HiLo-Token：面向高效图像编辑的输入自适应高低频 Token 压缩

Authors:Haoran You, Yotam Nitzan, Lingzhi Zhang, Yifan Gong, Mang-Tik Chiu, Connelly Barnes, Yan Kang, Yuqian Zhou, Haitian Zheng, Eli Shechtman, Sohrab Amirghodsi

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Abstract:Creative image editing tools, such as Adobe Photoshop's Remove and Generative Fill buttons, are central to everyday customer use and account for a major share of traffic. However, current generative AI models face significant latency challenges, which become even more pronounced as the field transitions from convolution-based U-Nets to Diffusion Transformers (DiTs). In our evaluation on hundreds of representative image editing samples spanning a wide range of mask ratios, the DiT module alone accounts for an average of 73% of the total model latency, even after being distilled from 50 timesteps down to 8 timesteps. To tackle this challenge, we propose \textbf{HiLo-Token}, an input-adaptive token compression framework that allocates a larger token budget to high-frequency, rich-context regions while assigning fewer tokens to low-frequency areas. Specifically, for the editing region specified by the user mask, we retain all tokens within a dilated mask to preserve strong locality and contextual relevance. Outside the editing region, we introduce a simple yet effective high-frequency token selection strategy based on spatial frequency to capture important local details, while using tokens from a heavily downsampled image to represent low-frequency components and preserve the coarse global structure. Extensive experiments on production-level evaluation data validate the effectiveness of the proposed method, which achieves 3.13$\times$, 2.59$\times$, and 1.67$\times$ DiT speedups on A100-80GB for image editing tasks across small, medium, and large mask ratio categories with average ratios of 6.38%, 15.92%, and 35.36%, respectively, without any regression in generation quality. In production, where the editing pipeline is served as disaggregated, independently autoscaled GPU workers, these per-request savings translate into a 33% reduction in the GPUs required to serve the models.

Submission history

From: Haoran You [view email]
[v1] Thu, 11 Jun 2026 20:45:26 UTC (19,097 KB)
[v2] Fri, 18 Sep 2026 18:33:32 UTC (19,127 KB)
[v3] Sat, 10 Oct 2026 17:55:26 UTC (18,979 KB)