VideoGenerator.from_pretrained(model_path, config) takes the same nested
settings as from_config; the 24 flat keywords, from_pretrained_kwargs_to_config,
FROM_PRETRAINED_KWARGS, and every flat_field in the schema are deleted.
nvfp4_fa4 is the typed field engine.attention.nvfp4_fa4, applied by a
resolution step. 65 call sites and the docs use the nested form; the three
kwargs golden cases are config cases with byte-identical results.
Every value is decided during resolution; no runtime code calls
with_override. The device offload policy (unified memory, MPS, layerwise
conflicts, lazy module load) runs as resolution steps in the main process
(fastvideo/api/device_policy.py), and the worker runs on the config it
receives. The LTX-2 refine defaults from model_index.json and the MiniMax-H3
schedule from fastvideo_inference.json are resolution steps
(fastvideo/api/checkpoint_defaults.py) that read local checkpoints or download
only those files; the H3 pipeline validates the schedule against the loaded
schedulers but no longer writes it. The preprocessing entry point passes the
downloaded local path as run state instead of a config override. Teacher and
critic models load with override_transformer_cls_name as a load argument.
The test isolation turns the device policy off, as it blocks downloads, so
that the goldens do not depend on the machine.
Against the 3f6893a0 goldens, every field still matches except boundary_ratio
and ltx2_vae_tiling (DESIGN section 9) and the fields that no longer exist.
The launcher and trainer YAML verifications report 0 unexplained differences.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
- 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
Every parameter, attribute, and keyword argument that carries the runtime
config is named resolved_config instead of fastvideo_args, including the
RPC kwargs keys between the executors and the workers. The module path
fastvideo.fastvideo_args is unchanged. The rename is mechanical (a
token-level rewrite) plus yapf line wrapping.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
