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https://github.com/willmiao/ComfyUI-Lora-Manager.git
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5ab0e88abc
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.
129 lines
4.6 KiB
JavaScript
129 lines
4.6 KiB
JavaScript
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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describe('ModelContextMenuMixin.getModelTypePrefix', () => {
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it('maps every known model type to its API route prefix', () => {
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expect(ModelContextMenuMixin.getModelTypePrefix.call({ modelType: 'lora' })).toBe('loras');
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expect(ModelContextMenuMixin.getModelTypePrefix.call({ modelType: 'checkpoint' })).toBe('checkpoints');
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expect(ModelContextMenuMixin.getModelTypePrefix.call({ modelType: 'embedding' })).toBe('embeddings');
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});
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it('falls back to the loras prefix for unknown types', () => {
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expect(ModelContextMenuMixin.getModelTypePrefix.call({ modelType: 'unknown' })).toBe('loras');
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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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