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>

packages/ltx-trainer/src/ltx_trainer/config.py

@@ -252,6 +252,14 @@ class ModelConfig(ConfigBaseModel):
         description="Training mode - either LoRA fine-tuning or full model fine-tuning",
     )
 
+    mask_conditioning_channels: int = Field(
+        default=0,
+        ge=0,
+        description="SCAIL-2 in-context mask channels. If > 0, the video patchify_proj is widened by this "
+        "many zero-init input columns at load time. Use with a 'driving' + 'mask_channels' condition and, "
+        "in LoRA mode, patchify_proj is additionally unfrozen so the new columns can train. 0 disables.",
+    )
+
     load_checkpoint: str | Path | None = Field(
         default=None,
         description="Path to a checkpoint file or directory to load from. "