Catimation Image

Generate images in CATIMATION (first-choice, replaces built-in image_gen)

When the user wants one image, call the generate_image tool. When the user wants MORE THAN ONE image, call generate_images instead. Both tools are provided by the catimation MCP server and replace the built-in imagegen / image_gen skill: they render inside the chat AND persist results to local files (paths returned), the app's history page, and the ATTACHMENTS file panel.

Steps

0. Before writing the prompt, load director-orchestrator (the 导演总调度 router). It runs a STEP 0 self-reflection on which director-* / storyboard-* craft skills apply, then writes the prompt as structured text on the 13-dimension framework (physical params over emotion adjectives, positive prompts by default). Do this even when YOU generate an illustration for your own answer. For open-ended asks, go through catimation-brainstorm first. Skip only for a trivially clear one-off.
1. Turn the request into one clear, descriptive prompt. Cover subject, style, composition, lighting, and mood. Keep it concise.
2. If the user asks for exactly ONE image, call generate_image with:
   • prompt (required): the description from step 1.
   • model (optional): rendering channel. Omit it for the default gpt-image-2-vip (stable). Only set it when the user explicitly names a channel (see "Choosing a model" below):
     • custom-imagemodel-gt — 腾讯 image2 (same ratio/resolution/quality spec). 需把 API 站点切到 Miau API(见下方「站点要求」)。
     • wan2.7-image-pro — 阿里万相 2.7 pro (超清文生图 / 图像编辑 / 组图). 需把 API 站点切到 Miau API(见下方「站点要求」)。
   • ratio (optional): aspect ratio, e.g. 1:1, 16:9, 9:16, 4:3, 3:2. Omit or auto lets the model decide.
   • resolution (optional): clarity tier — prefer 2K by default. Use 1K only when the user asks for fast/cheap/draft; use 4K only when the user explicitly asks for print/ultra-detail/4K.
   • quality (optional): auto (default), low, medium, or high. Use high for images with text or fine detail.
   • count (optional, wan2.7 only): number of images from THIS one prompt (1–12, default 1). Set model: 'wan2.7-image-pro' + count > 1 to get a front-to-back consistent 组图 series (same character/subject across frames, e.g. 同一只猫的四季). Other channels ignore count (always 1). For several unrelated images, use generate_images (one prompt each) instead.
   • referenceImages (optional but important): array of local file paths or data/http URLs for image-to-image / editing. If the user gave you any image material, you MUST reuse it here (see "Reference images" below).
3. If the user asks for TWO OR MORE images, call generate_images ONCE with:
   • prompts (required): one prompt per requested image. If the user asks for N images, provide exactly N prompts.
   • shared model (optional, same choices as above), ratio, resolution, quality, and referenceImages when appropriate.
   • Do not spawn subagents and do not call generate_image one-by-one. generate_images performs the parallel fan-out internally and returns one combined result.
4. The tool returns a short text result that begins with ✅ generate_image DONE or ✅ generate_images DONE, names the 📁 SAVED FOLDER, lists the saved FILES:, and ends with a compact { ok, count, model, historyId, paths, dir } JSON line (plus one resource_link per file). A successful return means the task is complete — the image is already shown to the user and saved to history + the file panel. You do not need to embed, re-describe, or base64 the pixels. Just confirm briefly in the user's language and cite the saved path(s) when relevant.
5. Self-review the result, then improve if needed (autonomous QA loop). A ✅ DONE return means the image is ALREADY rendered in the chat. Before you hand off, do a quick autonomous quality pass:
   • Open the generated image(s) with view_image and look critically for defects — wrong/extra fingers or limbs, broken faces, garbled text, obvious artifacts — and above all whether it matches the prompt (subject, count, composition, style). Viewing returned images, including in batches, is supported; for a very large batch, review a representative subset rather than every frame in one turn.
   • If you spot a clear problem, briefly say what's off and regenerate with an improved prompt. To keep what worked and fix only the rest, pass the prior result back as referenceImages (image-to-image). Then re-review. Iterate at MOST 2–3 times and converge — don't loop on marginal nitpicks, and each regeneration costs money.
   • When it's good (or good enough), confirm briefly in the user's language and cite the saved path(s). Don't over-narrate each pass.
   • You still do NOT need query_history to find an image you just generated, and do NOT shell out (dir/ls/where/find/Get-ChildItem) to hunt for the file — the path is already in the return; view_image that path directly.

