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feat(links): support ModelScope and TensorArt as model sources

Browse Source 
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.
This commit is contained in:
Will Miao
2026-09-14 07:24:08 +08:00
parent 84146b62fd
commit 5ab0e88abc
51 changed files with 2518 additions and 279 deletions
Download Patch File Download Diff File Expand all files Collapse all files
py/services/agent/skills/enrich_hf_metadata/prompt.md
+12 -9
View File
@@ -1,20 +1,23 @@
---
name: enrich_hf_metadata
title: "Enrich Metadata from HuggingFace"
title: "Enrich Metadata from Model Card"
description: >
Parse the HuggingFace model card via LLM to extract description, trigger
words, base model, tags, and preview image URL.
Parse the model card (README) from HuggingFace, ModelScope, or any other
supported model site via LLM to extract description, trigger words, base
model, tags, and preview image URL.
llm_required: true
---
You are an expert assistant for AI image generation models. Your task is to extract structured metadata from a HuggingFace model card (README.md).
You are an expert assistant for AI image generation models. Your task is to extract structured metadata from a model card (README).
## Model Information
- **Repository**: {{hf_url}}
- **Source site**: {{source_label}} ({{source_platform}})
- **Model page**: {{source_url}}
- **Model file path**: {{model_path}}
- **Model filename**: {{model_basename}}
- **Repository ID**: {{repo}}
- **Repository ID**: {{source_id}}
- **Repository raw-file base URL**: {{asset_base_url}}
## Current Metadata (may be incomplete)
@@ -39,7 +42,7 @@ name listed — do not invent aliases or modify variant suffixes.
{{base_models}}
## HuggingFace README Content
## Model Card Content
```
{{readme_content}}
@@ -92,7 +95,7 @@ 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 as `https://huggingface.co/{{repo}}/resolve/main/{filename}`. 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 no suitable image is found, 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.
@@ -121,7 +124,7 @@ Your confidence level in the extracted data:
## Important: Handling Collection Repos (multiple model files)
Many HuggingFace repos contain **multiple model files** in a single repository
Many model repositories contain **multiple model files** in a single repository
(e.g. a "LoRA collection" with different styles/characters in separate files).
The model file currently being enriched is: **`{{model_basename}}`**
py/services/agent/skills/enrich_hf_metadata/readme_processor.py
+39 -15
View File
@@ -1,8 +1,15 @@
"""HF README processing for the ``enrich_hf_metadata`` skill.
"""Model card (README) processing for the ``enrich_hf_metadata`` skill.
Provides README cleaning for LLM injection, gallery/image extraction from
multiple formats (YAML widget, markdown, HTML ``<img>``, gallery tables),
and section-based README trimming for collection repos.
The extractors default to Hugging Face asset URLs, but every one of them
accepts an explicit ``base_url`` so the same parsing works for any model
source (ModelScope, ...). See :mod:`py.services.model_sources`.
This module deliberately has no package-relative imports: it is also loaded
standalone by the README-processing test harness.
"""
from __future__ import annotations
@@ -15,12 +22,25 @@ from typing import Any, List, Tuple
_REPO_URL_PATTERN = re.compile(r"https?://huggingface\.co/([^/]+/[^/]+)")
def resolve_asset_base_url(repo: str, base_url: str | None = None) -> str:
"""Return the base URL used to resolve repository-relative assets.
Falls back to the historical Hugging Face layout when *base_url* is not
supplied, so existing callers keep their behaviour.
"""
if base_url:
return base_url.rstrip("/")
return f"https://huggingface.co/{repo}/resolve/main"
def extract_simple_markdown_images(
markdown_text: str,
repo: str,
existing_urls: set[str] | None = None,
default_width: int = 512,
default_height: int = 512,
base_url: str | None = None,
) -> list[dict[str, Any]]:
"""Extract standalone markdown images from the README body.
@@ -32,10 +52,10 @@ def extract_simple_markdown_images(
Returns a list of dicts in the same ``civitai.images`` format as
:func:`extract_gallery_images`.
