680 lines
27 KiB
Python
680 lines
27 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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from collections.abc import Mapping
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from copy import deepcopy
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from dataclasses import fields, is_dataclass
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from pathlib import Path
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from typing import Any
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from fastvideo.api.overrides import apply_overrides, normalize_overrides
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from fastvideo.api.parser import config_to_dict, load_raw_config, parse_config
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from fastvideo.api.request_metadata import (
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EXPLICIT_PATHS_ATTR,
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bind_generation_request_raw,
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get_explicit_paths,
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reset_tracking_roots,
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)
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from fastvideo.api.schema import (
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CompileConfig,
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ContinuationState,
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GenerationRequest,
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GeneratorConfig,
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InputConfig,
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OutputConfig,
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RequestRuntimeConfig,
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SamplingConfig,
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)
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from fastvideo.api.sampling_param import SamplingParam
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from fastvideo.fastvideo_args import FastVideoArgs
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from fastvideo.pipelines.basic.ltx2.stage_overrides import (
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refine_preset_override_fields,
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refine_stage_override_fields,
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)
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from fastvideo.utils import shallow_asdict
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_INPUT_FIELD_NAMES = {field.name for field in fields(InputConfig)}
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_SAMPLING_FIELD_NAMES = {field.name for field in fields(SamplingConfig)}
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_RUNTIME_FIELD_NAMES = {field.name for field in fields(RequestRuntimeConfig)}
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_OUTPUT_FIELD_NAMES = {field.name for field in fields(OutputConfig)}
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_MISSING = object()
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_LEGACY_REQUEST_ALIASES = {
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"neg_prompt": "negative_prompt",
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}
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_REQUEST_PIPELINE_OVERRIDE_FIELDS = frozenset({
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"embedded_cfg_scale",
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})
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REQUEST_BATCH_EXTRA_PASSTHROUGH_FIELDS = (
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"ltx2_audio_latents",
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"ltx2_audio_clean_latent",
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"ltx2_audio_denoise_mask",
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"audio_num_frames",
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"video_position_offset_sec",
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"vsa_mode",
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"vsa_dense_first_n_steps",
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"vsa_dense_layers",
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)
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# torch.compile kwargs that map to first-class CompileConfig fields.
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_COMPILE_TYPED_KEYS = ("backend", "fullgraph", "mode", "dynamic")
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# LTX-2 refine flat kwargs (init + per-request) known to FastVideoArgs.
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_LTX2_REFINE_FLAT_KEYS = (refine_preset_override_fields() | refine_stage_override_fields())
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def normalize_generator_config(config: GeneratorConfig | Mapping[str, Any], ) -> GeneratorConfig:
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if isinstance(config, GeneratorConfig):
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return config
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return parse_config(GeneratorConfig, config)
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def load_generator_config_from_file(
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path: str | Path,
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overrides: list[str] | Mapping[str, Any] | None = None,
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) -> GeneratorConfig:
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raw = load_raw_config(path)
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normalized_overrides = normalize_overrides(overrides)
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if _looks_like_run_or_serve_config(raw):
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if normalized_overrides:
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raw = apply_overrides(raw, normalized_overrides)
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return parse_config(GeneratorConfig, raw["generator"])
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if normalized_overrides:
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adjusted = normalized_overrides
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if all(key.startswith("generator.") for key in adjusted):
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adjusted = {key[len("generator."):]: value for key, value in adjusted.items()}
