  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. In this report we describe the architecture of the model, the data
collection procedure, the training technique, and the production system for
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. We also describe
extensions and applications of our model, including super resolution,
inpainting, image editing, image-to-video generation, and a distilled version
of Kandinsky 3.0 - Kandinsky 3.1, which does inference in 4 steps of the
reverse process and 20 times faster without visual quality decrease. By
side-by-side human preferences comparison, Kandinsky becomes better in text
understanding and works better on specific domains. The code is available at
https://github.com/ai-forever/Kandinsky-3
