12 citations · 16 across the 7 of their papers we have counts for
9 papers
When Text-to-Image Helps Editing: The Effects of Conditioning During Denoising
Lidia Troeshestova, Alexander Ustyuzhanin, Sergey Kastryulin
Unified models are trained for both instruction-based image editing and text-to-image (T2I) generation, but standard editing pipelines keep source-image conditioning throughout den…
Revisiting Classifier-Free Guidance Methods in Latent Diffusion Models
Artem Sergievskii, Artyom Turevich, Sergey Kastryulin
Inference-time quality-enhancement methods are an effective and widely adopted means of improving diffusion models without expensive retraining. We study a family of training-free…
DuET: Dual Expert Trajectories for Diffusion Image Editing
Lidia Troeshestova, Alexander Ustyuzhanin, Sergey Kastryulin
Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent source-image conditioning can limit…
Alchemist: Turning Public Text-to-Image Data into Generative Gold
Valerii Startsev, Alexander Ustyuzhanin, Alexey Kirillov +2
Pre-training equips text-to-image (T2I) models with broad world knowledge, but this alone is often insufficient to achieve high aesthetic quality and alignment. Consequently, super…
Does Diffusion Beat GAN in Image Super Resolution?
Denis Kuznedelev, Valerii Startsev, Daniil Shlenskii +1
There is a prevalent opinion that diffusion-based models outperform GAN-based counterparts in the Image Super Resolution (ISR) problem. However, in most studies, diffusion-based IS…
YaART: Yet Another ART Rendering Technology
Sergey Kastryulin, Artem Konev, Alexander Shishenya +20
In the rapidly progressing field of generative models, the development of efficient and high-fidelity text-to-image diffusion systems represents a significant frontier. This study…
QUASAR: QUality and Aesthetics Scoring with Advanced Representations
Sergey Kastryulin, Denis Prokopenko, Artem Babenko +1
This paper introduces a new data-driven, non-parametric method for image quality and aesthetics assessment, surpassing existing approaches and requiring no prompt engineering or fi…
PyTorch Image Quality: Metrics for Image Quality Assessment
Sergey Kastryulin, Jamil Zakirov, Denis Prokopenko +1
Image Quality Assessment (IQA) metrics are widely used to quantitatively estimate the extent of image degradation following some forming, restoring, transforming, or enhancing algo…
Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution
Aleksandr Belov, Joel Stadelmann, Sergey Kastryulin +1
We went below the MRI acceleration factors (a.k.a., k-space undersampling) reported by all published papers that reference the original fastMRI challenge, and then considered power…