Animagine XL V3.1

Animagine XL 3.1 is an update in the Animagine XL V3 series, enhancing the previous version, Animagine XL 3.0. This open-source, anime-themed text-to-image model has been improved for generating anime-style images with higher quality. It includes a broader range of characters from well-known anime series, an optimized dataset, and new aesthetic tags for better image creation. Built on Stable Diffusion XL, Animagine XL 3.1 aims to be a valuable resource for anime fans, artists, and content creators by producing accurate and detailed representations of anime characters. Model Details - Developed by: - In collaboration with: - Model type: Diffusion-based text-to-image generative model - Model Description: Animagine XL 3.1 generates high-quality anime images from textual prompts. It boasts enhanced hand anatomy, improved concept understanding, and advanced prompt interpretation. - License: - Fine-tuned from: Usage Guidelines Tag Ordering For optimal results, it's recommended to follow the structured prompt template because we train the model like this: 1girl/1boy, character name, from what series, everything else in any order. Special Tags Animagine XL 3.1 utilizes special tags to steer the result toward quality, rating, creation date and aesthetic. While the model can generate images without these tags, using them can help achieve better results. Quality Modifiers Quality tags now consider both scores and post ratings to ensure a balanced quality distribution. We've refined labels for greater clarity, such as changing 'high quality' to 'great quality'. Quality Modifier Score Criterion masterpiece > 95% best quality > 85% & ≤ 95% great quality > 75% & ≤ 85% good quality > 50% & ≤ 75% normal quality > 25% & ≤ 50% low quality > 10% & ≤ 25% worst quality ≤ 10% Rating Modifiers We've also streamlined our rating tags for simplicity and clarity, aiming to establish global rules that can be applied across different models. For example, the tag 'rating: general' is now simply 'general', and 'rating: sensitive' has been condensed to 'sensitive'. Rating Modifier Rating Criterion safe General sensitive Sensitive nsfw Questionable explicit, nsfw Explicit Year Modifier We've also redefined the year range to steer results towards specific modern or vintage anime art styles more accurately. This update simplifies the range, focusing on relevance to current and past eras. Year Tag Year Range newest 2021 to 2024 recent 2018 to 2020 mid 2015 to 2017 early 2011 to 2014 oldest 2005 to 2010 Aesthetic Tags We've enhanced our tagging system with aesthetic tags to refine content categorization based on visual appeal. These tags are derived from evaluations made by a specialized ViT (Vision Transformer) image classification model, specifically trained on anime data. For this purpose, we utilized the model , which assesses the aesthetic value of content before it undergoes training. This ensures that each piece of content is not only relevant and accurate but also visually appealing. Aesthetic Tag Score Range very aesthetic > 0.71 aesthetic > 0.45 & 0.27 & < 0.45 very displeasing ≤ 0.27 Recommended settings To guide the model towards generating high-aesthetic images, use negative prompts like: nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract] For higher quality outcomes, prepend prompts with: masterpiece, best quality, very aesthetic, absurdres it’s recommended to use a lower classifier-free guidance (CFG Scale) of around 5-7, sampling steps below 30, and to use Euler Ancestral (Euler a) as a sampler. Multi Aspect Resolution This model supports generating images at the following dimensions: Dimensions Aspect Ratio 1024 x 1024 1:1 Square 1152 x 896 9:7 896 x 1152 7:9 1216 x 832 19:13 832 x 1216 13:19 1344 x 768 7:4 Horizontal 768 x 1344 4:7 Vertical 1536 x 640 12:5 Horizontal 640 x 1536 5:12 Vertical Acknowledgements The development and release of Animagine XL 3.1 would not have been possible without the invaluable contributions and support from the following individuals and organizations: - : Our collaboration partner and sponsor. - : For providing the aesthetic classification model, . - : For their custom learning rate scheduler, adapted from . - : For their comprehensive training scripts. - Cagliostrolab Collaborators: For their dedication to model training, project management, and data curation. - Early Testers: For their valuable feedback and quality assurance efforts. - NovelAI: For their innovative approach to aesthetic tagging, which served as an inspiration for our implementation. Thank you all for your support and expertise in pushing the boundaries of anime-style image generation. Limitations While Animagine XL 3.1 represents a significant advancement in anime-style image generation, it is important to acknowledge its limitations: - Anime-Focused: This model is specifically designed for generating anime-style images and is not suitable for creating realistic photos. - Prompt Complexity: This model may not be suitable for users who expect high-quality results from short or simple prompts. The training focus was on concept understanding rather than aesthetic refinement, which may require more detailed and specific prompts to achieve the desired output. - Prompt Format: Animagine XL 3.1 is optimized for Danbooru-style tags rather than natural language prompts. For best results, users are encouraged to format their prompts using the appropriate tags and syntax. - Anatomy and Hand Rendering: Despite the improvements made in anatomy and hand rendering, there may still be instances where the model produces suboptimal results in these areas. - Dataset Size: The dataset used for training Animagine XL 3.1 consists of approximately 870,000 images. When combined with the previous