1. Introduction
We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications.
2. Model Summary
JanusFlow is a unified understanding and generation MLLM, which decouples visual encoding for multimodal understanding and generation, which is constructed based on DeepSeek-LLM-1.3b-base. For multimodal understanding, it uses the SigLIP-L as the vision encoder, which supports 384 x 384 image input. For image generation, JanusFlow uses rectified flow and SDXL-VAE to generate 384 x 384 images. The provided checkpoint is the EMA checkpoint after pre-training and supervised fine-tuning.
3. Quick Start
Please refer to Github Repository
4. License
This code repository is licensed under the MIT License. The use of JanusFlow models is subject to DeepSeek Model License.
5. Citation
@misc{ma2024janusflow,
title={JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation},
author={Yiyang Ma and Xingchao Liu and Xiaokang Chen and Wen Liu and Chengyue Wu and Zhiyu Wu and Zizheng Pan and Zhenda Xie and Haowei Zhang and Xingkai yu and Liang Zhao and Yisong Wang and Jiaying Liu and Chong Ruan},
journal={arXiv preprint arXiv:2411.07975},
year={2024}
}
6. Contact
If you have any questions, please raise an issue or contact us at [email protected].
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