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E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling

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Yayımlandı:arXiv.org (Dec 19, 2024), p. n/a
Yazar: Yuan, Zhihang
Diğer Yazarlar: Shang, Yuzhang, Zhang, Hanling, Tongcheng Fang, Xie, Rui, Xu, Bingxin, Yan, Yan, Shengen Yan, Dai, Guohao, Wang, Yu
Baskı/Yayın Bilgisi:
Cornell University Library, arXiv.org
Konular:
Parallel processing
Image quality
Image processing
Design factors
Noise reduction
Autoregressive processes
Flow mapping
Online Erişim:Citation/Abstract
Full text outside of ProQuest
Etiketler: Etiketle
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Özet:Recent advances in autoregressive (AR) models with continuous tokens for image generation show promising results by eliminating the need for discrete tokenization. However, these models face efficiency challenges due to their sequential token generation nature and reliance on computationally intensive diffusion-based sampling. We present ECAR (Efficient Continuous Auto-Regressive Image Generation via Multistage Modeling), an approach that addresses these limitations through two intertwined innovations: (1) a stage-wise continuous token generation strategy that reduces computational complexity and provides progressively refined token maps as hierarchical conditions, and (2) a multistage flow-based distribution modeling method that transforms only partial-denoised distributions at each stage comparing to complete denoising in normal diffusion models. Holistically, ECAR operates by generating tokens at increasing resolutions while simultaneously denoising the image at each stage. This design not only reduces token-to-image transformation cost by a factor of the stage number but also enables parallel processing at the token level. Our approach not only enhances computational efficiency but also aligns naturally with image generation principles by operating in continuous token space and following a hierarchical generation process from coarse to fine details. Experimental results demonstrate that ECAR achieves comparable image quality to DiT Peebles & Xie [2023] while requiring 10\(\times\) FLOPs reduction and 5\(\times\) speedup to generate a 256\(\times\)256 image.
ISSN:2331-8422
Kaynak:Engineering Database