"""
if not markdown_text or not repo:
if not markdown_text or not (repo or base_url):
return []
base_url = f"https://huggingface.co/{repo}/resolve/main"
base_url = resolve_asset_base_url(repo, base_url)
images: list[dict[str, Any]] = []
seen_urls: set[str] = set(existing_urls) if existing_urls else set()
@@ -89,20 +109,21 @@ def extract_html_img_tags(
existing_urls: set[str] | None = None,
default_width: int = 512,
default_height: int = 512,
base_url: str | None = None,
) -> list[dict[str, Any]]:
"""Extract image URLs from HTML ``<img src=\"...\">`` tags in the README.
Many HF collection repos (e.g. ``deadman44/Z-Image_LoRA``) use raw HTML
``<img>`` tags exclusively for their sample images, with no markdown
``![]()`` equivalents. This function finds those tags and constructs
resolvable HF URLs.
resolvable URLs.
Returns a list of dicts in the ``civitai.images`` format.
"""
if not markdown_text or not repo:
if not markdown_text or not (repo or base_url):
return []
base_url = f"https://huggingface.co/{repo}/resolve/main"
base_url = resolve_asset_base_url(repo, base_url)
images: list[dict[str, Any]] = []
seen_urls: set[str] = set(existing_urls) if existing_urls else set()
@@ -166,7 +187,7 @@ def extract_html_img_tags(
def extract_repo_from_hf_url(hf_url: str) -> str:
"""Extract ``user/repo`` from a HuggingFace URL."""
m = _REPO_URL_PATTERN.match(hf_url)
m = _REPO_URL_PATTERN.match(hf_url or "")
return m.group(1) if m else ""
@@ -175,21 +196,23 @@ def extract_gallery_images(
repo: str,
default_width: int = 512,
default_height: int = 512,
base_url: str | None = None,
) -> List[dict[str, Any]]:
"""Extract widget/gallery images from the YAML frontmatter of a HF README.
"""Extract widget/gallery images from the YAML frontmatter of a README.
Args:
markdown_text: Raw README content.
repo: HF repo identifier (``user/repo``).
repo: Repository identifier (``user/repo``).
default_width: Fallback width when the README provides no dimension.
default_height: Fallback height when the README provides no dimension.
base_url: Overrides the asset base URL (defaults to Hugging Face).
Returns a list of dicts compatible with the ``civitai.images`` metadata
format, each containing ``url`` (absolute HF URL), ``meta.prompt``,
format, each containing ``url`` (absolute), ``meta.prompt``,
``width``, ``height``, and ``type``. Returns an empty list when no
widget entries are found or when *repo* is empty.
"""
if not markdown_text or not repo:
if not markdown_text or not (repo or base_url):
return []
frontmatter = _extract_frontmatter(markdown_text)
@@ -197,7 +220,7 @@ def extract_gallery_images(
return []
images: List[dict[str, Any]] = []
base_url = f"https://huggingface.co/{repo}/resolve/main"
base_url = resolve_asset_base_url(repo, base_url)
w = default_width or 512
h = default_height or 512
@@ -279,10 +302,11 @@ def extract_gallery_table_images(
existing_urls: set[str] | None = None,
default_width: int = 512,
default_height: int = 512,
base_url: str | None = None,
) -> list[dict[str, Any]]:
"""Extract images from ``| Preview | Prompt |`` markdown gallery tables.
Many HF READMEs include a sample-gallery table in the body (outside
Many READMEs include a sample-gallery table in the body (outside
the YAML frontmatter) that shows generation examples with their
prompts. This function parses those tables and merges results with
the widget-sourced images from :func:`extract_gallery_images`.
@@ -291,10 +315,10 @@ def extract_gallery_table_images(
:func:`extract_gallery_images`. Already-seen URLs (from *existing_urls*)
are skipped.
"""
if not markdown_text or not repo:
if not markdown_text or not (repo or base_url):
return []
base_url = f"https://huggingface.co/{repo}/resolve/main"
base_url = resolve_asset_base_url(repo, base_url)
images: list[dict[str, Any]] = []
seen_urls: set[str] = set(existing_urls) if existing_urls else set()
lines = markdown_text.split("\n")