The release of Animagine XL 3.1, an open source anime-themed text-to-image model, marks significant progress in the field of image generation. This version has made many optimizations to address the shortcomings of previous versions, such as solving the problem of image overexposure, and improving the accuracy of image generation and user experience by adding aesthetic tags, update quality and year tags. In addition, the model training process and the size of the data set have been significantly improved, ensuring the high quality and diversity of generated images.
The newly released Animagine XL 3.1 is an open source animation-themed text-to-image model that has been upgraded and optimized to improve understanding of a wide range of animation works and styles. The new version solves the overexposure problem, adds aesthetic labels and updates the quality and year labels to generate images that are more in line with user needs. The label sorting method is used to improve the accuracy of the generated results. During the training process, 2x A100 80GB GPU was used for about 350 hours of training. Pre-training uses a data set containing 870,000 ordered and labeled images, which provides a deep knowledge base for the model. This model focuses on anime-style image generation, optimizing hand anatomy, image detail quality, and prompt parsing and conceptual understanding of output results.The improvements in Animagine XL 3.1 bring it to a higher level in animation-style image generation, and the open source feature also facilitates further research and application by developers and enthusiasts. It is believed that this model will play a greater role in the field of animation creation in the future and will continue to improve to bring users a better image generation experience.