# SPDX-License-Identifier: Apache-2.0 from __future__ import annotations from collections.abc import Mapping from copy import deepcopy from dataclasses import fields, is_dataclass from pathlib import Path from typing import Any from fastvideo.api.overrides import apply_overrides, normalize_overrides from fastvideo.api.parser import config_to_dict, load_raw_config, parse_config from fastvideo.api.request_metadata import ( EXPLICIT_PATHS_ATTR, bind_generation_request_raw, get_explicit_paths, reset_tracking_roots, ) from fastvideo.api.schema import ( CompileConfig, ContinuationState, GenerationRequest, GeneratorConfig, InputConfig, OutputConfig, RequestRuntimeConfig, SamplingConfig, ) from fastvideo.api.sampling_param import SamplingParam from fastvideo.fastvideo_args import FastVideoArgs from fastvideo.pipelines.basic.ltx2.stage_overrides import ( refine_preset_override_fields, refine_stage_override_fields, ) from fastvideo.utils import shallow_asdict _INPUT_FIELD_NAMES = {field.name for field in fields(InputConfig)} _SAMPLING_FIELD_NAMES = {field.name for field in fields(SamplingConfig)} _RUNTIME_FIELD_NAMES = {field.name for field in fields(RequestRuntimeConfig)} _OUTPUT_FIELD_NAMES = {field.name for field in fields(OutputConfig)} _MISSING = object() _LEGACY_REQUEST_ALIASES = { "neg_prompt": "negative_prompt", } _REQUEST_PIPELINE_OVERRIDE_FIELDS = frozenset({ "embedded_cfg_scale", }) # torch.compile kwargs that map to first-class CompileConfig fields. _COMPILE_TYPED_KEYS = ("backend", "fullgraph", "mode", "dynamic") # LTX-2 refine flat kwargs (init + per-request) known to FastVideoArgs. _LTX2_REFINE_FLAT_KEYS = (refine_preset_override_fields() | refine_stage_override_fields()) def normalize_generator_config(config: GeneratorConfig | Mapping[str, Any], ) -> GeneratorConfig: if isinstance(config, GeneratorConfig): return config return parse_config(GeneratorConfig, config) def load_generator_config_from_file( path: str | Path, overrides: list[str] | Mapping[str, Any] | None = None, ) -> GeneratorConfig: raw = load_raw_config(path) normalized_overrides = normalize_overrides(overrides) if _looks_like_run_or_serve_config(raw): if normalized_overrides: raw = apply_overrides(raw, normalized_overrides) return parse_config(GeneratorConfig, raw["generator"]) if normalized_overrides: adjusted = normalized_overrides if all(key.startswith("generator.") for key in adjusted): adjusted = {key[len("generator."):]: value for key, value in adjusted.items()} raw = apply_overrides(raw, adjusted) return parse_config(GeneratorConfig, raw) def legacy_from_pretrained_to_config( model_path: str, kwargs: Mapping[str, Any], ) -> GeneratorConfig: raw: dict[str, Any] = {"model_path": model_path} engine: dict[str, Any] = {} parallelism: dict[str, Any] = {} offload: dict[str, Any] = {} compile_config: dict[str, Any] = {} pipeline: dict[str, Any] = {} components: dict[str, Any] = {} quantization: dict[str, Any] = {} experimental: dict[str, Any] = {} preset_overrides: dict[str, Any] = {} preset_refine: dict[str, Any] = {} for key, value in kwargs.items(): if key == "revision": raw["revision"] = value elif key == "trust_remote_code": raw["trust_remote_code"] = value elif key == "num_gpus": engine["num_gpus"] = value elif key == "distributed_executor_backend": engine["execution_backend"] = value elif key in {"tp_size", "sp_size", "hsdp_replicate_dim", "hsdp_shard_dim", "dist_timeout"}: parallelism[key] = value elif key == "dit_cpu_offload": offload["dit"] = value elif key == "dit_layerwise_offload": offload["dit_layerwise"] = value elif key == "text_encoder_cpu_offload": offload["text_encoder"] = value elif key == "image_encoder_cpu_offload": offload["image_encoder"] = value elif key == "vae_cpu_offload": offload["vae"] = value elif key == "pin_cpu_memory": offload["pin_cpu_memory"] = value