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>
ResolvedGeneratorConfig becomes the runtime config of every mode. Resolution
builds the model's PipelineConfig once, freezes it, and attaches it as
resolved_config.pipeline_config; it also exposes mode, training_mode, and
inference_mode. VideoGenerator, fastvideo serve, the OpenAI server state,
the executors, the worker, and ComposedPipelineBase receive the resolved
config instead of a FastVideoArgs, and generator_config_to_fastvideo_args
is no longer on the inference path.
- Schema: ExecutionMode and WorkloadType move to fastvideo/api/schema.py;
typed homes for mode, output_type, boundary_ratio, master_port,
moba_config, and the LTX-2 refine settings; Enum parsing.
- Resolution: steps for the refine preset overrides, flat experimental
keys, the V-MoBA config, the validation that check_fastvideo_args did,
the num_gpus bump, the model's vae_tiling default, and a final
fill_runtime_defaults step for typed fields no earlier step decides.
- fastvideo/api/training_schema.py: TrainingRunConfig and
PreprocessRunConfig roots, their resolvers, and a --config plus dotted
override loader.
- fastvideo/api/device_policy.py: the device offload policy as functions
that return an overridden config; the worker keeps the result. The LTX-2
and MiniMax-H3 checkpoint defaults rebind the pipeline's config.
- fastvideo/api/flat_name_fallback.py answers the flat FastVideoArgs names
on the resolved config until the readers move to typed paths.
Golden changes: the decisions of the steps above are recorded, and
FastVideoArgs.ltx2_vae_tiling holds the model's vae_tiling (it held None;
no runtime code reads it). Against the 3f6893a0 goldens every
FastVideoArgs field, PipelineConfig, and SamplingParam matches, except
disable_autocast and boundary_ratio, which FastVideoArgs held at their
defaults because flattening routed them only to PipelineConfig.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
A request's embedded_cfg_scale (request extension or legacy generate_video
keyword) was applied to a deep copy of FastVideoArgs.pipeline_config in
VideoGenerator. The worker runs the pipeline with its own FastVideoArgs
(GpuWorker.execute_forward passes self.fastvideo_args), so the copy never
reached the denoising stages and the request value was ignored.
embedded_cfg_scale is now a per-request sampling value:
- the request field request.sampling.embedded_cfg_scale (the extension key
still works);
- SamplingParam.embedded_cfg_scale and ForwardBatch.embedded_cfg_scale;
- embedded_cfg_scale_for_batch(batch, fastvideo_args), which the denoising,
SR denoising, Wan DMD, and Flux2 text-encoding stages read. It returns the
batch value, else pipeline_config.embedded_cfg_scale, so training and
requests without the field are unchanged.
VideoGenerator no longer copies or changes FastVideoArgs per request, and
request_to_pipeline_overrides is removed. The golden snapshots record the new
SamplingParam field, and the request with extensions.embedded_cfg_scale now
carries 3.0 on SamplingParam instead of on a pipeline override.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
The from_pretrained keyword ltx2_refine_lora_path set
pipeline.components.lora_path, which reaches FastVideoArgs.lora_path and loads
as the main LoRA on the stage-1 transformer. The refine stage reads
FastVideoArgs.ltx2_refine_lora_path, which stayed unset. The conversion also
turned an empty string into None. An empty string is how callers such as the
Dreamverse GPU pool disable the refine LoRA, and None makes LTX2Pipeline load
the checkpoint default instead: fastvideo_refine_lora_path in model_index.json
is FastVideo/LTX2-Distilled-LoRA for the LTX-2 Diffusers checkpoints.
The refine LoRA gets its own field, pipeline.ltx2.refine.lora_path, whose
flat name is ltx2_refine_lora_path. None keeps the checkpoint default, and an
empty string disables the refine LoRA. The Dreamverse contract test, the
Dynamo contract example, and the parity inventory point at the field.
Golden changes: kwargs/ltx2_refine_lora_path now records the path on
ltx2_refine_lora_path instead of lora_path, and the added case
kwargs/ltx2_refine_lora_disabled pins the empty string.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
Parameters that belong to one model family, or to one component config,
could be set only through argparse flags or pipeline.experimental. They gain
typed GeneratorConfig fields, each with the flat name that FastVideoArgs or
PipelineConfig uses:
- pipeline.dit and pipeline.vae: dicts of DiT and VAE config field
overrides, passed as dit_config.<key> and vae_config.<key>
- pipeline.ltx2: VAE tile sizes, initial video and audio latent paths, noise
order, distilled sigmas, and refine.{transformer,noise,audio_noise}_path
- pipeline.minimax_h3: sequential load, video decode backend, TAEH3
checkpoint and chunk size, and the sequence-parallel VAE switches
- pipeline.longcat: the block sparse attention (BSA) settings
legacy_from_pretrained_to_config routes dit_config.* and vae_config.*
keywords into the new dicts. Every new scalar field defaults to None and is
not passed to FastVideoArgs while unset. Behavior is unchanged: the golden
config snapshots match.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
These inference parameters could be set only through argparse flags or the
untyped pipeline.experimental dict. Each one gains a typed GeneratorConfig
field whose flat name is its FastVideoArgs or PipelineConfig field, so YAML,
dotted CLI overrides, and from_pretrained keywords all reach it:
- engine.attention: backend, vsa_sparsity, vsa_tile_size, moba_config_path
- engine.precision: dit, vae, vae_decode, image_encoder, text_encoders
- engine.compile.regional (inference_torch_compile)
- pipeline: vae_sp, flow_shift, embedded_cfg_scale, dmd_denoising_steps
- pipeline.components.lora_target_modules
Every field defaults to None, which keeps the runtime or model default; a
None field is not passed to FastVideoArgs. pipeline.experimental still
accepts the flat names and still wins over the typed fields. The parity
inventory lists the typed paths, and the docs show the typed forms.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
