NesTok: Nested Self-Aligned 1D Tokenizer for Autoregressive Image Generation

arXiv:2609.36756 · cs.CV, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-29

Updated: 2026-10-01

Code: https://github.com/Jiawei804/NesTok

Terminology

• Alignment — 26× in this paper · explained in 1 episode
• Tokenization — 14× in this paper · explained in 2 episodes
• Distillation — 8× in this paper · explained in 2 episodes
• Quantization — 8× in this paper · explained in 12 episodes
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Transformer — 7× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Consistency — 4× in this paper · explained in 3 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 3× in this paper
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Latent Representations — 3× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer
• Improving Flexible Image Tokenizers for Autoregressive Image Generation
• REAR: Rethinking Visual Autoregressive Models via Generator-Tokenizer Consistency Regularization
• Auto-Encoding Variational Bayes
• DetailFlow: 1D Coarse-to-Fine Autoregressive Image Generation via Next-Detail Prediction
• One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression
• Semantic One-Dimensional Tokenizer for Image Reconstruction and Generation
• Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
• Chameleon: Mixed-Modal Early-Fusion Foundation Models
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
• MacTok: Robust Continuous Tokenization for Image Generation
• ResTok: Learning Hierarchical Residuals in 1D Visual Tokenizers for Autoregressive Image Generation

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