26 citations · 129 across the 44 of their papers we have counts for
46 papers
Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models
Beomsu Kim, Chieh-Hsin Lai, Bac Nguyen +3
World models predict future observations from current experience and actions, yet prediction can depend on observations seen far in the past. Episodic memory preserves past observa…
Does Uniform Discrete Diffusion Need Time?
Chunsan Hong, Chieh-Hsin Lai, Satoshi Hayakawa +3
Uniform discrete diffusion models (UDMs) commonly use explicit time conditioning, but we find that it can often be unnecessary in practice. In this paper, we first show that the po…
Efficient Reinforcement for Visual-Textual Thinking with Discrete Diffusion Model
Yoonjeon Kim, Yuhta Takida, Chieh-Hsin Lai +2
RL-based post-training has been widely adopted to enable interleaved visual and textual reasoning in unified multimodal models capable of both text and image generation. However, m…
Understanding and Accelerating the Training of Masked Diffusion Language Models
Chunsan Hong, Sanghyun Lee, Chieh-Hsin Lai +5
Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs are known to learn substantially more sl…
A Unified View of Score-Based and Drifting Models
Chieh-Hsin Lai, Bac Nguyen, Naoki Murata +5
Drifting models train one-step generators by optimizing a kernel-induced mean-shift discrepancy between the data and model distributions, with Laplace kernels used by default in pr…
Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment
Bac Nguyen, Yuhta Takida, Naoki Murata +4
Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between obj…
GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
Naoki Murata, Yuhta Takida, Chieh-Hsin Lai +4
Training-data attribution for vision generative models aims to identify which training data influenced a given output. While most methods score individual examples, practitioners o…
MeanFlow Transformers with Representation Autoencoders
Zheyuan Hu, Chieh-Hsin Lai, Ge Wu +2
MeanFlow (MF) is a diffusion-motivated generative model that enables efficient few-step generation by learning long jumps directly from noise to data. In practice, it is often used…
SONA: Learning Conditional, Unconditional, and Mismatching-Aware Discriminator
Yuhta Takida, Satoshi Hayakawa, Takashi Shibuya +6
Deep generative models have made significant advances in generating complex content, yet conditional generation remains a fundamental challenge. Existing conditional generative adv…
The Principles of Diffusion Models
Chieh-Hsin Lai, Yang Song, Dongjun Kim +2
This book presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse formulations arise from shared mathematic…
CMT: Mid-Training for Efficient Learning of Consistency, Mean Flow, and Flow Map Models
Zheyuan Hu, Chieh-Hsin Lai, Yuki Mitsufuji +1
Flow map models such as Consistency Models (CM) and Mean Flow (MF) enable few-step generation by learning the long jump of the ODE solution of diffusion models, yet training remain…
Denoising Multi-Beta VAE: Representation Learning for Disentanglement and Generation
Anshuk Uppal, Yuhta Takida, Chieh-Hsin Lai +1
Disentangled and interpretable latent representations in generative models typically come at the cost of generation quality. The -VAE framework introduces a hyperparameter t…
Improving Classifier-Free Guidance in Masked Diffusion: Low-Dim Theoretical Insights with High-Dim Impact
Kevin Rojas, Ye He, Chieh-Hsin Lai +3
Classifier-Free Guidance (CFG) is a widely used technique for conditional generation and improving sample quality in continuous diffusion models, and its extensions to discrete dif…
Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution
Yonghyun Park, Chieh-Hsin Lai, Satoshi Hayakawa +7
While diffusion models excel at image generation, their growing adoption raises critical concerns about copyright issues and model transparency. Existing attribution methods identi…
SteerMusic: Enhanced Musical Consistency for Zero-shot Text-guided and Personalized Music Editing
Xinlei Niu, Kin Wai Cheuk, Jing Zhang +8
Music editing is an important step in music production, which has broad applications, including game development and film production. Most existing zero-shot text-guided editing me…
VCT: Training Consistency Models with Variational Noise Coupling
Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai +2
Consistency Training (CT) has recently emerged as a strong alternative to diffusion models for image generation. However, non-distillation CT often suffers from high variance and i…
Music Foundation Model as Generic Booster for Music Downstream Tasks
WeiHsiang Liao, Yuhta Takida, Yukara Ikemiya +13
We demonstrate the efficacy of using intermediate representations from a single foundation model to enhance various music downstream tasks. We introduce SoniDo, a music foundation…
: Optimizing Sampling Schedule of Discrete Diffusion Models
Yong-Hyun Park, Chieh-Hsin Lai, Satoshi Hayakawa +2
Diffusion models have seen notable success in continuous domains, leading to the development of discrete diffusion models (DDMs) for discrete variables. Despite recent advances, DD…
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space
Yangming Li, Chieh-Hsin Lai, Carola-Bibiane Schönlieb +2
Deep Generative Models (DGMs), including Energy-Based Models (EBMs) and Score-based Generative Models (SGMs), have advanced high-fidelity data generation and complex continuous dis…
Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion
Bac Nguyen, Chieh-Hsin Lai, Yuhta Takida +4
By embedding discrete representations into a continuous latent space, we can leverage continuous-space latent diffusion models to handle generative modeling of discrete data. Howev…
G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving
Naoki Murata, Chieh-Hsin Lai, Yuhta Takida +4
Recent literature has effectively leveraged diffusion models trained on continuous variables as priors for solving inverse problems. Notably, discrete diffusion models with discret…
HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning
Ayano Hiranaka, Shang-Fu Chen, Chieh-Hsin Lai +6
Controllable generation through Stable Diffusion (SD) fine-tuning aims to improve fidelity, safety, and alignment with human guidance. Existing reinforcement learning from human fe…
A Survey on Diffusion Models for Inverse Problems
Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai +5
Diffusion models have become increasingly popular for generative modeling due to their ability to generate high-quality samples. This has unlocked exciting new possibilities for so…
Latent Diffusion Bridges for Unsupervised Musical Audio Timbre Transfer
Michele Mancusi, Yurii Halychanskyi, Kin Wai Cheuk +8
Music timbre transfer is a challenging task that involves modifying the timbral characteristics of an audio signal while preserving its melodic structure. In this paper, we propose…