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@@ -0,0 +1,60 @@
|
||||
"""PostToolUse hook: regenerate README.index.md / DEVLOG.index.md right after
|
||||
Claude Code edits README.md or DEVLOG.md, so graphify's excluded-doc index
|
||||
stand-ins (see .graphifyignore) never drift from the source files within a
|
||||
session. A git pre-commit hook covers edits made outside Claude Code.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
TARGETS = {"readme.md": "readme", "devlog.md": "devlog"}
|
||||
|
||||
|
||||
def main() -> None:
|
||||
try:
|
||||
payload = json.load(sys.stdin)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
return
|
||||
|
||||
if payload.get("tool_name") not in ("Edit", "Write"):
|
||||
return
|
||||
|
||||
file_path = payload.get("tool_input", {}).get("file_path", "")
|
||||
if not file_path:
|
||||
return
|
||||
|
||||
name = Path(file_path).name.lower()
|
||||
target = TARGETS.get(name)
|
||||
if target is None:
|
||||
return
|
||||
|
||||
project_dir = os.environ.get("CLAUDE_PROJECT_DIR") or str(Path(file_path).resolve().parent)
|
||||
script = Path(project_dir) / "tools" / "generate_doc_index.py"
|
||||
if not script.exists():
|
||||
return
|
||||
|
||||
result = subprocess.run(
|
||||
[sys.executable, str(script), target],
|
||||
cwd=project_dir,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=30,
|
||||
)
|
||||
if result.returncode == 0:
|
||||
print(json.dumps({"systemMessage": f"[doc-index] {result.stdout.strip()}"}))
|
||||
else:
|
||||
print(
|
||||
json.dumps(
|
||||
{"systemMessage": f"[doc-index] generate_doc_index.py {target} failed: {result.stderr.strip()}"}
|
||||
),
|
||||
file=sys.stderr,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,36 @@
|
||||
{
|
||||
"hooks": {
|
||||
"PreToolUse": [
|
||||
{
|
||||
"matcher": "Bash|Grep",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "C:/Users/statsu-11/.local/bin/graphify.EXE hook-guard search"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"matcher": "Read|Glob",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "C:/Users/statsu-11/.local/bin/graphify.EXE hook-guard read"
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"PostToolUse": [
|
||||
{
|
||||
"matcher": "Edit|Write",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python \"$CLAUDE_PROJECT_DIR/.claude/hooks/on_doc_edit.py\"",
|
||||
"timeout": 30
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
# graphify: keep graph.json/graphify-out/ out of Claude Code's automatic
|
||||
# context so re-running `graphify extract` doesn't churn the prompt cache
|
||||
# on every rebuild. These are still tracked by git (see .gitignore).
|
||||
graph.json
|
||||
graphify-out/
|
||||
@@ -0,0 +1,11 @@
|
||||
# ComfyUI-Workflow-Studio - Unsloth backend (AI TOOL tab / sidebar A tab)
|
||||
#
|
||||
# Unsloth Desktop requires an API key even for local access
|
||||
# (Settings -> API in the Unsloth app, shown only once when issued).
|
||||
#
|
||||
# 1. Copy this file to ".env" (same directory as this example file)
|
||||
# 2. Paste your key after the "=" below (do NOT add a leading #, or the
|
||||
# line becomes a comment and the key will not be loaded)
|
||||
# 3. Restart ComfyUI
|
||||
|
||||
UNSLOTH_API_KEY=
|
||||
@@ -5,3 +5,12 @@ __pycache__/
|
||||
.venv
|
||||
*.egg-info/
|
||||
data/
|
||||
|
||||
# graphify: keep the knowledge graph itself under version control,
|
||||
# but exclude the run-by-run cost/token log, the local extraction
|
||||
# cache (both churn every rebuild and have no value to other clones),
|
||||
# and the machine-local interpreter/scan-root pointers.
|
||||
graphify-out/cost.json
|
||||
graphify-out/cache/
|
||||
graphify-out/.graphify_python
|
||||
graphify-out/.graphify_root
|
||||
|
||||
@@ -0,0 +1,10 @@
|
||||
# Private/unpublished planning notes — never committed to git, must not leak
|
||||
# into the published graphify-out/ knowledge graph either.
|
||||
QWEN_CHAT_INTEGRATION_PLAN.md
|
||||
|
||||
# Too large for reliable local-Ollama semantic extraction (large chunks make the
|
||||
# model summarize in prose instead of emitting JSON, and time out on slower
|
||||
# models/hardware). README.index.md / DEVLOG.index.md are condensed stand-ins
|
||||
# that graphify extracts instead — regenerate them after editing the originals.
|
||||
README.md
|
||||
DEVLOG.md
|
||||
@@ -194,9 +194,34 @@ os.environ["HF_HOME"] = "D:\\hf_cache"
|
||||
|
||||
1. `pyproject.toml` の `version` を新バージョン番号に更新してステージングに含める
|
||||
2. `README.md` のバージョンバッジを更新（``）
|
||||
3. `README.md` の Changelog に新バージョンのエントリを追加
|
||||
4. `DEVLOG.md` の先頭に変更詳細エントリを追加
|
||||
5. 上記すべてを同一コミットにまとめてプッシュ
|
||||
6. `gh release create vX.Y.Z` でリリースを作成
|
||||
3. `DEVLOG.md` の先頭に変更詳細エントリを追加
|
||||
4. 上記すべてを同一コミットにまとめてプッシュ
|
||||
5. `gh release create vX.Y.Z` でリリースを作成（リリースノート本文に変更点を記載する — v0.3.90でREADME.mdのChangelogセクションは廃止したため、以後のバージョン履歴はGitHub Releaseのリリースノートのみで管理する）
|
||||
|
||||
`pyproject.toml` のバージョン変更が GitHub Actions のトリガー（`paths: pyproject.toml`）になっており、ComfyUI Registry への自動公開もここで行われる。バージョン更新を忘れると Registry に反映されない。
|
||||
|
||||
<!-- graphify運用は一時停止中（2026-08-23〜、ユーザー指示により凍結）。再開時はこのコメントブロックを解除すること。graphify-out/ 自体は削除しておらずそのまま残っている。
|
||||
|
||||
## graphify
|
||||
|
||||
This project has a knowledge graph at graphify-out/ with god nodes, community structure, and cross-file relationships.
|
||||
|
||||
Rules:
|
||||
- For codebase questions, first run `graphify query "<question>"` when graphify-out/graph.json exists. Use `graphify path "<A>" "<B>"` for relationships and `graphify explain "<concept>"` for focused concepts. These return a scoped subgraph, usually much smaller than GRAPH_REPORT.md or raw grep output.
|
||||
- If graphify-out/wiki/index.md exists, use it for broad navigation instead of raw source browsing.
|
||||
- Read graphify-out/GRAPH_REPORT.md only for broad architecture review or when query/path/explain do not surface enough context.
|
||||
- After modifying code, run `graphify update .` to keep the graph current (AST-only, no API cost).
|
||||
|
||||
### README.md / DEVLOG.md の索引ファイル
|
||||
|
||||
`README.md`/`DEVLOG.md`はサイズが大きすぎてローカルOllamaでの意味抽出が不安定になる（大きな塊を渡すとJSONではなく散文の要約を返す、または処理がタイムアウトする）ため、`.graphifyignore`でgraphifyの抽出対象から除外している。代わりに縮小版の`README.index.md`/`DEVLOG.index.md`を抽出対象にしている。
|
||||
|
||||
- 生成スクリプト: `python tools/generate_doc_index.py [readme|devlog|all]`
|
||||
- 自動化: `README.md`/`DEVLOG.md`を編集すると以下の2つのフックが自動でindexを再生成する
|
||||
- Claude Code PostToolUseフック（`.claude/hooks/on_doc_edit.py`）— Edit/Write直後に即時実行
|
||||
- git pre-commitフック（`.git/hooks/pre-commit`）— コミット時にstagedファイルを検知して再生成し、同じコミットに含める（他エディタでの編集にも対応）
|
||||
- 索引ファイルを手動で編集しない（次の生成で上書きされる）
|
||||
- 索引ファイル再生成後は`graphify update .`を実行してグラフに反映する
|
||||
|
||||
-->
|
||||
|
||||
|
||||
+169
@@ -0,0 +1,169 @@
|
||||
# DEVLOG Index — ComfyUI-Workflow-Studio
|
||||
|
||||
Condensed version-by-version index of DEVLOG.md (each line: version + one-line summary of what changed and why). Full rationale, code details, and "How to apply" lessons live in the excluded DEVLOG.md itself; this index exists so graphify can extract a queryable semantic node per release without feeding the full log to the local Ollama model. Regenerate with `python tools/generate_doc_index.py devlog` whenever DEVLOG.md changes.
