Pith. sign in

REVIEW 30 cited by

Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

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 2412.04431 v2 pith:DDTFBREX submitted 2024-12-05 cs.CV

Jian Han , Jinlai Liu , Yi Jiang , Bin Yan , Yuqi Zhang , Zehuan Yuan , Bingyue Peng , Xiaobing Liu This is my paper · ORCID
classification cs.CV
keywords infinityautoregressivebitwisescalingmodelingmodelssd3-mediumtokenizer
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present Infinity, a Bitwise Visual AutoRegressive Modeling capable of generating high-resolution, photorealistic images following language instruction. Infinity redefines visual autoregressive model under a bitwise token prediction framework with an infinite-vocabulary tokenizer & classifier and bitwise self-correction mechanism, remarkably improving the generation capacity and details. By theoretically scaling the tokenizer vocabulary size to infinity and concurrently scaling the transformer size, our method significantly unleashes powerful scaling capabilities compared to vanilla VAR. Infinity sets a new record for autoregressive text-to-image models, outperforming top-tier diffusion models like SD3-Medium and SDXL. Notably, Infinity surpasses SD3-Medium by improving the GenEval benchmark score from 0.62 to 0.73 and the ImageReward benchmark score from 0.87 to 0.96, achieving a win rate of 66%. Without extra optimization, Infinity generates a high-quality 1024x1024 image in 0.8 seconds, making it 2.6x faster than SD3-Medium and establishing it as the fastest text-to-image model. Models and codes will be released to promote further exploration of Infinity for visual generation and unified tokenizer modeling.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 30 Pith papers

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

  1. Generative Refinement Networks for Visual Synthesis

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    Hierarchical Binary Quantization plus global refinement AR yields 0.56 rFID reconstruction and 1.81 gFID class-conditional generation on ImageNet, with competitive T2I/T2V at 2B scale.

  2. FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL

    cs.CV 2025-06 conditional novelty 7.0 of 10

    FocusDiff improves autoregressive text-to-image generation by training on paired similar prompts with a modified GRPO objective, achieving state-of-the-art alignment on PairComp and gains on GenEval and T2I-CompBench.

  3. Progressive Checkerboards for Autoregressive Multiscale Image Generation

    cs.CV 2026-02 conditional novelty 6.0 of 10

    A balanced multiscale checkerboard sampling order for autoregressive image generation allows large scale-up factors without quality loss, because only the total number of serial steps matters.

  4. Scalable GANs with Transformers

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A transformer-only GAN trained in VAE latent space with multi-level noise supervision and width-scaled learning rates achieves FID 2.96 on ImageNet-256 in 40 epochs.

  5. A Unified Low-level Foundation Model for Enhancing Pathology Image Quality

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A prompt-guided diffusion model pretrained on 190 million pathology patches outperforms task-specific models across most restoration and virtual staining benchmarks.

  6. Localizing and Mitigating Memorization in Image Autoregressive Models

    cs.LG 2025-08 conditional novelty 6.0 of 10

    Memorization in image autoregressive models sits in early blocks at coarse scales for VAR models and in middle/late blocks for RAR models; halving the flagged neurons' weights cuts extractable images by 65 to 84 percent.

  7. HPSv3: Towards Wide-Spectrum Human Preference Score

    cs.CV 2025-08 conditional novelty 6.0 of 10

    HPSv3, trained on the new 1.08M-pair HPDv3 dataset, reaches 76.9% pairwise preference accuracy on its own test set and Spearman 0.94 against human model rankings, and is used to iteratively refine generated images (CoHP).

  8. Implementing Adaptations for Vision AutoRegressive Model

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Fine-tuned Vision AutoRegressive models mostly beat a strong diffusion baseline on downstream image generation, but DP fine-tuning yields poor FID scores.

  9. FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A method for multi-subject image personalization that fuses independently trained LoRA modules at inference time on visual autoregressive models.

  10. Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis

    cs.CV 2025-07 conditional novelty 6.0 of 10

    DisCon treats discrete image tokens as conditioning signals rather than targets, letting a continuous autoregressive model refine details and reach gFID 1.38 on ImageNet-256.

  11. Transition Matching: Scalable and Flexible Generative Modeling

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Transition Matching unifies flow matching and continuous autoregressive generation as discrete-time Markov processes, with three variants that improve text-to-image quality and speed.

  12. Instella-T2I: Pushing the Limits of 1D Discrete Latent Space Image Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    1D binary image latents reduce a 1024x1024 image to 128 discrete tokens and support text-to-image generation with diffusion and autoregressive models.

