MeanFlow

PyTorch implementation of Mean Flows for One-step Generative Modeling (MeanFlow) and Improved Mean Flows (iMF).

Note: Unofficial implementation, based on the papers above and the official JAX repo imeanflow.

Contributions and feedback are welcome! Feel free to open an issue or pull request.

Updates

2026.06.13

• MeanFlow and iMF training (meanflow.mode: "meanflow" / "i-meanflow")
• Dual-head DiT: u for MeanFlow, v for flow matching
• CFG scale as model input with CFG distillation (cfg_scale; None to disable)
• JVP under no_grad for dudt only; separate grad-enabled forward pass for optimization
• Config-based training for MNIST, CIFAR-10, and ImageNet latent

Usage

i-meanflow (meanflow.mode: "i-meanflow") is more stable and recommended for your projects. meanflow mode is kept for reference.

pip install torch accelerate torchvision einops tqdm diffusers

# single GPU
python train.py --config configs/mnist.py

# custom run name
python train.py --config configs/cifar10.py --run_suffix exp1

# multi-GPU
accelerate launch --num_processes 2 train.py --config configs/mnist.py

Config	Dataset
configs/mnist.py	MNIST
configs/cifar10.py	CIFAR-10
configs/imagenet_latent.py	ImageNet (latent, VAE)

Common config fields: n_steps, batch_size, grad_clip, mixed_precision, meanflow.mode, meanflow.cfg_scale.

Training logs are saved to logs/{run_name}/:

logs/{run_name}/
├── config.py
├── train.log      # loss, FM/MF loss, MF_V_MSE, grad norm, LR
├── images/
└── ckpts/

Examples

MNIST — 10k steps, 1-step sample:

MNIST — 6k steps, 1-step CFG (w=2.0):

CIFAR-10 — 200k steps, 1-step CFG (w=2.0):

TODO

• Basic training and inference
• Multi-GPU via Accelerate
• Classifier-Free Guidance
• Latent image training
• Improved MeanFlow (iMF)
• Triton JVP + Flash Attention

Known Issues

• jvp is currently incompatible with PyTorch's native Flash Attention (scaled_dot_product_attention).

• Solution in this repo:

  • Run the JVP pass under no_grad (for dudt only) without Flash Attention.
  • Run a separate gradient-enabled forward pass for optimization with Flash Attention.

• Other advanced solutions:

  • Triton-based kernels with JVP support (e.g. rcm) will allow Flash Attention within JVP.
  • Such kernels may offer additional speed and memory benefits, but are not currently supported in this repository.

Acknowledgement

Building upon Just-a-DiT and EzAudio.

iMF support is based on the official JAX repo imeanflow. Thanks to the authors for releasing their code.

Research projects built on this repository

• MeanAudio: fast text-to-audio generation with Mean Flows. See the paper.
• MeanVC: lightweight streaming zero-shot voice conversion. See the paper.
• MeanFlow-TSE: one-step target speaker extraction. See the paper.

Like This Project?

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Citation

If you find this repository useful in your research or projects, please consider citing the original MeanFlow papers as well as this implementation.

MeanFlow

@article{geng2025meanflow,
  title={Mean Flows for One-step Generative Modeling},
  author={Geng, Zhengyang and Shechtman, Eli and Kolter, J. Zico and He, Kaiming},
  journal={arXiv preprint arXiv:2505.13447},
  year={2025}
}

Improved MeanFlow

@article{geng2025improved,
  title={Improved Mean Flows: On the Challenges of Fastforward Generative Models},
  author={Geng, Zhengyang and Lu, Yiyang and Wu, Zongze and Shechtman, Eli and Kolter, J. Zico and He, Kaiming},
  journal={arXiv preprint arXiv:2512.02012},
  year={2025}
}

This Repository

@misc{meanflow_pytorch,
  title={MeanFlow: Unofficial PyTorch Implementation},
  author={haidog-yaqub},
  year={2025},
  howpublished={\url{https://github.com/haidog-yaqub/MeanFlow}},
}