4 papers
LensErase: A Foundation Model for Erase Anything
Yunuo Chen, Xiuyu Wu, Qinhong Yang +11
We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. LensErase supports diverse ob…
Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models
Dong Chen, Fangyun Wei, Ziyu Wan +18
We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with more than 6B parameters acro…
Minor DPO reject penalty to increase training robustness
Shiming Xie, Hong Chen, Fred Yu +3
Learning from human preference is a paradigm used in large-scale language model (LLM) fine-tuning step to better align pretrained LLM to human preference for downstream task. In th…
Minor SFT loss for LLM fine-tune to increase performance and reduce model deviation
Shiming Xie, Hong Chen, Fred Yu +2
Instruct LLM provide a paradigm used in large scale language model to align LLM to human preference. The paradigm contains supervised fine tuning and reinforce learning from human…