Dmitry Baranchuk

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I am a Lead Research Scientist at Yandex Research. I mainly work on the post-training stack for diffusion models across vision and language, with a focus on efficient generation at scale. I also enjoy exploring effective diffusion spaces across domains and analysing modern diffusion architectures.

In addition to my research, I organize seminars on generative models, post paper reviews, and teach the Visual GenAI course at the Yandex School of Data Analysis.

Previously: I received my Ph.D. in Computer Science at HSE University in December 2024, advised by Artem Babenko. In 2022, I was a research intern at Meta AI, where I worked on large-scale vector search and retrieval-augmented LLMs, advised by Matthijs Douze and Zeki Yalniz. Earlier, in 2019, I interned at ETH Zurich, working with Vincent Fortuin and Stephan Mandt on generative modeling for missing data imputation.

Selected Papers

Generative Modeling
  1. arXiv
    Representation-Space MMD for Diffusion Language Models
    Ilya Drobyshevskiy, Ilia Sudakov, Maksim Semenov, Denis Kuznedelev, Maksim Ignatov, Pavel Temirchev, Nikita Balagansky, Viacheslav Meshchaninov, Nikita Gushchin, and Dmitry Baranchuk
    Oct 2026
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  2. arXiv
    Registers Matter for Pixel-Space Diffusion Transformers
    Nikita Starodubcev, Ilia Sudakov, Ilya Drobyshevskiy, Artem Babenko, and Dmitry Baranchuk
    May 2026
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  3. ICLR
    Rethinking Global Text Conditioning in Diffusion Transformers
    Nikita Starodubcev, Daniil Pakhomov, Zongze Wu, Ilya Drobyshevskiy, Yuchen Liu, Zhonghao Wang, Yuqian Zhou, Zhe Lin, and Dmitry Baranchuk
    In International Conference on Learning Representations, Feb 2026
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  4. ICLR
    Scale-wise Distillation of Diffusion Models
    Nikita Starodubcev, Ilya Drobyshevskiy, Denis Kuznedelev, Artem Babenko, and Dmitry Baranchuk
    In International Conference on Learning Representations, Feb 2026
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  5. CVPR
    Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis
    Anton Voronov, Denis Kuznedelev, Mikhail Khoroshikh, Valentin Khrulkov, and Dmitry Baranchuk
    In Computer Vision and Pattern Recognition (CVPR), Jun 2025
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  6. NeurIPS
    Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps
    Nikita Starodubcev, Mikhail Khoroshikh, Artem Babenko, and Dmitry Baranchuk
    In Neural Information Processing Systems, Dec 2024
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  7. arXiv
    Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization
    Vage Egiazarian*, Denis Kuznedelev*, Anton Voronov*, Ruslan Svirschevski, Michael Goin, Daniil Pavlov, Dan Alistarh, and Dmitry Baranchuk
    Sep 2024
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  8. CVPR
    Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models
    Nikita Starodubcev, Artem Fedorov, Artem Babenko, and Dmitry Baranchuk
    In Computer Vision and Pattern Recognition (CVPR), Jun 2024
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  9. ICML
    TabDDPM: Modelling Tabular Data with Diffusion Models
    Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko
    In International Conference on Machine Learning, Jul 2023
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  10. ACL Demo
    Petals: Collaborative Inference and Fine-tuning of Large Models
    Alexander Borzunov*, Dmitry Baranchuk*, Tim Dettmers*, Max Ryabinin*, Younes Belkada*, Artem Chumachenko, Pavel Samygin, and Colin Raffel
    In Association for Computational Linguistics (System Demonstrations), Jul 2023
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  11. ICLR
    Label-Efficient Semantic Segmentation with Diffusion Models
    Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko
    In International Conference on Learning Representations, May 2022
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  12. AISTATS
    GP-VAE: Deep Probabilistic Time Series Imputation
    Vincent Fortuin*, Dmitry Baranchuk*, Gunnar Raetsch, and Stephan Mandt
    In International Conference on Artificial Intelligence and Statistics, Aug 2020
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Large-scale Vector Search
  1. ICCV
    DeDrift: Robust Similarity Search under Content Drift
    Dmitry Baranchuk, Matthijs Douze, Yash Upadhyay, and I. Zeki Yalniz
    In International Conference on Computer Vision (ICCV), Oct 2023
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  2. NeurIPS Competition
    Results of the NeurIPS’21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
    Harsha Vardhan Simhadri, George Williams, Martin Aumüller, Matthijs Douze, Artem Babenko, Dmitry Baranchuk, Qi Chen, Lucas Hosseini, Ravishankar Krishnaswamny, Gopal Srinivasa, Suhas Jayaram Subramanya, and Jingdong Wang
    In NeurIPS 2021 Competitions and Demonstrations Track, Dec 2022
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  3. ICML
    Learning to Route in Similarity Graphs
    Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, and Artem Babenko
    In International Conference on Machine Learning, Jun 2019
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  4. ECCV
    Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
    Dmitry Baranchuk, Artem Babenko, and Yury Malkov
    In European Conference on Computer Vision (ECCV), Sep 2018
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