JoliGEN: Generative AI Toolset with GANs and Diffusion for Real-World Applications
JoliGEN provides easy-to-use generative AI for image to image transformations.
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 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 to image translation to cope with scarce data
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.
Example results
Image translation while preserving the class
Mario to Sonic while preserving the action (running, jumping, ...)
Object insertion
Car insertion (BDD100K) with Diffusion
Glasses insertion (FFHQ) with Diffusion
Object removal
Glasses removal with GANs
Style transfer while preserving label boxes (e.g. cars, pedestrians, street signs, ...)
Day to night (BDD100K) with Transformers and GANs
Clear to snow (BDD100K) by applying a generator multiple times to add snow incrementally
Clear to overcast (BDD100K)
Clear to rainy (BDD100K)