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From the 1 of 18 linked papers with an AI index.

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20242026
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cs.CV2026

PixelPrune: Pixel-Level Adaptive Visual Token Reduction via Predictive Coding

Nan Wang, Zhiwei Jin, Chen Chen +1

Document understanding and GUI interaction are among the highest-value applications of Vision-Language Models (VLMs), yet they impose exceptionally heavy computational burden: fine…

cs.CV2026

Pluggable Pruning with Contiguous Layer Distillation for Diffusion Transformers

Jian Ma, Qirong Peng, Xujie Zhu +3

Diffusion Transformers (DiTs) have shown exceptional performance in image generation, yet their large parameter counts incur high computational costs, impeding deployment in resour…

cs.CV2025

X2Edit: Revisiting Arbitrary-Instruction Image Editing through Self-Constructed Data and Task-Aware Representation Learning

Jian Ma, Xujie Zhu, Zihao Pan +4

Existing open-source datasets for arbitrary-instruction image editing remain suboptimal, while a plug-and-play editing module compatible with community-prevalent generative models…

cs.CV2025

X2I: Seamless Integration of Multimodal Understanding into Diffusion Transformer via Attention Distillation

Jian Ma, Qirong Peng, Xu Guo +3

Text-to-image (T2I) models are well known for their ability to produce highly realistic images, while multimodal large language models (MLLMs) are renowned for their proficiency in…

cs.CV2025

LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models

Dingkun Zhang, Sijia Li, Chen Chen +2

In the era of AIGC, the demand for low-budget or even on-device applications of diffusion models emerged. In terms of compressing the Stable Diffusion models (SDMs), several approa…

cs.CV2025

SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation

Hongjian Liu, Qingsong Xie, TianXiang Ye +6

The iterative sampling procedure employed by diffusion models (DMs) often leads to significant inference latency. To address this, we propose Stochastic Consistency Distillation (S…