6 papers
MixFlow Training: Alleviating Exposure Bias with Slowed Interpolation Mixture
Hui Li, Fu-Yun Wang, Haoyuan Xia +4
This paper studies the training-testing discrepancy (a.k.a. exposure bias) problem for improving the diffusion models. During training, the input of a prediction network at one tra…
Image Diffusion Preview with Consistency Solver
Fu-Yun Wang, Hao Zhou, Liangzhe Yuan +8
The slow inference process of image diffusion models significantly degrades interactive user experiences. To address this, we introduce Diffusion Preview, a novel paradigm employin…
PromptRL: Prompt Matters in RL for Flow-Based Image Generation
Fu-Yun Wang, Han Zhang, Michael Gharbi +2
Flow matching models (FMs) have revolutionized text-to-image (T2I) generation, with reinforcement learning (RL) serving as a critical post-training strategy for alignment with rewa…
Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model
Team Seedance, Heyi Chen, Siyan Chen +194
Recent strides in video generation have paved the way for unified audio-visual generation. In this work, we present Seedance 1.5 pro, a foundational model engineered specifically f…
OSV: One Step is Enough for High-Quality Image to Video Generation
Xiaofeng Mao, Zhengkai Jiang, Fu-Yun Wang +5
Video diffusion models have shown great potential in generating high-quality videos, making them an increasingly popular focus. However, their inherent iterative nature leads to su…
Jasper and Stella: distillation of SOTA embedding models
Dun Zhang, Jiacheng Li, Ziyang Zeng +1
A crucial component in many deep learning applications, such as Frequently Asked Questions (FAQ) and Retrieval-Augmented Generation (RAG), is dense retrieval. In this process, embe…