Adds a target="video" path that trains a LoRA directly against the configured
video model so it loads on the video pipeline (image LoRAs can't apply to a Wan
DiT). _train_wan: encodes stills as 1-frame latents via the Wan 3D VAE
(latents_mean/std normalized), encodes the prompt via UMT5, loads the
transformer expert(s) in 4-bit (QLoRA) with gradient checkpointing, adds PEFT
LoRA to the attention projections, and trains a rectified-flow loss. Handles
Wan2.2's dual experts (transformer + transformer_2) via boundary_ratio routing,
and saves both expert LoRA layers (falls back to high-noise only on older
diffusers). Reuses the queue, eviction, thermal checkpoints and progress.

LoraTrainRequest gains target/quantize_4bit/num_frames; base-path resolution
gains a "video" category so it resolves the video model entry.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

An SD/SDXL image LoRA loaded onto a Wan video transformer matches no keys, so
set_adapters() raised "not in the list of present adapters: set()" and aborted
the whole request. Now each adapter is checked against the pipe's PEFT-capable
components after load; ones that registered nothing (wrong architecture) are
skipped with a clear message and generation proceeds with whatever is
compatible (or no LoRA). The request signature is still cached so the futile
load isn't retried every clip.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

The video model stays cached across clips, but LoRAs were loaded from disk
and fully unloaded on every clip — wasted I/O and fusion latency, since
consecutive clips of a match request the identical fighter+env LoRAs.

Cache the active LoRA signature on the pipe (_coderai_active_loras) and only
swap when it changes: a request with the same set reuses the loaded adapters,
a different set (or none) triggers a clean unload + reload. Replaces the
apply-then-unload-every-clip path.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Match re-render previously showed only a frozen text status. Now:
- _stage_videos_render reports per-clip start/end via clip_cb and overall
  done/total via progress_cb.
- _run_match_job seeds an items list and maps clip state into the job record.
- The match detail page renders a global progress bar plus one bar per
  clip/output (pending/rendering/done/failed), and these survive a reload
  via /active-jobs + resumeMatchJobs.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Route /v1/loras/train through queue_manager.acquire/release with a constant
"lora-train" model key. Concurrent training requests now queue and run one after
another (serialized by the scheduler, protecting the shared base cache) and
participate in the same scheduling/metrics as every other model request, instead
of being rejected with 409. _train_lock is kept as the in-flight signal that
_release_base_cache checks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Tag regen/train jobs with kind/name/jtype and add a /active-jobs endpoint listing
running ones. On Characters/Environments page load, resumeActiveJobs() re-attaches
the live progress display to any in-flight job for the matching card, so a reload
(or reopening the tab) keeps showing progress until completion.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Add a shared image lightbox (injected into every page via _page) and make the
reference-image thumbnails on the Characters/Environments pages clickable to view
them full-size. Click the backdrop or press Escape to close. Delete button still
works independently.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

- LoRA trainer: cache the SD/SDXL base on CPU between jobs so back-to-back
  trainings against the same base skip the disk reload, and the base holds no
  VRAM between jobs (moved to GPU only while training). Fixes the post-training
  eviction failure that forced the next image request into CPU/disk offload.
- Model manager: add register_external_vram_releaser() + last-resort eviction
  pass so a generation can reclaim the trainer's cached base when needed (skips
  while a job runs).
- Thermal: average 3 CPU samples spread across a 3s budget for the resume/
  cooldown decision (CPU sensors swing +/-10C); pause stays single-read to react
  fast. Bounded so it never blocks past 3s of the poll interval.
- Debug flags: --debug-web (uvicorn access lines), --debug-thermal ([thermal]
  [debug] checks), --debug-lora (per-step training loss to terminal); all off by
  default and independent of --debug.
- Admin: lora_train_base_model field on the Models page; saves apply live to the
  running server (build_runtime_kwargs/apply_model_entry_live) with no restart.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

- images.py: stop reusing cached prompt embeds for Z-Image. Its encode_prompt
  returns per-sample lists the pipeline consumes during a run, so cache-HIT
  reuse corrupted the batch (AssertionError: len(size) == bsz in unpatchify).
  Z-Image now always encodes natively; cache key also bound to id(pipeline).
- loras.py: fix wrong import (multi_model_manager lives in codai.models.manager,
  not codai.api.state) used for VRAM unload + base-model path resolution; add an
  up-front architecture guard so transformer/DiT models (Z-Image/Flux/SD3) fail
  with a clear message instead of crashing in the CLIP tokenizer.
- New train_base_model on LoraTrainRequest: train LoRAs against a separate
  UNet-based SD1.x/SDXL model while generation keeps using the DiT image model.
- gen_township_fighters.py: thread --lora-train-base-model / web field through
  train_lora, _train_profile_loras, stage_loras/stage_env_loras, all run + per-
  card call sites; CONFIG_FIELDS + live default_args apply on /start, /save-config.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Browsers routinely abort media (video seek/scrub) and SSE connections
mid-response, which surfaced as BrokenPipeError/ConnectionResetError
tracebacks spamming the terminal. Swallow these in the request handler's
handle_one_request() and finish() so they're ignored cleanly.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Matches:
- Outcomes are now per-match (<match>_<fighter>_<outcome>), generated for each
  match's two fighters, so each match's outcome scenes are its own/different.
- Support multiple matches for the same pair+environment via unique match names
  (numeric suffix); scanner and delete isolate siblings correctly.
- List view shows a lightweight preview (clip00 keyframe or metadata poster);
  detail view shows clips AND the match's outcomes per fighter, each with an
  editable prompt + re-render + remove. Clip/outcome prompts save to
  prompts.json. Legacy per-fighter outputs still listed.

