Claude
3adebb731f
Old project/workflow names and browser settings migrate on load; /funpack redirects to /fun. Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016acRss69eH1235eRdFTZHY
472 lines
20 KiB
Python
472 lines
20 KiB
Python
"""Named taste keys on disk, and pairing a run's capture with its rating.
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Layout: `<pack>/taste/<key>/<kind>.pt` holds `{"rows": [{"prompt_id", "reward",
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"rows": {name: tensor}}]}`; `<kind>.pending.pt` holds the latest run's capture and
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`<kind>.<prompt id>.pending.pt` the earlier runs' of the SAME Generate.
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A key is a folder, so deleting one is one folder and a kind is one file.
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A capture waits for a rating until the next Generate starts (`new_generation`), then is forgotten:
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nothing is learned from a clip nobody rated. A Generate can be several runs (one per scene that
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starts its own run), so every run of the latest Generate waits, each TAGGED with the ComfyUI prompt
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id it came from -- rating a clip whose capture was dropped is refused and said, never paired with
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the wrong run. Changing your mind on a clip already recorded updates its row instead of adding a
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second one.
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Keys are disposable (retrained per rental), so there is no migration from v4's
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`refinements/` files.
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"""
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import json
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import os
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import re
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import shutil
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import tempfile
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import threading
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import zipfile
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import zlib
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from pathlib import Path
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import torch
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from ..._core import config
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ROOT = config.ROOT / "taste"
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_KEY = re.compile(r"\A[A-Za-z0-9][A-Za-z0-9 _.-]{0,63}\Z")
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_LOCK = threading.RLock()
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REWARD = {"liked": 1.0, "disliked": -1.0}
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# Each rating's vote shrinks by this per later rating: half the vote sits in the
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# last ~7 (v4's RECENCY_DECAY; without it early ratings of one style outvoted a
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# new style until out-rated one-for-one).
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RECENCY_DECAY = 0.9
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# Two liked AND two disliked before a direction exists: one of each is two
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# points, not a direction.
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MIN_PER_GROUP = 2
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# A dislike can say WHICH half went wrong: "image" (the picture was bad) or "composition"
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# (the picture was fine, the shot plan was not). Plain disliked blames both.
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AXES = ("image", "composition")
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MAX_ROWS = 200
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def valid(key) -> bool:
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return isinstance(key, str) and bool(_KEY.match(key)) and not key.endswith((".", " ")) \
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and not key.lower().endswith(".json") # `latest.json` lives beside the keys: a key cannot share its name
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def _dir(key):
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if not valid(key):
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raise ValueError(f"{key!r} is not a usable taste key name")
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return ROOT / key
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def keys():
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if not ROOT.is_dir():
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return []
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return sorted(p.name for p in ROOT.iterdir() if p.is_dir() and valid(p.name))
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def _read(path, default):
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try:
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return torch.load(path, map_location="cpu", weights_only=True)
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except FileNotFoundError:
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return default
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def _write(path, data):
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path.parent.mkdir(parents=True, exist_ok=True)
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tmp = path.with_suffix(path.suffix + ".tmp")
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torch.save(data, tmp)
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os.replace(tmp, path)
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_loaded = {}
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def load(key, kind):
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"""Rated rows of a kind. Re-read only when the file changed: features ask at
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the start of every run, and a REINS log is tens of MB."""
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path = _dir(key) / f"{kind}.pt"
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try:
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stamp = path.stat().st_mtime_ns
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except FileNotFoundError:
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return {"rows": []}
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hit = _loaded.get(path)
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if hit is None or hit[0] != stamp:
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hit = _loaded[path] = (stamp, _read(path, {"rows": []}))
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return hit[1]
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def latest_key():
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"""The key the most recent capture went to, or None."""
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return _read_latest().get("key")
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def kind_path(key, kind):
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"""Where a kind's rated rows live on disk (it may not exist yet)."""
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return _dir(key) / f"{kind}.pt"
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def clear_kind(key, kind):
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"""Forget every rated row and the waiting captures of one kind."""
