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Qirong Peng

4 accepted papers

2026

Pluggable Pruning with Contiguous Layer Distillation for Diffusion Transformers

CVPR 2026

Diffusion Transformers (DiTs) have shown exceptional performance in image generation, yet their large parameter counts incur high computational costs, impeding deployment in resource-constrained settings. To address this, we propose Pluggable Pruning with Contiguous Layer Distillation (PPCL), a flex

Cited by 0SourcecodeScholar
2026

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

AAAI 2026technical

Existing open-source datasets for arbitrary-instruction image editing remain suboptimal, while a plug-and-play editing module compatible with community-prevalent generative models is notably absent. In this paper, we first introduce the X2Edit Dataset, a comprehensive dataset covering 14 diverse edi

Cited by 0SourcePDFScholar
2025

CritiPrefill: A Segment-wise Criticality-based Approach for Prefilling Acceleration in LLMs

ICASSP 2025accepted

Large language models have achieved notable success across various domains, yet efficient inference is still limited by the quadratic computation complexity of the attention mechanism. The inference consists of prefilling and decoding phases. Although several attempts have been made to accelerate de…

Cited by 0SourceScholar
2025

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

ICCV 2025poster

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 understanding and integrating multiple modalities. However, currently there is no straightforward and efficient framewo…