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Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation
Vladimir Ark · 2026-05-27 · via cs.AI updates on arXiv.org

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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.
Comments: Website: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2511.14993 [cs.CV]
  (or arXiv:2511.14993v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2511.14993

arXiv-issued DOI via DataCite

Submission history

From: Viacheslav Vasilev [view email]
[v1] Wed, 19 Nov 2025 00:23:22 UTC (27,561 KB)
[v2] Thu, 20 Nov 2025 13:09:51 UTC (27,561 KB)
[v3] Tue, 26 May 2026 15:07:50 UTC (27,560 KB)

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