SlimFlow

This is the official implementation of ECCV2024 paper

SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow

by Yuanzhi Zhu, Xingcaho Liu, Qiang Liu

This code is based on RectifiedFlow.

usage

Train 1-Rectified Flow

python ./train.py \
    --config ./configs/rectified_flow/cifar10_rf_gaussian.py  \
    --config.expr 1_rectified_flow \

Evaluation

evaluate FID of ckpts from config.eval.begin_ckpt in ckpt_dir

one step

python ./evaluation_fid.py \
    --config ./configs/rectified_flow/cifar10_rf_gaussian.py  \
    --ckpt_dir logs/1_rectified_flow \
    --config.eval.batch_size 512 --config.eval.num_samples 50000 \
    --config.eval.begin_ckpt 1 --config.eval.end_ckpt 0 \
    --config.sampling.sample_N 1 --config.sampling.use_ode_sampler euler \

rk45 by default

python ./evaluation_fid.py \
    --config ./configs/rectified_flow/cifar10_rf_gaussian.py  \
    --ckpt_dir logs/1_rectified_flow \
    --config.eval.batch_size 512 --config.eval.num_samples 50000 \
    --config.eval.begin_ckpt 1 --config.eval.end_ckpt 0 \

Image Sampling

sampling all ckpts in sampling_dir

python ./image_sampling.py \
    --config ./configs/rectified_flow/cifar10_rf_gaussian.py \
    --sampling_dir "logs/1_rectified_flow" \
    --config.eval.batch_size 64

Image Sampling Configurations

• Sample from 1flows: --config.sampling.use_ode_sampler rk45
• Sample from 2flows: [--config.sampling.use_ode_sampler rk45, --config.sampling.use_ode_sampler heun + --config.sampling.sample_N 3, --config.sampling.use_ode_sampler euler + --config.sampling.sample_N 1]
• Sample from distilled one-step models: --config.sampling.use_ode_sampler euler + --config.sampling.sample_N 1

Model Configurations

• ImageNet64 80.7M: --config.model.name DhariwalUNet --config.model.nf 128 --config.model.num_res_blocks 2 --config.model.ch_mult '(1, 2, 2, 4)' --config.data.num_classes 1000 --config.data.image_size 64 --config.model.attn_resolutions '32, 16'
• ImageNet 44.7MM: --config.model.name DhariwalUNet --config.model.nf 128 --config.model.num_res_blocks 2 --config.model.ch_mult '(1, 2, 2, 2)' --config.data.num_classes 1000 --config.data.image_size 64 --config.model.attn_resolutions '32, 16'
• FFHQ64 27.9M: --config.model.nf 128 --config.model.num_res_blocks 2 --config.data.image_size 64 --config.model.ch_mult '(1, 2, 2)'
• FFHQ64 15.7M: --config.model.nf 96 --config.model.num_res_blocks 2 --config.data.image_size 64 --config.model.ch_mult '(1, 2, 2)'
• FFHQ64 7.0M: --config.model.nf 64 --config.model.num_res_blocks 2 --config.data.image_size 64 --config.model.ch_mult '(1, 2, 2)'
• FFHQ64 3.4M: --config.model.nf 64 --config.model.num_res_blocks 1 --config.data.image_size 64 --config.model.ch_mult '(1, 1, 2)'
• CIFAR32 27.9M: --config.model.nf 128 --config.model.num_res_blocks 2 --config.data.image_size 32 --config.model.ch_mult '(1, 2, 2)'
• CIFAR32 15.7M: --config.model.nf 96 --config.model.num_res_blocks 2 --config.data.image_size 32 --config.model.ch_mult '(1, 2, 2)'
• CIFAR32 7.0M: --config.model.nf 64 --config.model.num_res_blocks 2 --config.data.image_size 32 --config.model.ch_mult '(1, 2, 2)'
• CIFAR32 3.4M: --config.model.nf 64 --config.model.num_res_blocks 1 --config.data.image_size 32 --config.model.ch_mult '(1, 1, 2)'

Generate Data Pair

z0-->z1 by default

python ./generate_data.py \
    --config ./configs/rectified_flow/cifar10_rf_gaussian.py  \
    --ckpt_path "logs/1_rectified_flow/checkpoints/checkpoint_14.pth" \
    --data_root "reflow_data/1_rectified_flow_50000/" \
    --config.sampling.total_number_of_samples 50000 --config.seed 0 \
    --config.training.batch_size 512 \
    --config.sampling.direction from_z0 \

config.sampling.direction has 3 options: 'from_z0', 'from_z1', 'random_paired'

Reflow to get 2-Rectified Flow with the Generated Data Pair

python ./train.py \
    --config ./configs/rectified_flow/cifar10_rf_gaussian.py  \
    --config.data.reflow_data_root "reflow_data/1_rectified_flow_50000/" \
    --config.flow.flow_t_schedule uniform \
    --config.expr 2_rectified_flow \
    --config.flow.h_flip=true \
    --config.flow.pre_train_model /logs/1_rectified_flow/checkpoints/checkpoint_14.pth \

Annealing Reflow

python ./train.py \
    --config ./configs/rectified_flow/cifar10_rf_gaussian.py  \
    --config.expr 2_rectified_flow_500001flow_flip_warmup_300000_28m \
    --config.flow.h_flip=true \
    --config.training.x0_randomness warmup_300000 \
    --config.training.snapshot_freq 50000 \
    --config.training.snapshot_sampling 10000 \
    --config.data.reflow_data_root "reflow_data/1_rectified_flow_50000/" \
    --config.model.nf 128 --config.model.num_res_blocks 2 \
    --config.model.ch_mult '(1, 2, 2)' \

must specify config.data.data_root for reflow training

if config.flow.pre_train_model is not specified, the model will be trained from scratch.

Distill to get one-step model

python ./train.py \
    --config ./configs/rectified_flow/cifar10_rf_gaussian.py  \
    --config.data.reflow_data_root "reflow_data/1_rectified_flow_50000/" \
    --config.flow.flow_t_schedule t0 \
    --config.training.loss_type lpips \
    --config.flow.use_teacher true \
    --config.expr 2_rectified_flow_500000bigflow_28m_distill_lpips_use_teacher \
    --config.flow.pre_train_model "./logs/2_rectified_flow_500001flow_flip_warmup_300000_28m/checkpoints/checkpoint_16.pth" \
    --config.model.nf 128 --config.model.num_res_blocks 2 \
    --config.model.ch_mult '(1, 2, 2)' \

Checkpoints

checkpoints can be found here on HuggingFace: https://huggingface.co/Yuanzhi/SlimFlow To sample from these checkpoints, please follow the instructions in the README.md of the HuggingFace model.

Citation

If you find this repo helpful, please cite:

@inproceedings{zhu2025slimflow,
  title={SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow},
  author={Zhu, Yuanzhi and Liu, Xingchao and Liu, Qiang},
  booktitle={European Conference on Computer Vision},
  pages={342--359},
  year={2025},
  organization={Springer}
}