6 papers · 1 filter
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…
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…