mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-09-20 18:51:26 -03:00
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
@@ -191,4 +191,60 @@ describe('ModelCard source globe (#1094)', () => {
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expect(openHuggingFace).toHaveBeenCalledWith('https://huggingface.co/user/repo');
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expect(openCivitai).not.toHaveBeenCalled();
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});
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it('points the globe at ModelScope for a ModelScope-linked model', () => {
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const card = mountCard(
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createModelCard,
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makeModel({
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from_civitai: false,
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civitai: {},
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source_platform: 'modelscope',
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source_url: 'https://modelscope.cn/models/user/repo',
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})
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);
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expect(card.dataset.has_civitai).toBe('false');
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expect(card.dataset.source_platform).toBe('modelscope');
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expect(card.dataset.hf_url).toBe('');
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expect(card.querySelector('.fa-globe').getAttribute('title')).toBe('View on ModelScope');
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});
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it('opens the ModelScope page when the globe is clicked', () => {
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const openSpy = vi.spyOn(window, 'open').mockImplementation(() => {});
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const card = mountCard(
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createModelCard,
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makeModel({
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from_civitai: false,
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civitai: {},
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source_platform: 'modelscope',
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source_url: 'https://modelscope.cn/models/user/repo',
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})
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);
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setupModelCardEventDelegation('loras');
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card.querySelector('.fa-globe').dispatchEvent(new MouseEvent('click', { bubbles: true }));
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expect(openSpy).toHaveBeenCalledWith(
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'https://modelscope.cn/models/user/repo',
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'_blank',
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'noopener,noreferrer'
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);
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expect(openCivitai).not.toHaveBeenCalled();
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expect(openHuggingFace).not.toHaveBeenCalled();
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openSpy.mockRestore();
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});
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it('points the globe at TensorArt for a TensorArt-linked model', () => {
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const card = mountCard(
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createModelCard,
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makeModel({
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from_civitai: false,
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civitai: {},
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source_platform: 'tensorart',
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source_url: 'https://tensor.art/models/827823520299086029',
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})
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);
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expect(card.querySelector('.fa-globe').getAttribute('title')).toBe('View on TensorArt');
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});
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});
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@@ -1,4 +1,4 @@
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import { describe, expect, it } from 'vitest';
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import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
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import { ModelContextMenuMixin } from '../../../static/js/components/ContextMenu/ModelContextMenuMixin.js';
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@@ -14,3 +14,115 @@ describe('ModelContextMenuMixin.getModelTypePrefix', () => {
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expect(ModelContextMenuMixin.getModelTypePrefix.call({})).toBe('loras');
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});
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});
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describe('ModelContextMenuMixin.updateEnrichMenuItem', () => {
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function setupMenu() {
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document.body.innerHTML = '<div id="menu"><div data-action="enrich-hf-llm"></div></div>';
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return { menu: document.getElementById('menu') };
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}
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function cardWith(dataset) {
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return { dataset };
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}
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it('enables enrichment for Hugging Face links', () => {
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const context = setupMenu();
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ModelContextMenuMixin.updateEnrichMenuItem.call(
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context,
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cardWith({ hf_url: 'https://huggingface.co/user/repo' })
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);
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const item = context.menu.querySelector('[data-action="enrich-hf-llm"]');
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expect(item.classList.contains('disabled')).toBe(false);
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expect(item.title).toBe('');
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});
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it('enables enrichment for ModelScope links', () => {
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const context = setupMenu();
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ModelContextMenuMixin.updateEnrichMenuItem.call(
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context,
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cardWith({
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source_platform: 'modelscope',
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source_url: 'https://modelscope.cn/models/user/repo',
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})
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);
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const item = context.menu.querySelector('[data-action="enrich-hf-llm"]');
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expect(item.classList.contains('disabled')).toBe(false);
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});
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it('disables enrichment for TensorArt and explains why', () => {
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const context = setupMenu();
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ModelContextMenuMixin.updateEnrichMenuItem.call(
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context,
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cardWith({
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source_platform: 'tensorart',
