Kandinsky 3.0 Technical Report
Authors: name: Vladimir Arkhipkin, name: Andrei Filatov, name: Viacheslav Vasilev, name: Anastasia Maltseva, name: Said Azizov, name: Igor Pavlov, name: Julia Agafonova, name: Andrey Kuznetsov, name: Denis Dimitrov

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 progress to achieve higher quality and realism of image
generation. Compared to previous versions of Kandinsky 2.x, Kandinsky 3.0
leverages a two times larger U-Net backbone, a ten times larger text encoder
and removes diffusion mapping. We describe the architecture of the model, the
data collection procedure, the training technique, and the production system of
user interaction. We focus on the key components that, as we have identified as
a result of a large number of experiments, had the most significant impact on
improving the quality of our model compared to the others. By our side-by-side
comparisons, Kandinsky becomes better in text understanding and works better on
specific domains. Project page: https://ai-forever.github.io/Kandinsky-3