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
Accept the causal-VAE-aligned 362-frame bucket (15.083 s) for 15-second H3 requests.
- Hoist MINIMAX_H3_MIN/MAX_ALIGNED_FRAMES next to align_num_frames and reuse them in the CUDA stage, the MLX runtime, and the LoRA example so the bound is consistent across entry points.
- State the accepted frame range in the error messages.
- Cover seconds="15" -> 362 at OpenAI admission and the MLX resolve_geometry bound.
- Update the Spark/openai cookbook frame caps from 345 to 362.
Known gaps: no GPU-lane coverage for the 362 bucket (SSIM runs 124 frames; the golden gate is a fixed-geometry fingerprint), and explicit num_frames=360 still hits the pre-existing grid gate.
