[Project Page] [arXiv] [HuggingFace]
Ever thinking about what is the prerequisite for a visual model achieving the impact of GPT in language? The prequisite should be its ability of zero-shot generalization to various applications, prompts, etc. Our RandAR is one of the attempts towards this objective.
🎲 RandAR is a decoder-only AR model generating image tokens in arbitrary orders.
🚀 RandAR supports parallel-decoding without additional fine-tuning and brings 2.5
🛠️ RandAR unlocks new capabilities for causal GPT-style transformers: inpainting, outpainting, zero-shot resolution extrapolation, and bi-directional feature encoding.
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[12/09/2024] 🎉 The initial code is released, including the tokenization/modeling/training pipeline. I found that augmentation & tokenization different from the LLaMAGEN's designs are better for FID. From the current speed of training, I expect to release model checkpoints and verified training/eval scripts before 12/18/2024.
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[12/02/2024] 📋 I am trying my best to re-implement the code and re-train the model as soon as I can. I plan to release the code before 12/09/2024 and the models afterwards. I am going to make my clusters running so fiecely that they will warm up the whole Illinois during this winter. 🔥🔥🔥
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[12/02/2024] 🎉 The paper appears on Arxiv.
Checkout our documentation DOCUMENTATION.md for more details.
If you find this work useful in your research, please consider citing:
@article{pang2024randar,
title={RandAR: Decoder-only Autoregressive Visual Generation in Random Orders},
author={Pang, Ziqi and Zhang, Tianyuan and Luan, Fujun and Man, Yunze and Tan, Hao and Zhang, Kai and Freeman, William T. and Wang, Yu-Xiong},
journal={arXiv preprint arXiv:2412.01827},
year={2024}
}Thank you to the open-source community for their explorations on autoregressive generation, especially LLaMAGen.