2 citations · 4 across the 5 of their papers we have counts for
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Kandinsky 3: Text-to-Image Synthesis for Multifunctional Generative Framework
Vladimir Arkhipkin, Viacheslav Vasilev, Andrei Filatov +9
Text-to-image (T2I) diffusion models are popular for introducing image manipulation methods, such as editing, image fusion, inpainting, etc. At the same time, image-to-video (I2V)…
Kandinsky 3.0 Technical Report
Vladimir Arkhipkin, Andrei Filatov, Viacheslav Vasilev +6
We present Kandinsky 3.0, a large-scale text-to-image generation model based on latent diffusion, continuing the series of text-to-image Kandinsky models and reflecting our progres…
Simple Control Baselines for Evaluating Transfer Learning
Andrei Atanov, Shijian Xu, Onur Beker +2
Transfer learning has witnessed remarkable progress in recent years, for example, with the introduction of augmentation-based contrastive self-supervised learning methods. While a…