[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

examples/train/configs/distribution_matching/kandinsky5/distill_dmd_qat.sh

@@ -19,7 +19,7 @@
 # The export is passed to all three models.*.init_from overrides: student,
 # teacher, and critic all load the same weights. Teacher/critic are
 # automatically masked back to dense attention and full-precision weights
-# by the _loading_teacher_critic_model gate in
+# by the loading_teacher_critic_model gate in
 # fastvideo/models/loader/component_loader.py (family-agnostic, no
 # Kandinsky5-specific handling needed) -- NOT by pointing them at a
 # different export.

examples/train/configs/distribution_matching/kandinsky5/dmd2_t2v_480p_qat.yaml

@@ -11,7 +11,7 @@
 #
 # - Teacher: frozen, full-precision Kandinsky5 (loaded from the stage-1 QAT
 #   checkpoint's base weights, or the original pretrained checkpoint --
-#   the _loading_teacher_critic_model gate in component_loader.py masks
+#   the loading_teacher_critic_model gate in component_loader.py masks
 #   quant_config and FASTVIDEO_ATTENTION_BACKEND for teacher/critic
 #   regardless, so they always run full precision / dense attention).
 # - Student: trainable, initialized from the stage-1 QAT finetune checkpoint.

examples/train/configs/fine_tuning/kandinsky5/README.md

@@ -90,7 +90,7 @@ conversion and uses it directly.)
 
 `--role student` exports the trained transformer weights (the only role
 that matters here: student/teacher/critic in stage 2 all load from the same
-checkpoint, and `_loading_teacher_critic_model` handles the full-precision
+checkpoint, and `loading_teacher_critic_model` handles the full-precision
 masking for teacher/critic at load time, not at export time). `--verify`
 strictly reloads the exported transformer immediately, so a key-mapping bug
 fails here instead of deep inside the stage-2 launch.
@@ -98,7 +98,7 @@ fails here instead of deep inside the stage-2 launch.
 Only the student is quantized (Attn-QAT); the teacher and critic stay full
 precision / dense attention. This is enforced in the loader
 (`fastvideo/models/loader/component_loader.py`, via the
-`_loading_teacher_critic_model` flag), which masks `quant_config` and clears
+`loading_teacher_critic_model` flag), which masks `quant_config` and clears
 `FASTVIDEO_ATTENTION_BACKEND` for teacher/critic -- the same global env var
 reaches only the student, with no per-model flags or Kandinsky5-specific
 handling. Validation runs the distilled student through
@@ -136,26 +136,31 @@ still says the base T2V pipeline and the registry
 (`fastvideo/registry.py`) has no way to auto-detect that a given directory
 is actually a DMD (four-step re-noise sampler) export -- it will resolve to
 `Kandinsky5T2VPipeline` and run the full-length sampler on DMD-distilled
-weights. Pass `override_pipeline_cls_name` and a `Kandinsky5DMDConfig`
-explicitly to select the right pipeline/sampler and get
-`dmd_denoising_steps` set (`Kandinsky5T2VConfig`'s default of `None` makes
+weights. Set `pipeline.components.override_pipeline_cls_name` and pass a
+`Kandinsky5DMDConfig` as `pipeline.experimental.pipeline_config` to select
+the right pipeline/sampler and get `pipeline.dmd_denoising_steps` set
+(`Kandinsky5T2VConfig`'s default of `None` makes
 `Kandinsky5DmdDenoisingStage` raise immediately):
 
 ```python
 from fastvideo import VideoGenerator
 from fastvideo.configs.pipelines.kandinsky5 import Kandinsky5DMDConfig
-from fastvideo.layers.quantization import get_quantization_config
 
-gen = VideoGenerator.from_pretrained(
-    "path/to/kandinsky5_dmd_checkpoint", num_gpus=1,
-    override_pipeline_cls_name="Kandinsky5DMDPipeline",
-    pipeline_config=Kandinsky5DMDConfig(),
-    transformer_quant=get_quantization_config("nvfp4_qat")(),
-    use_fsdp_inference=False,
-)
+gen = VideoGenerator.from_config({
+    "model_path": "path/to/kandinsky5_dmd_checkpoint",
+    "engine": {
+        "num_gpus": 1,
+        "use_fsdp_inference": False,
+        "quantization": {"transformer_quant": "nvfp4_qat"},
+    },
+    "pipeline": {
+        "components": {"override_pipeline_cls_name": "Kandinsky5DMDPipeline"},
+        "experimental": {"pipeline_config": Kandinsky5DMDConfig()},
+    },
+})
 # Kandinsky5DmdDenoisingStage ignores request.sampling.num_inference_steps
 # and guidance_scale -- it's a fixed 4-step, no-CFG sampler driven entirely
-# by pipeline_config.dmd_denoising_steps (set above). Adjust
+# by pipeline.dmd_denoising_steps, which Kandinsky5DMDConfig sets. Adjust
 # Kandinsky5DMDConfig(dmd_denoising_steps=[...]) instead if the checkpoint
 # was distilled/validated with a different schedule than the default
 # [1000, 750, 500, 250].
@@ -176,8 +181,9 @@ to a supported dense backend while the FP4 linear layers still run.
   and that gradients flow through a forward+backward pass.
 - `fastvideo/tests/api/test_kandinsky5_dmd_pipeline_resolution.py` --
   confirms an unmodified export resolves to `Kandinsky5T2VPipeline` (the bug)
-  and that `override_pipeline_cls_name="Kandinsky5DMDPipeline"` fixes it (the
-  documented workaround above), plus `Kandinsky5DMDConfig`'s
+  and that `pipeline.components.override_pipeline_cls_name:
+  Kandinsky5DMDPipeline` fixes it (the documented workaround above), plus
+  `Kandinsky5DMDConfig`'s
   `dmd_denoising_steps` default.
 - `fastvideo/tests/nightly/test_e2e_kandinsky5_dmd_t2v_overfit.py` -- a few
   steps of both training stages on a single synthetic sample, exercising the

examples/train/configs/overfit_minimax_h3_t2va.yaml

@@ -80,7 +80,9 @@ callbacks:
     num_videos_per_prompt: 1
     use_validation_media_conditioning: false
     offload_training_state: true
-    text_encoder_cpu_offload: true
-    vae_cpu_offload: true
+    engine:
+      offload:
+        text_encoder: true
+        vae: true
 
 pipeline: {}