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kind_path(key, kind).unlink(missing_ok=True)
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for path in [_pending_path(key, kind), *_dir(key).glob(f"{kind}.*.pending.pt")]:
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path.unlink(missing_ok=True)
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def current_prompt_id():
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"""The ComfyUI prompt running now -- set before any node executes."""
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try:
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from server import PromptServer
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return getattr(PromptServer.instance, "last_prompt_id", None)
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except Exception: # noqa: BLE001 -- tests, headless
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return None
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def _latest_path():
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return ROOT / "latest.json"
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MAX_PENDING = 64 # earlier runs of one Generate kept waiting, per kind
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_PID = re.compile(r"\A[A-Za-z0-9_-]{1,80}\Z") # a prompt id names a file: only plain ones may
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def _pending_path(key, kind, prompt_id=None):
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"""The latest run's waiting capture (no id), or an earlier run's of the same Generate."""
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return _dir(key) / (f"{kind}.pending.pt" if prompt_id is None else f"{kind}.{prompt_id}.pending.pt")
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def _prune_aside(key, kind):
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aside = sorted(_dir(key).glob(f"{kind}.*.pending.pt"), key=lambda p: p.stat().st_mtime_ns)
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for old in aside[:-MAX_PENDING]:
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old.unlink(missing_ok=True)
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def capture(key, kind, rows, prompt_id=None, keep=MAX_ROWS, only=None, mixed=False):
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"""This run's capture for `kind`, waiting for a rating until the next Generate starts.
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The previous run's capture of the same Generate is kept aside, not overwritten. Kept in the
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dtype given: a banked latent stays half precision. `keep` caps how many rated rows this kind
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holds, oldest dropped first. `only="liked"`: a kind that learns from liked clips alone does not
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bank a disliked one (it could only push the liked ones out under `keep`). `mixed`: its rows are
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legitimately of different shapes (a clip bank is matched by size), so import does not demand one.
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"""
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if not rows:
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return
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prompt_id = prompt_id or current_prompt_id()
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clean = {str(k): v.detach().cpu() for k, v in rows.items()}
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with _LOCK:
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latest = _pending_path(key, kind)
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before = _read(latest, None)
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earlier = before.get("prompt_id") if before else None
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if earlier is not None and earlier != prompt_id:
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if _PID.match(str(earlier)):
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os.replace(latest, _pending_path(key, kind, earlier))
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_write(latest, {"prompt_id": prompt_id, "rows": clean, "keep": int(keep), "only": only, "mixed": bool(mixed)})
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_prune_aside(key, kind)
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state = _read_latest()
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runs = state.get("runs", {})
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kinds = runs.setdefault(prompt_id, {}).setdefault(key, [])
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if kind not in kinds:
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kinds.append(kind)
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ROOT.mkdir(parents=True, exist_ok=True)
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_latest_path().write_text(json.dumps({"key": key, "prompt_id": prompt_id, "runs": runs, "forgot": state.get("forgot", [])}))
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def _held_by(path, prompt_id):
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"""Whether a waiting capture is `prompt_id`'s. An earlier run's carries its id in the name; only the latest
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(`<kind>.pending.pt`) is opened, and one that cannot be read is nobody's (it must not stop the rest going)."""
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if path.name.count(".") > 2:
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return path.name.split(".")[1] == prompt_id
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try:
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return (_read(path, None) or {}).get("prompt_id") == prompt_id
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except Exception: # noqa: BLE001
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return False
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def new_generation(keep=None):
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"""A Generate is starting: every capture still waiting for a rating is forgotten, but `keep`'s (the run this
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Generate already queued, which may have captured before this call). -> how many were dropped."""
