3 citations · 8 across the 14 of their papers we have counts for
6 papers · 1 filter
Video Diffusion Transformers are In-Context Learners
Zhengcong Fei, Di Qiu, Debang Li +2
This paper investigates a solution for enabling in-context capabilities of video diffusion transformers, with minimal tuning required for activation. Specifically, we propose a sim…
MovieCharacter: A Tuning-Free Framework for Controllable Character Video Synthesis
Di Qiu, Zheng Chen, Rui Wang +4
Recent advancements in character video synthesis still depend on extensive fine-tuning or complex 3D modeling processes, which can restrict accessibility and hinder real-time appli…
FLUX that Plays Music
Zhengcong Fei, Mingyuan Fan, Changqian Yu +1
This paper explores a simple extension of diffusion-based rectified flow Transformers for text-to-music generation, termed as FluxMusic. Generally, along with design in advanced Fl…
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…
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…