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feat(download): fill model metadata from the source API on download
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.
This commit is contained in:
@@ -266,4 +266,101 @@ describe('DownloadManager external model source downloads', () => {
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expect(manager._externalGroupKey(ms)).toBe('modelscope:u/r');
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expect(manager._externalGroupKey(hf)).not.toBe(manager._externalGroupKey(ms));
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});
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describe('post-transfer stage reporting', () => {
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it('ignores ordinary frames', () => {
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const updateProgress = vi.fn();
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expect(
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manager._applyMetadataStage(
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{ status: 'progress', progress: 40, bytes_per_second: 10 },
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updateProgress,
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0,
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'f.safetensors'
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)
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).toBe(false);
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expect(updateProgress).not.toHaveBeenCalled();
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});
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it('routes a metadata stage to the progress bar at 100%', () => {
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const updateProgress = vi.fn();
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expect(
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manager._applyMetadataStage(
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{ status: 'metadata', stage: 'source', platform: 'modelscope' },
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updateProgress,
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3,
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'f.safetensors'
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)
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).toBe(true);
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expect(updateProgress).toHaveBeenCalledWith(100, 3, 'f.safetensors', {}, {
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phase: 'metadata',
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stage: 'source',
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platform: 'modelscope',
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});
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});
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it('tolerates a stage frame with no stage or platform', () => {
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const updateProgress = vi.fn();
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expect(
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manager._applyMetadataStage({ status: 'metadata' }, updateProgress, 0, 'f')
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).toBe(true);
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expect(updateProgress).toHaveBeenCalledWith(100, 0, 'f', {}, {
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phase: 'metadata',
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stage: '',
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platform: '',
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});
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});
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it('surfaces a metadata frame received while the request is in flight', async () => {
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// End-to-end through the websocket handler: the backend keeps the socket
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// open while it hydrates, and the frame has to reach the progress bar.
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const sockets = [];
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class RecordingWebSocket {
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constructor(url) {
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this.url = url;
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this.onopen = null;
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this.onmessage = null;
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this.onerror = null;
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this.close = vi.fn();
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sockets.push(this);
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queueMicrotask(() => this.onopen && this.onopen());
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}
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}
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vi.stubGlobal('WebSocket', RecordingWebSocket);
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const updateProgress = vi.fn();
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mockLoadingManager.showDownloadProgress.mockReturnValue(updateProgress);
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mockApiClient.downloadModelSource.mockImplementation(async () => {
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sockets.at(-1).onmessage({
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data: JSON.stringify({
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status: 'metadata',
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stage: 'source',
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platform: 'modelscope',
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}),
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});
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return { success: true };
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});
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manager.sourcePlatform = 'modelscope';
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manager.sourceRepoId = 'u/r';
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manager.sourceSelectedFiles = ['a.safetensors'];
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await manager._downloadExternalRepoFiles({
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modelRoot: '/models',
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targetFolder: '',
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useDefaultPaths: false,
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});
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expect(updateProgress).toHaveBeenCalledWith(100, 0, 'a.safetensors', {}, {
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phase: 'metadata',
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stage: 'source',
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platform: 'modelscope',
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});
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mockLoadingManager.showDownloadProgress.mockReturnValue(vi.fn());
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});
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});
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});
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