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cs.CV2026
OrthoFuse: Training-free Riemannian Fusion of Orthogonal Style-Concept Adapters for Diffusion Models
Ali Aliev, Kamil Garifullin, Nikolay Yudin +5
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 da…
cs.CV2026
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…
cs.CV2025
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…