LOWN：基于证书引导的视觉路由高效扩散视觉语言模型
Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs
LOWN在扩散视觉语言模型解码时自适应跳过图像处理，当预测一致时减少计算量同时保持准确率。由于视觉标记在所研究基准中占89-97%输入，大多数解码计算用于重复处理相同图像。
Abstract
LOWN adaptively skips image processing during diffusion vision-language model decoding when predictions agree, reducing computation while preserving accuracy.
Diffusion vision-language models (DVLMs) process their entire input at every denoising step. Since visual tokens make up 89-97% of this input on the benchmarks we study, most of the decoding computation is spent repeatedly processing the same image. Visual token pruning methods partially relieve this burden by removing part of the visual tokens, but they lose the removed information and still process the remaining image at every step. To measure how often the image is actually needed, we compare LLaDA-V's predictions with and without the image at every decoding step and find that removing the image changes a token being unmasked at only 22.8% of steps on average and at as few as 7.9%. We therefore propose LOWN, a decoding method that decides at every step whether to process the image and how many tokens to unmask. LOWN predicts the masked tokens without the image and processes the image only when this prediction disagrees with the last image-conditioned prediction. Agreement certifies the earlier prediction for reuse, and the number of certified tokens sets how many are unmasked in that step. We further observe that the need for the image differs across benchmarks, so LOWN's adaptive decision is needed rather than a fixed schedule. Without training or a tuned threshold, LOWN retains 99.6% of LLaDA-V's score on ten benchmarks at 35.3% of the decoding FLOPs on average. LOWN also generalizes across LLaDA-based DVLMs, composes orthogonally with visual token pruning, and runs under key-value caching for further efficiency gains. Code and project page are available at https://hyeon9698.github.io/LOWN/
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来源: Hugging Face Daily Papers · huggingface.co