mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
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A ModelScope or Hugging Face download landed as a bare filename, hash and
source link; the model card stayed empty until the user ran "Enrich
Metadata with AI" by hand. But everything that makes a CivitAI download
useful — the display name, the description, the tags, the trigger words,
the example images, the preview — is already published by those sites'
public APIs, so asking for it at download time is deterministic work, not
model work.
Add `py/services/model_sources/hydration.py`, called by
`_save_source_metadata()` once the sidecar exists and the file is in the
scanner cache. It fetches the model card plus the site's card extras and
hands them to the same `PostProcessor` the AI skill uses, with an empty
`llm_output`, so the two paths cannot drift apart. What lands:
* `model_name` from the site's own display name (ModelScope's `Name`), so
the card stops showing the local filename — written only while the value
still equals the file stem, since once a user renames a model that
choice is theirs to keep
* `civitai.name` from the matched version's label (`showName`), which the
card renders as the version chip
* `civitai.description` / `modelDescription` from the author summary plus
the README as HTML
* `civitai.images` / `preview_url` from the per-file example images
* `civitai.trainedWords` from the per-file trigger words
* `base_model`, `tags` and `usage_tips` as before
Provenance stays honest: the pass records
`metadata_source = "source:<platform>"` rather than the skill's
`agent:enrich_hf_metadata`, and — because no provider ran — it no longer
stamps `llm_enriched_at`; that stamp is now conditional on the LLM
actually answering, which is what the field means. The five hand-rolled
`civitai` dict merges in the post-processor collapse into one
`_merge_civitai()` helper.
Two guards keep it safe. Only a model whose stored
`source_platform`/`source_url` match the repository being downloaded is
updated, so a local file that merely shares a name never receives another
model's card; and a file already on disk is topped up too, which
back-fills models downloaded before this existed. READMEs and detail
payloads describe the repository rather than the file, so a short-lived
process-wide `ModelSourceCache` (300 s, 32 entries) keeps a batch over one
repository to two HTTP requests. Every failure is logged and swallowed:
hydration can never fail a download.
Fix the hash policy while here. `_save_source_metadata()` went straight to
`MetadataManager.create_default_metadata()`, bypassing the per-type
factory on the owning scanner, so a checkpoint paid a full SHA256 inside
the download request — `CheckpointScanner`/`OtherScanner` deliberately
record `hash_status="pending"` with an empty `sha256` for their multi-GB
files. Metadata is now created through `scanner._create_default_metadata()`.
Hydration copes with the empty hash: `_matching_versions()` falls back to
the repository basename, which is exactly what the download just wrote.
Report both post-transfer stages, which advance no byte counter and so
read as a stall: the bar sat at 100% showing `0 B/s` for the seconds spent
hashing and fetching. `_report_phase()` broadcasts
`{"status": "metadata", "stage": "indexing" | "source", "platform": ...}`,
and `LoadingManager` names the stage in the status line (keeping the batch
position), retitles the item line, replaces the dead speed figure and runs
a sheen over the bar. `stage`/`platform` are machine-readable; the wording
is localised in the frontend.
Finally, `modelscope.ai` is its own catalogue rather than an alias of
`modelscope.cn` — `referall13/EM1` exists only on `.ai` and
`jj3550945163/Krea-2-LORA` only on `.cn` — so its URLs were rejected with
"Invalid model URL format". Register it as `ModelScopeIntlSource`
(`platform="modelscope-ai"`, `msai:` group prefix, its own default
download directory) and derive every URL either deployment builds from a
per-class `base_url`. `modelscope.com` stays an alias of `.cn`, which is
what it redirects to. The frontend source table, the link dialog hints and
the docs mirror the split.
Verified against the live APIs: both reported `.ai` repositories list
their files, read their READMEs and yield name / version / base model /
trigger words / example images. Backend 3092 passed; frontend 1259 JS +
91 Vue passed. The nine locales carry the new progress copy in the next
commit.
189 lines
6.6 KiB
JavaScript
189 lines
6.6 KiB
JavaScript
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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'modelscope-ai',
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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 ModelScope International as its own platform', () => {
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const info = parseModelSourceUrl(
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'https://www.modelscope.ai/models/referall13/EM1/files'
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);
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expect(info.platform).toBe('modelscope-ai');
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expect(info.groupPrefix).toBe('msai');
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expect(info.sourceId).toBe('referall13/EM1');
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expect(info.url).toBe('https://www.modelscope.ai/models/referall13/EM1');
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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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