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"""TOML configuration loading and model path confinement."""
from __future__ import annotations
import math
import tomllib
from dataclasses import dataclass
from pathlib import Path
from typing import Any
from .errors import ConfigError
VALID_SIZES = frozenset({"square_512", "landscape_512", "portrait_512"})
VALID_SCHEDULERS = frozenset({"dpmpp_2m", "ddim", "euler", "euler_a"})
@dataclass(frozen=True)
class LoRAConfig:
file: str
weight: float = 1.0
@dataclass(frozen=True)
class ModelConfig:
root: Path
checkpoint: str
vae: str | None
loras: tuple[LoRAConfig, ...]
def checkpoint_path(self) -> Path:
return self.root / "checkpoints" / self.checkpoint
def vae_path(self) -> Path | None:
return self.root / "vae" / self.vae if self.vae else None
def lora_paths(self) -> tuple[Path, ...]:
return tuple(self.root / "lora" / item.file for item in self.loras)
@dataclass(frozen=True)
class GenerationConfig:
steps: int = 25
cfg_scale: float = 7.0
scheduler: str = "dpmpp_2m"
default_size: str = "square_512"
@dataclass(frozen=True)
class ControlModelConfig:
enabled: bool
model: str | None
preprocessor: str | None = None
@dataclass(frozen=True)
class ControlNetConfig:
depth: ControlModelConfig
openpose: ControlModelConfig
inpaint: ControlModelConfig
def for_mode(self, mode: str) -> ControlModelConfig:
key = "openpose" if mode == "openpose_full" else mode
if key not in {"depth", "openpose", "inpaint"}:
raise ConfigError(f"unknown ControlNet mode: {mode}")
return getattr(self, key)
@dataclass(frozen=True)
class OutputConfig:
directory: Path
enabled: bool = False
@dataclass(frozen=True)
class AppConfig:
model: ModelConfig
generation: GenerationConfig
controlnet: ControlNetConfig
output: OutputConfig
def controlnet_path(self, mode: str) -> Path:
item = self.controlnet.for_mode(mode)
if not item.enabled or not item.model:
raise ConfigError(f"ControlNet mode is disabled: {mode}")
return self.model.root / "controlnet" / item.model
def _table(data: dict[str, Any], key: str, *, required: bool = False) -> dict[str, Any]:
value = data.get(key)
if value is None and not required:
return {}
if not isinstance(value, dict):
raise ConfigError(f"[{key}] must be a TOML table")
return value
def _string(table: dict[str, Any], key: str, *, required: bool = False) -> str | None:
value = table.get(key)
if value is None and not required:
return None
if not isinstance(value, str) or not value.strip():
raise ConfigError(f"{key} must be a non-empty string")
return value.strip()
def _filename(value: str | None, label: str) -> str | None:
if value is None:
return None
path = Path(value)
if path.name != value or path.is_absolute() or value in {".", ".."}:
raise ConfigError(f"{label} must be a file name, not a path")
return value
def _bool(table: dict[str, Any], key: str, default: bool) -> bool:
value = table.get(key, default)
if not isinstance(value, bool):
raise ConfigError(f"{key} must be a boolean")
return value
def _number(table: dict[str, Any], key: str, default: float) -> float:
value = table.get(key, default)
if isinstance(value, bool) or not isinstance(value, int | float):
raise ConfigError(f"{key} must be a number")
result = float(value)
if not math.isfinite(result):
raise ConfigError(f"{key} must be finite")
return result
def _control(table: dict[str, Any], name: str) -> ControlModelConfig:
enabled = _bool(table, "enabled", False)
model = _filename(_string(table, "model"), f"controlnet.{name}.model")
if enabled and model is None:
raise ConfigError(f"controlnet.{name}.model is required when enabled")
preprocessor = _string(table, "preprocessor")
if name == "depth" and preprocessor not in {None, "midas"}:
raise ConfigError("controlnet.depth.preprocessor must be 'midas'")
return ControlModelConfig(
enabled, model, preprocessor or ("midas" if name == "depth" else None)
)
def load_config(path: str | Path, *, validate_paths: bool = True) -> AppConfig:
"""Load a configuration file, validating all values and local model paths."""
