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Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models (Diff-Instruct)<br><sub>Official PyTorch implementation of the NeurIPS 2023 paper</sub>
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models<br> Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li and Zhihua Zhang. <br>https://openreview.net/forum?id=MLIs5iRq4w<br>
Abstract: Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs) have become the preferred option for generative modeling, with numerous pre-trained models available for a wide variety of datasets. Containing intricate information about data distributions, pre-trained DMs are valuable assets for downstream applications. In this work, we consider learning from pre-trained DMs and transferring their knowledge to other generative models in a data-free fashion. Specifically, we propose a general framework called Diff-Instruct to instruct the training of arbitrary generative models as long as the generated samples are differentiable with respect to the model parameters. Our proposed Diff-Instruct is built on a rigorous mathematical foundation where the instruction process directly corresponds to minimizing a novel divergence we call Integral Kullback-Leibler (IKL) divergence. IKL is tailored for DMs by calculating the integral of the KL divergence along a diffusion process, which we show to be more robust in comparing distributions with misaligned supports. We also reveal non-trivial connections of our method to existing works such as DreamFusion \citep{poole2022dreamfusion}, and generative adversarial training. To demonstrate the effectiveness and universality of Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion models and refining existing GAN models. The experiments on distilling pre-trained diffusion models show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. The experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models across various settings. Our official code is released through \url{https://github.com/pkulwj1994/diff_instruct}.
Code was based on Pytorch implementation of EDM diffusion model: https://github.com/NVlabs/edm.
Prepare conda env
git clone https://github.com/pkulwj1994/diff_instruct.git cd diff_instruct
source activate conda create -n di_v100 python=3.8 conda activate di_v100 pip install torch==1.12.1 torchvision==0.13.1 tqdm click psutil scipy
Pre-trained models
We use pre-trained EDM models:
Preparing datasets
Datasets are stored in the same format as in StyleGAN: uncompressed ZIP archives containing uncompressed PNG files and a metadata file dataset.json for labels. Custom datasets can be created from a folder containing images; see python dataset_tool.py --help for more information.
CIFAR-10: Download the CIFAR-10 python version and convert to ZIP archive:
python dataset_tool_edm.py --source=/data/downloads/cifar-10-python.tar.gz --dest=/data/datasets/cifar10-32x32.zip
ImageNet: Download the ImageNet Object Localization Challenge and convert to ZIP archive at 64x64 resolution:
python dataset_tool.py --source=/data/downloads/imagenet/ILSVRC/Data/CLS-LOC/train --dest=/data/datasets/imagenet-64x64.zip --resolution=64x64 --transform=center-crop
Distill single-step models for CIFAR10 unconditional generation on a single V100 GPU (result in an FID <= 4.5)
You can run diffusion distillation using di_train.py. For example:
# Train one-step DI model for unconditional CIFAR-10 using 1 GPUs
CUDA_VISIBLE_DEVICES=0 torchrun --standalone --nproc_per_node=1 --master_port=25678 di_train.py --outdir=/logs/di/ci10-uncond --data=/data/datasets/cifar10-32x32.zip --arch=ddpmpp --batch 128 --edm_model cifar10-uncond --cond=0 --metrics fid50k_full --tick 10 --snap 50 --lr 0.00001 --glr 0.00001 --init_sigma 1.0 --fp16=0 --lr_warmup_kimg -1 --ls 1.0 --sgls 1.0
In the experiment, the FID will be calculated automatically for each "snap" of rounds.
License
All material, including source code and pre-trained models, is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Citation
@inproceedings{
luo2023diffinstruct,
title={Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models},
author={Weijian Luo and Tianyang Hu and Shifeng Zhang and Jiacheng Sun and Zhenguo Li and Zhihua Zhang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=MLIs5iRq4w}
}
Development
This is a research reference implementation and is treated as a one-time code drop. As such, we do not accept outside code contributions in the form of pull requests.
Acknowledgments
We thank EDM paper ""Elucidating the Design Space of Diffusion-Based Generative Models"" for its great implementation of EDM diffusion models in https://github.com/NVlabs/edm. We thank Shuchen Xue, and Zhengyang Geng for constructive feedback on code implementations.