Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation
Authors: name: Vladimir Arkhipkin, name: Vladimir Korviakov, name: Nikolai Gerasimenko, name: Denis Parkhomenko, name: Viacheslav Vasilev, name: Alexey Letunovskiy, name: Maria Kovaleva, name: Nikolai Vaulin, name: Ivan Kirillov, name: Lev Novitskiy, name: Denis Koposov, name: Nikita Kiselev, name: Alexander Varlamov, name: Dmitrii Mikhailov, name: Vladimir Polovnikov, name: Andrey Shutkin, name: Ilya Vasiliev, name: Julia Agafonova, name: Anastasiia Kargapoltseva, name: Anna Dmitrienko, name: Anastasia Maltseva, name: Anna Averchenkova, name: Olga Kim, name: Tatiana Nikulina, name: Denis Dimitrov

This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis. The framework comprises three core line-up of models: Kandinsky 5.0 Image Lite - a line-up of 6B parameter image generation models, Kandinsky 5.0 Video Lite - a fast and lightweight 2B parameter text-to-video and image-to-video models, and Kandinsky 5.0 Video Pro - 19B parameter models that achieves superior video generation quality. We provide a comprehensive review of the data curation lifecycle - including collection, processing, filtering and clustering - for the multi-stage training pipeline that involves extensive pre-training and incorporates quality-enhancement techniques such as self-supervised fine-tuning (SFT) and reinforcement learning (RL)-based post-training. We also present novel architectural, training, and inference optimizations that enable Kandinsky 5.0 to achieve high generation speeds and state-of-the-art performance across various tasks, as demonstrated by human evaluation. As a large-scale, publicly available generative framework, Kandinsky 5.0 leverages the full potential of its pre-training and subsequent stages to be adapted for a wide range of generative applications. We hope that this report, together with the release of our open-source code and training checkpoints, will substantially advance the development and accessibility of high-quality generative models for the research community.