elif key == "enable_torch_compile": compile_config["enabled"] = value elif key == "enable_torch_compile_text_encoder": compile_config["text_encoder_enabled"] = value elif key == "enable_torch_compile_vae": compile_config["vae_enabled"] = value elif key == "enable_torch_compile_audio_vae": compile_config["audio_vae_enabled"] = value elif key == "torch_compile_kwargs": remaining: dict[str, Any] = (dict(deepcopy(value)) if isinstance(value, Mapping) else {}) for first_class in _COMPILE_TYPED_KEYS: if first_class in remaining: compile_config[first_class] = remaining.pop(first_class) if remaining: compile_config["extras"] = remaining elif key in { "torch_compile_kwargs_dit", "torch_compile_kwargs_text_encoder", "torch_compile_kwargs_vae", "torch_compile_kwargs_audio_vae", }: compile_config[key[len("torch_compile_kwargs_"):] + "_kwargs"] = (dict(deepcopy(value)) if isinstance(value, Mapping) else {}) elif key == "ltx2_vae_tiling": pipeline["vae_tiling"] = value elif key == "config_model_path": components["config_root"] = value elif key == "ltx2_refine_enabled": preset_refine["enabled"] = value elif key == "ltx2_refine_upsampler_path": # Empty string means "no upsampler"; keep typed None. components["upsampler_weights"] = value or None elif key == "ltx2_refine_lora_path": # Empty string means "no refine LoRA"; keep typed None. components["lora_path"] = value or None elif key == "ltx2_refine_add_noise": preset_refine["add_noise"] = value elif key == "ltx2_refine_num_inference_steps": preset_refine["num_inference_steps"] = value elif key == "ltx2_refine_guidance_scale": preset_refine["guidance_scale"] = value elif key in {"enable_stage_verification", "use_fsdp_inference", "disable_autocast"}: engine[key] = value elif key == "override_text_encoder_quant": quantization["text_encoder_quant"] = value elif key == "workload_type": pipeline["workload_type"] = value elif key == "lora_path": components["lora_path"] = value elif key == "override_pipeline_cls_name": components["override_pipeline_cls_name"] = value elif key == "override_transformer_cls_name": components["override_transformer_cls_name"] = value elif key == "pipeline_config": if isinstance(value, str): components["pipeline_config_path"] = value else: experimental[key] = deepcopy(value) elif key == "override_text_encoder_safetensors": components["text_encoder_weights"] = value elif key == "init_weights_from_safetensors": components["transformer_weights"] = value elif key == "init_weights_from_safetensors_2": components["transformer_2_weights"] = value else: experimental[key] = deepcopy(value) if parallelism: engine["parallelism"] = parallelism if offload: engine["offload"] = offload if compile_config: engine["compile"] = compile_config if quantization: engine["quantization"] = quantization if engine: raw["engine"] = engine if components: pipeline["components"] = components if preset_refine: preset_overrides["refine"] = preset_refine if preset_overrides: pipeline["preset_overrides"] = preset_overrides if experimental: pipeline["experimental"] = experimental if pipeline: raw["pipeline"] = pipeline return parse_config(GeneratorConfig, raw) def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, Any], ) -> FastVideoArgs: normalized = normalize_generator_config(config) unsupported = [] if normalized.pipeline.preset is not None: unsupported.append("pipeline.preset") if normalized.pipeline.preset_version is not None: unsupported.append("pipeline.preset_version") if normalized.pipeline.components.vae_weights is not None: unsupported.append("pipeline.components.vae_weights") if unsupported: joined = ", ".join(unsupported) raise NotImplementedError(f"VideoGenerator compatibility adapter does not support {joined} yet") engine = normalized.engine kwargs: