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
Published: 2025-11-19 00:23:22

Abstract: 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.