Search papers, labs, and topics across Lattice.
K, with 70% token merging ratio on Stable Diffusion 2.0, BiGain increases classification accuracy by 7.15% while also improving FID for generation by 0.34 (1.85%). Our comprehensive analyses indicate that balanced spectral retention, preserving high-frequency detail alongside low/mid-frequency semantic content is a reliable design rule for token compression in diffusion models. To our knowledge, BiGain is the first framework to jointly study and advance both generation and classification under accelerated diffusion, supporting lower-cost deployment of dual-purpose generative systems. **footnotetext: Equal contribution.
CMU Machine Learning1
9
4
4
LLM360 K2 unveils the black box of large language model training, offering a 65B parameter model that beats LLaMA-65B while using fewer resources, all under a fully transparent, open-source framework.
