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What is this Python project?
JoliGEN is an integrated framework for training custom generative AI image-to-image models
Main Features:
JoliGEN implements both GAN and Diffusion models for unpaired and paired image to image translation tasks, including domain and style adaptation with conservation of semantics such as image and object classes, masks, ...
JoliGEN generative AI capabilities are targeted at real world applications such as Controled Image Generation, Augmented Reality, Dataset Smart Augmentation and object insertion, Synthetic to Real transforms.
JoliGEN allows for fast and stable training with astonishing results. A server with REST API is provided that allows for simplified deployment and usage.
What's the difference between this Python project and similar ones?
This project combines various state-of-the-art methods and model architectures for training image-to-image generative models (such as CycleGAN, Palette, Cut...), open-source projects (like Segment Anything), and JoliBrain open-source work.
There isn't really a similar project, at least not that we've heard of.
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