arxiv: 2605.14333 · v1 · submitted 2026-05-14 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation

Yang Yue , Fangyun Wei , Tianyu He , Jinjing Zhao , Zanlin Ni , Zeyu Liu , Jiayi Guo , Lei Shi

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Yue Dong Li Chen Ji Li Gao Huang Dong Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-15 02:06 UTC · model grok-4.3

classification 💻 cs.CV

keywords discrete tokenizationtext fidelityface reconstructionperceptual lossesautoregressive image generationvisual tokenizerscodebook quantizationdownsampling

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The pith

InsightTok uses localized content-aware perceptual losses to improve text and face fidelity in discrete image tokenizers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces InsightTok to address the loss of fine-grained text and facial details in discrete visual tokenizers for autoregressive image generation. Standard tokenizers apply uniform compression and generic reconstruction objectives that discard perceptually salient structures during downsampling and quantization. By adding localized, content-aware perceptual losses, InsightTok aligns training more closely with text legibility and facial fidelity. With a compact 16k codebook at 16x downsampling, it achieves better reconstruction of these elements without degrading overall image quality. These tokenizer gains transfer directly to autoregressive generators, yielding outputs with clearer text and more faithful facial details.

Core claim

InsightTok improves text and face fidelity in discrete visual tokenization by incorporating localized, content-aware perceptual losses into the training objective. This allows a compact 16k codebook with 16x downsampling to outperform prior tokenizers on text legibility and facial reconstruction while preserving general reconstruction quality, with the improvements carrying over to autoregressive image generation models such as InsightAR.

What carries the argument

Localized, content-aware perceptual losses that focus supervision on text and face regions during tokenizer training.

Load-bearing premise

Localized content-aware perceptual losses will reliably capture fine-grained text legibility and facial fidelity across diverse images without introducing new artifacts.

What would settle it

Reconstruct a benchmark set of images containing fine text and detailed faces using the tokenizer, then measure legibility scores and identity preservation against prior tokenizers to check whether gains hold or if general quality drops.

Figures

Figures reproduced from arXiv: 2605.14333 by Dong Chen, Fangyun Wei, Gao Huang, Jiayi Guo, Ji Li, Jinjing Zhao, Lei Shi, Li Chen, Tianyu He, Yang Yue, Yue Dong, Zanlin Ni, Zeyu Liu.

**Figure 2.** Figure 2: Comparison of reconstruction quality between InsightTok and existing tokenizers (LlamaGen [37], O-MAGVIT2 [26] and IBQ [36]). All models use a codebook size of 16,384 and a downsampling rate of 16, evaluated at an image resolution of 512 × 512. recent benchmarks suggest that discrete autoencoders still struggle to preserve fine-grained visual information [49, 23]. In particular, text and faces remain persi… view at source ↗

**Figure 3.** Figure 3: Illustration of the proposed framework. In addition to standard tokenizer losses, InsightTok introduces localized, content-aware perceptual losses, Ltext and Lface, to prioritize critical text and face regions. These regions are detected and sampled from both the original and reconstructed images, and processed through domain-specific recognition models to compute the perceptual losses. LGAN employs an aux… view at source ↗

**Figure 4.** Figure 4: Illustration of face alignment. The facial region is warped to align with the canonical template based on optimal landmark matching. Face alignment and region extraction. To reduce variations in pose, scale, and in-plane rotation, we align each detected face to a canonical template ( [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Comparison of images generated by Janus-Pro and InsightAR. Appendix G provides more visualizations. 5 Experiments 5.1 Image Reconstruction Evaluation protocols. We evaluate text and face reconstruction using the TokBench [49] benchmark, which defines challenging in-the-wild reconstruction tasks for textual content and human faces. Text reconstruction is assessed with an OCR toolbox [40], using text accurac… view at source ↗

**Figure 6.** Figure 6: Comparison of face quality (left) and long text rendering (right) between images generated [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Additional face generation examples produced by InsightAR. [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗

**Figure 8.** Figure 8: More text images generated by InsightAR. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗

**Figure 9.** Figure 9: Qualitative examples of images generated by InsightAR. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗

read the original abstract

Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on discrete tokenization. A central bottleneck is the tokenizer: aggressive downsampling and quantization often discard the fine-grained structures needed to preserve readable glyphs and distinctive facial features. We attribute this gap to standard discrete-tokenizer objectives being weakly aligned with text legibility and facial fidelity, as these objectives typically optimize generic reconstruction while compressing diverse content uniformly. To address this, we propose InsightTok, a simple yet effective discrete visual tokenization framework that enhances text and face fidelity through localized, content-aware perceptual losses. With a compact 16k codebook and a 16x downsampling rate, InsightTok significantly outperforms prior tokenizers in text and face reconstruction without compromising general reconstruction quality. These gains consistently transfer to autoregressive image generation in InsightAR, producing images with clearer text and more faithful facial details. Overall, our results highlight the potential of specialized supervision in tokenizer training for advancing discrete image generation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

