5 citations · 13 across the 8 of their papers we have counts for
8 papers
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.…
Music Consistency Models
Zhengcong Fei, Mingyuan Fan, Junshi Huang
Consistency models have exhibited remarkable capabilities in facilitating efficient image/video generation, enabling synthesis with minimal sampling steps. It has proven to be adva…
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…
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning
Guisheng Liu, Yi Li, Zhengcong Fei +3
While impressive performance has been achieved in image captioning, the limited diversity of the generated captions and the large parameter scale remain major barriers to the real-…
DiT: Efficient Vision Transformers with Dynamic Token Routing
Yuchen Ma, Zhengcong Fei, Junshi Huang
Recently, the tokens of images share the same static data flow in many dense networks. However, challenges arise from the variance among the objects in images, such as large variat…