anima nsfw on Anyken
anima nsfw on Anyken: compare the model examples with your intended input and output. Review the capabilities documented below, then use the controls actually offered in the workspace. Available settings and credits are shown before submission.
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anima nsfw: Model Overview
Anima is a 2 billion parameter text-to-image model designed for anime and non-photorealistic illustration. It supports safety tags that allow users to specify content boundaries, including NSFW generation. Anima nsfw is trained on Danbooru-style tags and natural language captions, with specific tags for content safety levels.
Safety Tag System and Content Levels
Anima uses a four-level safety tag system: safe, sensitive, nsfw, and explicit. These tags are placed at the beginning of prompts in the quality/meta/year/safety tags section. The recommended positive prefix includes "safe" as default, while the negative prompt typically includes quality tags like "worst quality, low quality" but not safety tags by default. Users can adjust safety tags to match their intended content. Anima nsfw was trained on datasets that include these safety classifications, allowing it to understand and respect content boundaries. When using NSFW tags, the model generates content accordingly, though the base version may produce unexpected results with short prompts. The safety tag system gives users granular control over content generation, from completely safe illustrations to explicit material. This system is particularly important for artists and creators who need to generate content for different audiences or purposes.
Prompt Structure for NSFW Content
To generate NSFW content with Anima, users must include the appropriate safety tag in the prompt structure. The recommended tag order is: [quality/meta/year/safety tags] [1girl/1boy/1other etc] [character] [series] [artist] [general tags]. Safety tags like "nsfw" or "explicit" should be placed in the first section alongside quality and meta tags. Anima nsfw was trained with random tag dropout, meaning not every relevant tag needs to be included. However, for reliable NSFW generation, including the safety tag is essential. Users should also write sufficiently detailed prompts to avoid undesired content, as short prompts can give unexpected results. The model's understanding of safety tags comes from its training on Danbooru-style datasets, which include these classifications. For best results with NSFW content, combine safety tags with detailed descriptions of the desired scene, characters, and actions.