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raw = apply_overrides(raw, adjusted)
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return parse_config(GeneratorConfig, raw)
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def legacy_from_pretrained_to_config(
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model_path: str,
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kwargs: Mapping[str, Any],
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) -> GeneratorConfig:
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raw: dict[str, Any] = {"model_path": model_path}
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engine: dict[str, Any] = {}
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parallelism: dict[str, Any] = {}
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offload: dict[str, Any] = {}
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compile_config: dict[str, Any] = {}
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pipeline: dict[str, Any] = {}
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components: dict[str, Any] = {}
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quantization: dict[str, Any] = {}
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experimental: dict[str, Any] = {}
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preset_overrides: dict[str, Any] = {}
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preset_refine: dict[str, Any] = {}
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for key, value in kwargs.items():
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if key == "revision":
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raw["revision"] = value
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elif key == "trust_remote_code":
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raw["trust_remote_code"] = value
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elif key == "num_gpus":
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engine["num_gpus"] = value
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elif key == "distributed_executor_backend":
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engine["execution_backend"] = value
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elif key in {"tp_size", "sp_size", "hsdp_replicate_dim", "hsdp_shard_dim", "dist_timeout"}:
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parallelism[key] = value
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elif key == "dit_cpu_offload":
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offload["dit"] = value
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elif key == "dit_layerwise_offload":
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offload["dit_layerwise"] = value
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elif key == "text_encoder_cpu_offload":
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offload["text_encoder"] = value
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elif key == "image_encoder_cpu_offload":
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offload["image_encoder"] = value
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elif key == "vae_cpu_offload":
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offload["vae"] = value
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elif key == "pin_cpu_memory":
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offload["pin_cpu_memory"] = value
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elif key == "enable_torch_compile":
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compile_config["enabled"] = value
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elif key == "enable_torch_compile_text_encoder":
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compile_config["text_encoder_enabled"] = value
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elif key == "enable_torch_compile_vae":
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compile_config["vae_enabled"] = value
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elif key == "enable_torch_compile_audio_vae":
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compile_config["audio_vae_enabled"] = value
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elif key == "torch_compile_kwargs":
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remaining: dict[str, Any] = (dict(deepcopy(value)) if isinstance(value, Mapping) else {})
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for first_class in _COMPILE_TYPED_KEYS:
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if first_class in remaining:
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compile_config[first_class] = remaining.pop(first_class)
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if remaining:
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compile_config["extras"] = remaining
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elif key in {
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"torch_compile_kwargs_dit",
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"torch_compile_kwargs_text_encoder",
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"torch_compile_kwargs_vae",
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"torch_compile_kwargs_audio_vae",
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}:
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compile_config[key[len("torch_compile_kwargs_"):] +
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"_kwargs"] = (dict(deepcopy(value)) if isinstance(value, Mapping) else {})
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elif key == "ltx2_vae_tiling":
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pipeline["vae_tiling"] = value
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elif key == "config_model_path":
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components["config_root"] = value
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elif key == "ltx2_refine_enabled":
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preset_refine["enabled"] = value
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elif key == "ltx2_refine_upsampler_path":
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# Empty string means "no upsampler"; keep typed None.
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components["upsampler_weights"] = value or None
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elif key == "ltx2_refine_lora_path":
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# Empty string means "no refine LoRA"; keep typed None.
