Add SCAIL-2 training integration (Phase 3, ltx-trainer FlexibleStrategy)

Wire SCAIL-2 driving + in-context mask conditioning into the trainer via the
unified FlexibleStrategy, so the widened patchify_proj (Phase 2) can be trained.

- flexible.py: new DrivingConditionConfig (driving-latent concat with a RoPE
  width offset ΔW) and MaskChannelsConditionConfig (semantic masks -> per-token
  channels), added to the condition union and get_data_sources. Driving is
  prepended (cond-first, target stays at the tail for loss slicing); mask
  channels are written onto the driving tokens via Modality.cond_channels,
  reusing ltx-core encode_mask_channels. The noisy target keeps a zero mask.
- model_loader.load_transformer gains mask_conditioning_channels, widening the
  video patchify_proj with zero-init columns via a new live-module helper
  (widen_module_patchify_proj_for_mask_channels). ModelConfig exposes the field.
- trainer unfreezes patchify_proj in LoRA mode when mask channels are active
  (the new input columns are new base params LoRA cannot reach).
- configs/scail_animation_lora.yaml plus README / training-modes table rows.

Verified on CPU (verify_phase3_trainer.py): config round-trips, prepare_training
_inputs builds cond_channels [B,T,56] with the mask on the driving tokens, a tiny
widened model forwards and compute_loss returns a finite [B] loss, and the widen
helper is output-preserving at zero init. Real training needs Linux+GPU+checkpoint;
dataset preprocessing (driving latents + semantic masks) and validation-runner
wiring are left for later.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

docs/plan.md

@@ -45,8 +45,12 @@
 - `VideoConditionByMaskChannels`：(K+1) 語意 mask → 空間下採樣 + 時間 8× 堆疊 → 寫入尾端 driving token（target 保持零，符合論文）。
 - 驗證通過（`verify_mask_channels.py`）：向後相容、zero-init 加寬 forward == baseline、mask pipeline 不 crash。**僅驗證 plumbing，畫質需 Phase 3 微調。**
 
-### Phase 3 — 訓練整合（ltx-trainer）⬜ 未開始
-- dataset 產出 (target, driving, mask)，接上 Phase 1/2 conditioning，設微調 loss 與凍結策略。
+### Phase 3 — 訓練整合（ltx-trainer）✅ 已完成（程式碼路徑；實訓需 GPU）
+- **決策**：完整整合到 `FlexibleStrategy`；訓練 = LoRA + 解凍 `patchify_proj`（新 mask 欄位無法純 LoRA 訓練）。
+- 新 `DrivingConditionConfig` + `MaskChannelsConditionConfig`（`flexible.py`）；`_apply_driving_condition`(cond-first concat + ΔW) + `_build_mask_channels`(重用 `encode_mask_channels`) → `Modality.cond_channels`。
+- `load_transformer(mask_conditioning_channels=)` 用 `widen_module_patchify_proj_for_mask_channels` 加寬；`ModelConfig.mask_conditioning_channels`；trainer 在 LoRA 模式解凍 patchify_proj。
+- `configs/scail_animation_lora.yaml` + docs。CPU 單元驗證通過（`verify_phase3_trainer.py`）。
+- **本機無 GPU/Linux/checkpoint → 未跑實機訓練**；dataset 前處理（driving latents + 語意 mask）與 validation runner 接線未做。
 
 ### Phase 4 — Pipeline + CLI 包裝 ⬜ 未開始
 - 仿 `lipdub.py` 寫 `scail_animation.py` pipeline + arg parser。