pipeline.model is a tagged union: a mapping with exactly one key, which
names the model family (ltx2, minimax_h3, longcat, or generic) and selects
that family's options class. The former sibling sections pipeline.ltx2,
pipeline.minimax_h3, and pipeline.longcat, and the DiT/VAE architecture
override dicts pipeline.dit and pipeline.vae, live inside the family block.
The parser implements tagged unions as a general mechanism (a dataclass
with a TAGS table); the family is readable as pipeline.model.family.
Resolution validates the tag against the family that the registry chose
for model_path (validate_model_family raises and names the registry class
and the right block) and fills the family's empty block when the input has
none (fill_model_family), so readers never see None. Family-only fills and
derivations (the MiniMax-H3 parallel-VAE environment variables, the LTX-2
tile-size derivation and refine preset copy, the LongCat BSA mirrors) run
only for their family.
Against the 3f6893a0 goldens every PipelineConfig is identical and no
other field differs beyond the allowed categories; family-only settings
are no longer decided on models of another family. LingBot-Video reads
its refiner switch from pipeline.preset_overrides.refine.enabled and loads
the refiner by default when the checkpoint has one, as at 3f6893a0.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CbSuTKA9oX4YsECdcBPAsX
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>
- Docs, AGENTS.md files, and skills describe ResolvedGeneratorConfig,
resolve_inference_config, typed YAML, and --config plus dotted
overrides for training and preprocessing instead of FastVideoArgs and
flat flags.
- The local tests build resolved configs (or small fakes that expose typed
paths) instead of FastVideoArgs; the LoRA extraction scripts build
pipelines with from_pretrained(model_path, resolved_config=...).
- The mixkit README's NVFP4 snippet uses from_config with
engine.quantization.transformer_quant (the from_pretrained keyword it
used is rejected).
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
The SSIM, LoRA, performance, nightly, and local tests, two scripts, and the
pipeline parity skill template call VideoGenerator.generate with nested
requests and VideoGenerator.from_config with typed configs. The SSIM and
performance helpers convert their flat keyword tables into a typed config
and a request after the overrides merge. Text encoder precisions are passed
as lists, which the typed config parser requires.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
Every key that repository YAML configs, Python examples, the Dreamverse
backend, and tests passed through pipeline.experimental has a typed
GeneratorConfig field, so they use those fields instead:
engine.attention, engine.compile.regional, pipeline.flow_shift,
pipeline.embedded_cfg_scale, pipeline.dmd_denoising_steps,
pipeline.components.lora_nickname, pipeline.minimax_h3, and
pipeline.longcat. No repository config outside the tests uses
pipeline.experimental any more.
The flat FastVideoArgs keywords that each migrated YAML file and each
basic_fasth3.py option set produces match the previous ones. The golden
snapshots of the two Hunyuan scripts now record embedded_cfg_scale and
flow_shift as floats (6.0 instead of 6), because the typed fields are
floats. The guidance tensor is built as float32 either way. The golden case
untyped_experimental_keys is renamed attention_precision_and_flow_shift with
identical content, and keys_without_typed_fields covers keywords that still
go to pipeline.experimental.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
