- moduleloader builds the component-loading config and the inference
config as resolved configs (_build_training_resolved_config and
build_inference_resolved_config) instead of TrainingArgs objects mutated
after construction; the transformer override, weights path, and
teacher/critic flag are config values or load_module arguments.
keep_checkpoint_component_config keeps the trainer's PipelineConfig view
of the checkpoint-filled VAE and text-encoder configs.
- The validation callback builds its pipeline with
from_pretrained(path, resolved_config=...) and forwards with a resolved
config built per run; it no longer replaces or mutates the trainer's
pipeline_config, and generator_overrides takes nested GeneratorConfig
fields instead of flat names.
- Against the 3f6893a0 snapshots of the 67 trainer YAML files, every
PipelineConfig and value matches except ltx2_vae_tiling (the model's
vae_tiling) and the unread pretrained_model_name_or_path.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
- 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
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
Every example calls VideoGenerator.generate with a nested request instead
of generate_video keywords, and passes settings outside the from_pretrained
convenience keywords through VideoGenerator.from_config at their typed
paths. Each request and generator config equals what the keyword call built.
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
Every key that repository YAML configs, Python examples, the Dreamverse
backend, and tests passed through pipeline.experimental has a typed
GeneratorConfig field, so they use those fields instead:
engine.attention, engine.compile.regional, pipeline.flow_shift,
pipeline.embedded_cfg_scale, pipeline.dmd_denoising_steps,
pipeline.components.lora_nickname, pipeline.minimax_h3, and
pipeline.longcat. No repository config outside the tests uses
pipeline.experimental any more.
The flat FastVideoArgs keywords that each migrated YAML file and each
basic_fasth3.py option set produces match the previous ones. The golden
snapshots of the two Hunyuan scripts now record embedded_cfg_scale and
flow_shift as floats (6.0 instead of 6), because the typed fields are
floats. The guidance tensor is built as float32 either way. The golden case
untyped_experimental_keys is renamed attention_precision_and_flow_shift with
identical content, and keys_without_typed_fields covers keywords that still
go to pipeline.experimental.
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.
