rcg
This PyTorch-based self-supervised framework excels in generating unconditional images at 256x256 resolution on ImageNet. It closes the traditional gap between unconditional and class-conditional generation, enhancing self-representation generation techniques. Latest updates feature enhanced FID evaluation via the ADM suite and new training scripts for DiT-XL with RCG. Utilizing GPUs for efficient training, the framework also offers pre-trained weights and flexible customization options with various pixel generators such as MAGE, DiT, ADM, and LDM. Visit the project's repository for detailed setup and evaluation guidance for image generation projects.
