[refactor]: build resolved configs in the modular trainer

- moduleloader builds the component-loading config and the inference
  config as resolved configs (_build_training_resolved_config and
  build_inference_resolved_config) instead of TrainingArgs objects mutated
  after construction; the transformer override, weights path, and
  teacher/critic flag are config values or load_module arguments.
  keep_checkpoint_component_config keeps the trainer's PipelineConfig view
  of the checkpoint-filled VAE and text-encoder configs.
- The validation callback builds its pipeline with
  from_pretrained(path, resolved_config=...) and forwards with a resolved
  config built per run; it no longer replaces or mutates the trainer's
  pipeline_config, and generator_overrides takes nested GeneratorConfig
  fields instead of flat names.
- Against the 3f6893a0 snapshots of the 67 trainer YAML files, every
  PipelineConfig and value matches except ltx2_vae_tiling (the model's
  vae_tiling) and the unread pretrained_model_name_or_path.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7

apps/fastvideo_studio/src/components/jobs/CreateJobModal.tsx

@@ -131,8 +131,9 @@ export default function CreateJobModal({
   const editingJobId = editingJob?.id ?? null;
   const editingJobModelId = editingJob?.model_id ?? null;
 
-  // Layerwise offload and FSDP compete for the DiT weights and FastVideoArgs
-  // silently picks a winner (fastvideo_args.py:859); resolve it visibly here.
+  // Layerwise offload and FSDP compete for the DiT weights and the device offload
+  // policy (resolve_device_offload_conflicts in fastvideo/api/device_policy.py)
+  // silently picks a winner; resolve it visibly here.
   // dit_cpu_offload is deliberately not interlocked -- it is a modifier, not a
   // competing strategy.
   const handleDitLayerwiseOffloadChange = React.useCallback((next: boolean) => {