AnimagineXL v2

Overview Animagine XL 2.0 is an advanced latent text-to-image diffusion model designed to create high-resolution, detailed anime images. It's fine-tuned from Stable Diffusion XL 1.0 using a high-quality anime-style image dataset. This model, an upgrade from Animagine XL 1.0, excels in capturing the diverse and distinct styles of anime art, offering improved image quality and aesthetics.

Model Details Developed by: Linaqruf Model type: Diffusion-based text-to-image generative model Model Description: This is a model that excels in creating detailed and high-quality anime images from text descriptions. It's fine-tuned to understand and interpret a wide range of descriptive prompts, turning them into stunning visual art. License: CreativeML Open RAIL++-M License Finetuned from model: Stable Diffusion XL 1.0

Usage Guidelines Prompt Guidelines Animagine XL 2.0 responds effectively to natural language descriptions for image generation. For example:

A girl with mesmerizing blue eyes looks at the viewer. Her long, white hair is adorned with blue butterfly hair ornaments.

However, to achieve optimal results, it's recommended to use Danbooru-style tagging in your prompts, as the model is trained with images labeled using these tags. For instance:

1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck

This model incorporates quality and rating modifiers during dataset processing, influencing image generation based on specified criteria:

Quality Modifiers Quality Modifier Score Criterion masterpiece >150 best quality 100-150 high quality 75-100 medium quality 25-75 normal quality 0-25 low quality -5-0 worst quality <-5 Rating Modifiers Rating Modifier Rating Criterion

• general
• sensitive nsfw questionable nsfw explicit To guide the model towards generating high-aesthetic images, use negative prompts like:

lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry

For higher quality outcomes, prepend prompts with:

masterpiece, best quality

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 Examples Training and Hyperparameters Animagine XL was trained on a 1x A100 GPU with 80GB memory. The training process encompassed two stages: Feature Alignment Stage: Utilized 170k images to acquaint the model with basic anime concepts. Aesthetic Tuning Stage: Employed 83k high-quality synthetic datasets to refine the model's art style. Hyperparameters Global Epochs: 20 Learning Rate: 1e-6 Batch Size: 32 Train Text Encoder: True Image Resolution: 1024 (2048 x 512) Mixed-Precision: fp16 Note: The model's training configuration is subject to future enhancements.

Training Config Configuration Item Animagine XL 1.0 Animagine XL 2.0 GPU A100 40G A100 80G Dataset 8000 images 170k + 83k images Global Epochs Not Applicable 20 Learning Rate 4e-7 1e-6 Batch Size 16 32 Train Text Encoder False True Train Special Tags False True Image Resolution 1024 1024 Bucket Resolution 1024 x 256 2048 x 512 Caption Dropout 0.5 0 Direct Use The Animagine XL 2.0 model, with its advanced text-to-image diffusion capabilities, is highly versatile and can be applied in various fields:

Art and Design: This model is a powerful tool for artists and designers, enabling the creation of unique and high-quality anime-style artworks. It can serve as a source of inspiration and a means to enhance creative processes. Education: In educational contexts, Animagine XL 2.0 can be used to develop engaging visual content, assisting in teaching concepts related to art, technology, and media. Entertainment and Media: The model's ability to generate detailed anime images makes it ideal for use in animation, graphic novels, and other media production, offering a new avenue for storytelling. Research: Academics and researchers can leverage Animagine XL 2.0 to explore the frontiers of AI-driven art generation, study the intricacies of generative models, and assess the model's capabilities and limitations. Personal Use: Anime enthusiasts can use Animagine XL 2.0 to bring their imaginative concepts to life, creating personalized artwork based on their favorite genres and styles. Limitations The Animagine XL 2.0 model, while advanced in its capabilities, has certain limitations that users should be aware of:

Style Bias: The model exhibits a bias towards a specific art style, as it was fine-tuned using approximately 80,000 images with a similar aesthetic. This may limit the diversity in the styles of generated images. Rendering Challenges: There are occasional inaccuracies in rendering hands or feet, which may not always be depicted with high fidelity. Realism Constraint: Animagine XL 2.0 is not designed for generating realistic images, given its focus on anime-style content. Natural Language Limitations: The model may not perform optimally when prompted with natural language descriptions, as it is tailored more towards anime-specific terminologies and styles. Dataset Scope: Currently, the model is primarily effective in generating content related to the 'Honkai' series and 'Genshin Impact' due to the dataset's scope. Expansion to include more diverse concepts is planned for future iterations. NSFW Content Generation: The model is not proficient in generating NSFW content, as it was not a focus during the training process, aligning with the intention to promote safe and appropriate content generation. Acknowledgements We extend our gratitude to:

Chai AI: For the open-source grant (Chai AI) supporting our research. Kohya SS: For providing the essential training script. Camenduru Server Community: For invaluable insights and support. NovelAI: For inspiring the Quality Tags feature. Waifu DIffusion Team: for inspiring the optimal training pipeline with bigger datasets. Shadow Lilac: For the image classification model (shadowlilac/aesthetic-shadow) crucial in our quality assessment process.