ResolvedGeneratorConfig is the only runtime config. It answers only its
typed fields, pipeline_config, mode, training_mode, and inference_mode;
the transitional flat-name fallback is gone.
Deleted: fastvideo/fastvideo_args.py (FastVideoArgs, TrainingArgs, their
argparse, prepare/set/get_current_fastvideo_args), the flat-name fallback,
generator_config_to_fastvideo_args and the other flattening helpers,
PipelineConfig/model-config/PreprocessConfig/SamplingParam add_cli_args,
configs/utils.py, and the tests of the deleted code.
Materialization builds the model's PipelineConfig directly from the typed
values through an explicit table of typed path to PipelineConfig mirror
attribute (PIPELINE_CONFIG_MIRRORS); PipelineConfig.from_source replaces
from_kwargs. flat_field metadata stays only on the fields behind the 24
from_pretrained keywords; a pipeline.experimental key that names a mirror
attribute, and pipeline.preset / preset_version / components.vae_weights,
are rejected during resolution. The training root keeps the DiT on the
device through a resolution step instead of a from_pretrained override.
The goldens snapshot the end-state objects (resolved config, PipelineConfig,
decisions, request); the cli category is gone. Against the 3f6893a0
goldens, every field matches except disable_autocast, boundary_ratio,
ltx2_vae_tiling (DESIGN section 9), and the fields that no longer exist.
The schema parity inventory classifies the typed GeneratorConfig paths.
Co-Authored-By: Claude Fable 5.1 <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
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
