iFSQ: Improving FSQ for Image Generation with 1 Line of Code

• URL: http://arxiv.org/abs/2601.17124v2
• Date: Tue, 27 Jan 2026 13:16:28 GMT
• Title: iFSQ: Improving FSQ for Image Generation with 1 Line of Code
• Authors: Bin Lin, Zongjian Li, Yuwei Niu, Kaixiong Gong, Yunyang Ge, Yunlong Lin, Mingzhe Zheng, JianWei Zhang, Miles Yang, Zhao Zhong, Liefeng Bo, Li Yuan,
• Abstract summary: We show how to replace the activation function in FSQ with a distribution-matching mapping to enforce a uniform prior.<n>This simple strategy requires just one line of code yet mathematically guarantees both optimal bin utilization and reconstruction precision.<n>We extend our analysis by adapting Representation Alignment (REPA) to AR models, yielding LlamaGen-REPA.
• Score: 40.61338660155903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of image generation is currently bifurcated into autoregressive (AR) models operating on discrete tokens and diffusion models utilizing continuous latents. This divide, rooted in the distinction between VQ-VAEs and VAEs, hinders unified modeling and fair benchmarking. Finite Scalar Quantization (FSQ) offers a theoretical bridge, yet vanilla FSQ suffers from a critical flaw: its equal-interval quantization can cause activation collapse. This mismatch forces a trade-off between reconstruction fidelity and information efficiency. In this work, we resolve this dilemma by simply replacing the activation function in original FSQ with a distribution-matching mapping to enforce a uniform prior. Termed iFSQ, this simple strategy requires just one line of code yet mathematically guarantees both optimal bin utilization and reconstruction precision. Leveraging iFSQ as a controlled benchmark, we uncover two key insights: (1) The optimal equilibrium between discrete and continuous representations lies at approximately 4 bits per dimension. (2) Under identical reconstruction constraints, AR models exhibit rapid initial convergence, whereas diffusion models achieve a superior performance ceiling, suggesting that strict sequential ordering may limit the upper bounds of generation quality. Finally, we extend our analysis by adapting Representation Alignment (REPA) to AR models, yielding LlamaGen-REPA. Codes is available at https://github.com/Tencent-Hunyuan/iFSQ

Related papers

• Answer First, Reason Later: Aligning Search Relevance via Mode-Balanced Reinforcement Learning [7.006180736433431]
  Building a search relevance model that achieves both low latency and high performance is a long-standing challenge in the search industry.<n>We propose a novel textbfAnswer-First, Reason Later (AFRL) paradigm.<n>This paradigm requires the model to output the definitive relevance score in the very first token, followed by a structured logical explanation.
  arXiv  Detail & Related papers  (2026-02-10T17:28:12Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• Robust Residual Finite Scalar Quantization for Neural Compression [46.574899938569125]
  Finite Scalar Quantization (FSQ) offers simplified training but suffers from residual magnitude decay in multi-stage settings.<n>We propose Robust Residual Finite Scalar Quantization (RFSQ), addressing this fundamental limitation through two novel conditioning strategies.<n>Our experiments across audio and image modalities demonstrate RFSQ's effectiveness and generalizability.
  arXiv  Detail & Related papers  (2025-08-20T15:18:59Z)
• LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter [52.93785843453579]
  Blind face restoration from low-quality (LQ) images is a challenging task that requires high-fidelity image reconstruction and the preservation of facial identity.<n>We propose LAFR, a novel codebook-based latent space adapter that aligns the latent distribution of LQ images with that of HQ counterparts.<n>We show that lightweight finetuning of diffusion prior on just 0.9% of FFHQ dataset is sufficient to achieve results comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-05-29T14:11:16Z)
• Fast Autoregressive Models for Continuous Latent Generation [49.079819389916764]
  Autoregressive models have demonstrated remarkable success in sequential data generation, particularly in NLP.<n>Recent work, the masked autoregressive model (MAR) bypasses quantization by modeling per-token distributions in continuous spaces using a diffusion head.<n>We propose Fast AutoRegressive model (FAR), a novel framework that replaces MAR's diffusion head with a lightweight shortcut head.
  arXiv  Detail & Related papers  (2025-04-24T13:57:08Z)
• Frequency Autoregressive Image Generation with Continuous Tokens [31.833852108014312]
  We introduce the frequency progressive autoregressive (textbfFAR) paradigm and instantiate FAR with the continuous tokenizer.<n>We demonstrate the efficacy of FAR through comprehensive experiments on the ImageNet dataset.
  arXiv  Detail & Related papers  (2025-03-07T10:34:04Z)
• COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement [80.18490952057125]
  Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of large language models (LLMs) on complex tasks. We propose Context-Wise Order-Agnostic Language Modeling (COrAL) to overcome these challenges. Our approach models multiple token dependencies within manageable context windows, enabling the model to perform iterative refinement internally.
  arXiv  Detail & Related papers  (2024-10-12T23:56:19Z)
• You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment [45.62136459502005]
  We propose a network to perform full reference (FR) and no reference (NR) IQA. We first employ an encoder to extract multi-level features from input images. A Hierarchical Attention (HA) module is proposed as a universal adapter for both FR and NR inputs. A Semantic Distortion Aware (SDA) module is proposed to examine feature correlations between shallow and deep layers of the encoder.
  arXiv  Detail & Related papers  (2023-10-14T11:03:04Z)
• Feature Quantization Improves GAN Training [126.02828112121874]
  Feature Quantization (FQ) for the discriminator embeds both true and fake data samples into a shared discrete space. Our method can be easily plugged into existing GAN models, with little computational overhead in training.
  arXiv  Detail & Related papers  (2020-04-05T04:06:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.