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Naoki Murata

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15 papers

2 author rows

AAAI Conference 2026 Conference Paper

SteerMusic: Enhanced Musical Consistency for Zero-shot Text-Guided and Personalized Music Editing

Xinlei Niu
Kin Wai Cheuk
Jing Zhang
Naoki Murata
Chieh-Hsin Lai
Michele Mancusi
Woosung Choi
Giorgio Fabbro

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 methods rely on pretrained diffusion models by involving forward-backward diffusion processes. However, these methods often struggle to preserve the musical content. Additionally, text instructions alone usually fail to accurately describe the desired music. In this paper, we propose two music editing methods that improve the consistency between the original and edited music by leveraging score distillation. The first method, SteerMusic, is a coarse-grained zero-shot editing approach using delta denoising score. The second method, SteerMusic+, enables fine-grained personalized music editing by manipulating a concept token that represents a user-defined musical style. SteerMusic+ allows for the editing of music into user-defined musical styles that cannot be achieved by the text instructions alone. Experimental results show that our methods outperform existing approaches in preserving both music content consistency and editing fidelity. User studies further validate that our methods achieve superior music editing quality.

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TMLR Journal 2025 Journal Article

Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation

Yutong He
Alexander Robey
Naoki Murata
Yiding Jiang
Joshua Nathaniel Williams
George J. Pappas
Hamed Hassani
Yuki Mitsufuji

Prompt engineering is an effective but labor-intensive way to control text-to-image (T2I) generative models. Its time-intensive nature and complexity have spurred the development of algorithms for automated prompt generation. However, these methods often struggle with transferability across T2I models, require white-box access to the underlying model, or produce non-intuitive prompts. In this work, we introduce PRISM, an algorithm that automatically produces human-interpretable and transferable prompts that can effectively generate desired concepts given only black-box access to T2I models. Inspired by large language model (LLM) jailbreaking, PRISM leverages the in-context learning ability of LLMs to iteratively refine the candidate prompt distribution built upon the reference images. Our experiments demonstrate the versatility and effectiveness of PRISM in generating accurate prompts for objects, styles, and images across multiple T2I models, including Stable Diffusion, DALL-E, and Midjourney.

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TMLR Journal 2025 Journal Article

G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving

Naoki Murata
Chieh-Hsin Lai
Yuhta Takida
Toshimitsu Uesaka
Bac Nguyen
Stefano Ermon
Yuki Mitsufuji

Recent literature has effectively leveraged diffusion models trained on continuous variables as priors for solving inverse problems. Notably, discrete diffusion models with discrete latent codes have shown strong performance, particularly in modalities suited for discrete compressed representations, such as image and motion generation. However, their discrete and non-differentiable nature has limited their application to inverse problems formulated in continuous spaces. This paper presents a novel method for addressing linear inverse problems by leveraging generative models based on discrete diffusion as priors. We overcome these limitations by approximating the true posterior distribution with a variational distribution constructed from categorical distributions and continuous relaxation techniques. Furthermore, we employ a star-shaped noise process to mitigate the drawbacks of traditional discrete diffusion models with absorbing states, demonstrating that our method performs comparably to continuous diffusion techniques with lower GPU memory consumption.

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ICLR Conference 2025 Conference Paper

HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning

Ayano Hiranaka
Shang-Fu Chen
Chieh-Hsin Lai
Dongjun Kim
Naoki Murata
Takashi Shibuya 0001
Wei-Hsiang Liao 0001
Shao-Hua Sun

Controllable generation through Stable Diffusion (SD) fine-tuning aims to improve fidelity, safety, and alignment with human guidance. Existing reinforcement learning from human feedback methods usually rely on predefined heuristic reward functions or pretrained reward models built on large-scale datasets, limiting their applicability to scenarios where collecting such data is costly or difficult. To effectively and efficiently utilize human feedback, we develop a framework, HERO, which leverages online human feedback collected on the fly during model learning. Specifically, HERO features two key mechanisms: (1) Feedback-Aligned Representation Learning, an online training method that captures human feedback and provides informative learning signals for fine-tuning, and (2) Feedback-Guided Image Generation, which involves generating images from SD's refined initialization samples, enabling faster convergence towards the evaluator's intent. We demonstrate that HERO is 4x more efficient in online feedback for body part anomaly correction compared to the best existing method. Additionally, experiments show that HERO can effectively handle tasks like reasoning, counting, personalization, and reducing NSFW content with only 0.5K online feedback. The code and project page are available at [https://hero-dm.github.io/](https://hero-dm.github.io/).