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source_url: 'https://tensor.art/models/827823520299086029',
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})
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);
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const item = context.menu.querySelector('[data-action="enrich-hf-llm"]');
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expect(item.classList.contains('disabled')).toBe(true);
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expect(item.title).toContain('TensorArt');
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});
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it('disables enrichment when no source is linked', () => {
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const context = setupMenu();
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ModelContextMenuMixin.updateEnrichMenuItem.call(context, cardWith({}));
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const item = context.menu.querySelector('[data-action="enrich-hf-llm"]');
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expect(item.classList.contains('disabled')).toBe(true);
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expect(item.title).toContain('Link this model to a model source');
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});
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});
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describe('ModelContextMenuMixin._renderSupportedSources', () => {
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const originalFetch = global.fetch;
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beforeEach(() => {
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document.body.innerHTML = '<div id="hfSupportedSources">static fallback</div>';
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});
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afterEach(() => {
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global.fetch = originalFetch;
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});
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it('renders the server-provided example URLs', async () => {
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global.fetch = vi.fn().mockResolvedValue({
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ok: true,
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json: async () => [
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{ platform: 'huggingface', example_url: 'https://huggingface.co/user/repo' },
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{ platform: 'modelscope', example_url: 'https://modelscope.cn/models/user/repo' },
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{ platform: 'tensorart', example_url: 'https://tensor.art/models/123' },
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],
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});
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await ModelContextMenuMixin._renderSupportedSources.call({});
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const html = document.getElementById('hfSupportedSources').innerHTML;
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expect(html).toContain('https://huggingface.co/user/repo');
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expect(html).toContain('https://modelscope.cn/models/user/repo');
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expect(html).toContain('https://tensor.art/models/123');
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});
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it('keeps the static fallback when the request fails', async () => {
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global.fetch = vi.fn().mockRejectedValue(new Error('offline'));
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await ModelContextMenuMixin._renderSupportedSources.call({});
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expect(document.getElementById('hfSupportedSources').innerHTML).toBe('static fallback');
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});
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it('escapes markup from the server payload', async () => {
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global.fetch = vi.fn().mockResolvedValue({
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ok: true,
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json: async () => [{ example_url: '<img src=x onerror=alert(1)>' }],
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});
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await ModelContextMenuMixin._renderSupportedSources.call({});
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const html = document.getElementById('hfSupportedSources').innerHTML;
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expect(html).not.toContain('<img');
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expect(html).toContain('<img');
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});
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});
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@@ -0,0 +1,177 @@
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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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@@ -154,3 +154,155 @@ async def test_set_hf_url_rejects_non_repo_url(tmp_path, hf_env):
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payload = _json_payload(response)
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assert payload["success"] is False
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hf_env["cache_write"].assert_not_awaited()
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# ---------------------------------------------------------------------------
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# Multi-source linking (ModelScope / TensorArt)
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# ---------------------------------------------------------------------------
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async def _write_plain_model(model_path, sha: str = "d" * 64) -> None:
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await _write_model(
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model_path,
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{
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"file_name": "model",
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"model_name": "model",
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"file_path": str(model_path),
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"size": 32,
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"modified": 1.0,
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"sha256": sha,
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"base_model": "Unknown",
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"preview_url": "",
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},
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)
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@pytest.mark.asyncio
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async def test_set_hf_url_accepts_modelscope_and_stores_source_fields(tmp_path, hf_env):
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model_path = tmp_path / "ms_model.safetensors"
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await _write_plain_model(model_path)
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response = await HfHandler().set_hf_url(
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FakeRequest(
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json_data={
|
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"file_path": str(model_path),
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||||
"source_url": "https://modelscope.cn/models/jj3550945163/Krea-2-LORA",
|
||||
}
|
||||
)
|
||||
)
|
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|
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assert response.status == 200
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payload = _json_payload(response)
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assert payload["source_platform"] == "modelscope"
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assert payload["source_url"] == "https://modelscope.cn/models/jj3550945163/Krea-2-LORA"
|
||||
|
||||
saved = json.loads(open(_sidecar_path(model_path), encoding="utf-8").read())
|
||||
assert saved["source_platform"] == "modelscope"
|
||||
assert saved["source_url"] == "https://modelscope.cn/models/jj3550945163/Krea-2-LORA"
