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.

tests/frontend/utils/modelSourceHelpers.test.js

@@ -26,6 +26,7 @@ describe('modelSourceHelpers', () => {
     expect(MODEL_SOURCES.map((s) => s.platform)).toEqual([
       'huggingface',
       'modelscope',
+      'modelscope-ai',
       'tensorart',
     ]);
   });
@@ -44,6 +45,16 @@ describe('modelSourceHelpers', () => {
       expect(info.url).toBe('https://modelscope.cn/models/user/repo');
     });
 
+    it('recognises ModelScope International as its own platform', () => {
+      const info = parseModelSourceUrl(
+        'https://www.modelscope.ai/models/referall13/EM1/files'
+      );
+      expect(info.platform).toBe('modelscope-ai');
+      expect(info.groupPrefix).toBe('msai');
+      expect(info.sourceId).toBe('referall13/EM1');
+      expect(info.url).toBe('https://www.modelscope.ai/models/referall13/EM1');
+    });
+
     it('recognises TensorArt URLs and keeps only the numeric id', () => {
       const info = parseModelSourceUrl(
         'https://tensor.art/models/827823520299086029/Vivid-Impressions-Storybook-Sstyle-V1.0'

tests/frontend/utils/modelSourceUrlDetection.test.js

@@ -174,6 +174,54 @@ describe('DownloadManager.detectUrlType — external model source URLs', () => {
         expect(result.platform).toBe('huggingface');
     });
 
+    // modelscope.ai is a separate catalogue from modelscope.cn, not an alias,
+    // so it carries its own platform id all the way to the backend.
+    it('detects a ModelScope International repo URL', () => {
+        const result = DownloadManager.detectUrlType(
+            'https://www.modelscope.ai/models/referall13/EM1'
+        );
+        expect(result).toEqual({
+            type: 'model-source-repo',
+            platform: 'modelscope-ai',
+            repo: 'referall13/EM1',
+        });
+    });
+
+    it('detects a ModelScope International repo URL without the www prefix', () => {
+        const result = DownloadManager.detectUrlType(
+            'https://modelscope.ai/models/ErLubu/krea2_style_260911_02'
+        );
+        expect(result).toEqual({
+            type: 'model-source-repo',
+            platform: 'modelscope-ai',
+            repo: 'ErLubu/krea2_style_260911_02',
+        });
+    });
+
+    it('detects a ModelScope International file URL', () => {
+        const result = DownloadManager.detectUrlType(
+            'https://www.modelscope.ai/models/referall13/EM1/resolve/master/EM1_c1-st1000.safetensors'
+        );
+        expect(result).toEqual({
+            type: 'model-source-file',
+            platform: 'modelscope-ai',
+            repo: 'referall13/EM1',
+            revision: 'master',
+            filename: 'EM1_c1-st1000.safetensors',
+        });
+    });
+
+    it('keeps the two ModelScope deployments distinct', () => {
+        const mainland = DownloadManager.detectUrlType(
+            'https://modelscope.cn/models/referall13/EM1'
+        );
+        const intl = DownloadManager.detectUrlType(
+            'https://www.modelscope.ai/models/referall13/EM1'
+        );
+        expect(mainland.platform).toBe('modelscope');
+        expect(intl.platform).toBe('modelscope-ai');
+    });
+
     it('rejects path traversal in either platform', () => {
         expect(
             DownloadManager.detectUrlType('https://modelscope.cn/models/../etc/passwd')