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Aibek Alanov

14 accepted papers

2026

GAS: Improving Discretization of Diffusion ODEs via Generalized Adversarial Solver

Aleksandr Oganov, Ilya Bykov, Eva Neudachina, Mishan Aliev, Alexander Tolmachev, Alexander Sidorov +4
ICLR 2026poster

While diffusion models achieve state-of-the-art generation quality, they still suffer from computationally expensive sampling. Recent works address this issue with gradient-based optimization methods that distill a few-step ODE diffusion solver from the full sampling process, reducing the number of…

Cited by 0Source ↗code ↗Scholar ↗
2026

Guided Star-Shaped Masked Diffusion

Viacheslav Meshchaninov, Egor Shibaev, Artem Makoian, Ivan Klimov, Nikita Balagansky, Daniil Gavrilov +2
ICML 2026poster

The performance of pre-trained masked diffusion models is often constrained by their sampling procedure, which makes decisions irreversible and struggles in low-step generation regimes. We introduce a novel sampling algorithm that works with pre-trained models and, after a lightweight fine-tuning of…

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2026

LoRA meets Riemannion: Muon Optimizer for Parametrization-independent Low-Rank Adapters

Vladimir Bogachev, Vladimir Aletov, Alexander Molozhavenko, Denis Bobkov, Vera Soboleva, Aibek Alanov +1
ICLR 2026poster

This work presents a novel, fully Riemannian framework for Low-Rank Adaptation (LoRA) that geometrically treats low-rank adapters by optimizing them directly on the fixed-rank manifold. This formulation eliminates the parametrization ambiguity present in standard Euclidean optimizers. Our framework…

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2026

OrthoFuse: Training-free Riemannian Fusion of Orthogonal Style-Concept Adapters for Diffusion Models

Ali Aliev, Kamil Garifullin, Nikolay Yudin, Vera Soboleva, Alexander Molozhavenko, Ivan Oseledets +2
CVPR 2026

In a rapidly growing field of model training there is a constant practical interest in parameter-efficient fine-tuning and various techniques that use a small amount of training data to adapt the model to a narrow task. However, there is an open question: how to combine several adapters tuned for di

Cited by 0Source ↗code ↗Scholar ↗
2026

T-LoRA: Single Image Diffusion Model Customization Without Overfitting

Vera Soboleva, Aibek Alanov, Andrey Kuznetsov, Konstantin Sobolev
AAAI 2026technical

While diffusion model fine-tuning offers a powerful approach for customizing pre-trained models to generate specific objects, it frequently suffers from overfitting when training samples are limited, compromising both generalization capability and output diversity. This paper tackles the challenging

Cited by 11Source ↗PDF ↗Scholar ↗
2025

CLEAR: Character Unlearning in Textual and Visual Modalities

Alexey Dontsov, Dmitrii Korzh, Alexey Zhavoronkin, Boris Mikheev, Denis Bobkov, Aibek Alanov +3
ACL 2025finding

Machine Unlearning (MU) is critical for removing private or hazardous information from deep learning models. While MU has advanced significantly in unimodal (text or vision) settings, multimodal unlearning (MMU) remains underexplored due to the lack of open benchmarks for evaluating cross-modal data…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Group and Shuffle: Efficient Structured Orthogonal Parametrization

Mikhail Gorbunov, Kolya Yudin, Vera Soboleva, Aibek Alanov, Alexey Naumov, Maxim Rakhuba
NeurIPS 2024poster

The increasing size of neural networks has led to a growing demand for methods of efficient finetuning. Recently, an orthogonal finetuning paradigm was introduced that uses orthogonal matrices for adapting the weights of a pretrained model. In this paper, we introduce a new class of structured matri…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach

Maxim Nikolaev, Mikhail Kuznetsov, Dmitry Vetrov, Aibek Alanov
NeurIPS 2024poster

Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on. This task is challenging due to the need to adapt to various photo poses, the sensitivity of hairstyles, and the lack of objective metrics. The current state of the art…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2024

The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN Inversion and High Quality Image Editing

Denis Bobkov, Vadim Titov, Aibek Alanov, Dmitry Vetrov
CVPR 2024poster

The task of manipulating real image attributes through StyleGAN inversion has been extensively researched. This process involves searching latent variables from a well-trained StyleGAN generator that can synthesize a real image modifying these latent variables and then synthesizing an image with the…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2023

HIFI++: A Unified Framework for Bandwidth Extension and Speech Enhancement

Pavel Andreev, Aibek Alanov, Oleg Ivanov, Dmitry P. Vetrov
ICASSP 2023accepted

Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models. In this paper, we show that this success can be extended to other tasks of conditional audio generation. In particular, building upon HiFi vo…

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2023

Star-Shaped Denoising Diffusion Probabilistic Models

Andrey Okhotin, Dmitry Molchanov, Arkhipkin Sergeevich Vladimir, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov +1
NeurIPS 2023poster

Denoising Diffusion Probabilistic Models (DDPMs) provide the foundation for the recent breakthroughs in generative modeling. Their Markovian structure makes it difficult to define DDPMs with distributions other than Gaussian or discrete. In this paper, we introduce Star-Shaped DDPM (SS-DDPM). Its *s…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2023

StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation

Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry Vetrov
ICCV 2023poster

Domain adaptation of GANs is a problem of fine-tuning GAN models pretrained on a large dataset (e.g. StyleGAN) to a specific domain with few samples (e.g. painting faces, sketches, etc.). While there are many methods that tackle this problem in different ways, there are still many important question…

Cited by 9PDF ↗code ↗Scholar ↗
2022

HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks

Aibek Alanov, Vadim Titov, Dmitry P. Vetrov
NeurIPS 2022accept

Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training data. In this work, we significantly improve this framework by proposing an extremely compact parameter space for fine-tuni…

Cited by 34Source ↗PDF ↗code ↗Scholar ↗