perf(tagger): multi-thread CPU inference instead of pinning to one core

The ONNX session was built with intra_threads(1) unconditionally. That's
correct for DirectML (compute is on the GPU), but on the CPU execution
provider it pinned all matmul/conv work to a single core — ~1.78s/image,
~14s for an 8-image batch.

Derive the intra-op thread count from available_parallelism() for the CPU EP
(leaving 2 logical cores free for the UI and a possible concurrent scan;
tagging is the lowest-priority worker, so heavier workers are idle when it
runs). DirectML/Auto keep a single thread. Measured ~2.7x on a representative
machine (14s -> 5.3s per 8-image batch); sublinear because swinv2 inference is
memory-bandwidth-bound. The selected thread count is logged at load.

CHANGELOG.md

@@ -59,6 +59,10 @@ aims to follow [Semantic Versioning](https://semver.org/spec/v2.0.0.html)
 - **Explore cluster layout** — clusters are now sized by image count (busier
   clusters are larger and stack on top) and repositioned to avoid overlapping, so
   every cluster stays viewable and clickable even in dense libraries.
+- **Faster CPU tagging** — when AI tagging runs on the CPU provider (no usable
+  GPU) it now uses multiple cores instead of being pinned to one, several times
+  faster on multi-core machines. A couple of cores are left free so the rest of
+  the app stays responsive. GPU (DirectML) tagging is unchanged.
 
 ### Fixed