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import json
import re
from dataclasses import asdict, dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
from uuid import uuid4
def _now() -> str:
return datetime.now(timezone.utc).isoformat()
def _normal(value: str) -> str:
return re.sub(r"[^a-z0-9]+", " ", value.casefold()).strip()
@dataclass(slots=True)
class Asset:
id: str
kind: str
name: str
path: str
trainer: str = ""
dataset_id: str = ""
checkpoint: str = ""
epochs: int = 0
created_at: str = ""
@classmethod
def from_dict(cls, payload: dict[str, Any]) -> "Asset":
return cls(
id=str(payload.get("id") or uuid4().hex[:12]),
kind=str(payload.get("kind", "")),
name=str(payload.get("name", "")),
path=str(payload.get("path", "")),
trainer=str(payload.get("trainer", "")),
dataset_id=str(payload.get("dataset_id", "")),
checkpoint=str(payload.get("checkpoint", "")),
epochs=int(payload.get("epochs", 0) or 0),
created_at=str(payload.get("created_at") or _now()),
)
class AssetRegistry:
"""Persistent friendly-name index for datasets, models, and checkpoints."""
def __init__(self, root: Path) -> None:
self.path = root.resolve() / "data" / "assets.json"
self.assets: list[Asset] = []
self.load()
def load(self) -> None:
try:
payload = json.loads(self.path.read_text(encoding="utf-8"))
self.assets = [
Asset.from_dict(item)
for item in payload.get("assets", [])
if isinstance(item, dict)
]
except (OSError, ValueError, TypeError, json.JSONDecodeError):
self.assets = []
def save(self) -> None:
self.path.parent.mkdir(parents=True, exist_ok=True)
temporary = self.path.with_suffix(".tmp")
temporary.write_text(
json.dumps({"assets": [asdict(item) for item in self.assets]}, indent=2),
encoding="utf-8",
)
temporary.replace(self.path)
def register(
self,
*,
kind: str,
name: str,
path: str,
trainer: str = "",
dataset_id: str = "",
checkpoint: str = "",
epochs: int = 0,
persist: bool = True,
) -> Asset:
resolved = str(Path(path).expanduser().resolve())
existing = next(
(
item
for item in self.assets
if item.kind == kind and Path(item.path) == Path(resolved)
),
None,
)
asset = existing or Asset(uuid4().hex[:12], kind, name, resolved)
asset.name = name.strip() or Path(resolved).name
asset.trainer = trainer
asset.dataset_id = dataset_id
asset.checkpoint = checkpoint
asset.epochs = int(epochs)
asset.created_at = asset.created_at or _now()
if existing is None:
self.assets.insert(0, asset)
if persist:
self.save()
return asset
def ingest_result(self, result: dict[str, Any]) -> None:
entries = result.get("assets", [])
if not isinstance(entries, list):
return
for item in entries:
if not isinstance(item, dict):
continue
if item.get("kind") and item.get("path"):
values = {
key: item[key]
for key in (
"kind", "name", "path", "trainer", "dataset_id",
"checkpoint", "epochs",
)
if key in item
}
dataset_path = str(item.get("dataset_path", ""))
if item.get("kind") == "model" and dataset_path and Path(dataset_path).is_dir():
dataset = self.register(
kind="dataset",
name=Path(dataset_path).name,
path=dataset_path,
)
values["dataset_id"] = dataset.id
values.setdefault("name", Path(str(item["path"])).name)
self.register(**values)
def find(self, kind: str, query: str, *, trainer: str = "") -> list[Asset]:
wanted = _normal(query)
matches = []
exact = []
for item in self.assets:
if item.kind != kind or (trainer and item.trainer != trainer):
continue
haystacks = {_normal(item.name), _normal(Path(item.path).name)}
if wanted in haystacks:
exact.append(item)
elif any(wanted and wanted in value for value in haystacks):
matches.append(item)
return exact or matches
def discover(self, config: Any) -> None:
folders = config.get("tool_folders", {})
if not isinstance(folders, dict):
return
app_root = self.path.parent.parent
external_lora_root = app_root / "LoRAModelsHere"
if external_lora_root.is_dir():
for path in external_lora_root.rglob("*.safetensors"):
if path.is_file() and "_comfy" not in path.stem.casefold():
self.register(
kind="model",
name=path.stem.removesuffix("_cancelled"),
path=str(path),
