Stable Diffusion generates images from text prompts and is available through Replicate’s text-to-image API.
joliGEN
joliGEN provides generative AI tools for image and video tasks using GANs and diffusion models.

About joliGEN
joliGEN is a GitHub repository for an integrated framework for training custom generative AI image-to-image models. It implements GAN, diffusion, and consistency models for paired and unpaired image translation, including domain and style adaptation. The project aims to preserve semantic content such as image classes, object masks, and annotations during translation.
The homepage lists use cases including augmented reality, object insertion and removal, simulation-to-real translation, and image generation to enrich datasets. A server with a REST API is provided for deployment and use, and the project includes a Dockerized server. The homepage states support for both CPU and GPU. It also notes that the framework has a large number of options and parameters, and it points to Quickstarts for new users.
Key features
- Image-to-image translation with GAN, diffusion, and consistency models
- Paired and unpaired image translation for domain and style adaptation
- Preservation of bounding boxes, masks, and classes during translation
- Discriminator options including projected and vision-aided models
- REST API server with Docker packaging
- Output quality metrics including FID, PSNR, and KID
- Combines GAN, diffusion, and consistency model approaches in one framework.
- Provides a REST API server and a Dockerized server for deployment.
- Supports both CPU and GPU.
- Has a large scope of options and parameters, which the homepage notes can be overwhelming for new users.
joliGEN at a glance
- Website
- github.com/jolibrain/joliGEN
- Category
- AI & Machine Learning › AI Image Generators
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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