anima-prompting

name anima-prompting disable-model-invocation true description Prompting rules for the CircleStone Labs **Anima** anime / illustration model. Use when the user asks to generate / draw / render an anime / illustration / non-photoreal image on an Anima workflow. Anima uses score tags and an `@artist` prefix syntax; it is for anime / illustration, not photorealism. Anima Prompting Anima is a 2B anime / illustration text-to-image model (CircleStone Labs + Comfy Org), built on NVIDIA Cosmos with a qwen-text-encoder LLM adapter that has outsized influence on the generated image. Write the prompt and negative you pass to the generate_image tool per the rules below to drive Anima well on the first call. The score tags, @artist rule, and qwen-text-encoder behavior below are specific to Anima. Anima is trained on Danbooru-style tags of the Gelbooru flavor, natural-language captions, and mixes of the two (anime training data cuts off September 2025). All three work; pick whichever fits the request and always lead with quality + safety tags. Because of the qwen LLM text-encoder adapter, natural language is a first-class input mode , not a fallback - prose (or a quality-tag prefix followed by prose) often captures a scene's composition, spatial layout, and mood better than tags alone. The adapter is what makes detailed prompts pay off here far more than on a plain SDXL model - terse prompts waste the model's strongest feature. Safe default when unsure: use the anima workflow, begin the prompt with masterpiece, best quality, score_7, safe, , describe the subject in roughly 12-25 lowercase tags (or 2+ sentences of prose), and always pass the recommended negative from the negative recipe . Short prompts make Anima bland or unsafe, so spend the detail. Everything below is how to do better than this default when the request calls for it. The generate_image call Arg For Anima prompt Required. The full positive prompt including the masterpiece, best quality, score_7, safe prefix. negative Always pass one. Start from the recommended negative below. workflow Use anima when that workflow is configured; omit only if it is the defaultWorkflow . width / height 512-1536; default 1024x1024. Use portrait (e.g. 832x1216) for a single standing character. steps 30-50. Leave the workflow default (30) unless quality is lacking. cfg 4-5. Higher burns the image; do not push past ~5. seed Omit for a fresh random image; pass a prior seed to reproduce or vary one. You set the entire prompt string - it replaces the workflow's baked text, so the quality/safety prefix must be in your prompt every time. Do not pass inputImage (Anima here is text-to-image only). Positive prompt recipe Lead with the prefix: masterpiece, best quality, score_7, safe, Then the subject , as tags, natural language, or a mix. Lowercase tags, spaces not underscores - the only underscored tags are score tags ( score_7 ). Prefer the Gelbooru spelling when a tag differs between boorus. Be specific. Anima's base style is plain; short prompts give bland or unexpected (sometimes unsafe) output. For natural language aim for >=2 sentences. For tag style aim for >=8 substantive tags after the prefix. The model card warns explicitly: "The model may generate undesired content, especially if the prompt is short or lacking details." Name then describe characters. Fern from Sousou no Frieren, with long purple hair and purple eyes, wearing a black coat... - especially with multiple characters, describe each one's appearance or the model conflates them. Artists need @ : write @artist name (the @ is mandatory; without it the style barely registers). You may put quality/artist tags at the start of a natural-language prompt too. An @artist tag also locks a consistent style across seeds - pin one when you want the same house look from image to image (verified: same @artist holds the style steady across different seeds). Tag order (tag-style prompts) [quality / meta / year / safety] [1girl / 1boy / 1other] [character] [series] [@artist] [general tags] Order only matters between sections; within a section tags are free-order. You do not need every relevant tag - the model was trained with tag dropout. Natural-language and mixed prompts Anima's qwen text-encoder adapter reads full sentences, not just tags, so natural language is a first-class way to prompt it - often the better one for scenes, spatial relationships, mood, and multi-element compositions where a tag list gets ambiguous. The model card ships a dedicated natural-language tips section; the rules that matter: Keep the quality / safety prefix. Lead with masterpiece, best quality, score_7, safe, (or . ) even in a prose prompt - the rating tag still carries safety - then write the description as prose. Quality and @artist tags are fine at the start of a natural-language prompt. Be descriptive: aim for >=2 sentences. Pure natural language rewards detail; extremely short prose gives unexpected (sometimes unsafe) results. Spend the same detail budget as the tag recipe (~12-25 depictable items), just expressed as prose - appearance, wardrobe, setting, lighting, palette. Mix tags and prose freely, in any order. A hybrid is common and effective: open with quality + character / series tags, then a sentence or two of prose for pose, framing, and scene. The tool takes one string; comma- or period-join the two halves. Name a character, then describe their appearance ( Fern from Sousou no Frieren, with long purple hair and purple eyes, wearing a black coat... ). With multiple characters this is essential or the model conflates them. The one caution: do not bury a single character under a wall of scenery prose - keep scene description to a short clause and spend the detail on the character (see Anti-patterns ). Expanding a terse user request When the user asks for something short like "draw a cat girl" or "make me a witch", do not pass that string through. Expand it before calling generate_image : Prefix: add masterpiece, best quality, score_7, safe, . Subject count + type: infer 1girl / 1boy / 1other , plus solo if a single character. Character / series: only if the user named one ( Frieren from Sousou no Frieren ); otherwise skip. Appearance: invent 3-5 