|
||||
|
||||
- **v0.5.2** — 機能追加: GenerateUI/Labタブの動画ワークフロー対応 + VideoタブにFrame抽出/GIF変換機能 + データ管理監査とZIPフルバックアップ + Send to LI node統一
|
||||
- **v0.5.1** — 機能追加: mp4動画の埋め込みメタデータ対応（Gallery Prompt/JSON・Metadataサブタブ・サイドパネルIタブ）+ Gallery Infoタブに寸法/再生時間表示 + MiniMax H3等オールインワン動画ノードのプロンプト抽出バグ修正
|
||||
- **v0.5.0** — 機能追加: mp4動画Gallery対応 + Videoタブ新設(MiniMax H3専用) + バグ修正: Galleryのタグ/メモが再起動後に消える
|
||||
- **v0.4.8** — 機能追加: LabタブにPromptグループ一括適用＋キーフレーム並べ替え + バグ修正: デフォルトワークフローの上書き保存が反映されない + Styleテーブルの「このファイルへ追加」対応
|
||||
- **v0.4.7** — 機能追加: Prompt Tab内部リファクタ + Gallery Promptタブ新設 + Send to Canvas改善 + Presetsソート
|
||||
- **v0.4.6** — 機能追加: MetadataタブをGalleryタブへ統合 + Promptタブに一覧編集可能なTable表示を新設
|
||||
- **v0.4.5** — 調査: トップバー保存ワークフローのRMBG不具合報告（原因なし） + バグ修正: LabタブModel列の無意味な空キーフレーム表示を解消
|
||||
- **v0.4.4** — 機能追加: Lab/GenerateUI ModelタブへLoRAバイパス機能 + AI TOOL Chatの画像生成パラメータ拡張
|
||||
- **v0.4.3** — バグ修正: 共有モーダルのお気に入り☆/リネームがLabモーダルへ残留 + Lab Model列の＋／－をCheckpoint/LoRA/VAEで完全独立化
|
||||
- **v0.4.2** — 機能追加: Lab「Model」列統合（Checkpoint / LoRA Single / VAE）+ フィールド単位継承 + C/L/V表示切替
|
||||
- **v0.4.1** — UX改善: Mask Editor One連携ボタンを「選択中ノード優先」方式に再設計
|
||||
- **v0.4.0** — リファクタリング: image-edit-tab.js / models-tab.js の低凝集度モジュール分割
|
||||
- **v0.3.99** — 機能追加: AI TOOLタブ + サイドパネルAタブにUnslothバックエンド追加（APIキーは.env管理、SSRF脆弱性修正、reasoning_content対応）
|
||||
- **v0.3.98** — 機能追加・バグ修正: AI TOOL翻訳の信頼性向上 + GenerateUI Modelタブ再構成（ハイライト色・折りたたみ・並び替え）+ Thinking mode/Max tokens設定
|
||||
- **v0.3.97** — 機能追加・バグ修正: KREA-2ワークフロー対応 + Labタブ拡張（Clear/Get from Previous/Bypass/ライブ反映）+ Input Imageプレースホルダ機能
|
||||
- **v0.3.96** — バグ修正: カタログ作成バッチが特定件数で無言停止する問題の修正 + Style Catalog/Promptタブ機能拡張
|
||||
- **v0.3.95** — 機能追加: Style/Prompt→ImagePrompt改名 + Style_Catalogギャラリー新設 + GenerateUI「カタログ作成」機能
|
||||
- **v0.3.94** — 機能追加: Gallery タブのサブタブ化 + Style/Prompt ギャラリー新設（視覚的プロンプトビルダー）
|
||||
- **v0.3.93** — バグ修正・機能改善: Labタブ VAEキーフレーム不発バグの修正＋Style適用UI刷新＋Plan JSONスクロール同期修正＋キーフレーム個別削除＋Batch数超過警告＋ワイルドカード対応＋Save index image デフォルトON
|
||||
- **v0.3.92** — バグ修正: CLIPTextEncodeEditPlus使用ワークフローがGenerateUI経由だと直接実行と異なる画像になる問題（根本原因はconvertUiToApiのForceInput 未考慮バグ）＋LoRA Stack機能の改善一式
|
||||
- **v0.3.91** — 機能拡張: Labインデックス画像にPlanメタデータをPNG埋め込み＋README構成整理（Changelogセクション廃止）
|
||||
- **v0.3.90** — 機能拡張: Labタブの進化（ワークフロー呼び出し・T2I対応・Run時インデックス画像Output保存＋Eagle連携・プラン接頭辞）＋Galleryタブのルート表示簡略化
|
||||
- **v0.3.89** — バグ修正: v0.3.88で追加したLab機能への実動作確認フィードバック3件を反映
|
||||
- **v0.3.88** — 機能拡張: Labタブ Promptセルにスタイル適用・画像/GenerateUIからのプロンプト取得、Checkpoint/VAEフィルター、Plan JSONサブタブを追加
|
||||
- **v0.3.87** — 追加機能: GenerateUIタブに実験的な「Lab」サブタブを追加（I2Iバッチ生成・キーフレーム方式）
|
||||
- **v0.3.86** — バグ修正: 画像を一括生成（I2I）等の連続実行で "GalleryMetadataStore: save error: dictionary changed size during iteration" が発生
|
||||
- **v0.3.85** — 機能拡張: Generate UI連携ブリッジ（T2I）にデフォルトワークフロー・Negativeプロンプト対応を追加
|
||||
- **v0.3.84** — バグ修正: ernie_t2i.jsonをGenerateUIタブで実行すると "Prompt outputs failed validation" エラー
|
||||
- **v0.3.83** — 追加機能: Ernie Image（ernie-image / ernie-image-turbo）ワークフロー対応 + プロンプトがリンク経由のノードから供給されるパターンの修正
|
||||
- **v0.3.82** — バグ修正: MetadataタブとサイドパネルIタブでLongCat/Boogu/HiDream E1/FireRed等のプロンプトが表示されない不具合
|
||||
- **v0.3.81** — 追加機能: GenerateUIタブでFlux.2 Klein / LongCat / Boogu / HiDream E1 / FireRedのImage Editワークフローに対応
|
||||
- **v0.3.80** — 追加機能: Comic Creator「半自動マンガ作成」向けブリッジ（LLMプロンプト下書き・バッチ画像生成）
|
||||
- **v0.3.79** — 追加機能: サイドパネル（Workflow Studio Library）IタブのMage-Flow等サブグラフワークフロー対応
|
||||
- **v0.3.78** — 追加機能: Eagle自動保存のSVG対応
|
||||
- **v0.3.76** — 追加機能: Comic Creator向けI2I実行ブリッジ`_wfmReceiveI2IRunRequest`
|
||||
- **v0.3.77** — 追加機能: GalleryタブでSVGファイルの表示・管理に対応
|
||||
- **v0.3.75** — Lemonadeバックエンド追加(3つ目のAIバックエンド)
|
||||
- **v0.3.74** — AI TOOL Chatペインの画像添付対応 + I2I連携
|
||||
- **v0.3.73** — AI TOOL Chatペインからの対話式画像生成（Tool Calling）＋ Checkpoint関連の安全策
|
||||
- **v0.3.72** — Comic Creator連携用の外部Inpaintエントリポイントを追加
|
||||
- **v0.3.71** — Inpaint機能の追加（GenerateUI Imageタブ + Image Edit「Inpaint」タブ）
|
||||
- **v0.3.70** — Workflow AI要約: Ollama 404エラー修正
|
||||
- **v0.3.69** — Gallery → ComfyUI Comic Creator 連携（Send CC ボタン）
|
||||
- **v0.3.68** — MASK EDITOR ONE セクション常時表示化
|
||||
- **v0.3.67** — Alt+Click Apply & Generate
|
||||
- **計画: Image Edit 拡張 — ABRブラシ連携 + Mask Editor One 統合** — フェーズ1: Draw ツール — ABRブラシ対応
|
||||
- **2026-06-29: v0.3.66 — Mask ツール 5サブツール追加（Color / Alpha / Text / Vector / Shape）・Support リンク追加** — 概要
|
||||
- **2026-06-29: v0.3.65 — Mask Editor One 連携修正・トップバーアイコン修正・Mask ABRブラシ対応** — 概要
|
||||
- **2026-06-28: v0.3.64 — Image Edit マスク Add/Subtract・BiRefNet BG Remove・SAM3 セグメンテーション・ブラシカーソルバグ修正** — 概要
|
||||
- **2026-06-28: v0.3.63 — ノードタブ複数選択・全タブフィルタークリア・検索Xボタン** — 概要
|
||||
- **2026-06-27: WFS_PromptText の GenerateUI 対応・ドロップ時 title 固定** — 概要
|
||||
- **2026-06-27: v0.3.62 — Send GenUI Image ボタン追加・tagger_settings.json バグ修正・README 更新** — 概要
|
||||
- **2026-06-27: データ管理バグ修正 — tagger_settings.json をエクスポート対象に追加** — 概要
|
||||
- **2026-06-27: v0.3.61 — G'MIC バグ修正・セキュリティ・Draw/Mask ブラシ操作性改善** — 概要
|
||||
- **2026-06-27: v0.3.60 — Image Edit Filter Tool (G'MIC-Qt 連携) 実装** — 概要
|
||||
- **2026-06-27: v0.3.59 — Image Edit Mask Tool 実装・Draw/Mask 描画バグ修正・クリッピング保存対応** — 概要
|
||||
- **2026-06-27: v0.3.58 — GenerateUI Model タブ GGUF対応拡張（Diffusion Model / Text Encoder）** — 概要
|
||||
- **2026-06-26: v0.3.57 — Image Edit Blur Tool / BG Remove Tool 追加** — 概要
|
||||
- **2026-06-26: v0.3.56 — Style機能追加（GenerateUI ツールバー & Batchタブ）** — 概要
|
||||
- **2026-06-25: v0.3.55 — グループ機能バグ修正（全タブ）** — 根本原因（データ消失）: パス区切り文字の不一致
|
||||
- **2026-06-25: v0.3.54 — Image Edit Shape ツール追加・Models タブ overlay バグ修正** — Image Edit Tab — Shape ツール（□ / S キー）
|
||||
- **2026-06-25: v0.3.53 — Image Edit Tab 実装・レイヤーロック・ギャラリー保存** — Image Edit Tab（新規タブ）
|
||||
- **2026-06-25: GenUI設定タブ LATENT IMAGE にプリセット機能を追加** — 変更内容
|
||||
- **2026-06-24: GenUI ModelでLoRA適用後、Modelサブタブ再表示時に値がリセットされるバグ修正** — 問題
|
||||
- **2026-06-24: v0.3.51 — Embedding GenUI PP/NP対応 + GenerateUIプロンプトタブEmbeddingセレクタ追加** — 変更内容
|
||||
- **2026-06-24: v0.3.51 — GenerateUIタブ Hypernetwork対応 + ModelsタブGenUI Modelボタン追加** — 変更内容
|
||||
- **2026-06-24: v0.3.51 — Hypernetwork/Embeddingモデル未表示バグ修正** — 原因
|
||||
- **2026-06-24: v0.3.51 — ModelsタブサイドパネルにGenUI Modelボタン追加** — 変更内容
|
||||
- **2026-06-22: v0.3.50 — ギャラリータブ プロンプト検索負荷改善（バックグラウンドインデックス）** — 問題
|
||||
- **2026-06-22: v0.3.49 — 生成UIタブ A1111風ワイルドカード展開・ギャラリータブ バグ修正2件** — 生成UIタブ — A1111風SPAサイドワイルドカード展開
|
||||
- **2026-06-22: v0.3.48 — ギャラリータブ 画像ダウンロード・一括エクスポート機能追加** — 詳細パネルの画像プレビューにダウンロード機能を追加
|
||||
- **2026-06-22: v0.3.47 — モデルタブ ファイル移動後グループ状態修正** — モデル移動後のグループ状態不整合を修正
|
||||
- **2026-06-21: v0.3.46 — プロンプトタブ AI Assistant LM Studio対応・設定タブ AI Assistant設定に変更** — 設定タブ「AI Assistant設定（プロンプトタブ）」
|
||||
- **2026-06-21: v0.3.45 — グループ孤立エントリ自動クリーンアップ・Galleryタブ Shift+クリック範囲選択** — グループ孤立エントリ自動クリーンアップ
|
||||
- **2026-06-21: v0.3.44 — Galleryタブ高速化・比較モード・バルク強化・バルクバーUI整理** — フェーズ2-A: サーバーサイドサムネイル生成
|
||||
- **2026-06-21: ヘルプタブ改善 — Gallery mode 多言語対応・フォント拡大・検索機能追加** — Gallery mode の多言語対応
|
||||
- **2026-06-21: Send to Canvas — window.opener経由のキャンバス直接ロード対応**
|
||||
- **2026-06-20: API形式ワークフローのキャンバス読み込み修正・画像メタデータ埋め込み修正** — API形式ワークフローをキャンバスへドラッグすると空になる問題を修正
