# Parallelized Autoregressive Visual Generation

arXiv: https://arxiv.org/abs/2412.15119 · HTML: https://mlsanity.com/paper/2412.15119/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 74 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 30 of 57)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [MaskGIT: Masked Generative Image Transformer](https://mlsanity.com/paper/2202.04200/) (2022)
- [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://mlsanity.com/paper/2404.02905/) (2024)
- [Accelerating Large Language Model Decoding with Speculative Sampling](https://mlsanity.com/paper/2302.01318/) (2023)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Chameleon: Mixed-Modal Early-Fusion Foundation Models](https://mlsanity.com/paper/2405.09818/) (2024)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)

## Built on by (most cited first, 30 of 60)

- [Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation](https://mlsanity.com/paper/2506.09350/) (2025)
- [Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2505.02567/) (2025)
- [Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation](https://mlsanity.com/paper/2502.20388/) (2025)
- [GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning](https://mlsanity.com/paper/2505.17022/) (2025)
- [Neighboring Autoregressive Modeling for Efficient Visual Generation](https://mlsanity.com/paper/2503.10696/) (2025)
- [Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation](https://mlsanity.com/paper/2503.16430/) (2025)
- [Frequency Autoregressive Image Generation with Continuous Tokens](https://mlsanity.com/paper/2503.05305/) (2025)
- [Next Block Prediction: Video Generation via Semi-Autoregressive Modeling](https://mlsanity.com/paper/2502.07737/) (2025)
- [Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation](https://mlsanity.com/paper/2508.03334/) (2025)
- [Autoregressive Image Generation with Randomized Parallel Decoding](https://mlsanity.com/paper/2503.10568/) (2025)
- [DetailFlow: 1D Coarse-to-Fine Autoregressive Image Generation via Next-Detail Prediction](https://mlsanity.com/paper/2505.21473/) (2025)
- [Grouped Speculative Decoding for Autoregressive Image Generation](https://mlsanity.com/paper/2508.07747/) (2025)
- [DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer](https://mlsanity.com/paper/2507.04947/) (2025)
- [MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization](https://mlsanity.com/paper/2504.00999/) (2025)
- [Image Tokenizer Needs Post-Training](https://mlsanity.com/paper/2509.12474/) (2025)
- [Towards Better & Faster Autoregressive Image Generation: From the Perspective of Entropy](https://mlsanity.com/paper/2510.09012/) (2025)
- [LazyMAR: Accelerating Masked Autoregressive Models via Feature Caching](https://mlsanity.com/paper/2503.12450/) (2025)
- [UniFork: Exploring Modality Alignment for Unified Multimodal Understanding and Generation](https://mlsanity.com/paper/2506.17202/) (2025)
- [Robust Latent Matters: Boosting Image Generation with Sampling Error Synthesis](https://mlsanity.com/paper/2503.08354/) (2025)
- [FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge](https://mlsanity.com/paper/2505.14709/) (2025)
- [Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation](https://mlsanity.com/paper/2507.01957/) (2025)
- [Autoregressive Image Generation with Masked Bit Modeling](https://mlsanity.com/paper/2602.09024/) (2026)
- [Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation](https://mlsanity.com/paper/2509.15185/) (2025)
- [Speculative Jacobi-Denoising Decoding for Accelerating Autoregressive Text-to-image Generation](https://mlsanity.com/paper/2510.08994/) (2025)
- [VideoAR: Autoregressive Video Generation via Next-Frame & Scale Prediction](https://mlsanity.com/paper/2601.05966/) (2026)
- [Evolution of Video Generative Foundations](https://mlsanity.com/paper/2604.06339/) (2026)
- [DiCo: Revitalizing ConvNets for Scalable and Efficient Diffusion Modeling](https://mlsanity.com/paper/2505.11196/) (2025)
- [Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis](https://mlsanity.com/paper/2507.01756/) (2025)
- [ResTok: Learning Hierarchical Residuals in 1D Visual Tokenizers for Autoregressive Image Generation](https://mlsanity.com/paper/2601.03955/) (2026)
- [Autoregressive Image Generation with Linear Complexity: A Spatial-Aware Decay Perspective](https://mlsanity.com/paper/2507.01652/) (2025)

## Abstract (arXiv)

Autoregressive models have emerged as a powerful approach for visual generation but suffer from slow inference speed due to their sequential token-by-token prediction process. In this paper, we propose a simple yet effective approach for parallelized autoregressive visual generation that improves generation efficiency while preserving the advantages of autoregressive modeling. Our key insight is that parallel generation depends on visual token dependencies-tokens with weak dependencies can be generated in parallel, while strongly dependent adjacent tokens are difficult to generate together, as their independent sampling may lead to inconsistencies. Based on this observation, we develop a parallel generation strategy that generates distant tokens with weak dependencies in parallel while maintaining sequential generation for strongly dependent local tokens. Our approach can be seamlessly integrated into standard autoregressive models without modifying the architecture or tokenizer. Experiments on ImageNet and UCF-101 demonstrate that our method achieves a 3.6x speedup with comparable quality and up to 9.5x speedup with minimal quality degradation across both image and video generation tasks. We hope this work will inspire future research in efficient visual generation and unified autoregressive modeling. Project page: https://yuqingwang1029.github.io/PAR-project.