concrete details - hair color + length, eye color, outfit, distinctive accessory. Match the user's vibe; ask only if their wording is genuinely ambiguous about a load-bearing detail (e.g. "anime girl in a kimono - red or blue?"). Pose / framing: 1-2 tags - looking at viewer , standing , upper body , cowboy shot . Pick ONE framing tag. Setting: 2-4 tags or a clause - sunlit library, tall window, dust motes , or simple background, white background for a clean character shot. Lighting + palette: 1-3 tags - soft lighting , cinematic lighting , warm palette , muted colors . Era (optional): add year 2025 or newest for a modern look; skip for timeless. Artist (optional): only if the user named one - prepend @ (e.g. @some artist ). Negative: always pass the recommended negative; add extra fingers, text, watermark for cleaner output. The goal is roughly 12-25 substantive items in the positive prompt. If the user wants to iterate, pass the prior seed back to vary by tweaking tags rather than restarting. When the user supplies tags directly If the user hands you a tag list (or a tag list mixed with wildcards) instead of a terse English request, switch modes: Preserve every user-supplied tag verbatim. Do not paraphrase, deduplicate, reorder across sections, or "improve" their wording. Prepend the prefix if missing. Add masterpiece, best quality, score_7, safe, only if the user did not already include quality / safety tags. Add spatial / positional words only. Insert framing words like in the center of the frame , foreground , background , in front of , surrounded by , on either side to disambiguate where elements sit. Do not add weather, lighting, palette, or outfit details the user did not ask for. Preserve dynamic-prompt wildcards exactly. {A|B} , {A,|B,|C} , {1-3$$ A|B|C} , and {A,B}_noun are wildcard-extension syntax (ComfyUI Impact-Pack, sd-dynamic-prompts) - they expand at generation time, not in your prompt. Pass them through unchanged: never rewrite {standing|sitting} as "standing or sitting" , never pick one branch, never delete a branch. Append a one-sentence natural-language clause describing spatial relationships - Anima's qwen adapter benefits from prose, so a single closing sentence on top of the tag list usually improves coherence without adding embellishment. prompt: masterpiece, best quality, score_7, safe, 1girl, {standing|sitting}, classroom, desk, {morning|evening}, a young female student positioned in the center of the classroom in front of the desk, with the {morning|evening} lighting implied by the scene negative: worst quality, low quality, score_1, score_2, score_3, artist name Concatenate the tag list and the natural-language clause into one prompt string (comma- or period-joined) - the tool takes a single string, and Anima reads mixed input fine. Negative prompt recipe Start from the official recommendation and add situational terms: worst quality, low quality, score_1, score_2, score_3, artist name Add jpeg artifacts, blurry, lowres to clean up artifacts. Add the specific thing you don't want (e.g. multiple views, text, watermark, extra fingers ). Do not put a rating word ( safe , sensitive , nsfw , explicit ) in the negative. Safety is carried by the rating tag in the positive prefix, not by negating it here, and putting nsfw in the negative actively fights the positive the moment the scene is sensitive or higher. Keep the negative rating-free; every example below does. (On a safe shot, on a workflow that does not auto-scrub the negative, you may reinforce with nsfw here - but never above safe . When in doubt, leave it out and let the positive rating tag do the work.) Tag reference Group Values Quality (human) masterpiece , best quality , good quality , normal quality , low quality , worst quality Quality (Pony) score_9 ... score_1 (use human, Pony, both, or neither - all work) Safety safe , sensitive , nsfw , explicit Meta highres , absurdres , anime screenshot , official art , jpeg artifacts Time year 2025 (specific) or period: newest , recent , mid , early , old Weighting Parenthesis weighting; needs bigger numbers than SDXL: (chibi:2) , not (chibi:1.2) . See below. Weighting syntax. Anima runs on ComfyUI, which weights with parentheses only : (tag:1.4) to strengthen, (tag:0.6) to weaken, bare (tag) for ~1.1x, and nesting ( ((tag)) ~= 1.21x). Anima responds more weakly to a given weight than SDXL, so reach for bigger moves - (chibi:2) , not (chibi:1.2) . Square brackets are not weighting on ComfyUI : [tag] is parsed as ([tag]:1) , i.e. the brackets become literal tokens at weight 1, not an A1111-style de-emphasis. To push something down, use a fractional parenthesis weight like (background:0.6) , never [background] . Generation settings Setting Value Resolution 512x512 to 1536x1536; 1024x1024 default. Steps 30-50. CFG 4-5. Sampler er_sde is the neutral default; euler_a for softer lines; dpmpp_2m_sde_gpu for more variety. Scheduler Workflow default ( simple ) is a good neutral pick. Valid options vary by install (commonly simple , normal , karras , beta , kl_optimal ). NOTE: beta57 is a custom-node scheduler and is NOT on a stock ComfyUI - do not assume it exists. Sampler/scheduler live in the workflow file, not the tool args - mention a sampler only if asking the user to retune the workflow. Prompt enhancement ( enhance ) The generate_image tool has an opt-in enhance option that routes your prompt through a separate model before the render to rewrite it into Anima's native protocol - the score/safety-prefixed tag list this guide describes. It is opt-in. Pass enhance: true on the call (or a workflow/config default turns it on). When off, your prompt is sent as-is. Use it when the incoming prompt is thin (a few words) and you want a one-shot upgrade to a full tagged prompt. When you have already built a careful tagged prompt, leave enhance off - it only adds latency and risks drifting from your intent. Scene continuity. Pass the context arg alongside enhance to hand the enhancer background to honour (character facts, ongoing scene, wardrobe) without depicting it literally. It is ignored when enhance is off. Negative. The enhancer builds on whatever baseline negative you pass and returns a refined one - it should keep