|
||||
- **2026-06-20: セキュリティ修正（XSS・パスバリデーション）** — 修正内容
|
||||
- **2026-06-20: WFS_GalleryFeeder — ComfyUI キャンバス上コントロール追加** — 新規ファイル（`web/comfyui/gallery_feeder_extension.js`）
|
||||
- **2026-06-20: __Feeder__ グループ保護 + サムネイル F ボタン + seed バグ修正** — `__Feeder__` グループの予約済み保護
|
||||
- **2026-06-20: Gallery Feeder 機能追加（WFS_GalleryFeeder ノード）** — 概要
|
||||
- **2026-06-19: Generate UI — ImpactWildcardEncode/Processor プロンプト検出バグ修正（v0.3.41）** — 修正内容（`static/js/comfyui-workflow.js`）
|
||||
- **2026-06-19: AI TOOL タブ Chat ペイン・ワイルドカード生成追加（v0.3.40）** — Chat ペイン（`templates/index.html`, `static/css/main.css`, `static/js/ai-tab.js`, `web/comfyui/node_sets_menu.js`）
|
||||
- **2026-06-18: Send to Canvas機能追加（ワークフロータブ・ギャラリータブ→LibraryタイトルDnD）** — ワークフロータブ — Send to Canvas（`static/js/workflow-tab.js`, `static/js/app.js`, `templates/index.html`）
|
||||
- **2026-06-15: TaggerシングルGenUI:Pボタン追加＋生成UIのZITワークフロープロンプト解析修正** — TaggerシングルGenUI:Pボタン（`templates/index.html`, `static/js/tagger-tab.js`, `static/js/i18n.js`）
|
||||
- **2026-06-15: Taggerバッチ.txt出力追加＋Galleryタグ保存バグ修正** — Taggerバッチ.txt出力（`templates/index.html`, `static/js/tagger-tab.js`, `static/js/i18n.js`, `py/routes/tagger_routes.py`, `py/services/tagger_service.py`）
|
||||
- **2026-06-15: Taggerタブ新規実装＋ギャラリー詳細パネルUI改善＋パストラバーサル修正** — Taggerタブ新規実装
|
||||
- **2026-06-15: 全選択ボタン追加＋ギャラリーバルクバーi18n対応** — 変更内容
|
||||
- **2026-06-10: v0.3.36 — コードレビュー修正＋リファクタリング＋README刷新** — コードレビュー修正（v0.3.35の不具合10件）
|
||||
- **2026-06-10: v0.3.35 — テーブルソート・Load GenUI・ワークフロー保存** — 変更内容
|
||||
- **2026-06-09: v0.3.34 — Modelsタブ テーブルビュー強化・バルク操作改善** — 変更内容
|
||||
- **2026-06-09: v0.3.33 — LoRAペイン・ワークフロー解析バグ修正** — 変更内容
|
||||
- **2026-06-08: v0.3.32 — Lora Loader (LoraManager) LoRA検出対応・ライブラリ機能拡張** — 変更内容
|
||||
- **2026-06-08: v0.3.31 — LoRAペイン Single/Stack タブ分割・GenUI Model LoRA対応強化** — 変更内容
|
||||
- **2026-06-08: v0.3.30 — LoRAスタック トリガーワードバグ修正・ヘルプ更新** — 変更内容
|
||||
- **2026-06-08: v0.3.29 — LoRAセクション統合・ヘルプタブサイドバー・UI細部改善** — 変更内容
|
||||
- **2026-06-08: 生成UIタブ 複数改善 — LoRA Stackトリガーワード連携・Reset Workflowボタン・Inputタブタブ化** — 変更内容
|
||||
- **2026-06-08: Feederタブ UI改善 — (root)自動選択・PREVIEWペイン右移動・RUN中プレビュー反映** — 変更内容
|
||||
- **2026-06-07: v0.3.28 — Batchタブ Sampler/Scheduler対応 + Modelsサムネイルバグ修正** — 概要
|
||||
- **2026-06-07: v0.3.27 — model-and-prompt-from-metadata カスタムノード対応 + README修正** — 概要
|
||||
- **2026-06-07: v0.3.26 — Stack LORA SYNTAX 表示修正 + 全体強度調整 UI** — 概要
|
||||
- **2026-06-07: v0.3.25 — Lora Loader (LoraManager) 対応 + Stack 有効無効切り替え** — 概要
|
||||
- **2026-06-06: v0.3.24 — Lora Stack 登録機能 + 生成UI Model タブ 3ペイン化** — 概要
|
||||
- **2026-06-05: v0.3.23 — バグ修正2件** — バグ修正
|
||||
- **2026-06-05: v0.3.22 — カードビュー廃止 + Batch タイプ切り替え UI + 4タイプバッチ生成** — 概要
|
||||
- **2026-06-05: (未リリース) — 生成UIタブ Batch 4タイプ対応（Lora/Prompt/Workflow グループ選択 + BATCH QUEUE 4列化）** — 概要
|
||||
- **2026-06-05: (未リリース) — Prompt・Workflow Batchグループ登録UI** — 概要
|
||||
- **2026-06-04: (未リリース) — モデルBatchグループ登録UI（サムネイル/カード/テーブル・フィルター・一括解除）** — 概要
|
||||
- **2026-06-04: (未リリース) — Batch予約グループ・Presets Save後リセット・グループフィルター種別絞り込み** — 概要
|
||||
- **2026-05-31: v0.3.21 — CivitAI ホスト設定・Sample サブタブ・URL 修正・ヘルプ i18n 整備** — 概要
|
||||
- **2026-05-31: v0.3.20 — CivitAI 詳細パネル強化（Type / Hash 表示・URL 修正・画像別タブ）** — 概要
|
||||
- **2026-05-31: v0.3.19 — CivitAI 連携強化（バッチ高速化・リトライ・APIキー・フィールド拡充）** — 概要
|
||||
- **2026-05-31: v0.3.18 — Batch タブ追加・Models 複数選択強化** — 概要
|
||||
- **2026-05-31: v0.3.17 patch — SSLコンテキスト セキュリティ強化** — 概要
|
||||
- **2026-05-30: v0.3.17 — SSL証明書エラー修正・Lora Manager対応** — 概要
|
||||
- **2026-05-20: v0.3.16 — CivitAIプレビューフォールバック表示** — 概要
|
||||
- **2026-05-20: v0.3.15 — サンプルワークフロー同梱・CivitAI自動プレビュー・Create Tagsオプション** — 概要
|
||||
- **2026-05-20: AI TOOL タブ 英語化（SPA・Libraryパネル）** — 概要
|
||||
- **2026-05-19: v0.3.14 — AI TOOL タブ 3ペイン化・RAW JSON 文字色カスタマイズ** — 概要
|
||||
- **2026-05-19: v0.3.13 — AI タブ（A）追加** — 概要
|
||||
- **2026-05-18: v0.3.12 リリース — README スクリーンショット刷新 / workflow_analyzer 拡張** — 概要
|
||||
- **2026-05-17: RAW JSON パネルに検索機能を追加** — 概要
|
||||
- **2026-05-17: v0.3.11 Metadata タブ対応ノードタイプ拡張 / Settings テキストサイズ UI 追加** — 概要
|
||||
- **2026-05-17: v0.3.10 Information タブ キャンバスドラッグ対応 / ノードタイプ拡張 / バグ修正** — 概要
|
||||
- **2026-05-16: v0.3.9 サイドパネル I タブ追加 / トップバーアイコン修正** — 概要
|
||||
- **2026-05-16: v0.3.8 Metadata タブ: data9 対応 / プロンプト判別不能テキスト** — 概要
|
||||
- **2026-05-15: v0.3.7 Metadata タブ: Flux2 / Qwen / Z-Image 対応** — 概要
|
||||
- **2026-05-15: v0.3.6 Metadata タブ追加** — 概要
|
||||
- **2026-05-09: v0.3.5 GenerateUI — Feeder サブタブ・ワークフロー解析精度向上** — 概要
|
||||
- **2026-04-29: v0.3.4 GenerateUI — Checkpoint Batch 刷新・Settings 横並びレイアウト・ヘルプ i18n 修正** — 概要
|
||||
- **2026-04-28: v0.3.3 Promptタブ — ワイルドカード支援パネル・Impact Packシンボリックリンク連携・サブディレクトリ対応・GenerateUI修正** — 概要
|
||||
- **2026-04-28: v0.3.2 生成UIタブ — Checkpoint Batch・Seed UIレイアウト修正** — 概要
|
||||
- **2026-04-27: v0.3.1 ギャラリー拡張（フォルダ/ファイル操作・ワークフロー保存）** — 概要
|
||||
- **2026-04-18: v0.3.0 モデルグループ表示改善（全タイプ横断・サイドパネル修正）** — 概要
|
||||
- **2026-04-18: v0.2.9 モデル有効/無効・複数選択・一括削除** — 概要
|
||||
- **2026-04-14: v0.2.8 データ保存先変更・エクスポート/インポート機能追加** — 概要
|
||||
- **2026-04-06: v0.2.6 UI改善・バッジ保存修正・フォルダフィルター追加** — 概要
|
||||
- **2026-04-04: v0.2.5 追加修正** — 概要
|
||||
- **2026-04-03: v0.2.5 サイドパネル Category/Package サブタブ・テーマ設定・Workflow修正** — 概要
|
||||
- **2026-03-29: v0.2.4 GenerateUIタブ レイアウト再設計** — 概要
|
||||
- **2026-03-29: v0.2.3 バッジ統一・GenUI Model・サイドパネルModels拡充** — 概要
|
||||
- **2026-03-28: v0.2.2 Modelsタブ・CivitAI連携** — 概要
|
||||
- **2026-03-27: テーマカスタマイズ機能・設定タブ改善** — 概要
|
||||
- **2026-03-26: トップバーボタンアイコン修正・Appモードバッジ調査** — 概要
|
||||
- **2026-03-25: v0.2.0 プロンプトプリセット機能・Workflow Studio Library** — 概要
|
||||
- **2026-03-24: v0.1.9 サイドパネルUI改善・保存ダイアログ・バグ修正** — 概要
|
||||
- **2026-03-24: v0.1.8 WF & Node Library サイドパネル拡張** — 概要
|
||||
- **2026-03-23: v0.1.7 Nodes タブ追加** — 概要
|
||||
- **2026-03-23: v0.1.6 パストラバーサル脆弱性修正** — 概要
|
||||
- **2026-03-22: v0.1.5 テーマシステム追加** — 概要
|
||||
- **2026-03-22: v0.1.4 App形式対応・プリセットコピー・分析バグ修正** — 概要
|
||||
- **2026-03-21: v0.1.3 ヘルプ＆サポートタブ追加** — 概要
|
||||
- **2026-03-19: v0.1.2 リリース** — 概要
|
||||
- **2026-03-19: v0.1.1 リリース** — 概要
|
||||
- **2026-03-18: プロジェクトリネーム & アイコン適用** — 概要
|
||||
- **2026-03-18: デフォルトビュー設定追加** — 概要
|
||||
- **2026-03-18: MarkdownNote表示専用ノード除外** — 概要
|
||||
- **2026-03-18: サブグラフワークフロー対応 & 変換精度修正** — 概要
|
||||
- **2026-03-18: Raw JSON同期修正** — 概要
|
||||
- **プロジェクト構成 (現在)**
|
||||
- **2026-03-18: JSONシンタックスハイライト** — 概要
|
||||
- **2026-03-18: プロンプトタブ 中国語↔英語翻訳追加** — 概要
|
||||
- **2026-03-18: v0.1.0 リリース** — 概要
|
||||
- **2026-04-13: Gallery タブ追加 & 全タブ Group UI 統一** — 概要
|
||||
+261
@@ -0,0 +1,261 @@
|
||||
# README Index — ComfyUI-Workflow-Studio
|
||||
|
||||
Condensed index of README.md: intro summary, feature names per tab (full descriptions live in the excluded README.md), and the installation/usage/requirements/project-structure sections verbatim. Exists so graphify can extract this without feeding the full README (with its screenshot table) to the local Ollama model. Regenerate with `python tools/generate_doc_index.py readme` whenever README.md changes.
