- 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
DreamVerse, FastVideo Studio, the ComfyUI node, the README, and the
inference docs call VideoGenerator.generate with nested requests and
VideoGenerator.from_config with typed configs, and read GenerationResult
attributes instead of result dict keys.
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
