The BasilJapan model is a checkpoint-based stable diffusion model developed by Google AI. It is trained on a dataset of 1.5TB of natural images, and is able to generate high-quality images of both real and imaginary scenes.
The BasilJapan model is built on the diffusion framework, which is a type of generative model that works by gradually adding noise to an image until it reaches a desired level of realism. The BasilJapan model uses a checkpoint-based approach, which means that it saves intermediate states of the diffusion process as it runs. This allows the model to be restarted from any checkpoint, making it more efficient and easier to train.
The BasilJapan model has been shown to be effective at generating a variety of image types, including faces, landscapes, and objects. It is also able to generate images of real scenes that are indistinguishable from real photographs.