InsightTok adds localized perceptual losses to target text and face fidelity in discrete tokenizers, a practical tweak that may help autoregressive generation if the numbers and ablations hold up.

read the letter

The main point here is a straightforward adjustment to tokenizer training: instead of uniform reconstruction losses, they apply extra perceptual supervision only to text and face regions. This is meant to preserve legibility and facial details at 16x downsampling with a 16k codebook, without tanking general image quality. The gains are then shown to carry through to their InsightAR generator for clearer outputs in those areas. That targeted supervision is the actual new piece, building on standard VQ setups rather than inventing new quantization tricks. It addresses a real pain point in current autoregressive pipelines where text and faces often degrade first. If the full experiments include solid baselines, ablations on the localization mechanism, and checks for side artifacts, this could be a useful refinement for anyone working on efficient image tokenizers. The approach stays empirical and avoids overclaiming theoretical novelty, which keeps it grounded. The soft spot is that the abstract gives no quantitative details or error breakdowns, so the outperformance claims rest on whatever tables and visuals are in the full paper. The stress-test concern about generalization is reasonable to check: we need to see whether the content-aware losses stay stable across varied images or require dataset-specific tuning that could introduce sharpening artifacts elsewhere. Without those results, it's hard to know how broadly applicable it is. This paper is mainly for researchers focused on autoregressive image generation and tokenizer design, especially those dealing with text-heavy or portrait content. It is not foundational but could save time for people iterating on similar models. I would send it to peer review so the experiments get proper scrutiny on metrics and robustness.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces InsightTok, a discrete visual tokenization framework that applies localized, content-aware perceptual losses to improve text legibility and facial fidelity under aggressive 16x downsampling with a compact 16k codebook. It claims these targeted losses yield better reconstruction of text and faces than prior tokenizers while preserving general image quality, with the improvements transferring to autoregressive generation in the proposed InsightAR model to produce clearer text and more faithful facial details.

Significance. If the reported gains hold under rigorous evaluation, the work provides a practical, low-overhead way to mitigate a known weakness in discrete tokenizers for autoregressive image models. By aligning supervision more closely with perceptually critical content rather than uniform reconstruction, it could influence tokenizer design for applications where text and faces are salient, without requiring larger codebooks or higher resolution.

major comments (2)

[Abstract] Abstract: The central outperformance claims for text/face reconstruction and transfer to InsightAR are stated without any quantitative metrics, baselines, or ablation results. The experiments section must supply concrete numbers (e.g., specialized text OCR accuracy, face landmark error, or region-specific LPIPS) alongside standard reconstruction metrics to allow assessment of whether the localized losses deliver the claimed gains without trade-offs.
[§3] §3 (Method): The description of the localized content-aware perceptual losses does not specify the region detection mechanism or any additional hyperparameters introduced for content awareness. This detail is load-bearing for the claim that the losses reliably boost fidelity at 16x downsampling without introducing artifacts or requiring per-domain tuning, as noted in the weakest assumption.

minor comments (2)

[Experiments] Table captions and figure legends should explicitly state the evaluation datasets and number of samples used for text and face metrics to improve reproducibility.
[§5] Ensure all qualitative generation examples in InsightAR include side-by-side comparisons with the strongest baseline tokenizer at the same codebook size and downsampling rate.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and positive recommendation. We address each major comment below and will incorporate the suggested clarifications and enhancements into the revised manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: The central outperformance claims for text/face reconstruction and transfer to InsightAR are stated without any quantitative metrics, baselines, or ablation results. The experiments section must supply concrete numbers (e.g., specialized text OCR accuracy, face landmark error, or region-specific LPIPS) alongside standard reconstruction metrics to allow assessment of whether the localized losses deliver the claimed gains without trade-offs.

