190 citations · 470 across the 41 of their papers we have counts for
18 papers · 1 filter
Gaussian Mixture Solvers for Diffusion Models
Hanzhong Guo, Cheng Lu, Fan Bao +4
Recently, diffusion models have achieved great success in generative tasks. Sampling from diffusion models is equivalent to solving the reverse diffusion stochastic differential eq…
Finetuning Text-to-Image Diffusion Models for Fairness
Xudong Shen, Chao Du, Tianyu Pang +3
The rapid adoption of text-to-image diffusion models in society underscores an urgent need to address their biases. Without interventions, these biases could propagate a skewed wor…
Intriguing Properties of Data Attribution on Diffusion Models
Xiaosen Zheng, Tianyu Pang, Chao Du +2
Data attribution seeks to trace model outputs back to training data. With the recent development of diffusion models, data attribution has become a desired module to properly assig…
On Memorization in Diffusion Models
Xiangming Gu, Chao Du, Tianyu Pang +3
Due to their capacity to generate novel and high-quality samples, diffusion models have attracted significant research interest in recent years. Notably, the typical training objec…
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin +3
Low-rank adaptations (LoRA) are often employed to fine-tune large language models (LLMs) for new tasks. This paper investigates LoRA composability for cross-task generalization and…
AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation
Yunqing Zhao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +5
Few-shot image generation (FSIG) aims to learn to generate new and diverse images given few (e.g., 10) training samples. Recent work has addressed FSIG by leveraging a GAN pre-trai…