6 papers
Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers
Anh Nguyen, Ngan Nguyen, Duc Vu +11
Modern one-step diffusion models achieve impressive quality through distribution-based timestep distillation. Yet, they rely on a critical assumption: Teacher and Student must inha…
Self-Corrected Flow Distillation for Consistent One-Step and Few-Step Text-to-Image Generation
Quan Dao, Hao Phung, Trung Dao +2
Flow matching has emerged as a promising framework for training generative models, demonstrating impressive empirical performance while offering relative ease of training compared…
Cyberscurity Threats and Defense Mechanisms in IoT network
Trung Dao, Minh Nguyen, Son Do +1
The rapid proliferation of Internet of Things (IoT) technologies, projected to exceed 30 billion interconnected devices by 2030, has significantly escalated the complexity of cyber…
Improved Training Technique for Shortcut Models
Anh Nguyen, Viet Nguyen, Duc Vu +4
Shortcut models represent a promising, non-adversarial paradigm for generative modeling, uniquely supporting one-step, few-step, and multi-step sampling from a single trained netwo…
Supercharged One-step Text-to-Image Diffusion Models with Negative Prompts
Viet Nguyen, Anh Nguyen, Trung Dao +4
The escalating demand for real-time image synthesis has driven significant advancements in one-step diffusion models, which inherently offer expedited generation speeds compared to…
DiMSUM: Diffusion Mamba -- A Scalable and Unified Spatial-Frequency Method for Image Generation
Hao Phung, Quan Dao, Trung Dao +3
We introduce a novel state-space architecture for diffusion models, effectively harnessing spatial and frequency information to enhance the inductive bias towards local features in…