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"SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow", Yuanzhi Zhu, Xingchao Liu, Qiang Liu
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This is the official implementation of ECCV2024 paper
byYuanzhi Zhu,Xingcaho Liu,Qiang Liu
This code is based onRectifiedFlow.
python ./train.py \ --config ./configs/rectified_flow/cifar10_rf_gaussian.py \ --config.expr 1_rectified_flow \
evaluate FID of ckpts fromconfig.eval.begin_ckpt inckpt_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 insampling_dir
python ./image_sampling.py \ --config ./configs/rectified_flow/cifar10_rf_gaussian.py \ --sampling_dir"logs/1_rectified_flow" \ --config.eval.batch_size 64- 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
- 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)'
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'
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 specifyconfig.data.data_root for reflow training
ifconfig.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_teachertrue \ --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 can be found here on HuggingFace:https://huggingface.co/Yuanzhi/SlimFlowTo sample from these checkpoints, please follow the instructions in the README.md of the HuggingFace model.
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}}
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"SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow", Yuanzhi Zhu, Xingchao Liu, Qiang Liu
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