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.
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.
A model file could only ever be linked to huggingface.co: `set_hf_url`
validated the URL with a huggingface-only regex, the agent fetched the card
from a hardcoded HF URL, and the readme processor built every relative image
path off `https://huggingface.co/{repo}/resolve/main`. ModelScope publishes the
same model-card convention (README.md + YAML frontmatter, often carrying
`base_model:` and `trigger_words:`) behind a public, key-less API, so the
enrichment pipeline could already serve it - it was the plumbing that was
HF-shaped, not the idea.
Make the external source a first-class, provider-driven concept:
- New `py/services/model_sources/` registry. A `ModelSource` owns URL
recognition (lenient for stored values, strict for user input), the
canonical page URL, model-card fetching, the asset base URL and the
capability flags. `HuggingFaceSource` is the previous logic relocated;
`ModelScopeSource` reads `/models/{o}/{n}/resolve/{master|main}/README.md`
and falls back to `/api/v1/models/{o}/{n}/repo`. `TensorArtSource` is
link-only on purpose: tensor.art answers plain HTTP clients with a
Cloudflare challenge and its internal API (ap-east-1.tensorart.cloud /
cn.tensorart.net) rejects every /v1/model/* route with "invalid
authorization header", so it declares supports_enrichment=False rather than
failing silently later.
- Metadata gains `source_platform` + `source_url`; `hf_url` stays as a
read/write alias, written only for Hugging Face, so existing sidecars,
cached rows and third-party consumers keep working. Normalisation runs at
the scanner, the persistent cache (both directions, plus two new columns
behind an ALTER migration) and the linking handler - which is what stops a
user who switches sources from leaving a stale `hf_url` on a ModelScope
model.
- The agent pipeline keys off the provider instead of `hf_url`: the fast-fail
gate now explains *why* a model is skipped (no source / unknown source /
source without a reachable card), the prompt context exposes
source_url/source_id/source_label/asset_base_url while still filling the
legacy hf_url/repo aliases, and the four README image extractors take a
base_url (defaulting to HF) so relative paths resolve against the right
site. Version grouping generalises to hf: / ms: / ta: keys.
- `POST /api/lm/set-hf-url` keeps its path and its legacy payload keys but
accepts `source_url`, validates against every provider and returns the
platform. `GET /api/lm/model-sources` lets the UI render the supported-site
list from the server.
- Frontend: a `modelSourceHelpers` mirror of the registry drives the link
dialog, the card/modal globe (branded "View on ModelScope/TensorArt"), the
version-group key and the enrichment gate; the versions tab no longer sends
ms:/ta: keys to the CivitAI API.
TensorArt stays in the list because provenance is worth keeping even when the
card is unreadable - the dialog says so plainly ("Sites that don't expose one
(currently TensorArt) can only be linked") and the context menu disables
enrichment with a matching tooltip, instead of the user getting
"Unsupported URL".
Verified against the real ModelScope API: jj3550945163/Krea-2-LORA returns a
1882-byte card whose frontmatter carries base_model/tags/trigger_words, and
relative images resolve to .../resolve/master/....
Tests: backend 2815 passed; frontend 1130 JS + 91 Vue passed; pytest
tests/i18n and a Jinja compile pass over templates/. The nine locales carry
[TODO: Translate] for the new strings, completed in the next commit.
The bare call inside _build_prompt_context
would raise NameError because class methods don't close over class-level
scope. Use instead to trigger attribute lookup.
Update enrich_hf_metadata prompt.md clue locations for better LLM accuracy.
Update baseline report to v2 (mean 69.0, 46 models, +2.2pp vs baseline 71.1%).
Consolidate README snapshots into baselines/readmes/.
- agent_service._format_base_models: output bullet list instead of
JSON array for cleaner LLM parsing
- prompt.md mapping section: replace 14-row HF→CivitAI table with
compact rule set covering 14 mapping paths including new entries
for HiDream-ai, OnomaAIResearch/Illustrious, ideogram-ai/ideogram,
Tongyi-MAI/Z-Image-Turbo, and Wan-AI/Wan2.*
- base_model extraction instruction: add guidance to infer from
model filename, YAML tags, and README body text when YAML
frontmatter has no explicit base_model:
Merge skill.yaml (metadata) and prompt.md (prompt template) into a
single SKILL.md file with YAML frontmatter, matching the agent-skill
convention used by opencode and Claude Code.
- Add frontmatter parser (_parse_skill_file) to SkillRegistry
- Remove skill.yaml, prompt.md, empty skills/__init__.py
- Remove obsolete load_handler method
- Update tests for new format and cleaned-up fields
Introduce an agent skill framework for LLM-driven metadata enrichment:
- AgentCLI (py/agent_cli/): in-process wrappers around internal services
using standard relative imports, eliminating the need for sys.path hacks
- LLMService: centralized BYOK (bring-your-own-key) LLM client supporting
OpenAI, Ollama, and custom OpenAI-compatible endpoints
- PostProcessor: deterministic engine that applies LLM output via AgentCLI
(replaces old handler.py + _BASE_MODEL_ALIASES approach)
- SkillRegistry: filesystem-based skill discovery (skill.yaml + prompt.md)
- AgentService: orchestrates skill execution with WebSocket progress
- Frontend AgentManager: WebSocket listeners, skill execution, config UI
- Context menu entries (single + bulk) for "Enrich Metadata (Agent)"
- Settings UI for AI Provider configuration (BYOK)
- Full i18n support across 9 locales
Bug fixes found during review:
- aiohttp.web.json_response: status_code= -> status=
- settings_modal cancelEditApiKey: wrong argument position
- AgentManager.isLlmConfigured: allow Ollama without API key
- PostProcessor._merge_tags: lowercase all tags to match TagUpdateService
Will Miao