Pith sign in

REVIEW 9 cited by

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.01699 v2 pith:DCZW2CXP submitted 2024-10-02 cs.CV

Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding

Yao Teng , Han Shi , Xian Liu , Xuefei Ning , Guohao Dai , Yu Wang , Zhenguo Li , Xihui Liu This is my paper
classification cs.CV
keywords decodingauto-regressivegenerationtext-to-imagejacobimodelcriteriongenerate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

The current large auto-regressive models can generate high-quality, high-resolution images, but these models require hundreds or even thousands of steps of next-token prediction during inference, resulting in substantial time consumption. In existing studies, Jacobi decoding, an iterative parallel decoding algorithm, has been used to accelerate the auto-regressive generation and can be executed without training. However, the Jacobi decoding relies on a deterministic criterion to determine the convergence of iterations. Thus, it works for greedy decoding but is incompatible with sampling-based decoding which is crucial for visual quality and diversity in the current auto-regressive text-to-image generation. In this paper, we propose a training-free probabilistic parallel decoding algorithm, Speculative Jacobi Decoding (SJD), to accelerate auto-regressive text-to-image generation. By introducing a probabilistic convergence criterion, our SJD accelerates the inference of auto-regressive text-to-image generation while maintaining the randomness in sampling-based token decoding and allowing the model to generate diverse images. Specifically, SJD facilitates the model to predict multiple tokens at each step and accepts tokens based on the probabilistic criterion, enabling the model to generate images with fewer steps than the conventional next-token-prediction paradigm. We also investigate the token initialization strategies that leverage the spatial locality of visual data to further improve the acceleration ratio under specific scenarios. We conduct experiments for our proposed SJD on multiple auto-regressive text-to-image generation models, showing the effectiveness of model acceleration without sacrificing the visual quality. The code of our work is available here: https://github.com/tyshiwo1/Accelerating-T2I-AR-with-SJD/.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Where to Refine, When to Stop: Rethinking Redundancy via Latent Discrepancy for Efficient Visual Autoregressive Generation

    cs.CV 2026-05 unverdicted novelty 7.0

    LD-Pruning applies latent discrepancy to prune tokens and adaptively skip unconditional branches in VAR models for up to 2.35x faster inference with preserved quality.

  2. NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization

    cs.LG 2026-04 unverdicted novelty 7.0

    NI Sampling accelerates discrete diffusion language models up to 14.3 times by training a neural indicator to select which tokens to sample at each step using a trajectory-preserving objective.

  3. FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation

    cs.CV 2026-05 unverdicted novelty 6.0

    FlashAR achieves up to 22.9x speedup in 512x512 autoregressive image generation by post-training a pre-trained model with a complementary vertical head and dynamic fusion using only 0.05% of original training data.

  4. FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation

    cs.CV 2026-05 unverdicted novelty 6.0

    FlashAR accelerates autoregressive image generation up to 22.9x by post-training a pre-trained raster-scan model with a complementary vertical head and dynamic fusion for two-way next-token prediction.

  5. CASCADE: Context-Aware Relaxation for Speculative Image Decoding

    cs.CV 2026-05 unverdicted novelty 6.0

    CASCADE formalizes semantic interchangeability and convergence in target model representations to enable context-aware acceptance relaxation in tree-based speculative decoding, delivering up to 3.6x speedup on text-to...

  6. Visual Implicit Autoregressive Modeling

    cs.CV 2026-05 unverdicted novelty 6.0

    VIAR embeds implicit equilibrium layers in visual autoregressive models to achieve ImageNet FID 2.16 with 38.4% of VAR parameters and controllable inference compute.

  7. Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation

    cs.CV 2025-10 unverdicted novelty 6.0

    Speculative Coupled Decoding stabilizes draft sampling in Speculative Jacobi Decoding via an information-theoretic coupling step, delivering up to 4.2x image and 13.6x video speedups with no quality loss or training.

  8. CSD: Content-aware Speculative Decoding for Efficient Image Generation

    cs.CV 2026-06 unverdicted novelty 4.0

    CSD adds content-aware entropy relaxation and a distribution alignment filter to speculative decoding, raising acceptance rates in low-detail image areas while keeping output aligned with the target model.

  9. Cosmos World Foundation Model Platform for Physical AI

    cs.CV 2025-01 unverdicted novelty 3.0

    The Cosmos platform supplies open-source pre-trained world models and supporting tools for building fine-tunable digital world simulations to train Physical AI.