Required packages

November 5, 2025 · View on GitHub

[ICCV 2025] Stable Score Distillation

     

Formulating 2D image and 3D scene editing within diffusion models from the perspective of the score.      

Contents

Installation

Our environment was tested on Ubuntu 22, CUDA 11.7 with 3090.


conda create -n ssd python=3.8 -y 
conda activate ssd

conda install -c "nvidia/label/cuda-11.7.0" cuda-nvcc
conda install cuda-toolkit==11.7
pip install ninja
pip install cmake
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia

# Gaussian Splatting
cd gaussiansplatting
pip install submodules/diff-gaussian-rasterization
pip install submodules/simple-knn

# Required packages
cd ..
pip install tqdm
pip install plyfile
pip install mediapipe
pip install diffusers==0.27.2
pip install -r requirements_all.txt

We provide an environment.yaml file to help you verify.

Tips

  1. If the default resolution of 512×512 is not suitable, you can modify the line here by setting self.use_original_resolution to True, and adjust the resolution accordingly. For example, if the original (height, width) is (729, 985), you may change it to something like (512, 692).

  2. Some prompts may not work well with SD2.1. In such cases, you can try using IP2P instead.

  3. Most configurations are adapted from GaussianEditor.
    Our pipeline relies on three key configuration: cross-prompt, cross-trajectory, and prompt-enhancement.
    If the default values — cross-trajectory: 2.0 and enhance_scale: 5.5 — lead to suboptimal results, users can try adjusting the weights.

  4. We provide test data and the evaluation metric code.

Command Line

Please try our demo by running script/face.sh.

Citation

If you find our work helpful in your project, please cite:

@InProceedings{Zhu_2025_ICCV,
    author    = {Zhu, Haiming and Xu, Yangyang and Xu, Chenshu and Shen, Tingrui and Liu, Wenxi and Du, Yong and Yu, Jun and He, Shengfeng},
    title     = {Stable Score Distillation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2025},
    pages     = {16597-16606}
}

Acknowledgement

Most of our code is adapted from the excellent works of GaussianEditor and Threestudio. We sincerely thank the authors for their great contributions.

We also refer to the following projects: