16 citations · 65 across the 47 of their papers we have counts for
14 papers · 1 filter
Rethinking Negative Prompting for Concept Suppression: Unconditional Anchoring and Localized Guidance
Anubhav Jain, Yuya Kobayashi, Takashi Shibuya +4
Text-to-image diffusion models have rapidly transitioned from research prototypes to widely deployed tools. Along with their impressive capabilities, these models can also generate…
Classifier-Free Guidance inside the Attraction Basin May Cause Memorization
Anubhav Jain, Yuya Kobayashi, Takashi Shibuya +4
Diffusion models are prone to exactly reproduce images from the training data. This exact reproduction of the training data is concerning as it can lead to copyright infringement a…
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…
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…
Distillation of Discrete Diffusion through Dimensional Correlations
Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi +2
Diffusion models have demonstrated exceptional performances in various fields of generative modeling, but suffer from slow sampling speed due to their iterative nature. While this…
Variable Bitrate Residual Vector Quantization for Audio Coding
Yunkee Chae, Woosung Choi, Yuhta Takida +8
Recent state-of-the-art neural audio compression models have progressively adopted residual vector quantization (RVQ). Despite this success, these models employ a fixed number of c…
DisMix: Disentangling Mixtures of Musical Instruments for Source-level Pitch and Timbre Manipulation
Yin-Jyun Luo, Kin Wai Cheuk, Woosung Choi +8
Existing work on pitch and timbre disentanglement has been mostly focused on single-instrument music audio, excluding the cases where multiple instruments are presented. To fill th…
MoLA: Motion Generation and Editing with Latent Diffusion Enhanced by Adversarial Training
Kengo Uchida, Takashi Shibuya, Yuhta Takida +4
In text-to-motion generation, controllability as well as generation quality and speed has become increasingly critical. The controllability challenges include generating a motion o…
SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation
Koichi Saito, Dongjun Kim, Takashi Shibuya +4
Sound content creation, essential for multimedia works such as video games and films, often involves extensive trial-and-error, enabling creators to semantically reflect their arti…
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao +5
The diffusion model performs remarkable in generating high-dimensional content but is computationally intensive, especially during training. We propose Progressive Growing of Diffu…
Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric
Toshimitsu Uesaka, Taiji Suzuki, Yuhta Takida +3
In typical multimodal contrastive learning, such as CLIP, encoders produce one point in the latent representation space for each input. However, one-point representation has diffic…
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
Yuhta Takida, Yukara Ikemiya, Takashi Shibuya +8
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations. It is commonly performed with a variational autoencoding model,…