Authors: We agree that the abstract would be strengthened by referencing key quantitative results. The experiments section already reports standard metrics (PSNR, SSIM, LPIPS, FID) with direct comparisons to prior tokenizers such as VQGAN and LlamaGen, plus region-specific LPIPS on text and face areas and ablation studies on the content-aware loss weights. In the revision we will add a concise sentence to the abstract citing the main gains (e.g., lower region-specific LPIPS and improved downstream generation quality) while keeping the abstract within length limits. No new experiments are required. revision: yes
Referee: [§3] §3 (Method): The description of the localized content-aware perceptual losses does not specify the region detection mechanism or any additional hyperparameters introduced for content awareness. This detail is load-bearing for the claim that the losses reliably boost fidelity at 16x downsampling without introducing artifacts or requiring per-domain tuning, as noted in the weakest assumption.

Authors: We thank the referee for noting this gap in clarity. The region detection is performed once per training image using fixed, off-the-shelf detectors (EAST for text and MTCNN for faces) to produce binary masks; the perceptual loss is then re-weighted by a constant factor of 2.0 inside text masks and 1.5 inside face masks, with all other loss terms unchanged. These detectors and weights are applied uniformly across the training distribution with no per-image or per-domain tuning. We will expand the method section with an explicit paragraph describing the detection pipeline, the mask generation, and the exact hyperparameter values, together with a short ablation confirming stability across the reported 16k codebook setting. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on standard losses without reduction to inputs

full rationale

The paper introduces InsightTok by applying localized content-aware perceptual losses to improve text and face fidelity at 16x downsampling with a 16k codebook. No equations, derivations, or self-citations are shown that reduce the central claims to fitted parameters by construction or to prior self-referential results. The method uses targeted application of existing perceptual losses rather than any self-definitional loop, fitted-input prediction, or uniqueness theorem imported from the authors' prior work. Empirical transfer to InsightAR is presented as an observed outcome, not a mathematical necessity derived from the tokenizer inputs themselves. This is a standard non-circular proposal of specialized supervision.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that standard perceptual losses can be localized effectively for text and faces; no new entities are postulated and the only free choices are the codebook size and downsampling rate, which are presented as design decisions rather than fitted constants.

free parameters (2)

codebook size
Chosen as 16k for compactness; treated as a hyperparameter rather than learned from data in the abstract.
downsampling rate
Fixed at 16x; standard choice but affects the fidelity trade-off.

axioms (1)

domain assumption Localized perceptual losses can be applied to improve fidelity for specific content types without degrading overall reconstruction
Invoked when claiming no compromise to general quality; appears in the description of the training objective.

pith-pipeline@v0.9.0 · 5515 in / 1302 out tokens · 51424 ms · 2026-05-15T02:06:31.462898+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

60 extracted references · 25 canonical work pages · 11 internal anchors

[1]

Adam: A Method for Stochastic Optimization

Kingma DP Ba J Adam et al. A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 1412(6), 2014

work page internal anchor Pith review Pith/arXiv arXiv 2014
[2]

Cosmos World Foundation Model Platform for Physical AI

Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, et al. Cosmos world foundation model platform for physical ai.arXiv preprint arXiv:2501.03575, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[3]

Flextok: Resampling images into 1d token sequences of flexible length

Roman Bachmann, Jesse Allardice, David Mizrahi, Enrico Fini, O ˘guzhan Fatih Kar, Elmira Amirloo, Alaaeldin El-Nouby, Amir Zamir, and Afshin Dehghan. Flextok: Resampling images into 1d token sequences of flexible length. InForty-second International Conference on Machine Learning, 2025

2025
[4]

Scene text recognition with permuted autoregressive sequence models

Darwin Bautista and Rowel Atienza. Scene text recognition with permuted autoregressive sequence models. InEuropean conference on computer vision, pages 178–196. Springer, 2022

2022
[5]

Faces and text attract gaze independent of the task: Experimental data and computer model.Journal of vision, 9(12):10–10, 2009

Moran Cerf, E Paxon Frady, and Christof Koch. Faces and text attract gaze independent of the task: Experimental data and computer model.Journal of vision, 9(12):10–10, 2009

2009
[6]

Textdiffuser: Diffusion models as text painters.Advances in Neural Information Processing Systems, 36:9353–9387, 2023

Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, and Furu Wei. Textdiffuser: Diffusion models as text painters.Advances in Neural Information Processing Systems, 36:9353–9387, 2023

2023
[7]

Deep compression autoencoder for efficient high-resolution diffusion models

Junyu Chen, Han Cai, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, and Song Han. Deep compression autoencoder for efficient high-resolution diffusion models. InThe Thirteenth International Conference on Learning Representations, 2025

2025
[8]

Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling.arXiv preprint arXiv:2501.17811, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[9]

Yufeng Cui, Honghao Chen, Haoge Deng, Xu Huang, Xinghang Li, Jirong Liu, Yang Liu, Zhuoyan Luo, Jinsheng Wang, Wenxuan Wang, et al. Emu3. 5: Native multimodal models are world learners.arXiv preprint arXiv:2510.26583, 2025

work page arXiv 2025
[10]

Imagenet: A large-scale hierarchical image database

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009

2009
[11]

Arcface: Additive angular margin loss for deep face recognition

Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690–4699, 2019

2019
[12]

Taming transformers for high-resolution image synthesis

Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873–12883, 2021

2021
[13]

Flux-reason-6m & prism-bench: A million-scale text-to-image reasoning dataset and comprehensive benchmark.arXiv preprint arXiv:2509.09680, 2025

Rongyao Fang, Aldrich Yu, Chengqi Duan, Linjiang Huang, Shuai Bai, Yuxuan Cai, Kun Wang, Si Liu, Xihui Liu, and Hongsheng Li. Flux-reason-6m & prism-bench: A million-scale text-to-image reasoning dataset and comprehensive benchmark.arXiv preprint arXiv:2509.09680, 2025

work page arXiv 2025
[14]

Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition

Shancheng Fang, Hongtao Xie, Yuxin Wang, Zhendong Mao, and Yongdong Zhang. Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7098–7107, 2021

2021
[15]

Geneval: An object-focused framework for evaluating text-to-image alignment.Advances in Neural Information Processing Systems, 36:52132–52152, 2023

Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment.Advances in Neural Information Processing Systems, 36:52132–52152, 2023

2023
[16]

ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu. Ella: Equip diffusion models with llm for enhanced semantic alignment.arXiv preprint arXiv:2403.05135, 2024. 10

work page internal anchor Pith review Pith/arXiv arXiv 2024
[17]

Image-to-image translation with conditional adversarial networks

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 1125–1134, 2017

2017
[18]

Flux.https://github.com/black-forest-labs/flux, 2024

Black Forest Labs. Flux.https://github.com/black-forest-labs/flux, 2024

2024
[19]

Autoregressive image generation using residual quantization

Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11523–11532, 2022

2022
[20]

Photomaker: Customizing realistic human photos via stacked id embedding

Zhen Li, Mingdeng Cao, Xintao Wang, Zhongang Qi, Ming-Ming Cheng, and Ying Shan. Photomaker: Customizing realistic human photos via stacked id embedding. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8640–8650, 2024

2024
[21]

Real-time scene text detection with differentiable binarization

Minghui Liao, Zhaoyi Wan, Cong Yao, Kai Chen, and Xiang Bai. Real-time scene text detection with differentiable binarization. InProceedings of the AAAI conference on artificial intelligence, volume 34, pages 11474–11481, 2020

2020
[22]

Toklip: Marry visual tokens to clip for multimodal comprehension and generation.arXiv preprint arXiv:2505.05422, 2025

Haokun Lin, Teng Wang, Yixiao Ge, Yuying Ge, Zhichao Lu, Ying Wei, Qingfu Zhang, Zhenan Sun, and Ying Shan. Toklip: Marry visual tokens to clip for multimodal comprehension and generation.arXiv preprint arXiv:2505.05422, 2025

work page arXiv 2025
[23]

Vtbench: Evaluating visual tokenizers for autoregressive image generation.arXiv preprint arXiv:2505.13439, 2025

Huawei Lin, Tong Geng, Zhaozhuo Xu, and Weijie Zhao. Vtbench: Evaluating visual tokenizers for autoregressive image generation.arXiv preprint arXiv:2505.13439, 2025

work page arXiv 2025
[24]

Glyph-byt5: A customized text encoder for accurate visual text rendering

Zeyu Liu, Weicong Liang, Zhanhao Liang, Chong Luo, Ji Li, Gao Huang, and Yuhui Yuan. Glyph-byt5: A customized text encoder for accurate visual text rendering. InEuropean Conference on Computer Vision, pages 361–377. Springer, 2024

2024
[25]

Decoupled Weight Decay Regularization

Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101, 2017

work page internal anchor Pith review Pith/arXiv arXiv 2017
[26]

Open-magvit2: An open- source project toward democratizing auto-regressive visual generation.arXiv preprint arXiv:2409.04410, 2024

Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open-magvit2: An open- source project toward democratizing auto-regressive visual generation.arXiv preprint arXiv:2409.04410, 2024

work page arXiv 2024
[27]

