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feat(modelscope): read the model-detail API for card extras

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ModelScope's model card is not just README.md: the author's summary
(Description), the site-curated tags (OfficialTags), the internal
architecture enums (VisionFoundation/SubVisionFoundation) and — per
published version — the model filenames with that file's example images
(coverImages) and trigger words all live in the model-detail API.
AIGC repositories there frequently ship an auto-generated boilerplate
README and put the only useful text in Description, so reading just the
README yielded almost nothing.

Add `ModelSource.fetch_model_card_context()` returning a new
`ModelCardContext`, implemented by ModelScopeSource against the public
(no API key) detail endpoint. Example images are matched to the model's
basename through each version's `stats.fileList`, so every checkpoint in
a collection repository gets its own images rather than a sibling's.

Consume the context in the post-processor:

* example images seed `civitai.images` and, being per-file, take priority
  in the preview fallback chain
* the author summary becomes a paragraph in `modelDescription` and fills
  `civitai.description` when the LLM returns no short description
* site-curated tags are always merged in, which also fixes the official
  `character-enhancement` being dropped by the prompt's no-hyphen rule
* per-file trigger words are used before the repo-wide YAML
  `instance_prompt`
* an explicitly stated strength range is recovered by regex so
  `usage_tips` is populated even without an LLM

The prompt gains a Site-Provided Metadata section so the LLM can prefer
the site's first-hand data over its own guesses.
This commit is contained in:
Will Miao
2026-09-14 20:38:56 +08:00
parent e711e643f1
commit 35b291ab19
7 changed files with 1269 additions and 47 deletions
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py/services/agent/skills/enrich_hf_metadata/prompt.md
+38 -6
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@@ -25,6 +25,34 @@ You are an expert assistant for AI image generation models. Your task is to extr
{{current_metadata}}
```
## Site-Provided Metadata (any field may be empty)
The model site publishes the following **alongside** the README. It is
first-hand information recorded by the site itself, so it outranks anything
you would otherwise guess:
- **Author description**: {{source_description}}
- **Base model reported by the site**: {{source_base_model}}
- **Trigger words recorded for this file**: {{source_trigger_words}}
- **Site-curated tags**:
{{source_official_tags}}
- **Example image URLs for this file**:
{{source_example_images}}
Use it as follows:
- A weight or strength range stated in the **author description** belongs in
``usage_tips`` (and in ``notes``); do not leave ``usage_tips`` empty when the
description states one.
- When the author description exists, base ``short_description`` on it rather
than on the README, which on some sites is auto-generated boilerplate.
- Treat the **site-curated tags** as strong signals for ``tags``: they are
already a curated content vocabulary, so prefer them over invented words.
- Treat the **base model reported by the site** as a strong hint for
``base_model``, but still map it to the EXACT canonical name from the
available base-model list.
- Use the **example image URLs** when the README contains no usable image.
## User Priority Tags Reference
The user has configured the following list of **meaningful tag categories** for this model type (`{{model_type}}`):
@@ -55,10 +83,11 @@ Extract the following information from the README content above:
### base_model
The base model this model was trained on. Use EXACTLY one of the names from the **Available Base Models** list above. Do not invent new names or use aliases.
Check the YAML frontmatter for ``base_model:`` first. If the frontmatter has no ``base_model:``, look at the **model filename** (``{{model_basename}}``), YAML ``tags:``, README title and first paragraph for clues — the base model family is often embedded in the name
Check the **base model reported by the site** (above) and the YAML frontmatter ``base_model:`` first. If neither yields a match, look at the **model filename** (``{{model_basename}}``), YAML ``tags:``, README title and first paragraph for clues — the base model family is often embedded in the name
### trigger_words
The trigger words or activation prompts needed to use this LoRA. Look for:
- The **trigger words recorded for this file** in the site-provided metadata (most authoritative)
- `instance_prompt:` in the YAML frontmatter
- Phrases like "trigger word:", "trigger:", "use this prompt:", "activation prompt:"
- In collection repos: the trigger section **specific to this model file** (look near matching download links or anchor IDs)
@@ -66,12 +95,13 @@ The trigger words or activation prompts needed to use this LoRA. Look for:
Return as an array of strings. If none found, return an empty array `[]`. **Never** return `["None"]` or any placeholder value — a truly empty list means no trigger words exist.
### short_description
A concise 1-2 sentence summary of what this model does. Extract from the "Model description" section or the first paragraph. For collection repos, focus on the **specific model version** matching `{{model_basename}}`, not the repo as a whole. Return empty string if the README is too minimal.
A concise 1-2 sentence summary of what this model does. For collection repos, focus on the **specific model version** matching `{{model_basename}}`, not the repo as a whole. Prefer the **author description** from the site-provided metadata when it is present; otherwise extract from the "Model description" section or the first paragraph. Return empty string if the available content is too minimal.
### tags
3-8 relevant tags for categorizing this model. **Quality over quantity.**
Sources to consider:
- The **site-curated tags** from the site-provided metadata (these are already filtered content tags — prefer them)
- The YAML frontmatter `tags:` list (filter out technical ones — see below)
- The subject, style, character, or concept the model represents
- The model filename itself may give clues (e.g. "pokemon", "anime", "pixelart")
@@ -82,7 +112,9 @@ 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.
3. **All lowercase, no spaces, no hyphens** (use single words like `"photorealistic"`, `"anime"`, `"character"`).
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.
4. **Never invent a tag** that neither the site-provided metadata, the YAML frontmatter, nor the README text supports.
Return empty array if no meaningful content tags remain after filtering.
@@ -95,13 +127,13 @@ The URL of the most suitable preview image from the README. Look for:
- The YAML frontmatter `widget:` section (which often has `output.url` fields)
- In collection repos: the sample images listed **under the section** for this specific model version
- Generic `![alt](url)` in the body
Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL from the repository raw-file base URL (`{{asset_base_url}}`) plus the relative path. If no suitable image is found, return an empty string.
Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL from the repository raw-file base URL (`{{asset_base_url}}`) plus the relative path. If the README has no suitable image, fall back to the site-provided **example image URLs** for this file. If nothing is available, return an empty string.
### notes
A plain-text summary of the model card's key practical usage information. Combine trigger words, style modifiers, recommended parameters (steps, CFG, resolution, sampler), and any setup tips into a readable paragraph. For collection repos, focus on the **specific model version** matching `{{model_basename}}`. Return empty string if the README has no useful usage info.
A plain-text summary of the model card's key practical usage information. Combine trigger words, style modifiers, recommended parameters (steps, CFG, resolution, sampler), and any setup tips into a readable paragraph. For collection repos, focus on the **specific model version** matching `{{model_basename}}`. Include the **author description** from the site-provided metadata when it is present. Return empty string if there is no useful usage info.
### usage_tips
A JSON string with structured usage recommendations. Extract from the README any explicit ranges or recommended values (e.g. "Set LoRA strength: **0.85 - 1.4**", "CLIP strength: 0.5"). Possible fields (include only those you can determine):
A JSON string with structured usage recommendations. Extract from the **author description** (site-provided metadata) and the README any explicit ranges or recommended values (e.g. "Set LoRA strength: **0.85 - 1.4**", "CLIP strength: 0.5", "权重0.5-1.2"). Possible fields (include only those you can determine):
```json
{