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components["lora_path"] = value or None
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elif key == "ltx2_refine_add_noise":
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preset_refine["add_noise"] = value
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elif key == "ltx2_refine_num_inference_steps":
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preset_refine["num_inference_steps"] = value
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elif key == "ltx2_refine_guidance_scale":
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preset_refine["guidance_scale"] = value
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elif key in {"enable_stage_verification", "use_fsdp_inference", "disable_autocast"}:
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engine[key] = value
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elif key == "override_text_encoder_quant":
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quantization["text_encoder_quant"] = value
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elif key == "workload_type":
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pipeline["workload_type"] = value
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elif key == "lora_path":
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components["lora_path"] = value
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elif key == "lora_nickname":
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components["lora_nickname"] = value
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elif key == "lora_strength":
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components["lora_strength"] = value
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elif key == "override_pipeline_cls_name":
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components["override_pipeline_cls_name"] = value
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elif key == "override_transformer_cls_name":
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components["override_transformer_cls_name"] = value
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elif key == "pipeline_config":
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if isinstance(value, str):
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components["pipeline_config_path"] = value
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else:
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experimental[key] = deepcopy(value)
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elif key == "override_text_encoder_safetensors":
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components["text_encoder_weights"] = value
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elif key == "init_weights_from_safetensors":
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components["transformer_weights"] = value
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elif key == "init_weights_from_safetensors_2":
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components["transformer_2_weights"] = value
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else:
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experimental[key] = deepcopy(value)
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if parallelism:
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engine["parallelism"] = parallelism
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if offload:
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engine["offload"] = offload
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if compile_config:
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engine["compile"] = compile_config
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if quantization:
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engine["quantization"] = quantization
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if engine:
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raw["engine"] = engine
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if components:
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pipeline["components"] = components
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if preset_refine:
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preset_overrides["refine"] = preset_refine
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if preset_overrides:
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pipeline["preset_overrides"] = preset_overrides
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if experimental:
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pipeline["experimental"] = experimental
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if pipeline:
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raw["pipeline"] = pipeline
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return parse_config(GeneratorConfig, raw)
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def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, Any], ) -> FastVideoArgs:
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normalized = normalize_generator_config(config)
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unsupported = []
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if normalized.pipeline.preset is not None:
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unsupported.append("pipeline.preset")
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if normalized.pipeline.preset_version is not None:
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unsupported.append("pipeline.preset_version")
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if normalized.pipeline.components.vae_weights is not None:
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unsupported.append("pipeline.components.vae_weights")
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if unsupported:
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joined = ", ".join(unsupported)
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raise NotImplementedError(f"VideoGenerator compatibility adapter does not support {joined} yet")
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engine = normalized.engine
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kwargs: dict[str, Any] = {
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"model_path": normalized.model_path,
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"revision": normalized.revision,
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"trust_remote_code": normalized.trust_remote_code,
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"num_gpus": engine.num_gpus,
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"distributed_executor_backend": engine.execution_backend,
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"tp_size": engine.parallelism.tp_size,
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"sp_size": engine.parallelism.sp_size,
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"hsdp_replicate_dim": engine.parallelism.hsdp_replicate_dim,
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"hsdp_shard_dim": engine.parallelism.hsdp_shard_dim,
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"dist_timeout": engine.parallelism.dist_timeout,
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"dit_cpu_offload": engine.offload.dit,
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"dit_layerwise_offload": engine.offload.dit_layerwise,
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"text_encoder_cpu_offload": engine.offload.text_encoder,
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"image_encoder_cpu_offload": engine.offload.image_encoder,
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"vae_cpu_offload": engine.offload.vae,
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"pin_cpu_memory": engine.offload.pin_cpu_memory,
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"enable_torch_compile": engine.compile.enabled,
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"torch_compile_kwargs": _compile_config_to_torch_kwargs(engine.compile),
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"enable_stage_verification": engine.enable_stage_verification,
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"use_fsdp_inference": engine.use_fsdp_inference,
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"disable_autocast": engine.disable_autocast,
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}
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if normalized.pipeline.workload_type is not None:
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kwargs["workload_type"] = normalized.pipeline.workload_type
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if normalized.pipeline.vae_tiling is not None:
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kwargs["ltx2_vae_tiling"] = normalized.pipeline.vae_tiling
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if engine.compile.text_encoder_enabled is not None:
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kwargs["enable_torch_compile_text_encoder"] = (engine.compile.text_encoder_enabled)
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if engine.compile.vae_enabled is not None:
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kwargs["enable_torch_compile_vae"] = engine.compile.vae_enabled
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if engine.compile.audio_vae_enabled is not None:
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kwargs["enable_torch_compile_audio_vae"] = (engine.compile.audio_vae_enabled)
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if engine.compile.dit_kwargs:
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kwargs["torch_compile_kwargs_dit"] = deepcopy(engine.compile.dit_kwargs)
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if engine.compile.text_encoder_kwargs:
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kwargs["torch_compile_kwargs_text_encoder"] = deepcopy(engine.compile.text_encoder_kwargs)
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if engine.compile.vae_kwargs:
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kwargs["torch_compile_kwargs_vae"] = deepcopy(engine.compile.vae_kwargs)
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if engine.compile.audio_vae_kwargs:
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kwargs["torch_compile_kwargs_audio_vae"] = deepcopy(engine.compile.audio_vae_kwargs)
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quantization = engine.quantization
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if quantization is not None and quantization.text_encoder_quant is not None:
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kwargs["override_text_encoder_quant"] = quantization.text_encoder_quant
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if quantization is not None and quantization.transformer_quant is not None:
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# Resolve the typed quant name to a concrete ``QuantizationConfig``
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# instance and pin it on ``dit_config.quant_config``. The legacy
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# path expected callers to do this themselves via
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# ``pipeline_config.dit_config.quant_config = NVFP4Config()``; the
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# typed surface accepts a string and does the wiring here so
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# downstream code can rely on a single source of truth.
