9 papers
SALAD: Achieve High-Sparsity Attention via Efficient Linear Attention Tuning for Video Diffusion Transformer
Tongcheng Fang, Hanling Zhang, Ruiqi Xie +8
Diffusion Transformers have demonstrated remarkable performance in video generation. However, their long input sequences incur substantial latency due to the quadratic complexity o…
DSA-Tokenizer: Disentangled Semantic-Acoustic Tokenization via Flow Matching-based Hierarchical Fusion
Hanlin Zhang, Daxin Tan, Dehua Tao +5
Speech tokenizers are a key building block of fully discrete Speech LLMs. Existing tokenizers either prioritize semantic encoding, fuse semantic content with acoustic style insepar…
Cache-to-Cache: Direct Semantic Communication Between Large Language Models
Tianyu Fu, Zihan Min, Hanling Zhang +4
Multi-LLM systems harness the complementary strengths of diverse Large Language Models, achieving performance and efficiency gains that are not attainable by a single model. In exi…
VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models
Hanling Zhang, Yayu Zhou, Tongcheng Fang +4
Small Language Models (SLMs) provide computational advantages in resource-constrained environments, yet memory limitations remain a critical bottleneck for edge device deployment.…
VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate
Zhihang Yuan, Rui Xie, Yuzhang Shang +5
Diffusion Transformer(DiT)-based generation models have achieved remarkable success in video generation. However, their inherent computational demands pose significant efficiency c…
DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation
Zhihang Yuan, Siyuan Wang, Rui Xie +6
In this paper, we propose the Dynamic Latent Frame Rate VAE (DLFR-VAE), a training-free paradigm that can make use of adaptive temporal compression in latent space. While existing…
DiTFastAttnV2: Head-wise Attention Compression for Multi-Modality Diffusion Transformers
Hanling Zhang, Rundong Su, Zhihang Yuan +5
Text-to-image generation models, especially Multimodal Diffusion Transformers (MMDiT), have shown remarkable progress in generating high-quality images. However, these models often…
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling
Zhihang Yuan, Yuzhang Shang, Hanling Zhang +7
Recent advances in autoregressive (AR) models with continuous tokens for image generation show promising results by eliminating the need for discrete tokenization. However, these m…
DiTFastAttn: Attention Compression for Diffusion Transformer Models
Zhihang Yuan, Hanling Zhang, Pu Lu +6
Diffusion Transformers (DiT) excel at image and video generation but face computational challenges due to the quadratic complexity of self-attention operators. We propose DiTFastAt…