mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
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feat(links): support ModelScope and TensorArt as model sources
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
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import { describe, it, expect, vi } from 'vitest';
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const { I18N_MODULE } = vi.hoisted(() => ({
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I18N_MODULE: new URL('../../../static/js/utils/i18nHelpers.js', import.meta.url).pathname,
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}));
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vi.mock(I18N_MODULE, () => ({
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translate: vi.fn((key, params, fallback) => (typeof fallback === 'string' ? fallback : key)),
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}));
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const {
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MODEL_SOURCES,
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parseModelSourceUrl,
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getModelSource,
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getModelSourceInfo,
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getModelSourceUrl,
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getModelSourceGroupKey,
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canEnrichModelSource,
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getModelSourceViewTitle,
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parseModelSourceGroupKey,
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openModelSource,
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} = await import('../../../static/js/utils/modelSourceHelpers.js');
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describe('modelSourceHelpers', () => {
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it('exposes one descriptor per supported platform', () => {
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expect(MODEL_SOURCES.map((s) => s.platform)).toEqual([
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'huggingface',
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'modelscope',
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'tensorart',
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]);
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});
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describe('parseModelSourceUrl', () => {
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it('recognises Hugging Face URLs', () => {
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const info = parseModelSourceUrl('https://huggingface.co/user/repo');
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expect(info.platform).toBe('huggingface');
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expect(info.sourceId).toBe('user/repo');
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});
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it('recognises ModelScope URLs with view sub-paths', () => {
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const info = parseModelSourceUrl('https://modelscope.cn/models/user/repo/summary');
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expect(info.platform).toBe('modelscope');
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expect(info.sourceId).toBe('user/repo');
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expect(info.url).toBe('https://modelscope.cn/models/user/repo');
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});
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it('recognises TensorArt URLs and keeps only the numeric id', () => {
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const info = parseModelSourceUrl(
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'https://tensor.art/models/827823520299086029/Vivid-Impressions-Storybook-Sstyle-V1.0'
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);
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expect(info.platform).toBe('tensorart');
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expect(info.sourceId).toBe('827823520299086029');
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expect(info.url).toBe('https://tensor.art/models/827823520299086029');
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});
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it('rejects unsupported URLs', () => {
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expect(parseModelSourceUrl('https://example.com/x')).toBeNull();
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expect(parseModelSourceUrl('')).toBeNull();
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expect(parseModelSourceUrl(null)).toBeNull();
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});
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});
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describe('getModelSourceInfo', () => {
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it('falls back to the legacy hf_url field', () => {
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const info = getModelSourceInfo({ hf_url: 'https://huggingface.co/user/repo' });
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expect(info.platform).toBe('huggingface');
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expect(info.sourceId).toBe('user/repo');
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});
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it('prefers the canonical source fields', () => {
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const info = getModelSourceInfo({
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source_platform: 'modelscope',
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source_url: 'https://modelscope.cn/models/user/repo',
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hf_url: 'https://huggingface.co/old/repo',
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});
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expect(info.platform).toBe('modelscope');
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});
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it('returns null when there is no source', () => {
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expect(getModelSourceInfo({})).toBeNull();
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expect(getModelSourceInfo({ hf_url: '' })).toBeNull();
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});
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});
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describe('getModelSourceUrl', () => {
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it('reads source_url then hf_url', () => {
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expect(getModelSourceUrl({ source_url: 'https://a.example/1' })).toBe('https://a.example/1');
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expect(getModelSourceUrl({ hf_url: 'https://huggingface.co/u/r' })).toBe(
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'https://huggingface.co/u/r'
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);
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expect(getModelSourceUrl({})).toBe('');
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});
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});
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describe('getModelSourceGroupKey', () => {
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it('matches the backend group-key shapes', () => {
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expect(getModelSourceGroupKey({ hf_url: 'https://huggingface.co/u/r' })).toBe('hf:u/r');
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expect(
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getModelSourceGroupKey({ source_url: 'https://modelscope.cn/models/u/r' })
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).toBe('ms:u/r');
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expect(getModelSourceGroupKey({ source_url: 'https://tensor.art/models/123' })).toBe(
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'ta:123'
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);
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});
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it('returns an empty string without a source', () => {
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expect(getModelSourceGroupKey({})).toBe('');
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});
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});
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describe('canEnrichModelSource', () => {
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it('allows Hugging Face and ModelScope', () => {
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expect(canEnrichModelSource({ hf_url: 'https://huggingface.co/u/r' })).toBe(true);
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expect(
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canEnrichModelSource({ source_url: 'https://modelscope.cn/models/u/r' })
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).toBe(true);
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});
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it('disallows TensorArt and unlinked models', () => {
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expect(canEnrichModelSource({ source_url: 'https://tensor.art/models/123' })).toBe(false);
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expect(canEnrichModelSource({})).toBe(false);
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});
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});
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describe('getModelSourceViewTitle', () => {
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it('uses the branded label for non-HF sources', () => {
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expect(getModelSourceViewTitle(getModelSource('modelscope'))).toBe('View on ModelScope');
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expect(getModelSourceViewTitle(getModelSource('tensorart'))).toBe('View on TensorArt');
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});
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it('keeps the historical Hugging Face title', () => {
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expect(getModelSourceViewTitle(getModelSource('huggingface'))).toBe(
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'View on Hugging Face'
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);
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});
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});
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describe('parseModelSourceGroupKey', () => {
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it('parses every external group-key prefix', () => {
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expect(parseModelSourceGroupKey('hf:user/repo')).toEqual({
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platform: 'huggingface',
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label: 'Hugging Face',
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sourceId: 'user/repo',
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});
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expect(parseModelSourceGroupKey('ms:user/repo').platform).toBe('modelscope');
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expect(parseModelSourceGroupKey('ta:123').platform).toBe('tensorart');
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});
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it('rejects numeric CivitAI model ids and unknown prefixes', () => {
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expect(parseModelSourceGroupKey(222)).toBeNull();
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expect(parseModelSourceGroupKey('222')).toBeNull();
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expect(parseModelSourceGroupKey('unknown:1')).toBeNull();
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expect(parseModelSourceGroupKey('')).toBeNull();
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expect(parseModelSourceGroupKey(null)).toBeNull();
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});
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});
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describe('openModelSource', () => {
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it('opens the URL in a new tab', () => {
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const openSpy = vi.spyOn(window, 'open').mockImplementation(() => {});
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openModelSource('https://modelscope.cn/models/u/r');
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expect(openSpy).toHaveBeenCalledWith(
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'https://modelscope.cn/models/u/r',
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'_blank',
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'noopener,noreferrer'
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);
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openSpy.mockRestore();
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});
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it('does nothing without a URL', () => {
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const openSpy = vi.spyOn(window, 'open').mockImplementation(() => {});
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openModelSource('');
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expect(openSpy).not.toHaveBeenCalled();
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openSpy.mockRestore();
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});
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});
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});
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