64 citations · 268 across the 26 of their papers we have counts for
5 papers · 1 filter
DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention
Lianghui Zhu, Zilong Huang, Bencheng Liao +4
Diffusion models with large-scale pre-training have achieved significant success in the field of visual content generation, particularly exemplified by Diffusion Transformers (DiT)…
ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance
Jiannan Huang, Jun Hao Liew, Hanshu Yan +4
Recent text-to-image customization works have proven successful in generating images of given concepts by fine-tuning diffusion models on a few examples. However, tuning-based meth…
LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
Yujun Shi, Jun Hao Liew, Hanshu Yan +2
Accuracy and speed are critical in image editing tasks. Pan et al. introduced a drag-based image editing framework that achieves pixel-level control using Generative Adversarial Ne…
PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator
Hanshu Yan, Xingchao Liu, Jiachun Pan +3
We present Piecewise Rectified Flow (PeRFlow), a flow-based method for accelerating diffusion models. PeRFlow divides the sampling process of generative flows into several time win…
MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation
Weimin Wang, Jiawei Liu, Zhijie Lin +9
The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field. In this work, we introduce MagicVideo-V2 that inte…