|
||||
# No stale Hugging Face alias for a ModelScope model.
|
||||
assert saved.get("hf_url", "") == ""
|
||||
|
||||
cached_metadata = hf_env["cache_write"].await_args.args[1]
|
||||
assert cached_metadata["source_platform"] == "modelscope"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_set_hf_url_accepts_tensorart_url(tmp_path, hf_env):
|
||||
model_path = tmp_path / "ta_model.safetensors"
|
||||
await _write_plain_model(model_path, sha="e" * 64)
|
||||
|
||||
response = await HfHandler().set_hf_url(
|
||||
FakeRequest(
|
||||
json_data={
|
||||
"file_path": str(model_path),
|
||||
"source_url": (
|
||||
"https://tensor.art/models/827823520299086029/"
|
||||
"Vivid-Impressions-Storybook-Sstyle-V1.0"
|
||||
),
|
||||
}
|
||||
)
|
||||
)
|
||||
|
||||
assert response.status == 200
|
||||
payload = _json_payload(response)
|
||||
assert payload["source_platform"] == "tensorart"
|
||||
# The canonical page URL is stored, without the slug.
|
||||
assert payload["source_url"] == "https://tensor.art/models/827823520299086029"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_set_hf_url_canonicalises_modelscope_subpage(tmp_path, hf_env):
|
||||
model_path = tmp_path / "ms_sub.safetensors"
|
||||
await _write_plain_model(model_path, sha="f" * 64)
|
||||
|
||||
response = await HfHandler().set_hf_url(
|
||||
FakeRequest(
|
||||
json_data={
|
||||
"file_path": str(model_path),
|
||||
"source_url": "https://modelscope.cn/models/user/repo/summary",
|
||||
}
|
||||
)
|
||||
)
|
||||
|
||||
assert response.status == 200
|
||||
assert _json_payload(response)["source_url"] == "https://modelscope.cn/models/user/repo"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_set_hf_url_is_idempotent_for_modelscope(tmp_path, hf_env):
|
||||
model_path = tmp_path / "ms_twice.safetensors"
|
||||
await _write_plain_model(model_path, sha="1" * 64)
|
||||
|
||||
request = FakeRequest(
|
||||
json_data={
|
||||
"file_path": str(model_path),
|
||||
"source_url": "https://modelscope.cn/models/user/repo",
|
||||
}
|
||||
)
|
||||
await HfHandler().set_hf_url(request)
|
||||
await HfHandler().set_hf_url(request)
|
||||
|
||||
# The second call short-circuits without rewriting the cache entry.
|
||||
assert hf_env["cache_write"].await_count == 1
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_set_hf_url_switching_source_clears_hf_alias(tmp_path, hf_env):
|
||||
model_path = tmp_path / "switch.safetensors"
|
||||
await _write_plain_model(model_path, sha="2" * 64)
|
||||
|
||||
await HfHandler().set_hf_url(
|
||||
FakeRequest(
|
||||
json_data={
|
||||
"file_path": str(model_path),
|
||||
"source_url": "https://huggingface.co/user/repo",
|
||||
}
|
||||
)
|
||||
)
|
||||
await HfHandler().set_hf_url(
|
||||
FakeRequest(
|
||||
json_data={
|
||||
"file_path": str(model_path),
|
||||
"source_url": "https://modelscope.cn/models/user/repo",
|
||||
}
|
||||
)
|
||||
)
|
||||
|
||||
saved = json.loads(open(_sidecar_path(model_path), encoding="utf-8").read())
|
||||
assert saved["source_platform"] == "modelscope"
|
||||
assert saved.get("hf_url", "") == ""
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_model_sources_lists_capabilities():
|
||||
response = await HfHandler().get_model_sources(FakeRequest())
|
||||
sources = _json_payload(response)
|
||||
|
||||
by_platform = {s["platform"]: s for s in sources}
|
||||
assert set(by_platform) == {"huggingface", "modelscope", "tensorart"}
|
||||
assert by_platform["huggingface"]["supports_enrichment"] is True
|
||||
assert by_platform["modelscope"]["supports_enrichment"] is True
|
||||
# TensorArt is link-only: no accessible model card for the backend.
|
||||
assert by_platform["tensorart"]["supports_enrichment"] is False
|
||||
assert by_platform["modelscope"]["supports_download"] is False
|
||||
assert all(s["example_url"] for s in sources)
|
||||
|
||||
@@ -0,0 +1,182 @@
|
||||
"""Tests for source-aware AI enrichment orchestration.
