7 papers
Unified Lookup-Table Inference with Signed-Digit K/V Caches for Ternary LLMs
Ziang Duan, Jiajun Wu, Zetian Chen +8
Ternary LLMs make their weight-dominated projections compact and efficient, but attention remains a mismatch: its K/V cache is created online and is typically processed by a separa…
Anon: Extrapolating Adaptivity Beyond SGD and Adam
Yiheng Zhang, Kaiyan Zhao, Shaowu Wu +5
Adaptive optimizers such as Adam have achieved great success in training large-scale models like large language models and diffusion models. However, they often generalize worse th…
SegQuant: A Semantics-Aware and Generalizable Quantization Framework for Diffusion Models
Jiaji Zhang, Ruichao Sun, Hailiang Zhao +7
Diffusion models have demonstrated exceptional generative capabilities but are computationally intensive, posing significant challenges for deployment in resource-constrained or la…
Shiva-DiT: Residual-Based Differentiable Top- Selection for Efficient Diffusion Transformers
Jiaji Zhang, Hailiang Zhao, Guoxuan Zhu +9
Diffusion Transformers (DiTs) incur prohibitive computational costs due to the quadratic scaling of self-attention. Existing pruning methods fail to simultaneously satisfy differen…
HVAdam: A Full-Dimension Adaptive Optimizer
Yiheng Zhang, Shaowu Wu, Yuanzhuo Xu +4
Adaptive optimizers such as Adam have achieved great success in training large-scale models like large language models and diffusion models. However, they often generalize worse th…
Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization
Kyle Sargent, Kyle Hsu, Justin Johnson +2
Since the advent of popular visual generation frameworks like VQGAN and latent diffusion models, state-of-the-art image generation systems have generally been two-stage systems tha…