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
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
VideoGenerator.from_pretrained accepts the 24 common engine and offload
keywords in FROM_PRETRAINED_KWARGS. Any other keyword raises TypeError
with the config path to use with VideoGenerator.from_config. The
conversion of the removed flat keywords (LTX-2 refine names, component
config prefixes, pipeline_config objects, empty-string paths) is removed
from from_pretrained_kwargs_to_config.
The golden kwargs cases that used removed keywords move to a config
category of typed configs; their golden files are byte-identical. The
DreamVerse contract test and the GPU pool forward-translation tests
checked only the removed keyword mapping; the GPU pool tests keep the
typed-config flattening checks.
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 image routes call VideoGenerator.generate with a GenerationRequest
instead of passing flat generate_video keywords. The flat projection
_build_generation_kwargs in video_api.py had no runtime caller, so it is
removed, and its tests exercise build_generation_request instead.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
python -m fastvideo.entrypoints.openai.api_server parsed the hand-written
FastVideoArgs argparse flags, an inference input path next to the typed config
that fastvideo serve reads. It is removed; start the server with
fastvideo serve --config SERVE_CONFIG and dotted overrides such as
--server.port 8000. The GPU integration test starts the server that way, with
the same model, GPU count, and DiT offload.
FastVideoArgs.add_cli_args stays for the legacy training and preprocessing
scripts.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
build_generation_request flattened the serve config's default_request with
explicit_request_updates, merged the request body into the flat dict, and then
rebuilt a nested GenerationRequest through legacy_generate_call_to_request,
the generate_video keyword mapping. It now builds the nested request directly:
- default_request contributes its explicit fields with their sections, through
the new compat.explicit_request_raw;
- each value collected from the OpenAI request goes to a fixed typed path from
_REQUEST_PATHS, and any other collected value goes to extensions.
The resulting SamplingParam is unchanged. A request-body field still wins over
the same-named field in default_request.stage_overrides, because
request_to_sampling_param applies stage overrides to sampling fields of the same
name. The only difference in the typed request: an operator stage override
that the body does not set now stays under stage_overrides instead of moving
into sampling.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
VideoGenerator.__init__ takes resolved_config from the FastVideoArgs it
receives, so a generator that fastvideo serve builds through
from_fastvideo_args exposes the resolved config too, not only one built
through from_config.
from_fastvideo_args(...) with a FastVideoArgs that was not built from a typed
config now emits a DeprecationWarning that points to from_config. This is the
only public path that hands VideoGenerator a hand-built FastVideoArgs, and it
has to go before FastVideoArgs can be removed. The deprecated
fastvideo.entrypoints.openai.api_server flag entry point reaches it, as
before.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
resolve_generation_request in fastvideo/api/resolution.py runs resolution
steps over the settings sections of a GenerationRequest (sampling, runtime,
output, stage_overrides) and freezes them as a ResolvedRequest. The explicit
paths are the ones the request tracked while it was parsed or built. Prompts,
inputs, state, plan, and extensions are request data and are left out, so
large tensors are not copied.
fastvideo/api/request_resolution.py adds resolve_request(request, model_path).
Its step, fill_sampling_defaults[preset <name>], sets each setting that the
request did not write to the model's preset default
(SamplingParam.from_pretrained). This is the same source that
request_to_sampling_param starts from, and a test checks that the two agree
field by field.
VideoGenerator attaches the ResolvedRequest to each GenerationResult as
resolved_request, so the source of every setting can be looked up. The golden
request snapshots record request_resolution_decisions; nothing else changes.
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
generator_config_to_fastvideo_args passes the ResolvedGeneratorConfig to
FastVideoArgs through the resolved_config init-only argument. Such an
object keeps the config as resolved_config, skips the environment folds that
resolution has already decided, and becomes read-only after construction.
Its PipelineConfig becomes read-only too:
- Assigning a configuration field raises AttributeError and names
FastVideoArgs.override.
- Runtime state stays writable: model_paths, model_loaded, and the Ray
placement handles. So do nested component configs, which loaders fill
with checkpoint data.
- A FastVideoArgs built directly, as the training and argparse entry points
do, is unchanged and stays writable.
FastVideoArgs.override(source, values) is the one way to change fields after
construction. It records (source, values) in override_log and records the
change on resolved_config for fields that have a typed path. The writers that
changed configuration in place now go through it:
- device_policy:* for the unified-memory, lazy-load, MPS, and layerwise
offload decisions that each worker makes after binding its device;
- checkpoint:model_index.json for the LTX-2 refine defaults;
- checkpoint:fastvideo_inference.json for the FastH3 DMD schedule;
- request for the per-request embedded_cfg_scale copy.
Behavior change: an explicit false for engine.compile.regional or
pipeline.minimax_h3.vae_parallel_decode/encode now wins over
FASTVIDEO_INFERENCE_TORCH_COMPILE and FASTVIDEO_VAE_PARALLEL_*. The resolution
steps fill these fields only while they are unset, and FastVideoArgs no
longer folds the environment a second time.
A test run that froze every FastVideoArgs logged no other non-test writer in
the stages, pipelines, loader, worker, hooks, entrypoints, and platforms
suites.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
generator_config_to_fastvideo_args now runs resolve_inference_config, in
the new fastvideo/api/inference_resolution.py, before it flattens the
config. The resolution goes through the ordered, provenance-recording driver
in fastvideo/api/resolution.py. VideoGenerator.from_config keeps the frozen
result as resolved_config, so the source of every startup value can be
inspected.
A mapping input is the raw user input, and every leaf in it counts as
explicit. A GeneratorConfig object does not record which fields the caller
wrote. written_fields therefore treats the fields that differ from the
schema defaults as explicit, and parsing its output gives back an equal
config.
INFERENCE_RESOLUTION_STEPS is empty in this commit, so FastVideoArgs is
unchanged. The golden snapshots of the YAML and from_pretrained cases add
the list of resolution decisions, which is empty here.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
python -m fastvideo.entrypoints.openai.api_server parses the hand-written
FastVideoArgs argparse flags, a second input system next to the typed config
that fastvideo serve reads. It keeps working unchanged during the transition
and logs a warning that points to fastvideo serve --config with dotted
overrides.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