dict[str, Any] = { "model_path": normalized.model_path, "revision": normalized.revision, "trust_remote_code": normalized.trust_remote_code, "num_gpus": engine.num_gpus, "distributed_executor_backend": engine.execution_backend, "tp_size": engine.parallelism.tp_size, "sp_size": engine.parallelism.sp_size, "hsdp_replicate_dim": engine.parallelism.hsdp_replicate_dim, "hsdp_shard_dim": engine.parallelism.hsdp_shard_dim, "dist_timeout": engine.parallelism.dist_timeout, "dit_cpu_offload": engine.offload.dit, "dit_layerwise_offload": engine.offload.dit_layerwise, "text_encoder_cpu_offload": engine.offload.text_encoder, "image_encoder_cpu_offload": engine.offload.image_encoder, "vae_cpu_offload": engine.offload.vae, "pin_cpu_memory": engine.offload.pin_cpu_memory, "enable_torch_compile": engine.compile.enabled, "torch_compile_kwargs": _compile_config_to_torch_kwargs(engine.compile), "enable_stage_verification": engine.enable_stage_verification, "use_fsdp_inference": engine.use_fsdp_inference, "disable_autocast": engine.disable_autocast, } if normalized.pipeline.workload_type is not None: kwargs["workload_type"] = normalized.pipeline.workload_type if normalized.pipeline.vae_tiling is not None: kwargs["ltx2_vae_tiling"] = normalized.pipeline.vae_tiling if engine.compile.text_encoder_enabled is not None: kwargs["enable_torch_compile_text_encoder"] = (engine.compile.text_encoder_enabled) if engine.compile.vae_enabled is not None: kwargs["enable_torch_compile_vae"] = engine.compile.vae_enabled if engine.compile.audio_vae_enabled is not None: kwargs["enable_torch_compile_audio_vae"] = (engine.compile.audio_vae_enabled) if engine.compile.dit_kwargs: kwargs["torch_compile_kwargs_dit"] = deepcopy(engine.compile.dit_kwargs) if engine.compile.text_encoder_kwargs: kwargs["torch_compile_kwargs_text_encoder"] = deepcopy(engine.compile.text_encoder_kwargs) if engine.compile.vae_kwargs: kwargs["torch_compile_kwargs_vae"] = deepcopy(engine.compile.vae_kwargs) if engine.compile.audio_vae_kwargs: kwargs["torch_compile_kwargs_audio_vae"] = deepcopy(engine.compile.audio_vae_kwargs) quantization = engine.quantization if quantization is not None and quantization.text_encoder_quant is not None: kwargs["override_text_encoder_quant"] = quantization.text_encoder_quant if quantization is not None and quantization.transformer_quant is not None: # Resolve the typed quant name to a concrete ``QuantizationConfig`` # instance and pin it on ``dit_config.quant_config``. The legacy # path expected callers to do this themselves via # ``pipeline_config.dit_config.quant_config = NVFP4Config()``; the # typed surface accepts a string and does the wiring here so # downstream code can rely on a single source of truth. from fastvideo.layers.quantization import get_quantization_config _resolved_quant_cls = get_quantization_config(quantization.transformer_quant) kwargs["transformer_quant"] = _resolved_quant_cls() components = normalized.pipeline.components if components.pipeline_config_path is not None: kwargs["pipeline_config"] = components.pipeline_config_path if components.lora_path is not None: kwargs["lora_path"] = components.lora_path if components.override_pipeline_cls_name is not None: kwargs["override_pipeline_cls_name"] = components.override_pipeline_cls_name if components.override_transformer_cls_name is not None: kwargs["override_transformer_cls_name"] = components.override_transformer_cls_name if components.text_encoder_weights is not None: kwargs["override_text_encoder_safetensors"] = components.text_encoder_weights if components.transformer_weights is not None: kwargs["init_weights_from_safetensors"] = components.transformer_weights if components.transformer_2_weights is not None: kwargs["init_weights_from_safetensors_2"] = components.transformer_2_weights if components.config_root is not None: