43 citations · 71 across the 29 of their papers we have counts for
11 papers · 1 filter
GAS: Improving Discretization of Diffusion ODEs via Generalized Adversarial Solver
Aleksandr Oganov, Ilya Bykov, Eva Neudachina +7
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 o…
Guided Star-Shaped Masked Diffusion
Viacheslav Meshchaninov, Egor Shibaev, Artem Makoian +5
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 reg…
Heeding the Inner Voice: Aligning ControlNet Training via Intermediate Features Feedback
Nina Konovalova, Maxim Nikolaev, Andrey Kuznetsov +1
Despite significant progress in text-to-image diffusion models, achieving precise spatial control over generated outputs remains challenging. ControlNet addresses this by introduci…
LoRA meets Riemannion: Muon Optimizer for Parametrization-independent Low-Rank Adapters
Vladimir Bogachev, Vladimir Aletov, Alexander Molozhavenko +4
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 mani…
T-LoRA: Single Image Diffusion Model Customization Without Overfitting
Vera Soboleva, Aibek Alanov, Andrey Kuznetsov +1
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 s…
Inverse-and-Edit: Effective and Fast Image Editing by Cycle Consistency Models
Ilia Beletskii, Andrey Kuznetsov, Aibek Alanov
Recent advances in image editing with diffusion models have achieved impressive results, offering fine-grained control over the generation process. However, these methods are compu…
ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models
Dmitrii Sorokin, Maksim Nakhodnov, Andrey Kuznetsov +1
Recent advances in diffusion models have led to impressive image generation capabilities, but aligning these models with human preferences remains challenging. Reward-based fine-tu…
FastFace: Tuning Identity Preservation in Distilled Diffusion via Guidance and Attention
Sergey Karpukhin, Vadim Titov, Andrey Kuznetsov +1
In latest years plethora of identity-preserving adapters for a personalized generation with diffusion models have been released. Their main disadvantage is that they are dominantly…
DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization
Shamil Ayupov, Maksim Nakhodnov, Anastasia Yaschenko +2
Personalized diffusion models have shown remarkable success in Text-to-Image (T2I) generation by enabling the injection of user-defined concepts into diverse contexts. However, bal…
Beyond Fine-Tuning: A Systematic Study of Sampling Techniques in Personalized Image Generation
Vera Soboleva, Maksim Nakhodnov, Aibek Alanov
Personalized text-to-image generation aims to create images tailored to user-defined concepts and textual descriptions. Balancing the fidelity of the learned concept with its abili…
MaterialFusion: High-Quality, Zero-Shot, and Controllable Material Transfer with Diffusion Models
Kamil Garifullin, Maxim Nikolaev, Andrey Kuznetsov +1
Manipulating the material appearance of objects in images is critical for applications like augmented reality, virtual prototyping, and digital content creation. We present Materia…