|
||||
|
||||
Covers the fast-fail gate (:meth:`AgentService._enrichment_skip_reason`) and
|
||||
the prompt-context builder for non-Hugging Face model sources.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from unittest import mock
|
||||
|
||||
import pytest
|
||||
|
||||
from py.services.agent.agent_service import AgentService
|
||||
|
||||
|
||||
class TestEnrichmentSkipReason:
|
||||
def test_skips_when_no_source_linked(self):
|
||||
reason = AgentService._enrichment_skip_reason({})
|
||||
assert "source_url" in reason
|
||||
|
||||
def test_allows_huggingface(self):
|
||||
assert (
|
||||
AgentService._enrichment_skip_reason(
|
||||
{"hf_url": "https://huggingface.co/user/repo"}
|
||||
)
|
||||
== ""
|
||||
)
|
||||
|
||||
def test_allows_modelscope(self):
|
||||
assert (
|
||||
AgentService._enrichment_skip_reason(
|
||||
{
|
||||
"source_platform": "modelscope",
|
||||
"source_url": "https://modelscope.cn/models/user/repo",
|
||||
}
|
||||
)
|
||||
== ""
|
||||
)
|
||||
|
||||
def test_skips_tensorart_with_reason(self):
|
||||
reason = AgentService._enrichment_skip_reason(
|
||||
{
|
||||
"source_platform": "tensorart",
|
||||
"source_url": "https://tensor.art/models/827823520299086029",
|
||||
}
|
||||
)
|
||||
assert "TensorArt" in reason
|
||||
assert "not available" in reason
|
||||
|
||||
def test_skips_unknown_platform(self):
|
||||
reason = AgentService._enrichment_skip_reason(
|
||||
{"source_platform": "somewhere", "source_url": "https://somewhere.example/m/1"}
|
||||
)
|
||||
assert "somewhere" in reason
|
||||
|
||||
|
||||
class TestBuildPromptContext:
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_card_populates_source_variables(self):
|
||||
service = AgentService()
|
||||
readme = "---\nbase_model: krea/Krea-2-Turbo\n---\n# krea\n"
|
||||
|
||||
with (
|
||||
mock.patch(
|
||||
"py.services.model_sources.modelscope.ModelScopeSource.fetch_model_card",
|
||||
new=mock.AsyncMock(return_value=readme),
|
||||
) as mock_fetch,
|
||||
mock.patch(
|
||||
"py.metadata_ops.list_base_models",
|
||||
new=mock.AsyncMock(return_value=["Krea 2 Turbo"]),
|
||||
),
|
||||
mock.patch(
|
||||
"py.metadata_ops.identify_model_type",
|
||||
new=mock.AsyncMock(return_value="lora"),
|
||||
),
|
||||
mock.patch(
|
||||
"py.services.settings_manager.SettingsManager.get_priority_tag_config",
|
||||
return_value={"lora": "style, subject"},
|
||||
),
|
||||
):
|
||||
context = await service._build_prompt_context(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/models/loras/krea.safetensors",
|
||||
metadata={
|
||||
"source_platform": "modelscope",
|
||||
"source_url": "https://modelscope.cn/models/jj3550945163/Krea-2-LORA",
|
||||
"file_name": "krea",
|
||||
},
|
||||
registry=mock.Mock(),
|
||||
llm=mock.Mock(),
|
||||
)
|
||||
|
||||
mock_fetch.assert_awaited_once_with("jj3550945163/Krea-2-LORA")
|
||||
assert context["source_platform"] == "modelscope"
|
||||
assert context["source_id"] == "jj3550945163/Krea-2-LORA"
|
||||
assert context["source_label"] == "ModelScope"
|
||||
assert (
|
||||
context["asset_base_url"]
|
||||
== "https://modelscope.cn/models/jj3550945163/Krea-2-LORA/resolve/master"
|
||||
)
|
||||
assert readme in context["readme_content_full"]
