install the engines the presenter has been preferring all along

LatentSync and MuseTalk have been selectable by name for a while, and
`engine: "auto"` has ranked both ahead of wav2lip whenever there was a clip to
drive -- wav2lip works at 96x96 and upscales, which is the soft mouth on a 1080p
reel. Nothing ever installed them, so every selection ended at "neither on PATH
nor installed", and the call that would have been made could not have worked
either: LatentSync requires its checkpoint and UNet config as well as the three
paths, and MuseTalk takes a YAML task list and writes into a result directory
rather than taking paths at all.

Coderai orchestrates what it serves, so it installs these. The inference source
is vendored under third_party/ and pinned to a commit, for the reason LongCat's
is: an image someone else pulls has to run without network access to a third
party. The weights come from the Hub on first use, never during a render. Each
engine gets its own venv on its own python, because both pin stacks the server
cannot host.

Upstream MuseTalk takes its BiSeNet face-parsing checkpoint off a Google Drive id
and a pytorch.org URL; both come from a Hub mirror here, so the whole set has one
provenance.

_managed_engine_ready used to answer "an entrypoint and any .pt anywhere", which
reads an interrupted download as a working install. It now asks the module that
knows the file list, and the old heuristic keeps covering an engine an operator
installed by hand. /v1/video/presenter/engines reports what an uninstalled engine
is short of, so a client can tell "not yet" from "never here".

Verified live: LatentSync installed from nothing and lip-synced a 2s 480x480 clip
to real narration in 136s.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014T3rZnLhydX9QqUcfBbqLu