5 citations · 20 across the 17 of their papers we have counts for
18 papers
FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models
Bowen Xue, Zihan Min, Xingyang Li +8
Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications. Howeve…
Flash-KMeans: Fast and Memory-Efficient Exact K-Means
Shuo Yang, Haocheng Xi, Yilong Zhao +10
-means has historically been positioned primarily as an offline processing primitive, typically used for dataset organization or embedding preprocessing rather than as a first-c…
Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization
Haocheng Xi, Shuo Yang, Yilong Zhao +13
Despite rapid progress in autoregressive video diffusion, an emerging system algorithm bottleneck limits both deployability and generation capability: KV cache memory. In autoregre…
SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer
Junsong Chen, Yuyang Zhao, Jincheng Yu +17
We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720x1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution,…
LongLive: Real-time Interactive Long Video Generation
Shuai Yang, Wei Huang, Ruihang Chu +9
We present LongLive, a frame-level autoregressive (AR) framework for real-time and interactive long video generation. Long video generation presents challenges in both efficiency a…
DC-VideoGen: Efficient Video Generation with Deep Compression Video Autoencoder
Junyu Chen, Wenkun He, Yuchao Gu +12
We introduce DC-VideoGen, a post-training acceleration framework for efficient video generation. DC-VideoGen can be applied to any pre-trained video diffusion model, improving effi…
DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space
Wenkun He, Yuchao Gu, Junyu Chen +11
Existing text-to-image diffusion models excel at generating high-quality images, but face significant efficiency challenges when scaled to high resolutions, like 4K image generatio…
Radial Attention: Sparse Attention with Energy Decay for Long Video Generation
Xingyang Li, Muyang Li, Tianle Cai +11
Recent advances in diffusion models have enabled high-quality video generation, but the additional temporal dimension significantly increases computational costs, making training a…
Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation
Shuo Yang, Haocheng Xi, Yilong Zhao +10
Diffusion Transformers (DiTs) are essential for video generation but suffer from significant latency due to the quadratic complexity of attention. By computing only critical tokens…
STORM: Token-Efficient Long Video Understanding for Multimodal LLMs
Jindong Jiang, Xiuyu Li, Zhijian Liu +13
Recent advances in video-based multimodal large language models (Video-LLMs) have significantly improved video understanding by processing videos as sequences of image frames. Howe…
Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity
Haocheng Xi, Shuo Yang, Yilong Zhao +11
Diffusion Transformers (DiTs) dominate video generation but their high computational cost severely limits real-world applicability, usually requiring tens of minutes to generate a…
SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer
Enze Xie, Junsong Chen, Yuyang Zhao +11
This paper presents SANA-1.5, a linear Diffusion Transformer for efficient scaling in text-to-image generation. Building upon SANA-1.0, we introduce three key innovations: (1) Effi…
SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
Muyang Li, Yujun Lin, Zhekai Zhang +7
Diffusion models can effectively generate high-quality images. However, as they scale, rising memory demands and higher latency pose substantial deployment challenges. In this work…
SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers
Enze Xie, Junsong Chen, Junyu Chen +8
We introduce Sana, a text-to-image framework that can efficiently generate images up to 40964096 resolution. Sana can synthesize high-resolution, high-quality images with s…
Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models
Junyu Chen, Han Cai, Junsong Chen +6
We present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated…
Condition-Aware Neural Network for Controlled Image Generation
Han Cai, Muyang Li, Zhuoyang Zhang +3
We present Condition-Aware Neural Network (CAN), a new method for adding control to image generative models. In parallel to prior conditional control methods, CAN controls the imag…
DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
Muyang Li, Tianle Cai, Jiaxin Cao +7
Diffusion models have achieved great success in synthesizing high-quality images. However, generating high-resolution images with diffusion models is still challenging due to the e…
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models
Muyang Li, Ji Lin, Chenlin Meng +3
During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of com…