/inference_dit.yml

Furthermore, you can modify the inference parameters in inference_dit.yml, detailed as follows:

| Parameter | Recommended | Description |
| input_dir | - | The path o

……（后略，见原文）