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
- The 12 fastvideo/training/*_pipeline.py entry points load a
TrainingRunConfig from --config <yaml> plus dotted overrides (mode
finetuning or distillation) instead of TrainingArgs argparse flags.
- The training pipelines read self.resolved_config.training.<section>.<field>
and typed paths; the training_args alias, the inference_mode toggles,
and the writes to the training config are gone. Teacher and critic
models load with a with_override config and the loading_teacher_critic_model
argument; validation pipelines are built from their own resolved config
(resolve_validation_config), and the tracker receives to_dict().
- Each of the 37 training launchers has a YAML file next to it with the
values its flags held; shell variables stay as dotted overrides. Against
the 3f6893a0 TrainingArgs snapshots every value and PipelineConfig
matches, except mode/inference_mode (the stage readers saw training mode
before and after), the dropped flags without a reader, lora_alpha (now
derived from the rank by a step), and the vae_tiling and compile-kwargs
defaults.
- Validation stages read the validation pipeline's config: modules that
validation loads itself use the inference offload defaults, and the
bf16-autocast VAE decode of two distillation launchers becomes fp32.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
- v1_preprocess.py and v1_preprocessing_new.py load a PreprocessRunConfig
from --config <yaml> plus dotted overrides; their argparse flags are
gone. The preprocessing pipelines, workflows, datasets, and stages read
resolved_config.preprocess.* and typed paths instead of
preprocess_config and an argparse namespace.
- The overfit preprocessors build resolved configs instead of FastVideoArgs.
- Each preprocessing launcher has a YAML file next to it with the values
its flags held; shell variables stay as dotted overrides. Against the
3f6893a0 snapshots of the 17 launchers, every value and PipelineConfig
matches, except mode (preprocess instead of inference), ltx2_vae_tiling,
and text_only's vae_config.load_encoder. Five launchers that the old
parser rejected now carry their intended values; the never-defined
--model_type and --validation_dataset_file flags are dropped.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
- 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
VideoGenerator.generate(request) is the one inference entry point. The
generate_video keyword call, its mapping from flat keywords to a request
(legacy_generate_call_to_request and its helpers), and the tests of that
mapping are removed. Comments and docs that named generate_video describe
generate and request extensions instead.
scripts/ltx2_sr_alignment.py keeps its generate_video call, because its
reference run imports the separate FastVideo-internal package.
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
- Support official 8-step DMD schedule from fastvideo_inference.json in MLX pipeline
- Implement MiniMaxH3SchedulerState.from_dmd_steps for exact sigma ladder calculation
- Add auto-detection of fastvideo_inference.json in checkpoint converter AdaLN precomputation
- Add inter_step_cooldown_s and per-step MLX graph/cache cleanup to prevent thermal throttling
- Support configurable VAE tile sizes to guarantee 256px tiled decode without grid artifacts
- Skip non-existent shard files gracefully in Qwen3-VL conditioner