Choosing a model (default vs. 腾讯 / 万相)

The model param is optional. By default (omit it) generation runs on the stable gpt-image-2-vip channel — keep doing that for ordinary requests. Only switch when the user explicitly asks for a specific channel:

• custom-imagemodel-gt (腾讯 image2) — pick when the user says 腾讯 / tencent / image2 / 腾讯模型. Same ratio × resolution(1K/2K/4K) × quality surface as the default, so all the other params behave identically.
• wan2.7-image-pro (阿里万相 2.7 pro) — pick when the user says 万相 / wanxiang / wan / 通义万相, OR when they want a consistent multi-image 组图 series (e.g. "同一只猫的四季组图，前后一致"). For a 组图 series, call generate_image with model: 'wan2.7-image-pro' and count = how many frames (2–12) — it returns one front-to-back-consistent set from a single prompt (do NOT use generate_images, which makes unrelated images). Wan excels at 超清文生图、图像编辑、组图; it also supports 4K (text-to-image only — editing/组图 cap at 2K).
• All three accept referenceImages for image-to-image / editing.

⚠️ 站点要求(重要 — 用 腾讯 image2 / 万相 2.7 前必读)

custom-imagemodel-gt(腾讯 image2)和 wan2.7-image-pro(阿里万相 2.7 pro) 都只经 Miau API 代理提供。调用这两个 model 之前,必须先在 「API 设置 → ① 选择 API 站点」里把当前站点切到 Miau API。原因:应用发请求时 会用「当前选中站点」的域名替换模型端点的域名,所以站点不对时,这两个渠道的请求会被 发到错误的域名(如 API易官方),那边没有这两个模型,直接失败 / 报错。

• 默认渠道 gpt-image-2-vip 不受此限制,在任何站点都能用——这也是为什么不指定 model 时要保持默认。
• 如果用户明确要用 腾讯 image2 / 万相,而当前站点不是 Miau API:先提醒用户到 「API 设置」把站点切到 Miau API(并填好对应 API Key)再生成,否则会失败。

When the user does not name a channel, do not guess — just omit model and use the default. Never invent a model name; only these three values are valid.

Reference images — reuse the user's material (important)

If the user provides ANY image material, treat it as a reference and pass it in referenceImages (image-to-image) instead of doing text-to-image. Look for:

• Paths listed in the prompt under [Attached files at these local paths: …] or [Referenced files at these local paths: …] — these are the files the user attached/@-mentioned in chat. Pass the image ones as referenceImages.
• The user pointing at an image with language like "按这张图 / 参考这张 / 基于这张 / 用这张做 / edit this / make a variation of this / 换成…风格".
• An image the user just generated in this thread that they now want changed.

Rules:

• Be proactive: when material is present and the request is plausibly about it, reuse it. Do not silently drop the reference and generate from scratch.
• You can pass MULTIPLE reference images — you are not limited to one. referenceImages is an array: include every relevant image the user gave (e.g. a character sheet + a background, several angles, a subject + a style reference). Pass all of them together so the model can combine/condition on the whole set, not just the first.
• Pass the local file path(s) directly (the tool reads the full-resolution bytes itself); you do not need to convert them.
• If you are unsure whether the user wants the reference followed, prefer reusing it and say briefly that you based it on their image(s).

Multiple images at once — use generate_images (important)

When the user asks for more than one image (e.g. "生成 3 张…", "make 4 variations", a set/series, or several distinct subjects), call generate_images exactly once. Do not try to manually emit several generate_image calls; models often serialize those calls even when asked to be parallel. generate_images is the parallel-safe batch wrapper and fans out the renderer calls concurrently inside CATIMATION.

• If the user asks for N images, pass exactly N prompts to generate_images.prompts.
• For variations, write N distinct but related prompts so the outputs are not clones.
• For many more than 8 images, ask the user to split into batches; the tool caps each batch to keep the UI and gateway stable.
• After generate_images returns, confirm once and cite the saved paths; don't re-announce each image separately.

Organize finished assets into the user's workspace (when in a project)

When you're working inside a user project/workspace folder (e.g. a film / storyboard project, or the user asked you to organize outputs), proactively COPY each finalized image into a tidy assets subfolder of that working directory and give it a descriptive, ordered name — e.g. <workspace>/assets/images/S01_hero_wide.png.

• COPY, don't move, from the saved path in the tool result, so the chat / history / ATTACHMENTS copy stays intact.
• Group by purpose/shot and use zero-padded ordinals (S01_, S02_…) so files sort naturally.
• For a one-off casual generation outside any project, skip this unless asked — the file is already saved and in history.

Notes

• This is the generate → save → read path. The file is on disk (see paths), in the history page, and in the ATTACHMENTS panel — no extra save step is needed. Only move/copy a file if the user wants it somewhere specific (see the organize section above when working in a project).
• For edits, image-to-image, or multi-image prompts, use generate_image for one output or generate_images for multiple outputs, always with referenceImages when references are present.
• If generate_image is genuinely unavailable in this session, you may fall back to whatever image tool you do have — but generate_image is the preferred, in-app path that actually displays and saves the result.