2 citations · 12 across the 16 of their papers we have counts for
19 papers
Kandinsky S2V 1.0: Audio-Driven Avatar Video Generation
Nikolay Semenov, Artem Riabinin, Sergey Bassenko +8
We present Kandinsky S2V 1.0, an open family of speech-to-video models for controllable audio-driven human animation, comprising Kandinsky S2V 1.0 Lite (2B parameters) and Kandinsk…
Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation
Team Kandinsky, Julia Agafonova, Bulat Akhmatov +85
We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video Lite (3B parameters) and Kan…
Adding Voice Cloning to Text-to-Audio-Video Models with a Single Zero-Initialised Layer
Ivan Mikheev, Viacheslav Vasilev, Anna Dmitrienko +4
Text-to-audio-video (T2AV) generation models produce a video and its soundtrack from a textual description, but offer no control over whose voice speaks in the output. We show that…
Making Time Editable in Video Diffusion Transformers
Konstantin Kuklev, Viacheslav Vasilev, Alexander Kunitsyn +2
Modern Diffusion Transformers for video generation provide limited control over the progression of time and the editing of temporal dynamics. We propose a temporal-control methodol…
Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation
Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko +22
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 lin…
NABLA: Neighborhood Adaptive Block-Level Attention
Dmitrii Mikhailov, Aleksey Letunovskiy, Maria Kovaleva +6
Recent progress in transformer-based architectures has demonstrated remarkable success in video generation tasks. However, the quadratic complexity of full attention mechanisms rem…
VIVAT: Virtuous Improving VAE Training through Artifact Mitigation
Lev Novitskiy, Viacheslav Vasilev, Maria Kovaleva +2
Variational Autoencoders (VAEs) remain a cornerstone of generative computer vision, yet their training is often plagued by artifacts that degrade reconstruction and generation qual…
RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation
Viacheslav Vasilev, Julia Agafonova, Nikolai Gerasimenko +4
Text-to-image generation models have gained popularity among users around the world. However, many of these models exhibit a strong bias toward English-speaking cultures, ignoring…
GHOST 2.0: generative high-fidelity one shot transfer of heads
Alexander Groshev, Anastasiia Iashchenko, Pavel Paramonov +2
While the task of face swapping has recently gained attention in the research community, a related problem of head swapping remains largely unexplored. In addition to skin color tr…
Unleashing the power of novel conditional generative approaches for new materials discovery
Lev Novitskiy, Vladimir Lazarev, Mikhail Tiutiulnikov +6
For a very long time, computational approaches to the design of new materials have relied on an iterative process of finding a candidate material and modeling its properties. AI ha…
Kandinsky 3: Text-to-Image Synthesis for Multifunctional Generative Framework
Vladimir Arkhipkin, Viacheslav Vasilev, Andrei Filatov +9
Text-to-image (T2I) diffusion models are popular for introducing image manipulation methods, such as editing, image fusion, inpainting, etc. At the same time, image-to-video (I2V)…
ESQA: Event Sequences Question Answering
Irina Abdullaeva, Andrei Filatov, Mikhail Orlov +6
Event sequences (ESs) arise in many practical domains including finance, retail, social networks, and healthcare. In the context of machine learning, event sequences can be seen as…
Your Transformer is Secretly Linear
Anton Razzhigaev, Matvey Mikhalchuk, Elizaveta Goncharova +4
This paper reveals a novel linear characteristic exclusive to transformer decoders, including models such as GPT, LLaMA, OPT, BLOOM and others. We analyze embedding transformations…
OmniFusion Technical Report
Elizaveta Goncharova, Anton Razzhigaev, Matvey Mikhalchuk +6
Last year, multimodal architectures served up a revolution in AI-based approaches and solutions, extending the capabilities of large language models (LLM). We propose an \textit{Om…
MERA: A Comprehensive LLM Evaluation in Russian
Alena Fenogenova, Artem Chervyakov, Nikita Martynov +16
Over the past few years, one of the most notable advancements in AI research has been in foundation models (FMs), headlined by the rise of language models (LMs). As the models' siz…
FusionFrames: Efficient Architectural Aspects for Text-to-Video Generation Pipeline
Vladimir Arkhipkin, Zein Shaheen, Viacheslav Vasilev +3
Multimedia generation approaches occupy a prominent place in artificial intelligence research. Text-to-image models achieved high-quality results over the last few years. However,…
Kandinsky 3.0 Technical Report
Vladimir Arkhipkin, Andrei Filatov, Viacheslav Vasilev +6
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 progres…
The Shape of Learning: Anisotropy and Intrinsic Dimensions in Transformer-Based Models
Anton Razzhigaev, Matvey Mikhalchuk, Elizaveta Goncharova +3
In this study, we present an investigation into the anisotropy dynamics and intrinsic dimension of embeddings in transformer architectures, focusing on the dichotomy between encode…
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion
Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Maltseva +7
Text-to-image generation is a significant domain in modern computer vision and has achieved substantial improvements through the evolution of generative architectures. Among these,…