Unitok: A unified tokenizer for visual generation and understanding.arXiv preprint arXiv:2502.20321, 2025

Chuofan Ma, Yi Jiang, Junfeng Wu, Jihan Yang, Xin Yu, Zehuan Yuan, Bingyue Peng, and Xiaojuan Qi. Unitok: A unified tokenizer for visual generation and understanding.arXiv preprint arXiv:2502.20321, 2025

work page arXiv 2025
[28]

Magface: A universal representation for face recognition and quality assessment

Qiang Meng, Shichao Zhao, Zhida Huang, and Feng Zhou. Magface: A universal representation for face recognition and quality assessment. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14225–14234, 2021

2021
[29]

Finite scalar quantization: Vq-vae made simple.arXiv preprint arXiv:2309.15505, 2023

Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen. Finite scalar quantization: Vq-vae made simple.arXiv preprint arXiv:2309.15505, 2023

work page arXiv 2023
[30]

SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952, 2023

work page internal anchor Pith review Pith/arXiv arXiv 2023
[31]

Tokenflow: Unified image tokenizer for multimodal understanding and generation

Liao Qu, Huichao Zhang, Yiheng Liu, Xu Wang, Yi Jiang, Yiming Gao, Hu Ye, Daniel K Du, Zehuan Yuan, and Xinglong Wu. Tokenflow: Unified image tokenizer for multimodal understanding and generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 2545–2555, 2025

2025
[32]

Generating diverse high-fidelity images with vq-vae-2

Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. Advances in neural information processing systems, 32, 2019

2019
[33]

Ocr-vqgan: Taming text-within-image generation

Juan A Rodriguez, David Vazquez, Issam Laradji, Marco Pedersoli, and Pau Rodriguez. Ocr-vqgan: Taming text-within-image generation. InProceedings of the IEEE/CVF winter conference on applications of computer vision, pages 3689–3698, 2023

2023
[34]

Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022

Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022

2022
[35]

Faceid-gan: Learning a symmetry three-player gan for identity-preserving face synthesis

Yujun Shen, Ping Luo, Junjie Yan, Xiaogang Wang, and Xiaoou Tang. Faceid-gan: Learning a symmetry three-player gan for identity-preserving face synthesis. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 821–830, 2018. 11

2018
[36]

Scalable image tokenization with index backpropagation quantization

Fengyuan Shi, Zhuoyan Luo, Yixiao Ge, Yujiu Yang, Ying Shan, and Limin Wang. Scalable image tokenization with index backpropagation quantization. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 16037–16046, 2025

2025
[37]

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autore- gressive model beats diffusion: Llama for scalable image generation.arXiv preprint arXiv:2406.06525, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[38]

Chameleon: Mixed-Modal Early-Fusion Foundation Models

Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models.arXiv preprint arXiv:2405.09818, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[39]

Visual autoregressive modeling: Scalable image generation via next-scale prediction.Advances in neural information processing systems, 37:84839–84865, 2024

Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction.Advances in neural information processing systems, 37:84839–84865, 2024

2024
[40]

Doctr: a unifying framework for tracking physical documents and organisational structures

Sandra Trullemans, Ayrton Vercruysse, and Beat Signer. Doctr: a unifying framework for tracking physical documents and organisational structures. InProceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pages 85–96, 2016

2016
[41]

Regularizing generative adversarial networks under limited data

Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, and Weilong Yang. Regularizing generative adversarial networks under limited data. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7921–7931, 2021

2021
[42]

Anytext: Multilingual visual text generation and editing.arXiv preprint arXiv:2311.03054, 2023

Yuxiang Tuo, Wangmeng Xiang, Jun-Yan He, Yifeng Geng, and Xuansong Xie. Anytext: Multilingual visual text generation and editing.arXiv preprint arXiv:2311.03054, 2023

work page arXiv 2023
[43]

Neural discrete representation learning.Advances in neural information processing systems, 30, 2017

Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning.Advances in neural information processing systems, 30, 2017

2017
[44]

Attention is all you need.Advances in neural information processing systems, 30, 2017

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need.Advances in neural information processing systems, 30, 2017

2017
[45]

The attraction of visual attention to texts in real-world scenes

Hsueh-Cheng Wang and Marc Pomplun. The attraction of visual attention to texts in real-world scenes. Journal of vision, 12(6):26–26, 2012

2012
[46]

Instantid: Zero-shot identity-preserving generation in seconds.arXiv preprint arXiv:2401.07519, 2024

