| license: mit | |
| pipeline_tag: unconditional-image-generation | |
| # Adversarial Flow Models | |
| This repository contains the official checkpoints for the paper [Adversarial Flow Models](https://huggingface.co/papers/2511.22475). | |
| Adversarial Flow Models is a class of generative models that unifies Adversarial Models and Flow Models. This repository contains the pre-trained ImageNet-256px models described in the paper. | |
| - **GitHub Repository**: [ByteDance-Seed/Adversarial-Flow-Models](https://github.com/ByteDance-Seed/Adversarial-Flow-Models) | |
| - **Paper**: [Adversarial Flow Models](https://huggingface.co/papers/2511.22475) | |
| ## Usage | |
| Code and instructions for generation and training are available in the [official GitHub repository](https://github.com/ByteDance-Seed/Adversarial-Flow-Models). | |
| ## Repository Content | |
| * `models/` contains pre-trained ImageNet-256px checkpoints. | |
| * `eval/` contains pre-generated 50k samples for evaluations following ADM npz format. | |
| * `misc/` contains VAE and other checkpoints used in training. | |
| ## Citation | |
| ```bibtex | |
| @article{lin2025adversarial, | |
| title={Adversarial Flow Models}, | |
| author={Lin, Shanchuan and Yang, Ceyuan and Lin, Zhijie and Chen, Hao and Fan, Haoqi}, | |
| journal={arXiv preprint arXiv:2511.22475}, | |
| year={2025} | |
| } | |
| ``` |