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。

docs/tasks.md

@@ -30,10 +30,19 @@
 
 ## Phase 3 — 訓練整合（ltx-trainer）
 
+> **決策**：完整整合到 `FlexibleStrategy`；訓練方式 = **LoRA + 解凍 patchify_proj**（新 mask 欄位無法純 LoRA 訓練）。**本機無 GPU/Linux/checkpoint，只做到 CPU 單元驗證**，實訓需在 GPU 機器跑。
+
 | # | 任務 | 狀態 | 備註 |
 |---|---|---|---|
-| 3.1 | dataset 產出 (target, driving, mask) | ⬜ | |
-| 3.2 | 接上 conditioning + 微調 loss / 凍結策略 | ⬜ | |
+| 3.1 | 新增 Driving/MaskChannels ConditionConfig | ✅ | `flexible.py`：`DrivingConditionConfig`(latents_dir/mode/width_offset) + `MaskChannelsConditionConfig`(mask_dir/num_slots)，加入 union + `get_data_sources` |
+| 3.2 | strategy 接線 driving concat + cond_channels | ✅ | `_apply_driving_condition`(cond-first concat + ΔW width 偏移) + `_build_mask_channels`(重用 `encode_mask_channels`，寫前 N driving token) → `Modality.cond_channels` |
+| 3.3 | model_loader + ModelConfig 支援加寬 | ✅ | `widen_module_patchify_proj_for_mask_channels`(live module zero-init) + `load_transformer(mask_conditioning_channels=)` + `ModelConfig.mask_conditioning_channels` |
+| 3.4 | LoRA 模式解凍 patchify_proj | ✅ | `trainer._unfreeze_patchify_proj`：mask_channels>0 時把 video patchify_proj 設 trainable，讓新欄位隨 LoRA 一起訓 |
+| 3.5 | 範例 config + docs | ✅ | `configs/scail_animation_lora.yaml`；`configs/README.md` 與 `docs/training-modes.md` 表格 row |
+| 3.6 | CPU 單元驗證 | ✅ | `verify_phase3_trainer.py`：config round-trip、prepare_training_inputs 建 cond_channels[B,T,56]、driving 前置/mask placement、widened model forward + compute_loss finite、widen helper zero-init 等價 |
+| 3.7 | dataset 前處理（產 driving latents + 語意 mask） | ⬜ | 需 process_dataset 產出 `driving_latents/`（同 target 形狀）與 `char_masks/`(mask=[K+1,F_pix,H,W])；語意 mask 需分割模型，屬資料工程，未做 |
+| 3.8 | validation runner 接 driving/mask | ⬜ | 驗證期取樣尚未接 SCAIL 條件（config 內 validation 先停用），與 Phase 4 一起 |
+| 3.9 | 實機訓練跑通 | ⬜ | 需 Linux + GPU + checkpoint，本機無法 |
 
 ## Phase 4 — Pipeline + CLI