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ICLR Conference 2025 Conference Paper

Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric

Toshimitsu Uesaka
Taiji Suzuki
Yuhta Takida
Chieh-Hsin Lai
Naoki Murata
Yuki Mitsufuji

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 difficulty in capturing the relationship and the similarity structure of a huge amount of instances in the real world. For richer classes of the similarity, we propose the use of weighted point sets, namely, sets of pairs of weight and vector, as representations of instances. In this work, we theoretically show the benefit of our proposed method through a new understanding of the contrastive loss of CLIP, which we call symmetric InfoNCE. We clarify that the optimal similarity that minimizes symmetric InfoNCE is the pointwise mutual information, and show an upper bound of excess risk on downstream classification tasks of representations that achieve the optimal similarity. In addition, we show that our proposed similarity based on weighted point sets consistently achieves the optimal similarity. To verify the effectiveness of our proposed method, we demonstrate pretraining of text-image representation models and classification tasks on common benchmarks.

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ICLR Conference 2024 Conference Paper

Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion

Dongjun Kim
Chieh-Hsin Lai
Wei-Hsiang Liao 0001
Naoki Murata
Yuhta Takida
Toshimitsu Uesaka
Yutong He
Yuki Mitsufuji

Consistency Models (CM) (Song et al., 2023) accelerate score-based diffusion model sampling at the cost of sample quality but lack a natural way to trade-off quality for speed. To address this limitation, we propose Consistency Trajectory Model (CTM), a generalization encompassing CM and score-based models as special cases. CTM trains a single neural network that can -- in a single forward pass -- output scores (i.e., gradients of log-density) and enables unrestricted traversal between any initial and final time along the Probability Flow Ordinary Differential Equation (ODE) in a diffusion process. CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance and achieves new state-of-the-art FIDs for single-step diffusion model sampling on CIFAR-10 (FID 1.73) and ImageNet at 64X64 resolution (FID 1.92). CTM also enables a new family of sampling schemes, both deterministic and stochastic, involving long jumps along the ODE solution trajectories. It consistently improves sample quality as computational budgets increase, avoiding the degradation seen in CM. Furthermore, unlike CM, CTM's access to the score function can streamline the adoption of established controllable/conditional generation methods from the diffusion community. This access also enables the computation of likelihood. The code is available at https://github.com/sony/ctm.

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NeurIPS Conference 2024 Conference Paper

GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping

Junyoung Seo
Kazumi Fukuda
Takashi Shibuya
Takuya Narihira
Naoki Murata
Shoukang Hu
Chieh-Hsin Lai
Seungryong Kim

Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a model on. Recent research combining large-scale text-to-image (T2I) models with monocular depth estimation (MDE) has shown promise in handling in-the-wild images. In these methods, an input view is geometrically warped to novel views with estimated depth maps, then the warped image is inpainted by T2I models. However, they struggle with noisy depth maps and loss of semantic details when warping an input view to novel viewpoints. In this paper, we propose a novel approach for single-shot novel view synthesis, a semantic-preserving generative warping framework that enables T2I generative models to learn where to warp and where to generate, through augmenting cross-view attention with self-attention. Our approach addresses the limitations of existing methods by conditioning the generative model on source view images and incorporating geometric warping signals. Qualitative and quantitative evaluations demonstrate that our model outperforms existing methods in both in-domain and out-of-domain scenarios. Project page is available at https: //GenWarp-NVS. github. io.

