Summary of Kandinsky 3: Text-to-image Synthesis For Multifunctional Generative Framework, by Vladimir Arkhipkin et al.
Kandinsky 3: Text-to-Image Synthesis for Multifunctional Generative Framework
by Vladimir Arkhipkin, Viacheslav Vasilev, Andrei Filatov, Igor Pavlov, Julia Agafonova, Nikolai Gerasimenko, Anna Averchenkova, Evelina Mironova, Anton Bukashkin, Konstantin Kulikov, Andrey Kuznetsov, Denis Dimitrov
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Kandinsky 3 is a novel text-to-image (T2I) model based on latent diffusion, achieving high-quality and photorealism in image manipulation tasks like editing, fusion, inpainting, and outpainting. The architecture’s simplicity and efficiency enable its adaptation for various generation tasks. We extend the base T2I model to create a multifunctional system for text-guided inpainting/outpainting, image fusion, text-image fusion, image variations generation, I2V, and T2V generation. A distilled version of the T2I model enables inference in 4 steps without reducing image quality, making it 3 times faster than the base model. We release source code, checkpoints, and a user-friendly demo system for public testing. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Kandinsky 3 is a new way to make fake images from text. It’s really good at changing pictures, combining them, or creating new ones based on what someone writes. This tool can also turn text into videos or vice versa. The people who made it wanted to make sure it was easy to use and fast, so they created different versions that work well together. They’re sharing the code and a way to try it out for free. |
Keywords
» Artificial intelligence » Diffusion » Inference