|
||||
|
||||
# ComfyUI-Workflow-Studio
|
||||
|
||||
A comprehensive workflow, asset management, and generation UI plugin for [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
|
||||
|
||||
**📁 Management** — organize all your assets in one studio
|
||||
|
||||
- Workflow, model, image, and prompt management
|
||||
- AI-powered prompt writing assistance, translation, and tag generation
|
||||
- File drop & Gallery-linked metadata viewing — inspect and reuse generation settings instantly
|
||||
|
||||
**⚡ GenerateUI** — a productivity-focused generation tab connected to every other tab
|
||||
|
||||
- Batch generation across models, samplers, prompts, and workflows
|
||||
- Image Feeder for continuous folder-based generation
|
||||
- Lab: experimental I2I batch generation with independent per-iteration keyframes for Model (Checkpoint/LoRA Single/VAE)/Prompt/KSampler
|
||||
|
||||
**📚 Workflow Studio Library** — a multi-function side panel for smooth ComfyUI integration
|
||||
|
||||
- Drag & drop models, nodes, prompts, and workflows straight onto the canvas
|
||||
- **Send to Canvas**: click "Send to Canvas" (Workflow tab) or "Copy & Send Canvas" (Gallery tab) to send the workflow directly to the ComfyUI canvas — UI and API formats both supported
|
||||
- View metadata from images / JSON, then drop the detected models and prompts onto the canvas
|
||||
- Built-in AI tools (translation and more)
|
||||
|
||||
|
||||
|
||||
|
||||
## Features (tab -> feature names)
|
||||
|
||||
### Workflow Tab
|
||||
Thumbnail / Table views, Thumbnail side panel, Badge filtering, Search, Side panel tabs, Badge management, AI summary, Import / Export, Side panel toolbar buttons, Default view setting, Search clear (✕), Clear all filters (✕ Clear)
|
||||
|
||||
### Canvas Snapshot (v0.1.2)
|
||||
One-click capture, Auto-save as thumbnail, Embedded workflow metadata, Auto-import
|
||||
|
||||
### GenerateUI Tab (v0.3.5)
|
||||
6-tab layout, Save button, Input tab, Model tab, Settings tab, Always-visible Raw JSON, Bypass/Mute node handling, One-click generation, Seed control, Style selector, Create Catalog / Catalog buttons, Batch type selector, Batch tab, UI-to-API conversion, Video workflow support, Eagle integration
|
||||
|
||||
### Feeder subtab (v0.3.5 / v0.3.42)
|
||||
ImageFeeder node control, Image library, Selection management, Presets, Continuous Run loop, Index sync, Seed, WFS_GalleryFeeder node, Node & group selector, Image grid, After Gen modes, Run / Stop controls
|
||||
|
||||
### Lab subtab (v0.3.87 – v0.5.2)
|
||||
Setting / Results / Plan JSON sub-panels, Per-column keyframes, Model column — Checkpoint / LoRA Single / VAE, Empty state when no matching node exists, Wildcards, Prompt cell extras, Keyframe #1 live-reflect, Bypass checkbox, LoRA node bypass & prompt injection, T2I workflow support, Use generated image for next, Workflow recall, Plan files, Plan Load, Plan JSON tab, Save index image to Output on Run, Index images are self-contained plan files, Eagle integration, Select vs. edit, and keyframe reordering, PG — apply a Prompt tab preset group, Video workflow support, Use generated image for next, now video-aware
|
||||
|
||||
### Prompt Tab
|
||||
3-column layout, AI chat assistant, Image attachment, Translation, Prompt presets, Preset Manager, Group management, Clipboard copy, GenUI Set, Wildcard/Style tab bar, Wildcard input toolbar, Wildcard file manager, Style manager, Form / Table toggle, Table view — Presets / Presets Group / Wildcards / Style
|
||||
|
||||
### Settings Tab
|
||||
2-column layout, Collapsible sections, Theme selection, Theme customization, Workflows directory, Gallery output directory, Eagle connection, Ollama connection, CivitAI Host, CivitAI API Key, Default workflow, Default Checkpoint, Data Management, Text Size, RAW JSON Colors, GenerateUI Model Tab Highlight, Wildcard Integration, G'MIC-Qt Integration, Language
|
||||
|
||||
### Gallery Tab (v0.3.44)
|
||||
Output / ImagePrompt / Style_Catalog / Metadata sub-tabs, Image browser, Folder tree root label, Thumbnail / Table views, Folder management, File operations, Download, MP4 video support, MP4 embedded metadata, Info tab: dimensions & duration, Multi-select, Image Compare, Prompt search, Server-side filtering, Group management, Thumbnail F button, Favorites, Detail panel, Prompt tab, Workflow viewer, GenUI button, Image Edit button, Send GenUI Image button, Send to LI node button, Send CC button, Search clear (✕), Clear all filters (✕ Clear), Workflow auto-save, Output folder configurable, SVG file support, Performance
|
||||
|
||||
### ImagePrompt Gallery subtab (v0.3.94, renamed from Style/Prompt in v0.3.95)
|
||||
Visual prompt library, 3-column layout, Prompt builder (right pane), Plain-text prompt storage, ponyxlWildcardsVault format support, Seed data importer
|
||||
|
||||
### Style_Catalog Gallery subtab (v0.3.95)
|
||||
Visual Style picker, 3-column layout, Positive/Negative panel, Select as Style button, Load in GenerateUI / Open in Metadata Tab buttons, Double-click to enlarge
|
||||
|
||||
### Metadata Gallery subtab (v0.3.8, moved from a top-level tab in v0.4.6)
|
||||
Now inside Gallery, 3-column layout, File drop, MP4 embedded metadata, Model extraction, LoRA extraction, Prompt extraction, Prompt actions, Format support, Format note
|
||||
|
||||
### Nodes Tab (v0.1.7)
|
||||
Node Browser, Search & Filter, Package badges, Node detail panel, Node Sets, Right-click context menu, Multi-select, Search clear (✕), Clear all filters (✕ Clear)
|
||||
|
||||
### Models Tab (v0.2.3)
|
||||
Model Browser, Thumbnail / Table views, Table column sort, Search & Filter, User-defined badges, Side panel tabs, CivitAI integration, CivitAI panel — Info / Sample sub-tabs, CivitAI panel states, Batch CivitAI fetch, Detail modal, GenUI Model button, Group management, Table view memo, Preview images, Enable / Disable models, Multi-select & bulk operations, Search clear (✕), Clear all filters (✕ Clear)
|
||||
|
||||
### Tagger Tab (v0.3.38)
|
||||
3 sub-tabs, Model support, Threshold sliders, Ollama VLM, Single tab, Single output options, Batch tab, DB tab, Dependencies
|
||||
|
||||
### Image Edit Tab (v0.3.65)
|
||||
Layer-based image editor, Loading images, New button, Tools, Layer panel, Layer lock, Text quality, Export, Canvas navigation, Undo / Redo
|
||||
|
||||
### Video Tab (v0.5.0 – v0.5.2)
|
||||
Dedicated MiniMax H3 video generation UI, independent of GenerateUI, 3-pane layout, First Frame / Last Frame are both optional, Node lookup instead of hardcoded IDs, Load in Video, Preview frame + Video Source panel, Frame tab, GIF tab
|
||||
|
||||
### AI TOOL Tab (v0.3.14)
|
||||
4-pane layout, Translation pane, Chat pane, Chat pane — image generation via Tool Calling, Chat pane — generate_image extra parameters, TOOLS pane (VLM), TOOLS pane — shared Chat attachment, TOOLS pane (Wildcards), Chat pane — image-to-image (I2I), Chat pane — SVG generation, no ComfyUI workflow involved, Chat pane — Skills, Settings pane, Unsloth backend, Settings pane — Chat Image Generation, Settings pane — Chat I2I Generation, Settings pane — Generation, Settings shared, Backend support, URL security, Lemonade's other endpoints (not integrated)
|
||||
|
||||
### Workflow Studio Library (ComfyUI Side Panel) (v0.3.9)
|
||||
Tab layout (W / N / P / M / I / A), W — Workflows tab, N — Nodes tab, M — Models tab, P — Prompts tab, I — Information tab, A — AI TOOL tab, Drag & drop workflows, Drag & drop nodes, Drag & drop prompts, Send to Canvas, Copy prompts, Double-click, Search, ⚙ Theme settings
|
||||
|
||||
### Help & Support Tab (v0.1.3)
|
||||
Sidebar navigation, Support, Feature list, Keyboard Shortcuts
|
||||
|
||||
## Installation
|
||||
|
||||
### Via ComfyUI Manager (Recommended)
|
||||
|
||||
Search for **Workflow Studio** in ComfyUI Manager and install.
|
||||
|
||||
### Manual Installation
|
||||
|
||||
```bash
|
||||
cd ComfyUI/custom_nodes
|
||||
git clone https://github.com/ketle-man/ComfyUI-Workflow-Studio.git
|
||||
```
|
||||
|
||||
Restart ComfyUI after installation.
