3k citations · 10.2k across the 21 of their papers we have counts for
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One-Step Diffusion Distillation via Deep Equilibrium Models
Zhengyang Geng, Ashwini Pokle, J. Zico Kolter
Diffusion models excel at producing high-quality samples but naively require hundreds of iterations, prompting multiple attempts to distill the generation process into a faster net…
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
Improved Techniques for Training Consistency Models
Yang Song, Prafulla Dhariwal
Consistency models are a nascent family of generative models that can sample high quality data in one step without the need for adversarial training. Current consistency models ach…
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
Xiuying Wei, Yunchen Zhang, Yuhang Li +4
Post-training quantization~(PTQ) of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outl…
Consistency Models
Yang Song, Prafulla Dhariwal, Mark Chen +1
Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To over…