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>
indigo
github-actions[bot]
sync-bot