Davids048andClaude Opus 5.5
00a8e5bcb6
- 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
57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Assembly: build method + dataloader from a ``_target_``-based config."""
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from __future__ import annotations
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from typing import Any, TYPE_CHECKING
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from fastvideo.train.utils.instantiate import (
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instantiate,
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resolve_target,
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)
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from fastvideo.train.utils.config import RunConfig
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if TYPE_CHECKING:
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from fastvideo.train.utils.training_config import (
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TrainingConfig, )
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from fastvideo.train.methods.base import TrainingMethod
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def build_from_config(cfg: RunConfig, ) -> tuple[TrainingConfig, TrainingMethod, Any, int]:
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"""Build method + dataloader from a v3 run config.
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1. Instantiate each model in ``cfg.models`` via ``_target_``.
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2. Resolve the method class from ``cfg.method["_target_"]``
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and construct it with ``(cfg=cfg, role_models=...)``.
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3. Return ``(training_config, method, dataloader, start_step)``.
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"""
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from fastvideo.train.models.base import ModelBase
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# --- 1. Build role model instances ---
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role_models: dict[str, ModelBase] = {}
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for role, model_cfg in cfg.models.items():
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model = instantiate(model_cfg, training_config=cfg.training)
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if not isinstance(model, ModelBase):
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raise TypeError(f"models.{role}._target_ must resolve to a "
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f"ModelBase subclass, got {type(model).__name__}")
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role_models[role] = model
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# --- 2. Build method ---
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method_cfg = dict(cfg.method)
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method_target = str(method_cfg.pop("_target_"))
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method_cls = resolve_target(method_target)
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# The student model provides the dataloader.
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student = role_models.get("student")
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method = method_cls(
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cfg=cfg,
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role_models=role_models,
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)
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# --- 3. Gather dataloader and start_step ---
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dataloader = (getattr(student, "dataloader", None) if student is not None else None)
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start_step = int(getattr(student, "start_step", 0) if student is not None else 0)
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return cfg.training, method, dataloader, start_step
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