JoliGEN: Generative AI Toolset with GANs and Diffusion for Real-World Applications

JoliGEN is an integrated framework for training custom generative AI image-to-image models

Main Features

JoliGEN support 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 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.
JoliGEN has a large scope of options and parameters. To not get overwhelmed, start with Quick Start. There are then links to more detailed documentation on models, dataset formats, and data augmentation.

Use cases

AR and metaverse: replace any image element with super-realistic objects
Smart data augmentation: test / train sets augmentation
Image manipulation: seamlessly insert or remove objects/elements in images
Image to image translation while preserving semantic, e.g. existing source dataset annotations
Simulation to reality translation while preserving elements, metrics, ...
Image generation to enrich datasets, e.g. counter dataset imbalance, increase test sets, ...

This is achieved by combining conditioned generator architectures for fine-grained control, bags of discriminators, configurable neural networks and losses that ensure conservation of fundamental elements between source and target images.