HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing
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-adaptiv...
| Authors | Haoran You, Yotam Nitzan, Lingzhi Zhang et al. |
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| Primary category | cs.CV |
| Published | 11 June 2026 |
| Last updated at source | 10 October 2026 |
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