trainer="lora",
checkpoint=str(path),
persist=False,
)
base_model_root = app_root / "LoRA StableDiffusionModels Here"
if base_model_root.is_dir():
for path in base_model_root.iterdir():
is_model_file = path.is_file() and path.suffix.casefold() in {
".safetensors", ".ckpt", ".pt", ".bin"
}
is_diffusers_folder = path.is_dir() and (
(path / "model_index.json").is_file()
or (path / "unet" / "config.json").is_file()
)
if is_model_file or is_diffusers_folder:
self.register(
kind="base_model",
name=path.stem if path.is_file() else path.name,
path=str(path),
trainer="stable_diffusion",
persist=False,
)
flow_datasets = self._flow_dataset_paths()
collector = Path(str(folders.get("dataset_collector", ""))) / "Datasets"
if collector.is_dir():
for folder in collector.iterdir():
if folder.is_dir():
self.register(
kind="dataset", name=folder.name, path=str(folder), persist=False
)
for trainer, folder_name, output_name in (
("ddpm", "ddpm_trainer", "output"),
("lora", "lora_trainer", "output"),
("flow", "flow_trainer", "output_flow_models"),
):
root = Path(str(folders.get(folder_name, ""))) / output_name
if not root.is_dir():
continue
for folder in root.iterdir():
if not folder.is_dir():
continue
name = folder.name
dataset_path = ""
if trainer == "ddpm":
# DDPM writes a durable sidecar with the friendly model name and
# source dataset. Prefer it over a filesystem-safe folder name.
try:
metadata = json.loads((folder / "model_info.json").read_text(encoding="utf-8"))
name = str(metadata.get("model_name") or metadata.get("name") or name)
dataset_path = str(metadata.get("dataset_dir") or "")
except (OSError, ValueError, TypeError, json.JSONDecodeError):
pass
checkpoints = sorted(
folder.glob("checkpoint-*"),
key=lambda p: int(p.name.rsplit("-", 1)[-1])
if p.name.rsplit("-", 1)[-1].isdigit()
else -1,
)
elif trainer == "lora":
checkpoints = sorted(
(
path for path in folder.glob("*.safetensors")
if "_comfy" not in path.stem.casefold()
),
key=lambda p: p.stat().st_mtime,
)
if checkpoints:
name = checkpoints[-1].stem.removesuffix("_cancelled")
else:
checkpoints = []
try:
metadata = json.loads(
(folder / "flow_model_info.json").read_text(encoding="utf-8")
)
if metadata.get("model_type") != "rectified_flow":
continue
if not (folder / "unet" / "config.json").is_file():
continue
name = str(metadata.get("model_name") or metadata.get("name") or name)
dataset_path = flow_datasets.get(str(folder.resolve()), "")
except (OSError, ValueError, TypeError, json.JSONDecodeError):
continue
checkpoint = (
str(folder) if trainer == "flow" else str(checkpoints[-1]) if checkpoints else ""
)
dataset_id = ""
if dataset_path and Path(dataset_path).is_dir():
dataset_asset = self.register(
kind="dataset",
name=Path(dataset_path).name,
path=dataset_path,
persist=False,
)
dataset_id = dataset_asset.id
self.register(
kind="model",
name=name,
path=str(folder),
trainer=trainer,
dataset_id=dataset_id,
checkpoint=checkpoint,
persist=False,
)
self.save()
def _flow_dataset_paths(self) -> dict[str, str]:
"""Recover source datasets for Flow models created by ADAM in older runs."""
jobs_path = self.path.parent / "jobs.json"
try:
payload = json.loads(jobs_path.read_text(encoding="utf-8"))
jobs = payload.get("jobs", [])
except (OSError, ValueError, TypeError, json.JSONDecodeError):
return {}
links: dict[str, str] = {}
if not isinstance(jobs, list):
return links
for job in jobs:
if not isinstance(job, dict):
continue
plan = job.get("plan", {})
steps = plan.get("steps", []) if isinstance(plan, dict) else []
if not isinstance(steps, list):
continue
for step in steps:
if not isinstance(step, dict) or step.get("tool_id") != "flow_trainer":
continue
arguments = step.get("arguments", {})
if not isinstance(arguments, dict):
continue
output = str(arguments.get("output_dir", ""))
dataset = str(arguments.get("dataset_dir", ""))
if not output or not dataset or not Path(dataset).is_dir():
continue
try:
links[str(Path(output).expanduser().resolve())] = str(Path(dataset).expanduser().resolve())
except OSError:
continue
return links
|