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with _LOCK:
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dropped = 0
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for key in keys():
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for path in _dir(key).glob("*.pending.pt"):
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if keep is not None and _held_by(path, keep):
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continue
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path.unlink(missing_ok=True)
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dropped += 1
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state = _read_latest()
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if state:
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runs = state.get("runs", {})
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# remembered so a late rating can say this was why, and only then
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forgot = [*state.get("forgot", []), *(p for p in runs if p != keep)][-256:]
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_latest_path().write_text(json.dumps({"key": state.get("key"), "runs": {p: v for p, v in runs.items() if p == keep}, "forgot": forgot}))
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return dropped
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def _read_latest():
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try:
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return json.loads(_latest_path().read_text())
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except (FileNotFoundError, ValueError):
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return {}
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def blind(rows, to):
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"""`rows` as a learner that cannot judge the `to` axis sees them: a dislike blamed on
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that axis alone is neutral (reward 0, so it counts as neither liked nor disliked)."""
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if not to:
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return rows
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return [dict(r, reward=0.0) if r.get("axis") == to else r for r in rows]
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def _restore_pending(key, kind, prompt_id, rows, keep, only=None, mixed=False):
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"""A cleared rating puts the capture back to waiting (until the next Generate), so the clip can be
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rated again. The latest slot if it is free or already this run's, else aside under the prompt id."""
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latest = _pending_path(key, kind)
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held = _read(latest, None)
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if held and held.get("prompt_id") not in (None, prompt_id):
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if not _PID.match(str(prompt_id)):
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return
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latest = _pending_path(key, kind, prompt_id)
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_write(latest, {"prompt_id": prompt_id, "rows": rows, "keep": int(keep), "only": only, "mixed": bool(mixed)})
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_prune_aside(key, kind)
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def rate(prompt_id, rating, axis=None):
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"""-> {"recorded": [kinds], "updated": [kinds], "why": str|None}.
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`rating` None clears: the row is removed, nothing is learned from that clip -- and while its
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Generate is still the latest, the capture waits again so it can be rated again.
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`axis` only goes with "disliked" (see AXES); any other rating drops it.
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"""
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if not isinstance(prompt_id, str) or not prompt_id:
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raise ValueError("a rating names the clip it is about (no prompt id was given)")
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if rating is not None and rating not in REWARD:
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raise ValueError(f"{rating!r} is not a rating")
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if axis is not None and axis not in AXES:
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raise ValueError(f"{axis!r} is not a rating axis")
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if axis is not None and rating != "disliked":
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raise ValueError("only a dislike can name what went wrong")
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out = {"recorded": [], "updated": [], "why": None}
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with _LOCK:
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state = _read_latest()
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entry = state.get("runs", {}).get(prompt_id) or {}
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# A clip already recorded: change or remove its row, in every key/kind.
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for key in keys():
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for path in _dir(key).glob("*.pt"):
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if path.name.endswith(".pending.pt"):
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continue
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data = _read(path, {"rows": []})
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hit = [r for r in data["rows"] if r.get("prompt_id") == prompt_id]
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if not hit:
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continue
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if rating is None or (hit[-1].get("only") == "liked" and rating != "liked"):
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# a liked-only bank holds no dislike: it leaves, and may come back if liked again
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data["rows"] = [r for r in data["rows"] if r.get("prompt_id") != prompt_id]
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if path.stem in entry.get(key, []):
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_restore_pending(key, path.stem, prompt_id, hit[-1]["rows"], hit[-1].get("keep", MAX_ROWS),
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hit[-1].get("only"), hit[-1].get("mixed", False))
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else:
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for r in hit:
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r["reward"] = REWARD[rating]
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r.pop("axis", None)
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if axis:
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r["axis"] = axis
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_write(path, data)
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out["updated"].append(path.stem)
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if out["updated"]:
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return out
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if not entry:
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out["reason"] = "forgotten" if prompt_id in state.get("forgot", []) else "nothing" # a label the app can act on; `why` is for people
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out["why"] = ("a new Generate started before this clip was rated, and unrated clips are forgotten then"
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if out["reason"] == "forgotten" else
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"this run captured nothing: no learning feature acted on it, or ComfyUI reused an "
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"earlier result instead of sampling again")
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return out
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if rating is None:
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return out
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for key, kinds in entry.items():
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for kind in kinds:
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where = [_pending_path(key, kind)]
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if _PID.match(str(prompt_id)):
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where.append(_pending_path(key, kind, prompt_id))
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for pending_path in where:
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pending = _read(pending_path, None)
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if pending and pending.get("prompt_id") == prompt_id:
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break
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else:
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continue
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data = load(key, kind)
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keep = int(pending.get("keep", MAX_ROWS))
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if pending.get("only") == "liked" and rating != "liked":
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# nothing here learns from it, but a changed mind may still like it
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out.setdefault("skipped", []).append(kind)
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continue
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row = {"prompt_id": prompt_id, "reward": REWARD[rating], "rows": pending["rows"], "keep": keep,
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"only": pending.get("only"), "mixed": bool(pending.get("mixed"))}
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if axis:
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row["axis"] = axis
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data["rows"].append(row)
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data["rows"] = data["rows"][-keep:]
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_write(_dir(key) / f"{kind}.pt", data)
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pending_path.unlink(missing_ok=True)
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out["recorded"].append(kind)
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if not out["recorded"] and not out.get("skipped"):
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out["why"] = ("this clip's capture was not kept (more runs waited than are kept, or it was "
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"cleared); nothing was learned")
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return out
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def counts(key, kind, name=None):
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"""(liked, disliked) rows for a kind, optionally only rows holding `name`."""