|
||||
# Hugging Face aliases stay empty for a non-HF source.
|
||||
assert context["hf_url"] == ""
|
||||
assert context["repo"] == "jj3550945163/Krea-2-LORA"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_huggingface_keeps_legacy_aliases(self):
|
||||
service = AgentService()
|
||||
readme = "# card\n"
|
||||
|
||||
with (
|
||||
mock.patch(
|
||||
"py.services.model_sources.huggingface.HuggingFaceSource.fetch_model_card",
|
||||
new=mock.AsyncMock(return_value=readme),
|
||||
) as mock_fetch,
|
||||
mock.patch(
|
||||
"py.metadata_ops.list_base_models",
|
||||
new=mock.AsyncMock(return_value=[]),
|
||||
),
|
||||
mock.patch(
|
||||
"py.metadata_ops.identify_model_type",
|
||||
new=mock.AsyncMock(return_value="lora"),
|
||||
),
|
||||
mock.patch(
|
||||
"py.services.settings_manager.SettingsManager.get_priority_tag_config",
|
||||
return_value={},
|
||||
),
|
||||
):
|
||||
context = await service._build_prompt_context(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/models/loras/thing.safetensors",
|
||||
metadata={"hf_url": "https://huggingface.co/user/repo"},
|
||||
registry=mock.Mock(),
|
||||
llm=mock.Mock(),
|
||||
)
|
||||
|
||||
mock_fetch.assert_awaited_once_with("user/repo")
|
||||
assert context["source_platform"] == "huggingface"
|
||||
assert context["hf_url"] == "https://huggingface.co/user/repo"
|
||||
assert context["repo"] == "user/repo"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_tensorart_never_fetches_a_card(self):
|
||||
service = AgentService()
|
||||
|
||||
with (
|
||||
mock.patch(
|
||||
"py.services.model_sources.huggingface.HuggingFaceSource.fetch_model_card",
|
||||
new=mock.AsyncMock(),
|
||||
) as hf_fetch,
|
||||
mock.patch(
|
||||
"py.services.model_sources.modelscope.ModelScopeSource.fetch_model_card",
|
||||
new=mock.AsyncMock(),
|
||||
) as ms_fetch,
|
||||
mock.patch(
|
||||
"py.metadata_ops.list_base_models",
|
||||
new=mock.AsyncMock(return_value=[]),
|
||||
),
|
||||
mock.patch(
|
||||
"py.metadata_ops.identify_model_type",
|
||||
new=mock.AsyncMock(return_value="lora"),
|
||||
),
|
||||
mock.patch(
|
||||
"py.services.settings_manager.SettingsManager.get_priority_tag_config",
|
||||
return_value={},
|
||||
),
|
||||
):
|
||||
context = await service._build_prompt_context(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/models/loras/thing.safetensors",
|
||||
metadata={
|
||||
"source_platform": "tensorart",
|
||||
"source_url": "https://tensor.art/models/827823520299086029",
|
||||
},
|
||||
registry=mock.Mock(),
|
||||
llm=mock.Mock(),
|
||||
)
|
||||
|
||||
hf_fetch.assert_not_awaited()
|
||||
ms_fetch.assert_not_awaited()
|
||||
assert context["readme_content"] == ""
|
||||
assert context["source_platform"] == "tensorart"
|
||||
@@ -964,6 +964,8 @@ def _make_cache_entry(**overrides) -> Dict[str, Any]:
|
||||
"civitai": {"id": 111, "modelId": 222, "name": "v1"},
|
||||
"civitai_deleted": False,
|
||||
"skip_metadata_refresh": False,
|
||||
"source_platform": "",
|
||||
"source_url": "",
|
||||
"hf_url": "",
|
||||
"license_flags": 113,
|
||||
"hash_status": "completed",
|
||||
|
||||
@@ -0,0 +1,324 @@
|
||||
"""Tests for the external model-source provider registry.
|
||||
|
||||
Covers URL recognition for Hugging Face / ModelScope / TensorArt, the
|
||||
legacy ``hf_url`` → ``source_url`` normalisation, version-group keys, and
|
||||
each provider's model-card fetching and capability flags.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from py.services.model_sources import (
|
||||
HuggingFaceSource,
|
||||
ModelScopeSource,
|
||||
TensorArtSource,
|
||||
detect_source,
|
||||
get_source,
|
||||
get_source_platform,
|
||||
has_external_source,
|
||||
list_sources,
|
||||
normalize_metadata_source,
|
||||
resolve_source_ref,
|
||||
source_group_key,
|
||||
source_label,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# URL recognition
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestDetectSource:
|
||||
@pytest.mark.parametrize(
|
||||
("url", "platform", "source_id"),
|
||||
[
|
||||
("https://huggingface.co/user/repo", "huggingface", "user/repo"),
|
||||
("https://www.huggingface.co/user/repo", "huggingface", "user/repo"),
|
||||
(
|
||||
"https://huggingface.co/user/repo/resolve/main/model.safetensors",
|
||||
"huggingface",
|
||||
"user/repo",
|
||||
),
|
||||
(
|
||||
"https://modelscope.cn/models/jj3550945163/Krea-2-LORA",
|
||||
"modelscope",
|
||||
"jj3550945163/Krea-2-LORA",
|
||||
