3 citations · 6 across the 8 of their papers we have counts for
9 papers
Simplifying and Improving On-Policy Distillation for Few-Step Diffusion
Jiawei Zhang, Ziyu Wan, Nanye Ma +5
Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialized architectures. We show tha…
Benchmarking Visual State Tracking in Multimodal Video Understanding
Sihyun Yu, Nanye Ma, Pinzhi Huang +8
Understanding a video requires more than recognizing isolated moments, as humans continuously track entities, states, and events over time. This capacity for visual state tracking…
Mode Seeking meets Mean Seeking for Fast Long Video Generation
Shengqu Cai, Weili Nie, Chao Liu +8
Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form data is scarce and limited to…
Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders
Shengbang Tong, Boyang Zheng, Ziteng Wang +7
Representation Autoencoders (RAEs) have shown distinct advantages in diffusion modeling on ImageNet by training in high-dimensional semantic latent spaces. In this work, we investi…
Transition Matching Distillation for Fast Video Generation
Weili Nie, Julius Berner, Nanye Ma +3
Large video diffusion and flow models have achieved remarkable success in high-quality video generation, but their use in real-time interactive applications remains limited due to…
Flow Map Distillation Without Data
Shangyuan Tong, Nanye Ma, Saining Xie +1
State-of-the-art flow models achieve remarkable quality but require slow, iterative sampling. To accelerate this, flow maps can be distilled from pre-trained teachers, a procedure…
Diffusion Transformers with Representation Autoencoders
Boyang Zheng, Nanye Ma, Shengbang Tong +1
Latent generative modeling, where a pretrained autoencoder maps pixels into a latent space for the diffusion process, has become the standard strategy for Diffusion Transformers (D…
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps
Nanye Ma, Shangyuan Tong, Haolin Jia +8
Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model…
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
Nanye Ma, Mark Goldstein, Michael S. Albergo +3
We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which allows for…