Wire up real image embeddings and make embedding models first-class in the
VRAM lifecycle.

- api/embeddings.py: detect CLIP/SigLIP dual encoders and drive them through
  transformers get_text_features/get_image_features so text and images share
  one projected space (ST path kept for repos shipping a native recipe).
  request.image is now actually read (URL/data-URI/path/base64), vectors are
  appended after the text ones, and text-only models return a clear 400.
  Handle the transformers 5.x pooled-output return shape.
- Wrap the loaded model in _EmbeddingModel (unpacks as (backend, model) but
  exposes cleanup()) and register it via add_model() + record_vram_delta() on
  the request path, so it is measured, LRU-tracked, and cleanly evicted like
  every other model type instead of leaking as a bare tuple.
- admin: fix the model-load button for embeddings (was routed to the diffusers
  loader) to use _load_embedding_model, matching the request path.
- admin: backfill used_vram_gb after a download completes, since the entry is
  saved before the weights exist on disk; factor the estimate into a shared
  _estimate_used_vram_gb helper. A freshly-downloaded CLIP/SigLIP entry now
  always carries a reasonable estimate so pre-load eviction sizes correctly.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EPLnsRpNBzWCHLgkXATqRz