),
|
||||
(
|
||||
"https://www.modelscope.cn/models/jj3550945163/Krea-2-LORA/summary",
|
||||
"modelscope",
|
||||
"jj3550945163/Krea-2-LORA",
|
||||
),
|
||||
(
|
||||
"https://tensor.art/models/827823520299086029/Vivid-Impressions-Storybook-Sstyle-V1.0",
|
||||
"tensorart",
|
||||
"827823520299086029",
|
||||
),
|
||||
("https://tusi.cn/models/827823520299086029", "tensorart", "827823520299086029"),
|
||||
],
|
||||
)
|
||||
def test_recognises_supported_urls(self, url, platform, source_id):
|
||||
ref = detect_source(url)
|
||||
assert ref is not None
|
||||
assert ref.platform == platform
|
||||
assert ref.source_id == source_id
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"url",
|
||||
[
|
||||
"",
|
||||
None,
|
||||
"not-a-url",
|
||||
"https://example.com/x",
|
||||
"https://civitai.com/models/123",
|
||||
],
|
||||
)
|
||||
def test_ignores_unsupported_urls(self, url):
|
||||
assert detect_source(url) is None
|
||||
|
||||
def test_canonical_url_is_stable(self):
|
||||
assert detect_source("https://huggingface.co/u/r").url == "https://huggingface.co/u/r"
|
||||
assert (
|
||||
detect_source("https://modelscope.cn/models/u/r/summary").url
|
||||
== "https://modelscope.cn/models/u/r"
|
||||
)
|
||||
assert (
|
||||
detect_source("https://tensor.art/models/123/some-slug").url
|
||||
== "https://tensor.art/models/123"
|
||||
)
|
||||
|
||||
|
||||
class TestStrictParsing:
|
||||
@pytest.mark.parametrize(
|
||||
"url",
|
||||
[
|
||||
"https://huggingface.co/user/repo",
|
||||
"https://huggingface.co/user/repo/",
|
||||
"https://modelscope.cn/models/user/repo",
|
||||
"https://modelscope.cn/models/user/repo/summary",
|
||||
"https://tensor.art/models/827823520299086029",
|
||||
"https://tensor.art/models/827823520299086029/Vivid-Impressions",
|
||||
],
|
||||
)
|
||||
def test_accepts_user_facing_urls(self, url):
|
||||
assert detect_source(url, strict=True) is not None
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"url",
|
||||
[
|
||||
"https://huggingface.co/user/repo/resolve/main/model.safetensors",
|
||||
"https://example.com/x",
|
||||
"https://tensor.art/models/not-a-number",
|
||||
],
|
||||
)
|
||||
def test_rejects_non_page_urls(self, url):
|
||||
assert detect_source(url, strict=True) is None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Capabilities
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestCapabilities:
|
||||
def test_huggingface_supports_everything(self):
|
||||
source = get_source("huggingface")
|
||||
assert source.supports_enrichment is True
|
||||
assert source.supports_download is True
|
||||
|
||||
def test_modelscope_supports_enrichment_but_not_download(self):
|
||||
source = get_source("modelscope")
|
||||
assert source.supports_enrichment is True
|
||||
assert source.supports_download is False
|
||||
|
||||
def test_tensorart_is_link_only(self):
|
||||
source = get_source("tensorart")
|
||||
assert source.supports_enrichment is False
|
||||
assert source.supports_download is False
|
||||
|
||||
def test_registry_lists_every_source(self):
|
||||
platforms = {s.platform for s in list_sources()}
|
||||
assert platforms == {"huggingface", "modelscope", "tensorart"}
|
||||
|
||||
def test_labels_are_brand_names(self):
|
||||
assert source_label("huggingface") == "Hugging Face"
|
||||
assert source_label("modelscope") == "ModelScope"
|
||||
assert source_label("tensorart") == "TensorArt"
|
||||
assert source_label("unknown", "fallback") == "fallback"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Metadata normalisation
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestNormalizeMetadataSource:
|
||||
def test_derives_source_fields_from_legacy_hf_url(self):
|
||||
metadata = {"hf_url": "https://huggingface.co/user/repo"}
|
||||
normalize_metadata_source(metadata)
|
||||
assert metadata["source_platform"] == "huggingface"
|
||||
assert metadata["source_url"] == "https://huggingface.co/user/repo"
|
||||
assert metadata["hf_url"] == "https://huggingface.co/user/repo"
|
||||
|
||||
def test_canonicalises_modelscope_url_and_clears_hf_alias(self):
|
||||
metadata = {
|
||||
"source_platform": "modelscope",
|
||||
"source_url": "https://modelscope.cn/models/user/repo/summary",
|
||||
"hf_url": "https://huggingface.co/old/repo",
|
||||
}
|
||||
normalize_metadata_source(metadata)
|
||||
assert metadata["source_platform"] == "modelscope"
|
||||
assert metadata["source_url"] == "https://modelscope.cn/models/user/repo"