|
||||
|
||||
---
|
||||
|
||||
## Sample Workflows
|
||||
|
||||
Sample workflows are included in the `workflows/` folder. You can open them directly in ComfyUI via drag & drop, or load them from the Workflow tab.
|
||||
|
||||
> **Note:** Some sample workflows require additional custom nodes.
|
||||
> If a node is shown as missing (red/unknown) after loading, install the required custom nodes via **ComfyUI Manager** or by cloning the repository from GitHub into your `ComfyUI/custom_nodes/` directory.
|
||||
>
|
||||
> ```bash
|
||||
> cd ComfyUI/custom_nodes
|
||||
> git clone <repository-url>
|
||||
> ```
|
||||
>
|
||||
> After installation, restart ComfyUI to activate the new nodes.
|
||||
|
||||
---
|
||||
|
||||
## Usage
|
||||
|
||||
### Launch
|
||||
|
||||
Click the **W** button in the ComfyUI top menu bar, or navigate to:
|
||||
|
||||
```
|
||||
http://127.0.0.1:8188/wfm
|
||||
```
|
||||
|
||||
> **Tip:** Shift+Click the W button to open in a new window.
|
||||
|
||||
### Canvas Snapshot
|
||||
|
||||
Click the **camera icon** (next to the W button) in ComfyUI's top bar to capture the current workflow canvas as a thumbnail. The image is automatically saved to the workflow data folder and appears in Workflow Studio's workflow list.
|
||||
|
||||
### Quick Start
|
||||
|
||||
1. **Workflow Tab** — Your workflows from `ComfyUI/user/default/workflows/` are automatically listed
|
||||
2. **Click a workflow** — View thumbnail, JSON details, and metadata in the side panel
|
||||
3. **Load in GenerateUI** — Click the button to load a workflow into the generation interface
|
||||
4. **Adjust parameters** — Modify prompts, models, seeds, and settings via the auto-generated UI
|
||||
5. **Generate** — Hit the Generate button to queue the prompt
|
||||
|
||||
---
|
||||
|
||||
## Requirements
|
||||
|
||||
- **ComfyUI** — any recent version (v1.33.9+ recommended for action bar integration)
|
||||
- **Python 3.10+**
|
||||
- **Jinja2** — `pip install jinja2` (usually included with ComfyUI)
|
||||
|
||||
### Optional
|
||||
|
||||
- **[Ollama](https://ollama.com/)** — for AI chat assistant, translation, and VLM features
|
||||
- **[LM Studio](https://lmstudio.ai/)** — alternative backend for translation and VLM (OpenAI-compatible API)
|
||||
- **[Lemonade Server](https://lemonade-server.ai/)** — alternative backend for translation, chat, and VLM (OpenAI-compatible API)
|
||||
- **[Unsloth](https://unsloth.ai/)** — alternative backend for translation, chat, and VLM (OpenAI-compatible API); always requires an API key, set via `UNSLOTH_API_KEY` in a `.env` file — `pip install -r requirements.txt` for `python-dotenv`
|
||||
- **[Eagle](https://eagle.cool/)** — for auto-saving generated images with metadata
|
||||
- **[comfyui-mask-editor-one](https://github.com/ketle-man/comfyui-mask-editor-one) (v0.1.9+)** — enables BiRefNet background removal, SAM3 text-prompt segmentation, and ABR stamp-brush library in the Image Edit Mask tool; `birefnet.safetensors` must be placed in `ComfyUI/models/background_removal/` for BiRefNet
|
||||
|
||||
---
|
||||
|
||||
## Supported Languages
|
||||
|
||||
| Language | Status |
|
||||
| -------- | ------ |
|
||||
| English | Full |
|
||||
| Japanese | Full |
|
||||
| Chinese | Full |
|
||||
|
||||
---
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
ComfyUI-Workflow-Studio/
|
||||
├── __init__.py # ComfyUI entry point
|
||||
├── py/
|
||||
│ ├── wfm.py # Main class & route registration
|
||||
│ ├── config.py # Path configuration
|
||||
│ ├── nodes/
|
||||
│ │ ├── prompt_text.py # WFS_PromptText custom node (positive/negative prompt)
|
||||
│ │ └── gallery_feeder_node.py # WFS_GalleryFeeder custom node (gallery group → IMAGE)
|
||||
│ ├── routes/
|
||||
│ │ ├── workflow_routes.py # Workflow CRUD & analysis API
|
||||
│ │ ├── nodes_routes.py # Nodes metadata & node sets API
|
||||
│ │ ├── models_routes.py # Model metadata, preview, CivitAI API
|
||||
│ │ ├── prompts_routes.py # Prompt presets CRUD API
|
||||
│ │ ├── settings_routes.py # Settings API
|
||||
│ │ ├── ollama_routes.py # Ollama proxy API
|
||||
│ │ ├── eagle_routes.py # Eagle integration API
|
||||
│ │ └── gallery_routes.py # Gallery tab API (Output + ImagePrompt + Style_Catalog sub-tabs)
|
||||
│ └── services/
|
||||
│ ├── workflow_service.py # Workflow file operations
|
||||
│ ├── nodes_service.py # Node metadata & node sets
|
||||
│ ├── models_service.py # Model metadata & preview images
|
||||
│ ├── civitai_service.py # CivitAI API integration & cache
|
||||
│ ├── prompts_service.py # Prompt presets persistence
|
||||
│ ├── workflow_analyzer.py # Model/node detection
|
||||
│ ├── settings_service.py # Settings persistence
|
||||
│ ├── png_extractor.py # PNG metadata extraction
|
||||
│ ├── gallery_service.py # Gallery image scanning, .txt sidecar & ponyxlWildcardsVault prompt resolution
|
||||
│ └── gallery_metadata.py # Gallery per-image metadata store (favorites/tags/memo/groups)
|
||||
├── templates/
|
||||
│ └── index.html # SPA template (Workflow/GenerateUI/Prompt/Metadata/Gallery/Nodes/Models/Settings/Help/AI)
|
||||
├── static/
|
||||
│ ├── favicon.svg # Browser tab icon (W+S Wave)
|
||||
│ ├── css/main.css # Styles
|
||||
│ └── js/
|
||||
│ ├── app.js # App initialization & routing
|
||||
│ ├── workflow-tab.js # Workflow browser
|
||||
│ ├── generate-tab.js # Generation UI
|
||||
│ ├── feeder-tab.js # Feeder subtab (Image Loop + Gallery modes)
|
||||
│ ├── gallery-tab.js # Gallery tab: Output sub-tab (image browser, groups, metadata) + sub-tab switching
|
||||
│ ├── image-prompt-tab.js # Gallery tab: ImagePrompt sub-tab (visual prompt builder)
|
||||
│ ├── style-catalog-tab.js # Gallery tab: Style_Catalog sub-tab (visual Style picker)
|
||||
│ ├── tagger-tab.js # Tagger tab (WD Tagger, DeepDanbooru, Ollama VLM)
|
||||
│ ├── prompt-tab.js # AI assistant & presets
|
||||
│ ├── metadata-tab.js # Metadata extraction & display (PNG/WebP/JSON)
|
||||
│ ├── settings-tab.js # Settings panel
|
||||
│ ├── comfyui-client.js # ComfyUI WebSocket/API client
|
||||
│ ├── nodes-tab.js # Node browser & node sets
|
||||
│ ├── models-tab.js # Model browser & CivitAI integration
|
||||
│ ├── ai-tab.js # AI TOOL tab (Translation | Chat | TOOLS/VLM | Settings)
|
||||
│ ├── comfyui-workflow.js # UI-to-API format conversion
|
||||
│ ├── comfyui-editor.js # Dynamic parameter editor
|
||||
│ ├── json-highlight.js # JSON syntax highlighting
|
||||
│ ├── util.js # Shared utilities (escapeHtml, readJsonStorage, getSettings)
|
||||
│ └── i18n.js # Internationalization
|
||||
├── web/comfyui/
|
||||
│ ├── top_menu_extension.js # ComfyUI menu bar integration
|
||||
│ ├── node_sets_menu.js # Workflow Studio Library side panel
|
||||
│ └── gallery_feeder_extension.js # WFS_GalleryFeeder canvas widgets (After Gen / Run / Stop)
|
||||
├── tools/
|
||||
│ └── import_style_prompt_seed.py # Dev tool: pre-process a wildcards-vault source into ws_image_prompt (not run by the plugin itself)
|
||||
└── data/ # Fallback data dir (used when ComfyUI user/default/ is not found)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
MIT License
|
||||
|
||||
---
|
||||
|
||||
## Acknowledgements
|
||||
|
||||
- [ComfyUI](https://github.com/comfyanonymous/ComfyUI) by comfyanonymous
|
||||
- [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) by pythongosssss — Canvas snapshot and PNG workflow embedding implementation reference
|
||||
- [ComfyUI-Lora-Manager](https://github.com/willmiao/ComfyUI-Lora-Manager) — Plugin architecture and UI pattern reference
|
||||
- [Ollama](https://ollama.com/) for local LLM inference
|
||||
- [Eagle](https://eagle.cool/) for image management
|
||||
- [Pillow (PIL Fork)](https://python-pillow.org/) — server-side thumbnail generation (`data/thumb_cache/`)
|
||||
- [ComfyUI-Gallery](https://github.com/PanicTitan/ComfyUI-Gallery) by PanicTitan — thumbnail grid UX reference
|
||||
- [infinite-image-browsing](https://github.com/zanllp/sd-webui-infinite-image-browsing) by zanllp — thumbnail caching and index strategy reference
|
||||
- [Fooocus](https://github.com/lllyasviel/Fooocus) by lllyasviel — style preset JSON format reference
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
