6 papers
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…
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…