Kandinsky 3.0 Technical Report
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 progress to achieve higher quality and realism of image
generation. Compared to previous versions of Kandinsky 2.x, Kandinsky 3.0
leverages a two times larger U-Net backbone, a ten times larger text encoder
and removes diffusion mapping. We describe the architecture of the model, the
data collection procedure, the training technique, and the production system of
user interaction. We focus on the key components that, as we have identified as
a result of a large number of experiments, had the most significant impact on
improving the quality of our model compared to the others. By our side-by-side
comparisons, Kandinsky becomes better in text understanding and works better on
specific domains. Project page: https://ai-forever.github.io/Kandinsky-3
6571304087a92b76924c7a31
Vladimir Arkhipkin
admin_assigned
2023-12-07T12:10:24.449Z
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Arkhipkin Vladimir
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Andrei Filatov
admin_assigned
2023-12-07T12:06:30.086Z
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Andrei Filatov
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anvilarth
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Viacheslav Vasilev
admin_assigned
2023-12-07T12:06:36.824Z
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Viacheslav Vasilev
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vvasilev
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Anastasia Maltseva
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Said Azizov
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2023-12-08T11:21:29.424Z
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Said Azizov
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stazizov
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Igor Pavlov
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2023-12-07T12:11:09.412Z
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Igor Pavlov
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Julia Agafonova
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Andrey Kuznetsov
admin_assigned
2023-12-07T12:09:12.899Z
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Andrey Kuznetsov
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kuznetsoffandrey
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Denis Dimitrov
claimed_verified
2024-06-28T13:07:34.769Z
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Denis Dimitrov
user
dendimitrov
Kandinsky 3.0 Technical Report
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 progress to achieve higher quality and realism of image
generation. Compared to previous versions of Kandinsky 2.x, Kandinsky 3.0
leverages a two times larger U-Net backbone, a ten times larger text encoder
and removes diffusion mapping. We describe the architecture of the model, the
data collection procedure, the training technique, and the production system of
user interaction. We focus on the key components that, as we have identified as
a result of a large number of experiments, had the most significant impact on
improving the quality of our model compared to the others. By our side-by-side
comparisons, Kandinsky becomes better in text understanding and works better on
specific domains. Project page: https://ai-forever.github.io/Kandinsky-3
Kandinsky 3.0, a large-scale text-to-image model based on latent diffusion, improves quality and realism through a larger architecture and advanced text understanding.
latent diffusion U-Net text encoder diffusion mapping text-to-image model architecture text understanding specific domains