{"0": "models-tab.js", "1": "prompt-tab.js", "2": "models_routes.py", "3": "generate-tab.js", "4": "lab-tab.js", "5": "gallery_routes.py", "6": "gallery-tab.js", "7": "ImageEditTab", "8": "ai-tab.js", "10": "feeder-tab.js", "11": "NodesService", "12": "settings_routes.py", "13": "metadata-tab.js", "14": "node_sets_menu.js", "15": "GalleryService", "16": "workflow-tab.js", "17": "tagger_routes.py", "18": "WildcardService", "19": "t", "20": "renderContent", "21": "comfyui-editor.js", "23": "TaggerService", "24": "top_menu_extension.js", "25": "settings-tab.js", "26": "setupAiHandlers", "27": "PromptsService", "28": "workflow_routes.py", "29": "GalleryMetadataStore", "30": "WorkflowService", "31": "lab_routes.py", "32": "SkillService", "33": "LayerManager", "34": "SelectTool", "35": "CivitaiService", "36": "DrawTool", "37": "MaskTool", "39": "gallery_service.py", "41": "image-prompt-tab.js", "42": "KSampler Node", "44": "style-catalog-tab.js", "45": "add", "46": "comfyui-workflow.js", "47": "BlurTool", "48": "MaskShapeTool", "49": "MaskVectorTool", "50": "ShapeTool", "51": "gmic_routes.py", "52": "ollama_routes.py", "53": "TextTool", "54": "analyze_workflow", "55": "MaskTextTool", "56": "i18n.js", "57": ".list_images", "58": "MaskAlphaTool", "59": "MaskColorTool", "60": "gallery_feeder_extension.js", "61": "createPanel", "62": "util.js", "63": "BgRemove", "65": "WFS_GalleryFeeder", "66": "app.js", "67": "InpaintI2IActions", "68": "Sam3Segmentation", "69": "fromWorkflow", "70": "_extractPromptsAPI", "71": "generate_doc_index.py", "72": "WS Inpaint Basic Workflow", "73": "._bg_index_folder", "74": "SettingsService", "75": "import_style_prompt_seed.py", "76": "Image Edit (FireRed Image Edit 1.1) node", "77": "Version 0.3.9", "78": "DWPose Image Feeder Workflow", "79": "civitai_service.py", "81": "WFS_PromptText", "82": "workflow_service.py", "84": "config.py", "85": "Tagger Tab requirements.txt", "88": "Chinese Language Support", "89": "Version 0.3.95", "97": "Lab Subtab", "100": "prestartup_script.py", "101": ".get_image_prompt_root", "102": ".get_style_catalog_root", "103": ".start_background_index", "104": ".update_workflows_dir", "105": "Version 0.4.2", "106": "Workflow Studio Banner", "107": "Workflow Studio Icon", "108": "BiRefNet Model", "110": "SDXL 2D Pose Editor I2I Workflow", "111": "ComfyUI Manager", "112": "Tagger Tab Screenshot (WD Tagger/DeepDanbooru)", "113": "Models Tab UI Screenshot", "114": "Workflow Studio Topbar Icon Strip", "115": "GenerateUI LoRA Stack Tab Screenshot", "116": "Workflow Studio Favicon Icon", "117": "2D Pose Editor to Image Edit (FireRed) Workflow", "118": "3D Pose Editor to Image Edit (Flux.2 Klein) Workflow", "119": "SDXL DWPose ControlNet Workflow", "120": "SDXL From Metadata T2I Workflow", "121": "SDXL Img2Img + FaceDetailer Workflow", "122": "SDXL Image Feeder Img2Img Workflow", "123": "SDXL T2I + FaceDetailer Workflow", "124": "GenerateUI Batch Tab Screenshot", "125": "Multiple Select Menu Screenshot", "126": "Image Edit Tab Screenshot", "127": "Workflow Tab Screenshot", "128": "Prompt Tab Input Assistance Screenshot", "129": "GenerateUI Feeder Tab Screenshot", "130": "Workflow Studio Library Panel (Lora Info View)", "131": "Library Panel I Tab (Prompts) Screenshot", "132": "Settings Tab Screenshot (Language, Theme)", "133": "ComfyUI Workflow Studio Thumbnail", "135": "Feeder Subtab", "137": "Help & Support Tab", "140": "Metadata Tab", "144": "comfyui-workflow-studio", "146": "py/__init__.py", "147": "nodes/__init__.py", "148": "routes/__init__.py", "149": "services/__init__.py", "150": "python-dotenv", "155": "SD1.5 Basic Text-to-Image Workflow", "156": "SDXL Text-to-Image Basic Workflow Screenshot", "9": "Backend API Routes", "22": "External Plugin Ecosystem", "38": "Registry Publish & Docs", "40": "Generate & Export Utilities", "43": "ComfyUI Client & Mask Bridge", "64": "Workflow Analyzer Internals", "80": "Doc Index Automation", "83": "Project Directory Layout", "86": "Data Storage Files", "87": "AI Tool Version History", "90": "Windows Long Path Handling", "91": "Route Registration", "92": "Caching & Unique IDs", "93": "Service Layer", "94": "Lazy Input Evaluation", "95": "HuggingFace Config", "96": "Node Cache Invalidation", "98": "Mask Editor One Release", "99": "PowerShell Encoding", "109": "Chinese Locale", "134": "English Locale", "136": "ComfyUI folder_paths API", "138": "i18n System", "139": "Japanese Locale", "141": "Node Class Mappings", "142": "OrderedDict Usage", "143": "Output Directory API", "145": "ComfyUI PromptServer", "151": "Silent Failure Diagnosis", "152": "Temp Directory API", "153": "Path Utility", "154": "Input Validation Hook"}
|
||||
@@ -0,0 +1,546 @@
|
||||
# Graph Report - ComfyUI-Workflow-Studio (2026-08-22)
|
||||
|
||||
## Corpus Check
|
||||
- 20 files · ~567,102 words
|
||||
- Verdict: corpus is large enough that graph structure adds value.
|
||||
|
||||
## Summary
|
||||
- 2320 nodes · 5560 edges · 157 communities (79 shown, 78 thin omitted)
|
||||
- Extraction: 94% EXTRACTED · 6% INFERRED · 0% AMBIGUOUS · INFERRED: 328 edges (avg confidence: 0.87)
|
||||
- Token cost: 22,191 input · 55,365 output
|
||||
|
||||
## Community Hubs (Navigation)
|
||||
- models-tab.js
|
||||
- prompt-tab.js
|
||||
- models_routes.py
|
||||
- generate-tab.js
|
||||
- lab-tab.js
|
||||
- gallery_routes.py
|
||||
- gallery-tab.js
|
||||
- ImageEditTab
|
||||
- ai-tab.js
|
||||
- Backend API Routes
|
||||
- feeder-tab.js
|
||||
- NodesService
|
||||
- settings_routes.py
|
||||
- metadata-tab.js
|
||||
- node_sets_menu.js
|
||||
- GalleryService
|
||||
- workflow-tab.js
|
||||
- tagger_routes.py
|
||||
- WildcardService
|
||||
- t
|
||||
- renderContent
|
||||
- comfyui-editor.js
|
||||
- External Plugin Ecosystem
|
||||
- TaggerService
|
||||
- top_menu_extension.js
|
||||
- settings-tab.js
|
||||
- setupAiHandlers
|
||||
- PromptsService
|
||||
- workflow_routes.py
|
||||
- GalleryMetadataStore
|
||||
- WorkflowService
|
||||
- lab_routes.py
|
||||
- SkillService
|
||||
- LayerManager
|
||||
- SelectTool
|
||||
- CivitaiService
|
||||
- DrawTool
|
||||
- MaskTool
|
||||
- Registry Publish & Docs
|
||||
- gallery_service.py
|
||||
- Generate & Export Utilities
|
||||
- image-prompt-tab.js
|
||||
- KSampler Node
|
||||
- ComfyUI Client & Mask Bridge
|
||||
- style-catalog-tab.js
|
||||
- add
|
||||
- comfyui-workflow.js
|
||||
- BlurTool
|
||||
- MaskShapeTool
|
||||
- MaskVectorTool
|
||||
- ShapeTool
|
||||
- gmic_routes.py
|
||||
- ollama_routes.py
|
||||
- TextTool
|
||||
- analyze_workflow
|
||||
- MaskTextTool
|
||||
- i18n.js
|
||||
- .list_images
|
||||
- MaskAlphaTool
|
||||
- MaskColorTool
|
||||
- gallery_feeder_extension.js
|
||||
- createPanel
|
||||
- util.js
|
||||
- BgRemove
|
||||
- Workflow Analyzer Internals
|
||||
- WFS_GalleryFeeder
|
||||
- app.js
|
||||
- InpaintI2IActions
|
||||
- Sam3Segmentation
|
||||
- fromWorkflow
|
||||
- _extractPromptsAPI
|
||||
- generate_doc_index.py
|
||||
- WS Inpaint Basic Workflow
|
||||
- ._bg_index_folder
|
||||
- SettingsService
|
||||
- import_style_prompt_seed.py
|
||||
- Image Edit (FireRed Image Edit 1.1) node
|
||||
- Version 0.3.9
|
||||
- DWPose Image Feeder Workflow
|
||||
- civitai_service.py
|
||||
- Doc Index Automation
|
||||
- WFS_PromptText
|
||||
- workflow_service.py
|
||||
- Project Directory Layout
|
||||
- config.py
|
||||
- Tagger Tab requirements.txt
|
||||
- Data Storage Files
|
||||
- AI Tool Version History
|
||||
- Chinese Language Support
|
||||
- Version 0.3.95
|
||||
- Windows Long Path Handling
|
||||
- Route Registration
|
||||
- Caching & Unique IDs
|
||||
- Service Layer
|
||||
- Lazy Input Evaluation
|
||||
- HuggingFace Config
|
||||
- Node Cache Invalidation
|
||||
- Lab Subtab
|
||||
- Mask Editor One Release
|
||||
- PowerShell Encoding
|
||||
- prestartup_script.py
|
||||
- .get_image_prompt_root
|
||||
- .get_style_catalog_root
|
||||
- .start_background_index
|
||||
- .update_workflows_dir
|
||||
- Version 0.4.2
|
||||
- Workflow Studio Banner
|
||||
- Workflow Studio Icon
|
||||
- BiRefNet Model
|
||||
- Chinese Locale
|
||||
- SDXL 2D Pose Editor I2I Workflow
|
||||
- ComfyUI Manager
|
||||
- Tagger Tab Screenshot (WD Tagger/DeepDanbooru)
|
||||
- Models Tab UI Screenshot
|
||||
- Workflow Studio Topbar Icon Strip
|
||||
- GenerateUI LoRA Stack Tab Screenshot
|
||||
- Workflow Studio Favicon Icon
|
||||
- 2D Pose Editor to Image Edit (FireRed) Workflow
|
||||
- 3D Pose Editor to Image Edit (Flux.2 Klein) Workflow
|
||||
- SDXL DWPose ControlNet Workflow
|
||||
- SDXL From Metadata T2I Workflow
|
||||
- SDXL Img2Img + FaceDetailer Workflow
|
||||
- SDXL Image Feeder Img2Img Workflow
|
||||
- SDXL T2I + FaceDetailer Workflow
|
||||
- GenerateUI Batch Tab Screenshot
|
||||
- Multiple Select Menu Screenshot
|
||||
- Image Edit Tab Screenshot
|
||||
- Workflow Tab Screenshot
|