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TMLR Journal 2024 Journal Article

HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes

Yuhta Takida
Yukara Ikemiya
Takashi Shibuya
Kazuki Shimada
Woosung Choi
Chieh-Hsin Lai
Naoki Murata
Toshimitsu Uesaka

Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations. It is commonly performed with a variational autoencoding model, VQ-VAE, which can be further extended to hierarchical structures for making high-fidelity reconstructions. However, such hierarchical extensions of VQ-VAE often suffer from the codebook/layer collapse issue, where the codebook is not efficiently used to express the data, and hence degrades reconstruction accuracy. To mitigate this problem, we propose a novel unified framework to stochastically learn hierarchical discrete representation on the basis of the variational Bayes framework, called hierarchically quantized variational autoencoder (HQ-VAE). HQ-VAE naturally generalizes the hierarchical variants of VQ-VAE, such as VQ-VAE-2 and residual-quantized VAE (RQ-VAE), and provides them with a Bayesian training scheme. Our comprehensive experiments on image datasets show that HQ-VAE enhances codebook usage and improves reconstruction performance. We also validated HQ-VAE in terms of its applicability to a different modality with an audio dataset.

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ICLR Conference 2024 Conference Paper

Manifold Preserving Guided Diffusion

Yutong He
Naoki Murata
Chieh-Hsin Lai
Yuhta Takida
Toshimitsu Uesaka
Dongjun Kim
Wei-Hsiang Liao 0001
Yuki Mitsufuji

Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training. In this paper, we propose Manifold Preserving Guided Diffusion (MPGD), a training-free conditional generation framework that leverages pretrained diffusion models and off-the-shelf neural networks with minimal additional inference cost for a broad range of tasks. Specifically, we leverage the manifold hypothesis to refine the guided diffusion steps and introduce a shortcut algorithm in the process. We then propose two methods for on-manifold training-free guidance using pre-trained autoencoders and demonstrate that our shortcut inherently preserves the manifolds when applied to latent diffusion models. Our experiments show that MPGD is efficient and effective for solving a variety of conditional generation applications in low-compute settings, and can consistently offer up to 3.8× speed-ups with the same number of diffusion steps while maintaining high sample quality compared to the baselines.

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IJCAI Conference 2024 Conference Paper

MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models

Yixiao Zhang
Yukara Ikemiya
Gus Xia
Naoki Murata
Marco A. Martínez-Ramírez
Wei-Hsiang Liao
Yuki Mitsufuji
Simon Dixon

Recent advances in text-to-music generation models have opened new avenues in musical creativity. However, the task of editing these generated music remains a significant challenge. This paper introduces a novel approach to edit music generated by such models, enabling the modification of specific attributes, such as genre, mood, and instrument, while maintaining other aspects unchanged. Our method transforms text editing to the latent space manipulation, and adds an additional constraint to enforce consistency. It seamlessly integrates with existing pretrained text-to-music diffusion models without requiring additional training. Experimental results demonstrate superior performance over both zero-shot and certain supervised baselines in style and timbre transfer evaluations. We also show the practical applicability of our approach in real-world music editing scenarios.

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NeurIPS Conference 2024 Conference Paper

PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher

Dongjun Kim
Chieh-Hsin Lai
Wei-Hsiang Liao
Yuhta Takida
Naoki Murata
Toshimitsu Uesaka
Yuki Mitsufuji
Stefano Ermon

The diffusion model performs remarkable in generating high-dimensional content but is computationally intensive, especially during training. We propose Progressive Growing of Diffusion Autoencoder (PaGoDA), a novel pipeline that reduces the training costs through three stages: training diffusion on downsampled data, distilling the pretrained diffusion, and progressive super-resolution. With the proposed pipeline, PaGoDA achieves a $64\times$ reduced cost in training its diffusion model on $8\times$ downsampled data; while at the inference, with the single-step, it performs state-of-the-art on ImageNet across all resolutions from $64\times64$ to $512\times512$, and text-to-image. PaGoDA's pipeline can be applied directly in the latent space, adding compression alongside the pre-trained autoencoder in Latent Diffusion Models (e. g. , Stable Diffusion). The code is available at https: //github. com/sony/pagoda.

