iFSQ：通过一行代码改进图像生成的FSQ
iFSQ: Improving FSQ for Image Generation with 1 Line of Code
AI总结：
iFSQ通过一行代码改进图像生成的FSQ，解决了重建保真度与信息效率的平衡问题，并揭示了离散与连续表示的最佳平衡点及AR与扩散模型的性能差异。
摘要：
图像生成领域目前分为两类：基于离散令牌的自回归（AR）模型和利用连续潜在变量的扩散模型。这种分裂源于VQ-VAEs与VAEs的区别，阻碍了统一建模和公平基准测试。有限标量量化（FSQ）提供了一个理论桥梁，但原始FSQ存在关键缺陷：其等间隔量化可能导致激活崩溃。这种不匹配迫使在重建保真度和信息效率之间做出取舍。在本工作中，我们通过简单地将原始FSQ中的激活函数替换为分布匹配映射来解决这一困境，以强制采用均匀先验。称为iFSQ，这种简单策略只需一行代码，却在数学上保证了最优的bin利用和重建精度。利用iFSQ作为受控基准，我们揭示了两个关键见解：（1）离散和连续表示之间的最佳平衡大约为每维度4位。（2）在相同的重建约束下，AR模型表现出快速的初始收敛，而扩散模型达到更优的性能上限，这表明严格的序列顺序可能限制了生成质量的上限。最后，我们通过将表示对齐（REPA）适应到AR模型中，得到LlamaGen-REPA。代码可在https://github.com/Tencent-Hunyuan/iFSQ获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
