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fix(agent): keep each tag's own wording instead of forcing single words

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The tags instruction demanded "all lowercase, no spaces, no hyphens" with
single-word examples.  That clause arrived in the same commit that added
the priority_tags cross-reference, so it reads as a crude way of pushing
the model towards that (entirely single-word) vocabulary rather than as a
requirement in its own right — and nothing in the codebase depends on it:

* `_merge_tags` only lowercases and de-duplicates;
* `resolve_priority_tag` matches aliases exactly, and the priority config
  syntax already supports multi-word entries and aliases;
* the tag FTS index tokenises on non-alphanumerics, so a hyphenated tag is
  indexed as two tokens and stays searchable;
* tags never reach a ComfyUI prompt — that is `trainedWords`.

It also fought the priority_tags rule it was meant to support.  Handed the
site-curated `character-enhancement`, satisfying both rules produced
`character` as well; the run added generic priority-list tags and dropped
the site's own wording.  The spelling used by the site, the frontmatter or
the author is now kept verbatim — hyphenated, multi-word or non-Latin —
and no separator-free synonym is invented for a tag already included.

Measured on a Krea 2 portrait LoRA, the proposal went from nine tags
(four of them generic priority-list words) to six grounded ones.
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Will Miao
2026-09-14 21:22:31 +08:00
parent 51de85a6ca
commit f0ee30fc68
1 changed files with 1 additions and 1 deletions
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py/services/agent/skills/enrich_hf_metadata/prompt.md
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@@ -112,7 +112,7 @@ Sources to consider:
2. **Cross-reference against the priority_tags reference.** Only include a tag if it meaningfully describes what the model actually creates (subject, style, character type) and is semantically close to one of the priority_tags. If none of the README's tags match meaningful categories, prefer returning a smaller set or an empty array over including low-value tags. 2. **Cross-reference against the priority_tags reference.** Only include a tag if it meaningfully describes what the model actually creates (subject, style, character type) and is semantically close to one of the priority_tags. If none of the README's tags match meaningful categories, prefer returning a smaller set or an empty array over including low-value tags.
3. **All lowercase, no spaces, no hyphens** (use single words like `"photorealistic"`, `"anime"`, `"character"`). This rule applies to Latin-script tags; when the model's own tags are in another script (e.g. Chinese), keep them verbatim instead of dropping or translating them. 3. **All lowercase, and keep each tag's own wording.** Prefer the spelling already used by the site, the frontmatter, or the author — including hyphenated and multi-word tags such as `"sci-fi"`, `"semi-realistic"`, `"character-enhancement"` or `"art style"`. Do **not** strip separators or invent a single-word variant of a tag you are already including (e.g. do not emit both `"character-enhancement"` and `"character"`). When a tag is written in another script (e.g. Chinese), likewise keep it verbatim instead of translating it.
4. **Never invent a tag** that neither the site-provided metadata, the YAML frontmatter, nor the README text supports. 4. **Never invent a tag** that neither the site-provided metadata, the YAML frontmatter, nor the README text supports.