kwargs["config_model_path"] = components.config_root if components.upsampler_weights is not None: kwargs["ltx2_refine_upsampler_path"] = components.upsampler_weights preset_overrides = deepcopy(normalized.pipeline.preset_overrides) refine = preset_overrides.pop("refine", None) if isinstance(refine, Mapping): for key in _LTX2_REFINE_FLAT_KEYS: if key in refine: kwargs[f"ltx2_refine_{key}"] = refine[key] kwargs.update(preset_overrides) kwargs.update(deepcopy(normalized.pipeline.experimental)) return FastVideoArgs.from_kwargs(**kwargs) def normalize_generation_request(request: GenerationRequest | Mapping[str, Any], ) -> GenerationRequest: normalized = (request if isinstance(request, GenerationRequest) else parse_config(GenerationRequest, request)) if not hasattr(normalized, EXPLICIT_PATHS_ATTR): # Request wasn't bound through the parser (e.g. constructed # directly). Treat every currently-set field as explicit. bind_generation_request_raw(normalized, _serialize_generation_request(normalized)) return normalized def legacy_generate_call_to_request( prompt: str | None, sampling_param: SamplingParam | None, *, mouse_cond: Any | None = None, keyboard_cond: Any | None = None, grid_sizes: Any | None = None, legacy_kwargs: Mapping[str, Any] | None = None, ) -> GenerationRequest: raw = _sampling_param_to_request_raw(sampling_param) if prompt is not None: raw["prompt"] = prompt for key, value in (legacy_kwargs or {}).items(): _apply_request_field(raw, key, value) if mouse_cond is not None: raw.setdefault("inputs", {})["mouse_cond"] = mouse_cond if keyboard_cond is not None: raw.setdefault("inputs", {})["keyboard_cond"] = keyboard_cond if grid_sizes is not None: raw.setdefault("inputs", {})["grid_sizes"] = grid_sizes normalized = parse_config(GenerationRequest, raw) bind_generation_request_raw(normalized, raw) return normalized def request_to_sampling_param( request: GenerationRequest, *, model_path: str, ) -> SamplingParam: if request.plan is not None: raise NotImplementedError("GenerationRequest.plan is not wired into VideoGenerator yet") sampling_param = SamplingParam.from_pretrained(model_path) if request.state is not None: _validate_continuation_state(request.state) sampling_param.continuation_state = request.state if request.output.return_state: sampling_param.return_continuation_state = True updates = explicit_request_updates(request) for key, value in updates.items(): if hasattr(sampling_param, key): setattr(sampling_param, key, deepcopy(value)) elif key in _REQUEST_PIPELINE_OVERRIDE_FIELDS: continue elif value == _SCHEMA_DEFAULT_UPDATES.get(key, _MISSING): # Schema-default field that isn't on SamplingParam; tolerated # because direct GenerationRequest(...) construction has no # way to distinguish "user set" from "schema default". continue else: raise ValueError(f"Request field {key!r} is not supported by sampling params for {model_path}") sampling_param.__post_init__() sampling_param.check_sampling_param() return sampling_param def expand_request_prompt_batch(request: GenerationRequest, ) -> list[GenerationRequest]: if not isinstance(request.prompt, list): return [request] requests: list[GenerationRequest] = [] for index, prompt in enumerate(request.prompt): single_request = deepcopy(request) # deepcopy preserves the tracking-root cycle, but re-pin roots # defensively so that subsequent setattrs record on the copy. reset_tracking_roots(single_request) single_request.prompt = prompt _fan_out_batched_input_value(request, single_request, "image_path", index) _fan_out_batched_input_value(request, single_request, "video_path", index) requests.append(single_request) return requests def _looks_like_run_or_serve_config(raw: Mapping[str, Any]) -> bool: return isinstance(raw.get("generator"), Mapping) def _compile_config_to_torch_kwargs(compile_config: CompileConfig, ) -> dict[str, Any]: """Flatten typed ``CompileConfig`` back to a ``torch_compile_kwargs`` dict that the legacy ``FastVideoArgs`` path still expects. Typed first-class fields (:attr:`backend`, :attr:`fullgraph`, :attr:`mode`, :attr:`dynamic`) are only emitted when the user set them explicitly (non-``None``). ``extras`` is merged on top for any uncommon kwargs. """ out: dict[str, Any] = {} for key in _COMPILE_TYPED_KEYS: value = getattr(compile_config, key) if value is not None: out[key] = value if compile_config.extras: out.update(deepcopy(compile_config.extras)) return out def _sampling_param_to_request_raw(sampling_param: SamplingParam | None, ) -> dict[str, Any]: if sampling_param is None: return {} raw: dict[str, Any] = {} for key, value in shallow_asdict(sampling_param).items(): if key == "prompt": continue _apply_request_field(raw, key, deepcopy(value)) return raw def _apply_request_field( raw: dict[str, Any], key: str, value: Any, ) -> None: key = _LEGACY_REQUEST_ALIASES.get(key, key) if key == "negative_prompt": raw["negative_prompt"] = value return if key in _INPUT_FIELD_NAMES: raw.setdefault("inputs", {})[key] = value return if key in _SAMPLING_FIELD_NAMES: raw.setdefault("sampling", {})[key] = value return if key in _RUNTIME_FIELD_NAMES: raw.setdefault("runtime", {})[key] = value return if key in _OUTPUT_FIELD_NAMES: raw.setdefault("output", {})[key] = value return raw.setdefault("extensions", {})[key] = value def request_to_pipeline_overrides(request: GenerationRequest) -> dict[str, Any]: overrides: dict[str, Any] = {} for key, value in explicit_request_updates(request).items(): if key in _REQUEST_PIPELINE_OVERRIDE_FIELDS: overrides[key] = deepcopy(value) return overrides def explicit_request_updates(request: GenerationRequest) -> dict[str, Any]: """Project a ``GenerationRequest`` down to *explicitly set* fields only. Returns a flat kwargs dict suitable for merging into a generator call. The projection uses ``_fastvideo_explicit_paths`` (populated during ``parse_config`` / raw binding) so schema defaults on the dataclass are **not** emitted — only paths the caller/operator actually wrote. This is what makes ``ServeConfig.default_request`` work as an operator-pinned baseline rather than a full override: a YAML with just ``sampling.seed: 42`` yields ``{"seed": 42}``, not the full sampling config with its 15 schema defaults. Precondition: the request must carry ``_fastvideo_explicit_paths`` — populated by :func:`fastvideo.api.parser.parse_config` or :func:`fastvideo.api.compat.normalize_generation_request`. Calling on a raw ``GenerationRequest()`` asserts. """ assert hasattr(request, EXPLICIT_PATHS_ATTR), ("GenerationRequest reached explicit_request_updates without tracking; " "every entry point must route through normalize_generation_request " "or parse_config first") paths = get_explicit_paths(request) raw = _build_sparse_raw_from_paths(request, paths) return _extract_request_updates(raw) def _build_sparse_raw_from_paths( request: GenerationRequest, paths: frozenset[str], ) -> dict[str, Any]: result: dict[str, Any] = {} for path in paths: parts = path.split(".") value = _read_dotted_path(request, parts) if value is _MISSING: continue _set_dotted_path(result, parts, deepcopy(value)) return result def _read_dotted_path(obj: Any, parts: list[str]) -> Any: for part in parts: if is_dataclass(obj) and not isinstance(obj, type): if not hasattr(obj, part): return _MISSING obj = getattr(obj, part) elif isinstance(obj, Mapping): if part not in obj: return _MISSING obj = obj[part] else: return _MISSING return obj def _set_dotted_path( target: dict[str, Any], parts: list[str], value: Any, ) -> None: cursor = target for part in parts[:-1]: nxt = cursor.get(part) if not isinstance(nxt, dict): nxt = {} cursor[part] = nxt cursor = nxt cursor[parts[-1]] = value