  13. SpectralAR: Spectral Autoregressive Visual Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    An autoregressive image generator that tokenizes images in the DCT frequency domain into nested 1D spectral sequences and generates them coarse-to-fine, reaching 3.02 gFID with 64 tokens on ImageNet-1K.

  14. Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Fine-tuning Qwen-2.5-VL on the new FakeXplained dataset of 8,772 AI-generated images with box-and-caption artifact annotations yields an explainable detector with 98.1% accuracy and 37.8% IoU.

  15. HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    HMAR is an image generator that builds each resolution scale from the previous scale and refines it with masked prediction, matching or improving ImageNet FID/IS versus VAR with faster training and inference.

  16. Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack

    cs.CR 2025-06 conditional novelty 6.0 of 10

    LBW embeds watermarks into autoregressive image token maps by biasing token sampling toward a secret green list and detects them with a z-test on green-token counts.

  17. DiSA: Diffusion Step Annealing in Autoregressive Image Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Autoregressive image diffusion models can use far fewer denoising steps for later tokens without losing quality, yielding 1.4-2.5x speedup from step annealing and up to 10x when combined with fewer autoregressive steps.

  18. OmniGenBench: A Benchmark for Omnipotent Multimodal Generation across 50+ Tasks

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A 57-task benchmark for multimodal image generation that uses automated visual parsers and an LLM judge to show GPT-4o-Native leads current models.

  19. FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-design

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A post-training floating-point quantization framework with grouped Hadamard rotation and learned smoothing brings 4-bit visual autoregressive image generation to near-FP16 quality, plus a matching FPGA accelerator.

  20. UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    UniGen shows a 1.5B model trained on open data can beat larger systems on image understanding and generation once it verifies its own outputs with chain-of-thought and Best-of-N selection.

  21. AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings

    cs.CV 2025-05 conditional novelty 6.0 of 10

    AKRMap jointly learns a 2D projection and an adaptive kernel so contour maps of cross-modal metrics like CLIPScore and human preference scores become more accurate.

  22. Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance

    cs.CV 2025-09 conditional novelty 5.0 of 10

    IGG, an attention-based reweighting of classifier-free guidance, concentrates guidance on important tokens and modestly improves FID/IS in scale-wise autoregressive image generation.

  23. DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer

    cs.CV 2025-07 conditional novelty 5.0 of 10

    DC-AR generates 512x512 images in 12 masked autoregressive steps plus 20 diffusion refinement steps, using a 32x compressed 2D tokenizer, and reports gFID 5.49 on MJHQ-30K.

  24. CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation

    cs.CV 2025-06 conditional novelty 5.0 of 10

    CycleVAR adapts a pretrained visual autoregressive model to unpaired image translation using softmax-relaxed quantization and source-token prefixes, achieving FID scores competitive with CycleGAN-Turbo.

  25. DFBench: Benchmarking Deepfake Image Detection Capability of Large Multimodal Models

    cs.CV 2025-06 conditional novelty 5.0 of 10

    DFBench adds a 540,000-image benchmark with 12 modern generators, partial edits, and distorted real images, and its three-model LMM ensemble, MoA-DF, reaches near-perfect recall on its own test split.

  26. Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image Generation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A new benchmark and agent framework for complex text-to-image generation, with an unvalidated AI-judge evaluation and claims that the agent outperforms GPT-4o on the authors' own benchmark.

  27. Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization

    cs.CV 2025-05 conditional novelty 5.0 of 10

    GRPO fine-tuning with aesthetic and CLIP rewards raises VAR aesthetic scores by about one point and appears to produce painting-like images despite ImageNet pretraining.

  28. MolPIF: A Parameter Interpolation Flow Model for Molecule Generation

    cs.LG 2025-07 conditional novelty 4.0 of 10

    MolPIF generates 3D ligands by interpolating the parameters of Gaussian coordinate and Dirichlet atom-type distributions, reporting stronger docking scores and geometric fidelity than prior flow and diffusion models o...

  29. SkipVAR: Accelerating Visual Autoregressive Modeling via Adaptive Frequency-Aware Skipping

    cs.CV 2025-06 conditional novelty 4.0 of 10

    SkipVAR selects, per sample, between step skipping and unconditional branch replacement using handcrafted frequency features and a trained logistic regression, to accelerate visual autoregressive generation.

  30. Auto-Regressively Generating Multi-View Consistent Images

    cs.CV 2025-06

Pith tools