Papers by Arkhipkin Vladimir
Kandinsky 3: Text-to-Image Synthesis for Multifunctional Generative Framework (2024.emnlp-demo)
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Arkhipkin Vladimir, Viacheslav Vasilev, Andrei Filatov, Igor Pavlov, Julia Agafonova, Nikolai Gerasimenko, Anna Averchenkova, Evelina Mironova, Bukashkin Anton, Konstantin Kulikov, Andrey Kuznetsov, Denis Dimitrov
| Challenge: | Text-to-image (T2I) diffusion models are popular for image manipulation, but also for video generation. |
| Approach: | They propose a novel T2I diffusion model based on latent diffusion that extends the base model for various applications. |
| Outcome: | The proposed model achieves high quality and photorealism and is 3 times faster than the base model. |
Kandinsky: An Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion (2023.emnlp-demo)
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Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Maltseva, Vladimir Arkhipkin, Igor Pavlov, Ilya Ryabov, Angelina Kuts, Alexander Panchenko, Andrey Kuznetsov, Denis Dimitrov
| Challenge: | Experimental evaluations demonstrate FID score of 8.03 on the COCO-30K dataset, marking our model as the top open source performer in terms of measurable image generation quality. |
| Approach: | They propose a latent diffusion-based model that combines image prior and latent diffusive techniques to create a text-to-image architecture. |
| Outcome: | The proposed model achieves the highest FID score among open-source models . it is compared with the state-of-the-art models on the COCO-30K dataset . |