VRAM estimation (manager.py):
- Weight the effective quant multiplier by REAL per-component parameter
  shares (new _component_param_shares scans safetensors by component folder)
  instead of a blind 70/30 split. Wan2.2 is 99.6% quantizable (two 14B
  experts + text encoder 4-bit, only the 0.13B VAE dense), so the old 0.475x
  multiplier inflated ~25.8 GB -> 42.7 GB and forced needless offload. Now
  ~0.28x -> ~25.8 GB. VAE forced dense (conv-only, bnb can't quantize).

Auto offload decision (video.py):
- 'auto': when peak footprint exceeds free VRAM, go straight to `model` CPU
  offload (active component on GPU, near full-GPU speed) — no full-GPU gamble,
  no slow balanced+disk path.
- 'auto-borderline' (new mode): same, except a marginal overshoot (<=3 GB)
  tries full-GPU first to keep both experts resident and use free VRAM,
  falling back to model offload on OOM.

Acceleration LoRA (acceleration.py + video.py):
- Keep the distill/Lightning LoRA as an ACTIVE RUNTIME ADAPTER instead of
  fusing. Fusing into CPU-offloaded bitsandbytes 4-bit weights triggers a
  dequant->merge->requant per Linear on the CPU — minutes/hours per expert,
  appearing to hang (high CPU, empty VRAM). Runtime adapters apply at forward
  time on the GPU at negligible cost and natively cover transformer_2.
- _sync_video_loras preserves the accel adapters across per-request LoRA swaps
  and re-includes them in every set_adapters; _unload_video_loras deletes only
  per-request adapters, keeping accel.

UI (models.html):
- Add "Auto borderline-aware" offload strategy option + updated hint.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>