11 citations · 22 across the 23 of their papers we have counts for
10 papers · 1 filter
Self-Evaluation Unlocks Any-Step Text-to-Image Generation
Xin Yu, Xiaojuan Qi, Zhengqi Li +6
We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data simi…
Group Diffusion: Enhancing Image Generation by Unlocking Cross-Sample Collaboration
Sicheng Mo, Thao Nguyen, Richard Zhang +7
In this work, we explore an untapped signal in diffusion model inference. While all previous methods generate images independently at inference, we instead ask if samples can be ge…
What matters for Representation Alignment: Global Information or Spatial Structure?
Jaskirat Singh, Xingjian Leng, Zongze Wu +4
Representation alignment (REPA) guides generative training by distilling representations from a strong, pretrained vision encoder to intermediate diffusion features. We investigate…
Improved Mean Flows: On the Challenges of Fastforward Generative Models
Zhengyang Geng, Yiyang Lu, Zongze Wu +3
MeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its ``fastforward'' nature introduces key challenges in both the training obje…
MoGAN: Improving Motion Quality in Video Diffusion via Few-Step Motion Adversarial Post-Training
Haotian Xue, Qi Chen, Zhonghao Wang +4
Video diffusion models achieve strong frame-level fidelity but still struggle with motion coherence, dynamics and realism, often producing jitter, ghosting, or implausible dynamics…
Learning an Image Editing Model without Image Editing Pairs
Nupur Kumari, Sheng-Yu Wang, Nanxuan Zhao +7
Recent image editing models have achieved impressive results while following natural language editing instructions, but they rely on supervised fine-tuning with large datasets of i…
Identifying Prompted Artist Names from Generated Images
Grace Su, Sheng-Yu Wang, Aaron Hertzmann +3
A common and controversial use of text-to-image models is to generate pictures by explicitly naming artists, such as "in the style of Greg Rutkowski". We introduce a benchmark for…
Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
Xun Huang, Zhengqi Li, Guande He +2
We introduce Self Forcing, a novel training paradigm for autoregressive video diffusion models. It addresses the longstanding issue of exposure bias, where models trained on ground…
Long-Context State-Space Video World Models
Ryan Po, Yotam Nitzan, Richard Zhang +5
Video diffusion models have recently shown promise for world modeling through autoregressive frame prediction conditioned on actions. However, they struggle to maintain long-term m…
SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
Rohit Gandikota, Zongze Wu, Richard Zhang +3
We present SliderSpace, a framework for automatically decomposing the visual capabilities of diffusion models into controllable and human-understandable directions. Unlike existing…