|
||||
# A stale HF alias must not make a ModelScope model look like HF.
|
||||
assert metadata["hf_url"] == ""
|
||||
|
||||
def test_preserves_unknown_url_for_unknown_platform(self):
|
||||
metadata = {"source_url": "https://example.com/model/1", "source_platform": "other"}
|
||||
normalize_metadata_source(metadata)
|
||||
assert metadata["source_url"] == "https://example.com/model/1"
|
||||
assert metadata["source_platform"] == "other"
|
||||
|
||||
def test_empty_metadata_gets_default_fields(self):
|
||||
metadata: dict = {}
|
||||
normalize_metadata_source(metadata)
|
||||
assert metadata["source_platform"] == ""
|
||||
assert metadata["source_url"] == ""
|
||||
|
||||
def test_infers_platform_from_url_when_missing(self):
|
||||
metadata = {"source_url": "https://modelscope.cn/models/user/repo"}
|
||||
normalize_metadata_source(metadata)
|
||||
assert metadata["source_platform"] == "modelscope"
|
||||
|
||||
|
||||
class TestResolveSourceRef:
|
||||
def test_resolves_from_canonical_fields(self):
|
||||
ref = resolve_source_ref(
|
||||
{"source_platform": "modelscope", "source_url": "https://modelscope.cn/models/u/r"}
|
||||
)
|
||||
assert ref is not None
|
||||
assert ref.platform == "modelscope"
|
||||
assert ref.source_id == "u/r"
|
||||
|
||||
def test_resolves_from_legacy_hf_url(self):
|
||||
ref = resolve_source_ref({"hf_url": "https://huggingface.co/u/r"})
|
||||
assert ref is not None
|
||||
assert ref.platform == "huggingface"
|
||||
|
||||
def test_returns_none_without_any_source(self):
|
||||
assert resolve_source_ref({}) is None
|
||||
assert resolve_source_ref({"hf_url": ""}) is None
|
||||
|
||||
|
||||
class TestHelpers:
|
||||
def test_has_external_source_accepts_both_field_shapes(self):
|
||||
assert has_external_source({"hf_url": "https://huggingface.co/u/r"}) is True
|
||||
assert has_external_source({"source_url": "https://modelscope.cn/models/u/r"}) is True
|
||||
assert has_external_source({"source_url": ""}) is False
|
||||
assert has_external_source({}) is False
|
||||
|
||||
def test_get_source_platform_infers_from_url(self):
|
||||
assert get_source_platform({"hf_url": "https://huggingface.co/u/r"}) == "huggingface"
|
||||
assert get_source_platform({"source_platform": "tensorart"}) == "tensorart"
|
||||
assert get_source_platform({}) == ""
|
||||
|
||||
def test_group_keys_match_legacy_hf_shape(self):
|
||||
assert source_group_key({"hf_url": "https://huggingface.co/u/r"}) == "hf:u/r"
|
||||
assert (
|
||||
source_group_key({"source_url": "https://modelscope.cn/models/u/r"}) == "ms:u/r"
|
||||
)
|
||||
assert (
|
||||
source_group_key({"source_url": "https://tensor.art/models/123"}) == "ta:123"
|
||||
)
|
||||
|
||||
def test_group_key_is_none_without_source(self):
|
||||
assert source_group_key({}) is None
|
||||
assert source_group_key({"hf_url": "https://example.com/x"}) is None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Model card fetching
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestFetchModelCard:
|
||||
@pytest.mark.asyncio
|
||||
async def test_huggingface_tries_main_then_master(self, monkeypatch):
|
||||
calls: list[str] = []
|
||||
|
||||
async def fake_fetch_text(url: str, **_kwargs) -> str:
|
||||
calls.append(url)
|
||||
if url.endswith("/master/README.md"):
|
||||
return "# card"
|
||||
return ""
|
||||
|
||||
monkeypatch.setattr("py.services.model_sources.huggingface.fetch_text", fake_fetch_text)
|
||||
|
||||
card = await HuggingFaceSource().fetch_model_card("user/repo")
|
||||
|
||||
assert card == "# card"
|
||||
assert calls == [
|
||||
"https://huggingface.co/user/repo/raw/main/README.md",
|
||||
"https://huggingface.co/user/repo/raw/master/README.md",
|
||||
]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_prefers_resolve_url(self, monkeypatch):
|
||||
