5 citations · 12 across the 7 of their papers we have counts for
4 papers · 1 filter
Scaling Diffusion Transformers to 16 Billion Parameters
Zhengcong Fei, Mingyuan Fan, Changqian Yu +2
In this paper, we present DiT-MoE, a sparse version of the diffusion Transformer, that is scalable and competitive with dense networks while exhibiting highly optimized inference.…
Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models
Zhengcong Fei, Mingyuan Fan, Changqian Yu +2
Transformers have catalyzed advancements in computer vision and natural language processing (NLP) fields. However, substantial computational complexity poses limitations for their…
Scalable Diffusion Models with State Space Backbone
Zhengcong Fei, Mingyuan Fan, Changqian Yu +1
This paper presents a new exploration into a category of diffusion models built upon state space architecture. We endeavor to train diffusion models for image data, wherein the tra…
Tuning-Free Inversion-Enhanced Control for Consistent Image Editing
Xiaoyue Duan, Shuhao Cui, Guoliang Kang +4
Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e.g., changing postures) to the main objects in the input image without changing…