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from fastvideo.layers.quantization import get_quantization_config
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_resolved_quant_cls = get_quantization_config(quantization.transformer_quant)
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kwargs["transformer_quant"] = _resolved_quant_cls()
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components = normalized.pipeline.components
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if components.pipeline_config_path is not None:
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kwargs["pipeline_config"] = components.pipeline_config_path
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if components.lora_path is not None:
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kwargs["lora_path"] = components.lora_path
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kwargs["lora_nickname"] = components.lora_nickname
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kwargs["lora_strength"] = components.lora_strength
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if components.override_pipeline_cls_name is not None:
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kwargs["override_pipeline_cls_name"] = components.override_pipeline_cls_name
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if components.override_transformer_cls_name is not None:
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kwargs["override_transformer_cls_name"] = components.override_transformer_cls_name
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if components.text_encoder_weights is not None:
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kwargs["override_text_encoder_safetensors"] = components.text_encoder_weights
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if components.transformer_weights is not None:
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kwargs["init_weights_from_safetensors"] = components.transformer_weights
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if components.transformer_2_weights is not None:
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kwargs["init_weights_from_safetensors_2"] = components.transformer_2_weights
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if components.config_root is not None:
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kwargs["config_model_path"] = components.config_root
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if components.upsampler_weights is not None:
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kwargs["ltx2_refine_upsampler_path"] = components.upsampler_weights
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preset_overrides = deepcopy(normalized.pipeline.preset_overrides)
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refine = preset_overrides.pop("refine", None)
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if isinstance(refine, Mapping):
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for key in _LTX2_REFINE_FLAT_KEYS:
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if key in refine:
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kwargs[f"ltx2_refine_{key}"] = refine[key]
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if "enabled" in refine:
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kwargs["refine_enabled"] = refine["enabled"]
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kwargs.update(preset_overrides)
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kwargs.update(deepcopy(normalized.pipeline.experimental))
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return FastVideoArgs.from_kwargs(**kwargs)
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def normalize_generation_request(request: GenerationRequest | Mapping[str, Any], ) -> GenerationRequest:
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normalized = (request if isinstance(request, GenerationRequest) else parse_config(GenerationRequest, request))
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if not hasattr(normalized, EXPLICIT_PATHS_ATTR):
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# Request wasn't bound through the parser (e.g. constructed
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# directly). Treat every currently-set field as explicit.
