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Relational Diffusion Distillation for Efficient Image PDF

This repository provides the implementation for our paper "Relational Diffusion Distillation for Efficient Image". Our approach introduces a novel relational distillation method for distilling fewer steps diffusion models, focusing on efficiency and performance.

Environment

Python 3.8.18, torch 2.1.0

Training

Train the base model

python -m torch.distributed.launch --nproc_per_node=4 train_base.py \
    --flagfile ./config/CIFAR10_BASE.txt \
    --gpu_id 0,1,2,3 --logdir ./logs/CIFAR10/1024

Distill using PD

python -m torch.distributed.launch --nproc_per_node=4 PD.py \
    --flagfile ./config/CIFAR10_PD.txt --gpu_id 0,1,2,3 \
    --logdir ./logs/CIFAR10/512 --base_ckpt ./logs/CIFAR10/1024

...

python -m torch.distributed.launch --nproc_per_node=4 PD.py \
    --flagfile ./config/CIFAR10_PD.txt --gpu_id 0,1,2,3 \
    --logdir ./logs/CIFAR10/4 --base_ckpt ./logs/CIFAR10/8

To use RDD, train the classifier using classifier/train.py first

python train.py --model densenet201

Distill using RDD

python -m torch.distributed.launch --nproc_per_node=4 train_rdd.py \
    --flagfile ./config/CIFAR10_PD.txt --gpu_id 0,1,2,3 --num_gpus 4 \
    --logdir ./logs/8to4/rdd --base_ckpt ./logs/CIFAR10/8 \
    --classifier densenet201 --classifier_path ./classifier/result/cifar10/densenet201 \
    --num_workers 8 --feature --total_steps 20000 \
    --sample_step 5000 --save_step 5000 \
    --lr 5e-5 --wd 0. --loss_type mp2p --temperature 0.9

Evaluation

To eval, run score/get_npz.py first or download from google drive

python get_npz.py --dataset cifar10

Eval

# 8-step DDIM
python ddim_eval.py --flagfile ./config/CIFAR10_EVAL.txt --logdir ./logs/CIFAR10/1024 --stride 128
# 4-step PD
python ddim_eval.py --flagfile ./config/CIFAR10_EVAL.txt --logdir ./logs/CIFAR10/4
# 4-step RDD
python ddim_eval.py --flagfile ./config/CIFAR10_EVAL.txt --logdir ./logs/8to4/rdd

Pre-trained Models

RCFD provide some pre-trained models (1024-step base model, 8-step PD-obtained model, and densenet201) in google drive. We use the same model as them.

Citation

If you find this repository useful, please consider citing the following paper:

@misc{feng2024relationaldiffusiondistillationefficient,
      title={Relational Diffusion Distillation for Efficient Image Generation}, 
      author={Weilun Feng and Chuanguang Yang and Zhulin An and Libo Huang and Boyu Diao and Fei Wang and Yongjun Xu},
      year={2024},
      eprint={2410.07679},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.07679}, 
}

Acknowledgment

This codebase is heavily borrowed from RCFD , pytorch-ddpm and diffusion_distiller.