Moebius: 0.2B image inpainting model with 10B-level performance

The Moebius model has been released, featuring a 0.2 billion parameter architecture specifically designed for image inpainting tasks. Despite its smaller size, it achieves performance levels comparable to models with around 10 billion parameters, indicating significant efficiency in training and inference. This advancement is crucial for practitioners as it allows for high-quality image generation with reduced computational resources.