8 papers · 1 filter
Avatar-Forever: Decoupled Parallel Training for High-Quality Real-Time Infinite Avatars
Ruibin Li, Tao Yang, Zhiyuan Ma +3
Existing streaming video systems often rely on sequential, distillation-centered training pipelines to enable few-step long-video generation. However, this paradigm suffers from tw…
Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation
Ruibin Li, Tao Yang, Fangzhou Ai +4
Streaming video generation (SVG) distills a pretrained bidirectional video diffusion model into an autoregressive model equipped with sliding window attention (SWA). However, SWA i…
CoCoEdit: Content-Consistent Image Editing via Region Regularized Reinforcement Learning
Yuhui Wu, Chenxi Xie, Ruibin Li +3
Image editing has achieved impressive results with the development of large-scale generative models. However, existing models mainly focus on the editing effects of intended object…
Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
Tianhe Wu, Ruibin Li, Lei Zhang +1
Distribution matching distillation (DMD) facilitates few-step image generation by aligning a distilled student with a reference multi-step teacher. In practice, however, optimizing…
Self-transcendence: Is External Feature Guidance Indispensable for Accelerating Diffusion Transformer Training?
Lingchen Sun, Rongyuan Wu, Zhengqiang Zhang +4
Recent works such as REPA have shown that guiding diffusion models with external semantic features (e.g., DINO) can significantly accelerate the training of diffusion transformers…
Many-for-Many: Unify the Training of Multiple Video and Image Generation and Manipulation Tasks
Ruibin Li, Tao Yang, Yangming Shi +4
Diffusion models have shown impressive performance in many visual generation and manipulation tasks. Many existing methods focus on training a model for a specific task, especially…