- LTX-2: the refine settings are read from pipeline.ltx2.refine.* and
pipeline.components.upsampler_weights. The checkpoint's model_index.json
defaults fill the fields that are still None, and the remaining switches
get their defaults, in one recorded override that rebinds the pipeline's
config. An explicit input value now always wins over the checkpoint
default; the flat sentinels (the generic refine_* names and the class
default of the step count) are gone.
- MiniMax-H3: the sequential load, decode backend, TAEH3, parallel VAE,
offload, and checkpoint schedule settings are read from typed paths.
- Wan, MMAudio, MagiHuman, Stable Audio: flow_shift, dmd_denoising_steps,
output_type, workload_type, offload, and autocast settings are read from
typed paths. MagiHuman no longer reads the never-set log_level_progress
name.
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
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
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
The schema class MiniMaxH3Config had the same name as the MiniMax-H3 DiT
config class in fastvideo/configs/models/dits/minimax_h3.py. The family
partitions of PipelineSelection are renamed LTX2Options, LTX2RefineOptions,
MiniMaxH3Options, and LongCatOptions, so their names cannot be confused with
the model and pipeline config classes.
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
Each GeneratorConfig field that has a name in the flat keyword API
(VideoGenerator.from_pretrained kwargs and FastVideoArgs fields) declares it
through flat_field(...) metadata. legacy_from_pretrained_to_config and
generator_config_to_fastvideo_args read those names instead of keeping two
hand-written mappings, so adding a typed field with a flat name needs no
compat change.
The keywords that need a conversion keep explicit branches: the
torch_compile_kwargs split, the transformer_quant instance, the LTX-2 refine
preset keys, empty-string refine paths, and a non-string pipeline_config.
_SPECIALLY_MAPPED_PATHS lists every field without a flat name, and
test_generator_config_flat_names.py fails when a field has neither.
Behavior is unchanged: the 150 golden config snapshots match. The flat
keyword dict no longer passes None for revision, dist_timeout, and
lazy_module_load, and always passes the default LoRA nickname and strength and
empty per-component compile dicts; each equals the FastVideoArgs default.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
