13 papers
ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs
Zizhong Ding, Junxian Li, Kai Liu +4
Visual token pruning reduces the inference cost of multimodal large language models, but a fixed token ratio is poorly matched to text-rich inputs. In OCR-centric tasks, decisive e…
FOCUS: FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling
Xianglong Yan, Hong Liu, Chengzhu Bao +4
Large language models (LLMs) achieve remarkable performance but are expensive to deploy due to their enormous size. FP4 quantization, with formats such as MXFP4 and NVFP4, offers a…
WorldDynCache: Risk-Controlled Latent Dynamics Approximation for Diffusion World Model
Leyang Chen, Junyi Wu, Shaoqiu Zhang +1
Diffusion world models generate high-quality futures, but re- peated transformer evaluations make inference prohibitively slow. Existing caches reuse intermediate features, selecti…
FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing
Junyi Wu, Zhiteng Li, Haotong Qin +2
Text-guided image editing with diffusion models has achieved remarkable quality but often suffers from prohibitive latency. We introduce \textbf{FlashEdit}, a real-time localized i…
Quantized Visual Geometry Grounded Transformer
Weilun Feng, Haotong Qin, Mingqiang Wu +8
Learning-based 3D reconstruction models, represented by Visual Geometry Grounded Transformers (VGGTs), have made remarkable progress with the use of large-scale transformers. Their…
AdaTSQ: Pushing the Pareto Frontier of Diffusion Transformers via Temporal-Sensitivity Quantization
Shaoqiu Zhang, Zizhong Ding, Kaicheng Yang +6
Diffusion Transformers (DiTs) have emerged as the state-of-the-art backbone for high-fidelity image and video generation. However, their massive computational cost and memory footp…