<h1 id="about-hyperanima-vice">About HyperAnima Vice</h1>HyperAnima Vice is a Turbo checkpoint based on Anima by CircleStone Labs, tuned for a more anime-forward, flat-shaded visual style.Compared with HyperAnima Turbo, this version leans more heavily into clean linework, graphic shapes, cel-style shading and stronger anime stylization, while keeping the speed and prompt flexibility of the Anima family.It is well suited for character art, portraits, posters, key visuals and stylized scenes, with a consistent look that favors bold presentation, expressive faces and clean composition.HyperAnima Vice is built for fast generation, producing strong results at very low step counts and low CFG.<h2 id="key-features">Key Features</h2><ul><li>Strong anime-inspired visual identity</li><li>Cleaner and more visible linework</li><li>Flat and cel-shaded rendering</li><li>Bold shapes and graphic contrast</li><li>Expressive faces and stylized character design</li><li>Clean composition and strong silhouette readability</li><li>Works with characters, portraits, creatures and scenes</li><li>Supports TAG-style prompting</li><li>Supports natural-language prompting</li><li>Designed for very low step counts</li></ul><h2 id="recommended-settings">Recommended Settings</h2>Sampler: ER SDE Scheduler: BETA Steps: 8–12 CFG: 1–2Sweet spot: <code>10 steps / CFG 1.5</code>Higher step counts are generally unnecessary.<h2 id="recommended-prompting">Recommended Prompting</h2>Recommended positive prefix: <code>masterpiece, best quality, score_7, safe,</code>Recommended negative: <code>worst quality, low quality, score_1, score_2, score_3, artist name, blurry, jpeg artifacts, chromatic aberration</code><h2 id="prompting">Prompting</h2>HyperAnima Vice supports both TAG-style prompts and natural language, just like other Anima-based models.Both approaches can also be mixed in the same prompt.For best results, use TAGs for character traits, appearance, outfit and composition, while using natural language to guide mood, environment, lighting and scene context.The model responds especially well to prompts that clearly describe the subject, pose, expression, framing and background.

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