config_path = Path(path).expanduser().resolve()
try:
with config_path.open("rb") as stream:
raw = tomllib.load(stream)
except (OSError, tomllib.TOMLDecodeError) as exc:
raise ConfigError(f"cannot read config {config_path}: {exc}") from exc
model_raw = _table(raw, "model", required=True)
root_value = _string(model_raw, "root", required=True)
assert root_value is not None
root = Path(root_value).expanduser()
if not root.is_absolute():
root = config_path.parent / root
root = root.resolve()
checkpoint = _filename(_string(model_raw, "checkpoint", required=True), "model.checkpoint")
assert checkpoint is not None
vae = _filename(_string(model_raw, "vae"), "model.vae")
lora_raw = model_raw.get("lora", [])
if not isinstance(lora_raw, list):
raise ConfigError("model.lora must be an array of tables")
loras: list[LoRAConfig] = []
for index, value in enumerate(lora_raw):
if not isinstance(value, dict):
raise ConfigError(f"model.lora[{index}] must be a table")
file = _filename(_string(value, "file", required=True), f"model.lora[{index}].file")
weight = _number(value, "weight", 1.0)
if not -4.0 <= weight <= 4.0:
raise ConfigError(f"model.lora[{index}].weight must be between -4 and 4")
assert file is not None
loras.append(LoRAConfig(file, weight))
if len({item.file for item in loras}) != len(loras):
raise ConfigError("LoRA file names must be unique")
generation_raw = _table(raw, "generation")
steps_value = generation_raw.get("steps", 25)
if (
isinstance(steps_value, bool)
or not isinstance(steps_value, int)
or not 1 <= steps_value <= 150
):
raise ConfigError("generation.steps must be an integer between 1 and 150")
cfg_scale = _number(generation_raw, "cfg_scale", 7.0)
if not 0 <= cfg_scale <= 30:
raise ConfigError("generation.cfg_scale must be between 0 and 30")
scheduler = _string(generation_raw, "scheduler") or "dpmpp_2m"
if scheduler not in VALID_SCHEDULERS:
raise ConfigError(f"unsupported scheduler: {scheduler}")
default_size = _string(generation_raw, "default_size") or "square_512"
if default_size not in VALID_SIZES:
raise ConfigError(f"unsupported default_size: {default_size}")
control_raw = _table(raw, "controlnet")
controlnet = ControlNetConfig(
depth=_control(_table(control_raw, "depth"), "depth"),
openpose=_control(_table(control_raw, "openpose"), "openpose"),
inpaint=_control(_table(control_raw, "inpaint"), "inpaint"),
)
output_raw = _table(raw, "output")
output_value = _string(output_raw, "directory") or "./outputs"
output_dir = Path(output_value).expanduser()
if not output_dir.is_absolute():
output_dir = config_path.parent / output_dir
config = AppConfig(
model=ModelConfig(root, checkpoint, vae, tuple(loras)),
generation=GenerationConfig(steps_value, cfg_scale, scheduler, default_size),
controlnet=controlnet,
output=OutputConfig(output_dir.resolve(), _bool(output_raw, "enabled", False)),
)
if validate_paths:
validate_model_paths(config)
return config
def validate_model_paths(config: AppConfig) -> None:
"""Fail before GPU initialization when a configured model file is absent."""
if not config.model.root.is_dir():
raise ConfigError(f"model root directory not found: {config.model.root}")
required = [config.model.checkpoint_path()]
vae = config.model.vae_path()
if vae:
required.append(vae)
required.extend(config.model.lora_paths())
for mode in ("depth", "openpose", "inpaint"):
item = config.controlnet.for_mode(mode)
if item.enabled:
required.append(config.controlnet_path(mode))
missing = [str(path) for path in required if not path.is_file()]
if missing:
raise ConfigError("model file(s) not found: " + ", ".join(missing))
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