def _extract_request_updates(raw: Mapping[str, Any]) -> dict[str, Any]: updates: dict[str, Any] = {} if "negative_prompt" in raw: updates["negative_prompt"] = deepcopy(raw["negative_prompt"]) for section_name in ("inputs", "sampling", "runtime", "output"): section = raw.get(section_name) if not isinstance(section, Mapping): continue for key, value in section.items(): updates[key] = deepcopy(value) stage_overrides = raw.get("stage_overrides") if stage_overrides: updates.update(_flatten_stage_overrides(stage_overrides)) extensions = raw.get("extensions") if isinstance(extensions, Mapping): for key, value in extensions.items(): updates[key] = deepcopy(value) return updates def _flatten_stage_overrides(stage_overrides: Any) -> dict[str, Any]: if not isinstance(stage_overrides, Mapping): raise ValueError("GenerationRequest.stage_overrides must be a mapping") flattened: dict[str, Any] = {} for stage_name, overrides in stage_overrides.items(): if not isinstance(overrides, Mapping): raise ValueError(f"GenerationRequest.stage_overrides.{stage_name} must be a mapping") for key, value in overrides.items(): if key in flattened and flattened[key] != value: raise ValueError(f"Conflicting stage override for {key!r} across stages") flattened[key] = deepcopy(value) return flattened def _serialize_generation_request(request: GenerationRequest) -> dict[str, Any]: return deepcopy(config_to_dict(request)) _SCHEMA_DEFAULT_UPDATES = _extract_request_updates(config_to_dict(GenerationRequest())) _KNOWN_CONTINUATION_KINDS: set[str] = set() def register_continuation_kind(kind: str) -> None: """Register a :class:`ContinuationState.kind` as recognized. PR 7 wires the envelope through; per-kind payload deserializers live with each model family (e.g. ``fastvideo.pipelines.basic.ltx2. continuation.LTX2ContinuationState``). The registry lets the public-API compat layer validate the kind early, before the state reaches the pipeline. """ if not isinstance(kind, str) or not kind: raise ValueError("ContinuationState kind must be a non-empty string") _KNOWN_CONTINUATION_KINDS.add(kind) def _validate_continuation_state(state: ContinuationState) -> None: if not isinstance(state.kind, str) or not state.kind: raise ValueError("GenerationRequest.state.kind must be a non-empty string; got " f"{state.kind!r}") if not isinstance(state.payload, Mapping): raise ValueError(f"GenerationRequest.state.payload must be a mapping; got " f"{type(state.payload).__name__}") if state.kind not in _KNOWN_CONTINUATION_KINDS: known = sorted(_KNOWN_CONTINUATION_KINDS) raise ValueError(f"Unknown ContinuationState kind {state.kind!r}; registered " f"kinds: {known}. Import the model family that owns this kind " "(e.g. `import fastvideo.pipelines.basic.ltx2.continuation`) " "to register it, or drop the state field.") def _fan_out_batched_input_value( source_request: GenerationRequest, target_request: GenerationRequest, field_name: str, index: int, ) -> None: value = getattr(source_request.inputs, field_name) if not isinstance(value, list): return _validate_batched_input_length(source_request.prompt, value, field_name) setattr(target_request.inputs, field_name, deepcopy(value[index])) def _validate_batched_input_length( prompts: str | list[str] | None, values: list[Any], field_name: str, ) -> None: if not isinstance(prompts, list): return if len(values) != len(prompts): raise ValueError(f"GenerationRequest.inputs.{field_name} must have the same length as request.prompt") __all__ = [ "explicit_request_updates", "generator_config_to_fastvideo_args", "legacy_from_pretrained_to_config", "legacy_generate_call_to_request", "load_generator_config_from_file", "normalize_generation_request", "normalize_generator_config", "register_continuation_kind", "request_to_pipeline_overrides", "request_to_sampling_param", ]