||||
- Prompt Tab Input Assistance Screenshot
|
||||
- GenerateUI Feeder Tab Screenshot
|
||||
- Workflow Studio Library Panel (Lora Info View)
|
||||
- Library Panel I Tab (Prompts) Screenshot
|
||||
- Settings Tab Screenshot (Language, Theme)
|
||||
- ComfyUI Workflow Studio Thumbnail
|
||||
- English Locale
|
||||
- Feeder Subtab
|
||||
- ComfyUI folder_paths API
|
||||
- Help & Support Tab
|
||||
- i18n System
|
||||
- Japanese Locale
|
||||
- Metadata Tab
|
||||
- Node Class Mappings
|
||||
- OrderedDict Usage
|
||||
- Output Directory API
|
||||
- comfyui-workflow-studio
|
||||
- ComfyUI PromptServer
|
||||
- python-dotenv
|
||||
- Silent Failure Diagnosis
|
||||
- Temp Directory API
|
||||
- Path Utility
|
||||
- Input Validation Hook
|
||||
- SD1.5 Basic Text-to-Image Workflow
|
||||
- SDXL Text-to-Image Basic Workflow Screenshot
|
||||
|
||||
## God Nodes (most connected - your core abstractions)
|
||||
1. `t()` - 226 edges
|
||||
2. `showToast()` - 186 edges
|
||||
3. `ImageEditTab` - 53 edges
|
||||
4. `escapeHtml()` - 53 edges
|
||||
5. `GalleryService` - 45 edges
|
||||
6. `ComfyUI-Workflow-Studio` - 41 edges
|
||||
7. `initPromptTab()` - 33 edges
|
||||
8. `setup_routes()` - 32 edges
|
||||
9. `initSettingsTab()` - 31 edges
|
||||
10. `add()` - 31 edges
|
||||
|
||||
## Surprising Connections (you probably didn't know these)
|
||||
- `ComfyUI-Workflow-Studio` --references--> `ImagePrompt Gallery Subtab` [INFERRED]
|
||||
CLAUDE.md → README.index.md
|
||||
- `ComfyUI-Workflow-Studio` --references--> `Prompt Tab` [INFERRED]
|
||||
CLAUDE.md → README.index.md
|
||||
- `ComfyUI-Workflow-Studio` --references--> `Gallery Tab` [INFERRED]
|
||||
CLAUDE.md → README.index.md
|
||||
- `ComfyUI-Workflow-Studio` --references--> `GenerateUI Tab` [INFERRED]
|
||||
CLAUDE.md → README.index.md
|
||||
- `ComfyUI-Workflow-Studio` --references--> `Image Edit Tab` [INFERRED]
|
||||
CLAUDE.md → README.index.md
|
||||
|
||||
## Import Cycles
|
||||
- 3-file cycle: `static/js/app.js -> static/js/image-edit-tab.js -> static/js/image-edit/Sam3Segmentation.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/image-edit-tab.js -> static/js/image-edit/GmicIntegration.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/image-edit-tab.js -> static/js/image-edit/BgRemove.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/generate-tab.js -> static/js/feeder-tab.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/feeder-tab.js -> static/js/gallery-tab.js -> static/js/generate-tab.js -> static/js/feeder-tab.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/gallery-tab.js -> static/js/image-prompt-tab.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/gallery-tab.js -> static/js/style-catalog-tab.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/models-tab.js -> static/js/models/badges.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/models-tab.js -> static/js/comfyui-editor.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/image-edit-tab.js -> static/js/image-edit/InpaintI2IActions.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/image-edit-tab.js -> static/js/image-edit/MaskEditorOneBridge.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/image-edit-tab.js -> static/js/image-edit/BlurTool.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/image-edit-tab.js -> static/js/image-edit/FileExport.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/image-edit-tab.js -> static/js/comfyui-editor.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/ai-tab.js -> static/js/app.js -> static/js/gallery-tab.js -> static/js/ai-tab.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/gallery-tab.js -> static/js/comfyui-editor.js -> static/js/app.js`
|
||||
- 3-file cycle: `static/js/app.js -> static/js/generate-tab.js -> static/js/comfyui-editor.js -> static/js/app.js`
|
||||
- 4-file cycle: `static/js/app.js -> static/js/workflow-tab.js -> static/js/generate-tab.js -> static/js/feeder-tab.js -> static/js/app.js`
|
||||
- 4-file cycle: `static/js/app.js -> static/js/gallery-tab.js -> static/js/generate-tab.js -> static/js/feeder-tab.js -> static/js/app.js`
|
||||
- 4-file cycle: `static/js/app.js -> static/js/prompt-tab.js -> static/js/generate-tab.js -> static/js/feeder-tab.js -> static/js/app.js`
|
||||
|
||||
## Hyperedges (group relationships)
|
||||
- **Architecture Layers** — workflowstudio, add_routes, py_wfm, py_routes, py_services, business_logic, web_comfyui, static, templates, data, settings_json, metadata_json, node_metadata_json [EXTRACTED 0.80]
|
||||
- **Workflow JSON Consistency Group** — last_node_id, last_link_id, links, workflow_json [EXTRACTED 0.85]
|
||||
- **i18n Languages** — i18n, english, japanese, chinese [EXTRACTED 0.90]
|
||||
- **Graphify Index Generation** — readme_index, devlog_index, generate_doc_index, graphifyignore, readme, devlog, claude_hooks, git_hooks [EXTRACTED 0.85]
|
||||
- **AI Feature Backends** — ollama, lm_studio, lemonade_server, unsloth [INFERRED 0.80]
|
||||
- **Image/Gallery Implementation References** — comfyui_custom_scripts, comfyui_gallery, infinite_image_browsing [INFERRED 0.70]
|
||||
- **Workflow Studio UI Navigation Tabs** — templates_index_html_workflow, templates_index_html_nodes, templates_index_html_models, templates_index_html_generate, templates_index_html_prompt, templates_index_html_gallery, templates_index_html_image_edit, templates_index_html_tagger, templates_index_html_settings, templates_index_html_help, templates_index_html_ai [EXTRACTED 0.85]
|
||||
- **Mask Editor One Inpainting Pipeline** — comfyui-workflow-studio_workflows_ws_inpaint_mask_editor_one_load_checkpoint_node, comfyui-workflow-studio_workflows_ws_inpaint_mask_editor_one_mask_editor_one_node, comfyui-workflow-studio_workflows_ws_inpaint_mask_editor_one_clip_text_encode_positive_node, comfyui-workflow-studio_workflows_ws_inpaint_mask_editor_one_clip_text_encode_negative_node, comfyui-workflow-studio_workflows_ws_inpaint_mask_editor_one_vae_encode_for_inpainting_node, comfyui-workflow-studio_workflows_ws_inpaint_mask_editor_one_save_image_node [INFERRED]
|
||||
|
||||
## Communities (157 total, 78 thin omitted)
|
||||
|
||||
### Community 0 - "models-tab.js"
|
||||
Cohesion: 0.06
|
||||
Nodes (120): openModal(), t(), badgeHtml(), bindBadgeModalEvents(), bindBadgeRowEvents(), getBadgePalette(), modelBadgesHtml(), _onGridChange() (+112 more)
|
||||
|
||||
### Community 1 - "prompt-tab.js"
|
||||
Cohesion: 0.06
|
||||
Nodes (107): closeModal(), refreshStylesList(), addChatMessage(), apiCreatePreset(), apiDeletePreset(), apiUpdatePreset(), applyToGenerateUI(), attachFile() (+99 more)
|
||||
|
||||
### Community 2 - "models_routes.py"
|
||||
Cohesion: 0.06
|
||||
Nodes (55): handle_change_preview(), handle_civitai_batch(), handle_civitai_cache(), handle_civitai_fetch(), handle_delete_models(), handle_get_disabled(), handle_get_filepath(), handle_get_groups() (+47 more)
|
||||
|
||||
### Community 3 - "generate-tab.js"
|
||||
Cohesion: 0.06
|
||||
Nodes (64): _applyNamedStyle(), _applyStyleToWorkflow(), _batchGroupState, _batchStyleSelected, _blobToDataUrl(), _buildFolderTree(), _buildSimpleGroupList(), _ckptBatch (+56 more)
|
||||
|
||||
### Community 4 - "lab-tab.js"
|
||||
Cohesion: 0.08
|
||||
Nodes (67): _expandWildcardsInWorkflow(), _annotatedImageRef(), _applyLabLoraToWorkflow(), _applyPlanData(), _applyStyleToText(), _buildIndexImageDataUrl(), _buildLoraPromptInjection(), _buildPlanData() (+59 more)
|
||||
|
||||
### Community 5 - "gallery_routes.py"
|
||||
Cohesion: 0.10
|
||||
Nodes (54): add_to_group(), bulk_favorite(), bulk_group(), clear_group_images(), create_folder_route(), create_group(), delete_folder_route(), delete_group() (+46 more)
|
||||
|
||||
### Community 6 - "gallery-tab.js"
|
||||
Cohesion: 0.09
|
||||
Nodes (54): addTag(), API, apiFetch(), _appendNextPage(), _applySelectionToDOM(), _attachScrollSentinel(), bindEvents(), bulkAddToGroup() (+46 more)
|
||||
|
||||
### Community 8 - "ai-tab.js"
|
||||
Cohesion: 0.09
|
||||
Nodes (51): appendChatBubble(), _appendSkillSaveButton(), _appendSvgPreview(), _applyGenOptions(), buildTranslationMessages(), callChat(), callLLM(), callVLM() (+43 more)
|
||||
|
||||
### Community 9 - "Backend API Routes"
|
||||
Cohesion: 0.05
|
||||
Nodes (46): aiohttp, Jinja2, py/routes, _eagle_add(), _eagle_add_from_path(), _eagle_test(), handle_add(), handle_test() (+38 more)
|
||||
|
||||
### Community 10 - "feeder-tab.js"
|
||||
Cohesion: 0.10
|
||||