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rows = [r for r in load(key, kind)["rows"] if name is None or str(name) in r["rows"]]
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return (sum(r["reward"] > 0 for r in rows), sum(r["reward"] < 0 for r in rows))
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def direction(key, kind, name):
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"""-> (unit vector | None, n_liked, n_disliked).
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Recency-weighted mean(liked) - mean(disliked) over the rows holding `name`,
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split by the SIGN of the rating only. Raw rows, not normalised: a hidden
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state's magnitude carries content (v4's trajectory-probe lesson).
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"""
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name = str(name)
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rows = [r for r in load(key, kind)["rows"] if name in r["rows"]]
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aged = [(r["rows"][name], r["reward"], RECENCY_DECAY ** (len(rows) - 1 - i))
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for i, r in enumerate(rows)]
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liked = [(d, a) for d, w, a in aged if w > 0]
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disliked = [(d, a) for d, w, a in aged if w < 0]
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if len(liked) < MIN_PER_GROUP or len(disliked) < MIN_PER_GROUP:
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return None, len(liked), len(disliked)
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def wmean(group):
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ds = torch.stack([d for d, _ in group]).float()
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a = torch.tensor([a for _, a in group], dtype=ds.dtype)
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return (ds * a.view(-1, *([1] * (ds.dim() - 1)))).sum(0) / a.sum()
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diff = wmean(liked) - wmean(disliked)
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norm = diff.norm()
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if not torch.isfinite(norm) or norm <= 1e-8:
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return None, len(liked), len(disliked)
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return diff / norm, len(liked), len(disliked)
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def _forget_key_runs(key):
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"""The key's waiting runs died with its folder; other keys' runs stay ratable."""
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state = _read_latest()
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if not state:
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return
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runs = {}
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for pid, per_key in state.get("runs", {}).items():
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left = {k: v for k, v in per_key.items() if k != key}
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if left:
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runs[pid] = left
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state["runs"] = runs
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if state.get("key") == key:
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state["key"] = None
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_latest_path().write_text(json.dumps(state))
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def delete(key):
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with _LOCK:
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shutil.rmtree(_dir(key), ignore_errors=True)
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_forget_key_runs(key)
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# --- moving a key between machines -------------------------------------------
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#
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# A key is a folder of `<kind>.pt` files, so exporting is a zip of them. Importing reads a zip a
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# stranger may have made: only plainly named `.pt` files, bounded in count and size, each one
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# loadable with weights_only (no pickled code) before anything touches the real folder.
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_KIND_FILE = re.compile(r"\A[A-Za-z0-9_]{1,48}\.pt\Z")
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MAX_FILES = 64
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MAX_BYTES = 4 * 1024 ** 3
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def export_key(key, out_path):
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"""Zip `key`'s rated rows into `out_path` (waiting captures are not part of what was learned)."""