Prompts page: reframed as master prompts that auto-write a unique prompt per
clip/outcome of every match; static templates marked fallback-only.

Terminal: per-card action jobs (regenerate, train LoRA, match render) now echo
their progress to the terminal too.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Matches page:
- Lightweight list view (no embedded players); per-match/per-fighter detail
  loads only that item's videos.
- Edit a match: change fighters, environment, short/long targets, and each
  clip's prompt (saved to prompts.json). Fighter-outputs detail edits outcome
  prompts.
- Remove videos: per clip, per final, whole match, single output, or all of a
  fighter's outputs (keeps plan/keyframes for re-render). Traversal-guarded.

Global Prompts page (/prompts):
- Edit the script's global templates — LLM fight/outcome system prompts and the
  static fight-shot + win/ko_win/retire/draw fallback templates. Persisted to
  <out-dir>/prompts_config.json, applied live and re-loaded on every run, with
  reset-to-defaults.

Web UI:
- Only auto-open a browser when --browser is passed (avoids spawning a terminal
  text browser on headless servers).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

LoRA training (codai/api/loras.py):
- Fix instant crash "mat1 and mat2 have the same dtype (Half != float)":
  components loaded in their checkpoint's native dtype (fp16 text encoders +
  fp32 UNet). Force consistent fp32 (stable LoRA reference) for both SD1.5 and
  SDXL, and free VAE/text encoders after pre-encoding to keep memory in budget.

Web UI (tools/gen_township_fighters.py):
- Per-profile LoRA training launched from the Characters/Environments cards
  (steps/rank, live server progress, status badge); the "Train LoRAs" step now
  trains regardless of the consistency setting and logs what it does. Profiles
  are uploaded to CoderAI first so server-side training has reference images.
- New Matches page: final short/long videos, single clips, and outcome outputs
  per match. Re-render clips/outputs with the video model (reuses the render
  path, honoring keyframe/LoRA settings) or reassemble finals from existing
  clips (no model).
- Replace browser alert/confirm/prompt with proper in-page modal dialogs
  (uiConfirm/uiAlert/uiPrompt) on every page.
- Apply changed connection/model settings (image/video/text model) to the live
  session on Save config / Start, so per-profile jobs use them immediately.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Web UI (tools/gen_township_fighters.py):
- Characters and Environments management pages: edit every meta field
  (data-driven form, not a fixed set), remove a profile, delete individual
  reference images — synced to CoderAI (DELETE/PATCH).
- Regenerate references: background job generates N new images guided by the
  kept references (IP-Adapter) and APPENDS them, preserving non-deleted ones.
- Upload your own reference image files (magic-byte validated, appended).
- Clear run banner + status-pill label indicating which run/step is executing.
- Config: -s/--save and -c/--config; web Save button writes server-side and,
  when launched with -c, defaults to that same config path.

Environment parity / consistency:
- Per-environment identity LoRAs (loras.py: environment field, env image
  resolver, "place" instance prompt; script stage_env_loras, env_loras.json,
  applied to keyframes + video alongside fighter LoRAs).
- Environment IP-Adapter: environment_profiles on the image request
  (imagerequest.py) resolved into the IP-Adapter reference set (images.py),
  so environment regeneration can match kept references.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

CLI: -s/--save FILE writes selected generation options to a JSON config
and exits; -c/--config FILE loads them as defaults (command-line args
still override, via a two-phase parse).

Web UI: "Save config" button writes the current form options to a file
on the server (relative paths land in the output dir, absolute paths as-is)
via a new /save-config endpoint. Web- and CLI-saved configs are
interchangeable (shared CONFIG_FIELDS / canonical mapping).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

- Character profiles: extract from images/video or generate from text prompt;
  up to 6 profiles per image/video generation via IP-Adapter conditioning
- Environment profiles: same multi-slot system for scene/background conditioning
- Multi-character dialog for video: per-character TTS synthesis with voice profile
  support, sequential or manual timing, ffmpeg amix, Wav2Lip/SadTalker lip sync
- 2D↔3D conversion: image/video to stereo/anaglyph/depth/mesh and back;
  text/image to 3D model (GLB); turntable video rendering
- Generation archive: auto-save all outputs with configurable retention and
  hourly cleanup; browse/delete from web UI and API
- Voice profile enhancements: extract from video, PATCH update, GET by name;
  TTS endpoint now accepts voice_profile for F5-TTS cloning
- Image generation progress tracking via GET /v1/images/progress
- Web Studio: Profiles tab (Characters, Environments, Voices) with full managers;
  multi-slot character/environment selectors in all panels; character dialog
  section in all video panels; archive settings in Settings page
- Fix: Profiles tab sub-tab switching now correctly updates the content panel
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>