Open-source alternatives to Data Efficient Gans

Various Improvements to make StyleGAN2 more suitible to be trained on Google Colab Supports Non-Square images, for example, 768x512, which basically as 6x4 (x2^7), or 640x384 as 5x3 (x2^7), etc. Supports vertical mirror augmentation Supports train from latest pkl automatically Optimized dataset…

Generated using only 100 images of Obama, grumpy cats, pandas, the Bridge of Sighs, the Medici Fountain, the Temple of Heaven, without pre-training.

Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)

Imaginaire is a PyTorch image synthesis library and neural image translation framework designed to generate high-resolution synthetic visual content. It functions as a deep learning visual generator that maps semantic images and videos into photorealistic versions using both supervised and unsupervised methods. The project includes a specialized tool for rendering 3D environments, which converts block-based world representations into photorealistic scenes while maintaining long-term visual consistency. It further supports photorealistic video translation that utilizes reference images to ensu

StyleGAN2 is a TensorFlow generative adversarial network and image synthesis model designed to produce high-resolution synthetic visual content. It functions as a deep learning architecture that learns patterns from image datasets to synthesize new images. The project includes a latent space projection tool for mapping existing images to latent vectors to analyze their representation within a generative model. It also provides an image quality evaluation framework to measure the visual fidelity and diversity of synthetic outputs. The system covers the full generative pipeline, including imag

Download the conditional WikiArt model

This is a generative AI model library containing a collection of PyTorch and TensorFlow implementations for creating synthetic data and modeling complex probability distributions. It serves as a multi-framework repository of deep learning models designed for learning and replicating data patterns. The project provides specialized implementation suites for several generative architectures. This includes Generative Adversarial Networks using competing generator and discriminator models, Variational Autoencoder frameworks that map data to a latent space, and Restricted Boltzmann Machine and Deep