This is the official implementation of ECCV2024 paper
by Yuanzhi Zhu, Xingcaho Liu, Qiang Liu
This code is based on RectifiedFlow.
python ./train.py \
--config ./configs/rectified_flow/cifar10_rf_gaussian.py \
--config.expr 1_rectified_flow \evaluate FID of ckpts from config.eval.begin_ckpt in ckpt_dir
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 \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 \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 64python ./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'
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 \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.
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)' \If you find this repo helpful, please cite:
@article{zhu2024slimflow,
title={SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow},
author={Zhu, Yuanzhi and Liu, Xingchao and Liu, Qiang},
journal={arXiv preprint arXiv:2407.12718},
year={2024}
}
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