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ScaleLong 🐉 <br> <sub><small>Official PyTorch implementation of our paper:<br> "ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection" (NeurIPS 2023)</small></sub>

💡ScaleLong (paper link) is a simple yet effective method that can stabilize the training of UNet-based models for diffusion model (potentially including other tasks that utilize UNet, such as medical segmentation, image segmentation, image-to-image translation, and more). Its core concept involves applying regular or adaptive scaling to the long skip connections of the UNet architecture.

<img src="image/fm.png" alt="drawing" width="800"/>

ScaleLong is highly straightforward to implement. Specifically, as depicted in the diagram below, the main focus lies on the file libs/uvit.py.

  1. Original UNet vs $1/\sqrt{2}$-CS:

    <img src="image/sqrt2.png" alt="drawing" width="800"/>

  2. Original UNet vs CS:

    <img src="image/cs.png" alt="drawing" width="800"/>

  3. Original UNet vs LS:

    <img src="image/ls.png" alt="drawing" width="800"/>

where the $1/\sqrt{2}$-CS is a magical technology that has been widely applied to some well-known diffusion models, including Imagen, SR3, Score-based generative modeling by SDE, as well as some GAN-based generative models, such as ProgressiveGAN, StyleGAN, and StyleGAN-2.

Dependency

pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116  # install torch-1.13.1
pip install accelerate==0.12.0 absl-py ml_collections einops wandb ftfy==6.1.1 transformers==4.23.1

# xformers is optional, but it would greatly speed up the attention computation.
pip install -U xformers
pip install -U --pre triton

Preparation Before Training

Autoencoder

Download stable-diffusion directory from this link (which contains image autoencoders converted from Stable Diffusion). Put the downloaded directory as assets/stable-diffusion in this codebase. The autoencoders are used in latent diffusion models.

Reference statistics for FID

Download fid_stats directory from this link (which contains reference statistics for FID). Put the downloaded directory as assets/fid_stats in this codebase. In addition to evaluation, these reference statistics are used to monitor FID during the training process.

Training

The following is the training command or you can run run.py:

export CUDA_VISIBLE_DEVICES=0
            # assign a gpu
accelerate launch --multi_gpu --num_processes 1 --mixed_precision fp16 --main_process_port 1024 train.py\
                  --config=cifar10_config.py --config.nnet.scalelong=0 --config.nnet.kappa=0.5\
                  --config.train.batch_size=64
            # num_processes: the number of gpu
            # config: training/model config
            # config.nnet.scalelong: method for scaling the long skip connection
                      # 0:    'orgin'
                      # 1:    '1/sqrt(2)-CS'
                      # 2:    'CS'
                      # 3:    'LS'
                      # 4:    'LS (non-share)'
            # config.nnet.kappa: the scaling coefficient for CS (we suggest kappa in [0.5, 0.95])
            # config.train.batch_size: batch size

References

If you find the code useful for your research, please consider citing

@inproceedings{huang2022scale,
 title={ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection},
 author={Huang, Zhongzhan and Pan, Zhou and Yan, Shuicheng and Lin, Liang},
 booktitle = {NeurIPS},
 year={2023}
}

This implementation is based on