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folder = _dir(key)
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if not folder.is_dir():
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raise ValueError(f"there is no taste key called {key!r}")
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with _LOCK:
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found = [p for p in sorted(folder.glob("*.pt")) if _KIND_FILE.match(p.name)]
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if not found:
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raise ValueError(f"{key!r} has not learned anything yet: there is nothing to export")
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with zipfile.ZipFile(out_path, "w", zipfile.ZIP_STORED) as zf:
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for path in found:
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zf.write(path, path.name)
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return out_path
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def _sound_rows(data):
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"""True when `data` is what the learners consume: a dict of rated rows, each with a number
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for a reward and tensors for what was captured. Loading it safely is not enough -- a row of
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the wrong shape would break every later rating."""
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if not isinstance(data, dict) or not isinstance(data.get("rows"), list):
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return False
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shapes = {}
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for row in data["rows"]:
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if not isinstance(row, dict) or isinstance(row.get("reward"), bool) or not isinstance(row.get("reward"), (int, float)):
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return False
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if not isinstance(row.get("prompt_id"), str) or not row["prompt_id"]:
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return False
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if row.get("axis") not in (None, *AXES):
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return False
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captured = row.get("rows")
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if not isinstance(captured, dict) or not all(isinstance(v, torch.Tensor) for v in captured.values()):
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return False
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# What is stacked later must agree: one name, one shape (a key trained across two model
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# families would otherwise abort every sampling that reads it).
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if row.get("mixed") is True:
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continue # a clip bank: rows of different sizes are the point
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for name, v in captured.items():
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if shapes.setdefault(name, tuple(v.shape)) != tuple(v.shape):
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return False
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return True
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def import_key(key, zip_path, overwrite=False):
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"""-> number of kinds imported. FileExistsError if `key` exists and `overwrite` is off."""
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dest = _dir(key)
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try:
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zf = zipfile.ZipFile(zip_path)
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except zipfile.BadZipFile as exc:
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raise ValueError("that is not an exported taste key (not a zip file)") from exc
|
|
with zf:
|
|
infos = zf.infolist()
|
|
if not infos or len(infos) > MAX_FILES:
|
|
raise ValueError(f"an exported key holds 1 to {MAX_FILES} files, this has {len(infos)}")
|
|
for info in infos:
|
|
if not _KIND_FILE.match(info.filename):
|
|
raise ValueError(f"{info.filename!r} is not part of a taste key")
|
|
if sum(i.file_size for i in infos) > MAX_BYTES:
|
|
raise ValueError("that key is larger than 4 GB")
|
|
ROOT.mkdir(parents=True, exist_ok=True)
|
|
stage = Path(tempfile.mkdtemp(prefix=".import-", dir=ROOT))
|
|
try:
|
|
for info in infos:
|
|
target = stage / info.filename
|
|
try:
|
|
with zf.open(info) as src, open(target, "wb") as out:
|
|
shutil.copyfileobj(src, out)
|
|
except (zipfile.BadZipFile, zlib.error, RuntimeError, NotImplementedError, EOFError) as exc:
|
|
raise ValueError(f"{info.filename} cannot be read from the zip ({type(exc).__name__}): "
|
|
f"it is damaged, encrypted or compressed in an unsupported way") from exc
|
|
try:
|
|
data = torch.load(target, map_location="cpu", weights_only=True)
|
|
except Exception as exc: # noqa: BLE001
|
|
raise ValueError(f"{info.filename} is not a readable taste file ({type(exc).__name__})") from exc
|
|
if not _sound_rows(data):
|
|
raise ValueError(f"{info.filename} does not hold rated rows of the shape FunUI reads")
|
|
with _LOCK:
|
|
if dest.exists():
|
|
if not overwrite:
|
|
raise FileExistsError(key)
|
|
shutil.rmtree(dest)
|
|
os.replace(stage, dest)
|
|
# Anything waiting to be rated under this name died with the old folder.
|
|
_forget_key_runs(key)
|
|
return len(infos)
|
|
finally:
|
|
shutil.rmtree(stage, ignore_errors=True)
|