calls: list[str] = []
|
||||
|
||||
async def fake_fetch_text(url: str, **_kwargs) -> str:
|
||||
calls.append(url)
|
||||
return "---\nbase_model: krea/Krea-2-Turbo\n---\n# krea"
|
||||
|
||||
monkeypatch.setattr("py.services.model_sources.modelscope.fetch_text", fake_fetch_text)
|
||||
|
||||
card = await ModelScopeSource().fetch_model_card("u/r")
|
||||
|
||||
assert card.startswith("---")
|
||||
assert calls == ["https://modelscope.cn/models/u/r/resolve/master/README.md"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_falls_back_to_repo_api(self, monkeypatch):
|
||||
calls: list[str] = []
|
||||
|
||||
async def fake_fetch_text(url: str, **_kwargs) -> str:
|
||||
calls.append(url)
|
||||
if "/api/v1/models/" in url:
|
||||
return "# from api"
|
||||
return ""
|
||||
|
||||
monkeypatch.setattr("py.services.model_sources.modelscope.fetch_text", fake_fetch_text)
|
||||
|
||||
card = await ModelScopeSource().fetch_model_card("u/r")
|
||||
|
||||
assert card == "# from api"
|
||||
assert "resolve/master/README.md" in calls[0]
|
||||
assert (
|
||||
"https://modelscope.cn/api/v1/models/u/r/repo?Revision=master&FilePath=README.md"
|
||||
in calls
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_tensorart_never_fetches(self):
|
||||
# TensorArt enrichment is disabled: the provider must not issue any
|
||||
# HTTP request, so it deliberately does not import `fetch_text`.
|
||||
import importlib
|
||||
|
||||
module = importlib.import_module("py.services.model_sources.tensorart")
|
||||
assert not hasattr(module, "fetch_text")
|
||||
assert await TensorArtSource().fetch_model_card("123") == ""
|
||||
|
||||
|
||||
class TestAssetBaseUrl:
|
||||
def test_huggingface_uses_main_revision(self):
|
||||
assert (
|
||||
HuggingFaceSource().asset_base_url("u/r")
|
||||
== "https://huggingface.co/u/r/resolve/main"
|
||||
)
|
||||
|
||||
def test_modelscope_uses_master_revision(self):
|
||||
assert (
|
||||
ModelScopeSource().asset_base_url("u/r")
|
||||
== "https://modelscope.cn/models/u/r/resolve/master"
|
||||
)
|
||||
@@ -292,6 +292,61 @@ Content
|
||||
)
|
||||
assert images[0]["meta"]["prompt"] == "a cat"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_gallery_images_use_modelscope_asset_base_url(self, processor):
|
||||
"""A ModelScope-linked model resolves relative images against ModelScope."""
|
||||
readme = """---
|
||||
widget:
|
||||
- text: "a cat"
|
||||
output:
|
||||
url: images/cat.png
|
||||
---
|
||||
Content
|
||||
"""
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.MIN_LLM_OUTPUT,
|
||||
metadata={
|
||||
"from_civitai": False,
|
||||
"source_platform": "modelscope",
|
||||
"source_url": "https://modelscope.cn/models/user/repo",
|
||||
},
|
||||
readme_content=readme,
|
||||
)
|
||||
applied = mock_apply.call_args[0][1]
|
||||
images = applied.get("civitai", {}).get("images", [])
|
||||
assert len(images) == 1
|
||||
assert images[0]["url"] == (
|
||||
"https://modelscope.cn/models/user/repo/resolve/master/images/cat.png"
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_base_model_overwrites_existing_modelscope_model(self, processor):
|
||||
"""ModelScope is an external source, so the LLM may overwrite base_model."""
|
||||
llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=False),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=llm,
|
||||
metadata={
|
||||
"base_model": "SD 1.5",
|
||||
"source_platform": "modelscope",
|
||||
"source_url": "https://modelscope.cn/models/user/repo",
|
||||
},
|
||||
)
|
||||
assert mock_apply.call_args[0][1]["base_model"] == "Flux.1 D"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_gallery_images_skipped_without_hf_url(self, processor):
|
||||
"""Gallery images NOT extracted when the model has no HF source."""
|
||||
|
||||
Reference in New Issue
Block a user