11 citations · 21 across the 10 of their papers we have counts for
12 papers
FlashControl: One-Step Controllable Generator via Distillation-Friendly Single-Stream Teachers
Ngan Nguyen, Dung Nguyen, Quan Dao +4
Diffusion models have become a leading approach in visual generative modeling, particularly effective in text-to-image and controllable generation. Their main drawback is slow infe…
Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions
Quyen Tran, Hai Nguyen, Quan Dao +4
Analytic Continual Learning (ACL) offers a computationally efficient alternative to gradient-based approaches. Recent ACL methods are based on Recursive Least Squares (RLS) and hav…
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…
MPDiT: Multi-Patch Global-to-Local Transformer Architecture For Efficient Flow Matching and Diffusion Model
Quan Dao, Dimitris Metaxas
Transformer architectures, particularly Diffusion Transformers (DiTs), have become widely used in diffusion and flow-matching models due to their strong performance compared to con…
Improved Training Technique for Latent Consistency Models
Quan Dao, Khanh Doan, Di Liu +2
Consistency models are a new family of generative models capable of producing high-quality samples in either a single step or multiple steps. Recently, consistency models have demo…
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…
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…
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models
Xiaoxiao He, Quan Dao, Ligong Han +14
Discrete diffusion models have achieved success in tasks like image generation and masked language modeling but face limitations in controlled content editing. We introduce DICE (D…
A High-Quality Robust Diffusion Framework for Corrupted Dataset
Quan Dao, Binh Ta, Tung Pham +1
Developing image-generative models, which are robust to outliers in the training process, has recently drawn attention from the research community. Due to the ease of integrating u…
Flow Matching in Latent Space
Quan Dao, Hao Phung, Binh Nguyen +1
Flow matching is a recent framework to train generative models that exhibits impressive empirical performance while being relatively easier to train compared with diffusion-based m…
Anti-DreamBooth: Protecting users from personalized text-to-image synthesis
Thanh Van Le, Hao Phung, Thuan Hoang Nguyen +3
Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personal…
Wavelet Diffusion Models are fast and scalable Image Generators
Hao Phung, Quan Dao, Anh Tran
Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances. However, their slow training and inferen…