3 citations · 7 across the 17 of their papers we have counts for
9 papers · 1 filter
EdiVal-Agent: An Object-Centric Framework for Automated, Fine-Grained Evaluation of Multi-Turn Editing
Tianyu Chen, Yasi Zhang, Zhi Zhang +13
Instruction-based image editing has advanced rapidly, yet reliable and interpretable evaluation remains a bottleneck. Current protocols either (i) depend on paired reference images…
Few-Step Diffusion via Score identity Distillation
Mingyuan Zhou, Yi Gu, Zhendong Wang
Diffusion distillation has emerged as a promising strategy for accelerating text-to-image (T2I) diffusion models by distilling a pretrained score network into a one- or few-step ge…
Enhancing and Accelerating Diffusion-Based Inverse Problem Solving through Measurements Optimization
Tianyu Chen, Zhendong Wang, Mingyuan Zhou
Diffusion models have recently demonstrated notable success in solving inverse problems. However, current diffusion model-based solutions typically require a large number of functi…
Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step
Mingyuan Zhou, Huangjie Zheng, Yi Gu +2
Score identity Distillation (SiD) is a data-free method that has achieved SOTA performance in image generation by leveraging only a pretrained diffusion model, without requiring an…
Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization
Yi Gu, Zhendong Wang, Yueqin Yin +2
Aligning large language models with human preferences has emerged as a critical focus in language modeling research. Yet, integrating preference learning into Text-to-Image (T2I) g…
Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation
Mingyuan Zhou, Zhendong Wang, Huangjie Zheng +1
Diffusion-based text-to-image generation models trained on extensive text-image pairs have demonstrated the ability to produce photorealistic images aligned with textual descriptio…
A Dense Reward View on Aligning Text-to-Image Diffusion with Preference
Shentao Yang, Tianqi Chen, Mingyuan Zhou
Aligning text-to-image diffusion model (T2I) with preference has been gaining increasing research attention. While prior works exist on directly optimizing T2I by preference data,…
Improving In-Context Learning in Diffusion Models with Visual Context-Modulated Prompts
Tianqi Chen, Yongfei Liu, Zhendong Wang +4
In light of the remarkable success of in-context learning in large language models, its potential extension to the vision domain, particularly with visual foundation models like St…
Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling
Huangjie Zheng, Zhendong Wang, Jianbo Yuan +5
Diffusion models excel at generating photo-realistic images but come with significant computational costs in both training and sampling. While various techniques address these comp…