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bind_generation_request_raw(normalized, _serialize_generation_request(normalized))
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return normalized
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def legacy_generate_call_to_request(
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prompt: str | None,
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sampling_param: SamplingParam | None,
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*,
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mouse_cond: Any | None = None,
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keyboard_cond: Any | None = None,
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grid_sizes: Any | None = None,
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legacy_kwargs: Mapping[str, Any] | None = None,
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) -> GenerationRequest:
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raw = _sampling_param_to_request_raw(sampling_param)
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if prompt is not None:
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raw["prompt"] = prompt
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for key, value in (legacy_kwargs or {}).items():
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_apply_request_field(raw, key, value)
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if mouse_cond is not None:
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raw.setdefault("inputs", {})["mouse_cond"] = mouse_cond
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if keyboard_cond is not None:
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raw.setdefault("inputs", {})["keyboard_cond"] = keyboard_cond
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if grid_sizes is not None:
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raw.setdefault("inputs", {})["grid_sizes"] = grid_sizes
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normalized = parse_config(GenerationRequest, raw)
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bind_generation_request_raw(normalized, raw)
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return normalized
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def request_to_sampling_param(
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request: GenerationRequest,
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*,
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model_path: str,
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) -> SamplingParam:
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if request.plan is not None:
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raise NotImplementedError("GenerationRequest.plan is not wired into VideoGenerator yet")
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sampling_param = SamplingParam.from_pretrained(model_path)
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if request.state is not None:
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_validate_continuation_state(request.state)
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sampling_param.continuation_state = request.state
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if request.output.return_state:
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sampling_param.return_continuation_state = True
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updates = explicit_request_updates(request)
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for key, value in updates.items():
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if hasattr(sampling_param, key):
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setattr(sampling_param, key, deepcopy(value))
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elif key in _REQUEST_PIPELINE_OVERRIDE_FIELDS or key in REQUEST_BATCH_EXTRA_PASSTHROUGH_FIELDS:
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continue
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elif value == _SCHEMA_DEFAULT_UPDATES.get(key, _MISSING):
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# Schema-default field that isn't on SamplingParam; tolerated
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# because direct GenerationRequest(...) construction has no
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# way to distinguish "user set" from "schema default".
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continue
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else:
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raise ValueError(f"Request field {key!r} is not supported by sampling params for {model_path}")
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sampling_param.__post_init__()
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sampling_param.check_sampling_param()
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return sampling_param
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def expand_request_prompt_batch(request: GenerationRequest, ) -> list[GenerationRequest]:
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if not isinstance(request.prompt, list):
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return [request]
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requests: list[GenerationRequest] = []
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for index, prompt in enumerate(request.prompt):
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single_request = deepcopy(request)
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# deepcopy preserves the tracking-root cycle, but re-pin roots
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# defensively so that subsequent setattrs record on the copy.
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reset_tracking_roots(single_request)
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single_request.prompt = prompt
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_fan_out_batched_input_value(request, single_request, "image_path", index)
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_fan_out_batched_input_value(request, single_request, "video_path", index)
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requests.append(single_request)
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return requests
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def _looks_like_run_or_serve_config(raw: Mapping[str, Any]) -> bool:
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return isinstance(raw.get("generator"), Mapping)
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def _compile_config_to_torch_kwargs(compile_config: CompileConfig, ) -> dict[str, Any]:
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"""Flatten typed ``CompileConfig`` back to a ``torch_compile_kwargs``
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dict that the legacy ``FastVideoArgs`` path still expects.
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Typed first-class fields (:attr:`backend`, :attr:`fullgraph`,
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:attr:`mode`, :attr:`dynamic`) are only emitted when the user set
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them explicitly (non-``None``). ``extras`` is merged on top for any
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uncommon kwargs.
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"""
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out: dict[str, Any] = {}
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for key in _COMPILE_TYPED_KEYS:
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value = getattr(compile_config, key)
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|
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 request_to_batch_extra(request: GenerationRequest) -> dict[str, Any]:
|
|
"""Extract typed-request extensions consumed through ``ForwardBatch.extra``."""
|
|
return {
|
|
key: deepcopy(value)
|
|
for key, value in explicit_request_updates(request).items() if key in REQUEST_BATCH_EXTRA_PASSTHROUGH_FIELDS
|
|
}
|
|
|
|
|
|
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__ = [
|
|
"REQUEST_BATCH_EXTRA_PASSTHROUGH_FIELDS",
|
|
"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_batch_extra",
|
|
"request_to_pipeline_overrides",
|
|
"request_to_sampling_param",
|
|
]
|