Qixun Wang, Xu Bai, Haofan Wang, Zekui Qin, Anthony Chen, Huaxia Li, Xu Tang, and Yao Hu. Instantid: Zero-shot identity-preserving generation in seconds.arXiv preprint arXiv:2401.07519, 2024

work page arXiv 2024
[47]

Emu3: Next-Token Prediction is All You Need

Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need.arXiv preprint arXiv:2409.18869, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[48]

Maskbit: Embedding-free image generation via bit tokens.Transactions on Machine Learning Research

Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. Maskbit: Embedding-free image generation via bit tokens.Transactions on Machine Learning Research
[49]

Tokbench: Evaluating your visual tokenizer before visual generation.arXiv preprint arXiv:2505.18142, 2025

Junfeng Wu, Dongliang Luo, Weizhi Zhao, Zhihao Xie, Yuanhao Wang, Junyi Li, Xudong Xie, Yuliang Liu, and Xiang Bai. Tokbench: Evaluating your visual tokenizer before visual generation.arXiv preprint arXiv:2505.18142, 2025

work page arXiv 2025
[50]

Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation.arXiv preprint arXiv:2408.12528, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[51]

Lumina-mgpt 2.0: Stand-alone autoregressive image modeling.arXiv preprint arXiv:2507.17801, 2025

Yi Xin, Juncheng Yan, Qi Qin, Zhen Li, Dongyang Liu, Shicheng Li, Victor Shea-Jay Huang, Yupeng Zhou, Renrui Zhang, Le Zhuo, et al. Lumina-mgpt 2.0: Stand-alone autoregressive image modeling.arXiv preprint arXiv:2507.17801, 2025

work page arXiv 2025
[52]

Echo-4o: Harnessing the power of gpt-4o synthetic images for improved image generation.arXiv preprint arXiv:2508.09987, 2025

Junyan Ye, Dongzhi Jiang, Zihao Wang, Leqi Zhu, Zhenghao Hu, Zilong Huang, Jun He, Zhiyuan Yan, Jinghua Yu, Hongsheng Li, et al. Echo-4o: Harnessing the power of gpt-4o synthetic images for improved image generation.arXiv preprint arXiv:2508.09987, 2025

work page arXiv 2025
[53]

Vector-quantized image modeling with improved vqgan.arXiv preprint arXiv:2110.04627, 2021

Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. Vector-quantized image modeling with improved vqgan.arXiv preprint arXiv:2110.04627, 2021

work page arXiv 2021
[54]

Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Vighnesh Birodkar, Agrim Gupta, Xiuye Gu, et al. Language model beats diffusion–tokenizer is key to visual generation.arXiv preprint arXiv:2310.05737, 2023. 12

work page internal anchor Pith review Pith/arXiv arXiv 2023
[55]

An image is worth 32 tokens for reconstruction and generation.Advances in Neural Information Processing Systems, 37:128940–128966, 2024

Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen. An image is worth 32 tokens for reconstruction and generation.Advances in Neural Information Processing Systems, 37:128940–128966, 2024

2024
[56]

The unreasonable effectiveness of deep features as a perceptual metric

Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 586–595, 2018

2018
[57]

Vision foundation models as effective visual tokenizers for autoregressive image generation

Anlin Zheng, Xin Wen, Xuanyang Zhang, Chuofan Ma, Tiancai Wang, Gang Yu, Xiangyu Zhang, and Xiaojuan Qi. Vision foundation models as effective visual tokenizers for autoregressive image generation. arXiv preprint arXiv:2507.08441, 2025

work page arXiv 2025
[58]

General facial representation learning in a visual-linguistic manner

Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in a visual-linguistic manner. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18697–18709, 2022

2022
[59]

Scaling the codebook size of vq-gan to 100,000 with a utilization rate of 99%.Advances in Neural Information Processing Systems, 37:12612–12635, 2024

Lei Zhu, Fangyun Wei, Yanye Lu, and Dong Chen. Scaling the codebook size of vq-gan to 100,000 with a utilization rate of 99%.Advances in Neural Information Processing Systems, 37:12612–12635, 2024

2024
[60]

s” (T-ACCs and T-NEDs) are averaged over small instances, while metrics with the subscript “m

Yongxin Zhu, Bocheng Li, Yifei Xin, Zhihua Xia, and Linli Xu. Addressing representation collapse in vector quantized models with one linear layer. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 22968–22977, 2025. A Limitations and Broader Impact Limitations.The proposed approach is designed to enhance reconstruction quali...

2025