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ICLR Conference 2024 Conference Paper

SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer

Yuhta Takida
Masaaki Imaizumi
Takashi Shibuya 0001
Chieh-Hsin Lai
Toshimitsu Uesaka
Naoki Murata
Yuki Mitsufuji

Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution. We derive *metrizable conditions*, sufficient conditions for the discriminator to serve as the distance between the distributions, by connecting the GAN formulation with the concept of sliced optimal transport. Furthermore, by leveraging these theoretical results, we propose a novel GAN training scheme called the Slicing Adversarial Network (SAN). With only simple modifications, a broad class of existing GANs can be converted to SANs. Experiments on synthetic and image datasets support our theoretical results and the effectiveness of SAN as compared to the usual GANs. We also apply SAN to StyleGAN-XL, which leads to a state-of-the-art FID score amongst GANs for class conditional generation on CIFAR10 and ImageNet 256$\times$256. The code is available at https://github.com/sony/san.

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ICML Conference 2023 Conference Paper

FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation

Chieh-Hsin Lai
Yuhta Takida
Naoki Murata
Toshimitsu Uesaka
Yuki Mitsufuji
Stefano Ermon

Score-based generative models (SGMs) learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise. These perturbed data densities are linked together by the Fokker-Planck equation (FPE), a partial differential equation (PDE) governing the spatial-temporal evolution of a density undergoing a diffusion process. In this work, we derive a corresponding equation called the score FPE that characterizes the noise-conditional scores of the perturbed data densities (i. e. , their gradients). Surprisingly, despite the impressive empirical performance, we observe that scores learned through denoising score matching (DSM) fail to fulfill the underlying score FPE, which is an inherent self-consistency property of the ground truth score. We prove that satisfying the score FPE is desirable as it improves the likelihood and the degree of conservativity. Hence, we propose to regularize the DSM objective to enforce satisfaction of the score FPE, and we show the effectiveness of this approach across various datasets.

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ICML Conference 2023 Conference Paper

GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration

Naoki Murata
Koichi Saito
Chieh-Hsin Lai
Yuhta Takida
Toshimitsu Uesaka
Yuki Mitsufuji
Stefano Ermon

Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements. However, existing approaches require knowledge of the linear operator. In this paper, we propose GibbsDDRM, an extension of Denoising Diffusion Restoration Models (DDRM) to a blind setting in which the linear measurement operator is unknown. GibbsDDRM constructs a joint distribution of the data, measurements, and linear operator by using a pre-trained diffusion model for the data prior, and it solves the problem by posterior sampling with an efficient variant of a Gibbs sampler. The proposed method is problem-agnostic, meaning that a pre-trained diffusion model can be applied to various inverse problems without fine-tuning. In experiments, it achieved high performance on both blind image deblurring and vocal dereverberation tasks, despite the use of simple generic priors for the underlying linear operators.

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ICML Conference 2022 Conference Paper

SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

Yuhta Takida
Takashi Shibuya 0001
Wei-Hsiang Liao 0001
Chieh-Hsin Lai
Junki Ohmura
Toshimitsu Uesaka
Naoki Murata
Shusuke Takahashi

One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned discrete representation uses only a fraction of the full capacity of the codebook, also known as codebook collapse. We hypothesize that the training scheme of VQ-VAE, which involves some carefully designed heuristics, underlies this issue. In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE). In SQ-VAE, we observe a trend that the quantization is stochastic at the initial stage of the training but gradually converges toward a deterministic quantization, which we call self-annealing. Our experiments show that SQ-VAE improves codebook utilization without using common heuristics. Furthermore, we empirically show that SQ-VAE is superior to VAE and VQ-VAE in vision- and speech-related tasks.

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