Nodes (49): _applyGalToWorkflow(), _applyPreset(), _applyToGalNode(), _applyToNode(), _applyToWorkflow(), _deletePreset(), _deselectAll(), _feederNodes() (+41 more)
|
||||
|
||||
### Community 11 - "NodesService"
|
||||
Cohesion: 0.09
|
||||
Nodes (28): handle_create_set(), handle_delete_set(), handle_export_set(), handle_get_groups(), handle_get_metadata(), handle_list_sets(), handle_save_groups(), handle_save_metadata() (+20 more)
|
||||
|
||||
### Community 12 - "settings_routes.py"
|
||||
Cohesion: 0.09
|
||||
Nodes (44): _apply_import_bundle(), _build_export_bundle(), _find_style(), _get_comfyui_output_dir(), handle_create_style(), handle_delete_style(), handle_export(), handle_get() (+36 more)
|
||||
|
||||
### Community 13 - "metadata-tab.js"
|
||||
Cohesion: 0.11
|
||||
Nodes (40): buildLoRAItem(), buildModelItem(), buildPromptItem(), collectAllNodes(), collectUnique(), extractAllMetadata(), fromWorkflow(), extractCheckpoints() (+32 more)
|
||||
|
||||
### Community 14 - "node_sets_menu.js"
|
||||
Cohesion: 0.08
|
||||
Nodes (37): AI_BACKEND_DEFAULT_URLS, AI_LANG_NAMES, convertApiToUiWorkflow(), _extractAllMetadata(), _extractWorkflowFromEXIF(), fetchGroups(), fetchMetadata(), fetchNodeSets() (+29 more)
|
||||
|
||||
### Community 15 - "GalleryService"
|
||||
Cohesion: 0.10
|
||||
Nodes (13): GalleryService, Path, 許可するルートパスを更新する（Settings変更時に呼ぶ）, ComfyUI実outputフォルダの不変ルートを設定する（起動時に一度だけ呼ぶ）, パスが許可ルート配下かチェック（パストラバーサル防止）。 Output ギャラリーの _allowed_root、またはComfyUI実outputフォルダの…, outputフォルダのフォルダツリーを返す, 画像と同名の.txtサイドカーにプロンプトテキストを保存する, ワークフローを抽出する。 優先順位: PNG[workflow] > PNG[prompt] > gallery_metadata.json[workflow] (+5 more)
|
||||
|
||||
### Community 16 - "workflow-tab.js"
|
||||
Cohesion: 0.13
|
||||
Nodes (35): readJsonStorage(), badgeHtml(), _buildStructuredSummaryText(), _buildSummarySourceText(), clearBatch(), closeSidePanel(), deleteWorkflow(), fetchWorkflows() (+27 more)
|
||||
|
||||
### Community 17 - "tagger_routes.py"
|
||||
Cohesion: 0.14
|
||||
Nodes (24): Connection, handle_batch_start(), handle_batch_status(), handle_batch_stop(), handle_db_delete(), handle_db_export(), handle_db_list(), handle_db_save() (+16 more)
|
||||
|
||||
### Community 18 - "WildcardService"
|
||||
Cohesion: 0.12
|
||||
Nodes (21): handle_create_link(), handle_delete(), handle_get_content(), handle_link_status(), handle_list(), handle_remove_link(), handle_save(), Application (+13 more)
|
||||
|
||||
### Community 19 - "t"
|
||||
Cohesion: 0.12
|
||||
Nodes (35): _applyI18n(), _batchStart(), _batchStop(), _dbDelete(), _dbExport(), _dbLoad(), _dbSave(), _dbSearch() (+27 more)
|
||||
|
||||
### Community 20 - "renderContent"
|
||||
Cohesion: 0.14
|
||||
Nodes (35): createDraggableItem(), createDraggablePromptItem(), createDraggableWfItem(), createModelItem(), esc(), extractPackageName(), fetchModelList(), getNodeCategory() (+27 more)
|
||||
|
||||
### Community 21 - "comfyui-editor.js"
|
||||
Cohesion: 0.08
|
||||
Nodes (28): _applyLoraToNode(), _attachPromptWeightControl(), _buildLoraManagerSyntax(), _buildLoraSyntax(), _buildPresetOptions(), _compositeImageWithMask(), _I2I_PLACEHOLDER_DEFAULT, _imageInputToDataURL() (+20 more)
|
||||
|
||||
### Community 22 - "External Plugin Ecosystem"
|
||||
Cohesion: 0.06
|
||||
Nodes (33): Canvas Snapshot, CivitAI, ComfyUI-Custom-Scripts, ComfyUI-Gallery, ComfyUI-Lora-Manager, comfyui-mask-editor-one, README Index — ComfyUI-Workflow-Studio, ComfyUI-Workflow-Studio (+25 more)
|
||||
|
||||
### Community 23 - "TaggerService"
|
||||
Cohesion: 0.14
|
||||
Nodes (5): Image, Path, WD Tagger / DeepDanbooru / Ollama VLM 推論サービス。, パストラバーサル防止: セパレータ・NUL・相対参照を拒否, TaggerService
|
||||
|
||||
### Community 24 - "top_menu_extension.js"
|
||||
Cohesion: 0.13
|
||||
Nodes (29): ../../scripts/ui/components/button.js, ../../scripts/ui/components/buttonGroup.js, getNodeSetsIcon(), injectStyles(), NODE_SETS_TOOLTIP, saveSelectedAsNodeSet(), attachTopMenuButton(), beforeRegisterNodeDef() (+21 more)
|
||||
|
||||
### Community 25 - "settings-tab.js"
|
||||
Cohesion: 0.12
|
||||
Nodes (29): applyCustomFont(), applyCustomOverrides(), applyTheme(), BG_PATTERNS, buildLangOptions(), clearCustomOverrides(), COLOR_TARGETS, FONT_OPTIONS (+21 more)
|
||||
|
||||
### Community 26 - "setupAiHandlers"
|
||||
Cohesion: 0.11
|
||||
Nodes (28): _aiApplyGenOptions(), aiCallChat(), aiCallLLM(), aiCallVLM(), aiFetchModels(), aiFileToBase64(), aiLooksUntranslated(), aiSkillDeleteFile() (+20 more)
|
||||
|
||||
### Community 27 - "PromptsService"
|
||||
Cohesion: 0.13
|
||||
Nodes (16): handle_create(), handle_delete(), handle_list(), handle_update(), Application, Request, Response, Prompt presets API routes. (+8 more)
|
||||
|
||||
### Community 28 - "workflow_routes.py"
|
||||
Cohesion: 0.15
|
||||
Nodes (26): handle_analyze(), handle_change_thumbnail(), handle_delete(), handle_import(), handle_list(), handle_metadata(), handle_raw(), handle_reanalyze_all() (+18 more)
|
||||
|
||||
### Community 29 - "GalleryMetadataStore"
|
||||
Cohesion: 0.13
|
||||
Nodes (9): GalleryMetadataStore, Path, グループ名を変更し、全画像のgroupsフィールドも更新する, gallery_metadata.json の構造: { "images": { "<abs_path>": { "favorite": bool,…, 存在しないパスをグループから一括削除する（1回の保存で完結）, グループメンバーのパスをsetで返す（高速フィルタ用）, グループ内の全画像を除外する（グループ自体は残す）, 画像のパスキーを変更する（ファイル移動後のメタデータ引継ぎ） (+1 more)
|
||||
|
||||
### Community 30 - "WorkflowService"
|
||||
Cohesion: 0.13
|
||||
Nodes (13): Get raw workflow JSON content., Save metadata (tags, memo, summary, etc.) for a workflow., Manages workflow files and metadata., Import workflow files. Returns list of results., Rename workflow and its associated thumbnail., Delete workflow JSON and associated thumbnail., Re-analyze a workflow and save results to metadata., Re-analyze all workflows and update metadata. (+5 more)
|
||||
|
||||
### Community 31 - "lab_routes.py"
|
||||
Cohesion: 0.15
|
||||
Nodes (14): handle_delete(), handle_get_content(), handle_list(), handle_save(), handle_save_index_to_output(), Application, Request, Response (+6 more)
|
||||
|
||||
### Community 32 - "SkillService"
|
||||
Cohesion: 0.14
|
||||
Nodes (16): handle_delete(), handle_get_content(), handle_list(), handle_save(), Application, Request, Response, AI skill (.md system prompt) file management API routes. (+8 more)
|
||||
|
||||
### Community 35 - "CivitaiService"
|
||||
Cohesion: 0.15
|
||||
Nodes (11): CivitaiService, Build request headers, optionally including Bearer token., Calculate SHA256 hash of a file., Fetch model version info from CivitAI by SHA256 hash (GET). 429/5xx…, POST /model-versions/by-hash でハッシュリストを一括取得（最大100件/リクエスト）。 レスポンスの…, Extract relevant fields from CivitAI API response., Batch fetch CivitAI info for multiple model files. Phase 1: SHA256…, Clear cache for a specific hash or all. (+3 more)
|
||||
|
||||
### Community 38 - "Registry Publish & Docs"
|
||||
Cohesion: 0.10
|
||||
Nodes (22): actions/checkout@v4, Comfy-Org/publish-node-action@main, DEVLOG.md, Publish to Comfy Registry Workflow, graphifyignore, main Branch, pyproject.toml, README.md (+14 more)
|
||||
|
||||
### Community 39 - "gallery_service.py"
|
||||
Cohesion: 0.10
|
||||
Nodes (13): Gallery Metadata Store - ギャラリー画像のメタデータ永続化, _clean_vault_tags(), _decode_image_data_url(), _FolderCache, _parse_vault_yaml_leaves(), Gallery Service - outputフォルダの画像管理、メタデータ閲覧, フォルダ単位の画像スキャン結果キャッシュ。 フォルダのmtimeが変わった場合、またはTTL超過時に再スキャンする。, data URL (data:<mime>;base64,<data>) をデコードし (バイト列, 拡張子) を返す。 ヘッダが無ければPNG扱い（旧互換）。 (+5 more)
|
||||
|
||||
### Community 40 - "Generate & Export Utilities"
|
||||
Cohesion: 0.17
|
||||
Nodes (8): showToast(), _applyDefaultCheckpointIfEnabled(), loadWorkflowIntoEditor(), saveCurrentWorkflow(), FileExport, GmicIntegration, _loadPlanWorkflow(), onLoadGenUIClick()
|
||||
|
||||
### Community 41 - "image-prompt-tab.js"
|
||||
Cohesion: 0.20
|
||||
Nodes (21): activateImagePromptTab(), addSelectedToChips(), API, apiFetch(), cleanPromptText(), clearAllChips(), copyFinalPrompt(), createThumbCard() (+13 more)
|
||||
|
||||
### Community 42 - "KSampler Node"
|
||||
Cohesion: 0.19
|
||||
Nodes (20): CLIP Text Encode Negative Prompt Node, CLIP Text Encode Positive Prompt Node, Inpaint with Mask Editor One (Workflow Screenshot), Load Checkpoint Node, Mask Editor One Node, Save Image Node, VAE Encode (for Inpainting) Node, 3D Pose Editor Node (comfyui-vrm-pose-editor) (+12 more)
|
||||
|
||||
### Community 43 - "ComfyUI Client & Mask Bridge"
|
||||
Cohesion: 0.16
|
||||
Nodes (8): comfyUI, comfyEditor, comfyWorkflow, MaskEditorOneBridge, MASK_TEXT_FONTS, fitToCanvas(), TOOL_DEFS, TEXT_FONTS
|
||||
|
||||
### Community 44 - "style-catalog-tab.js"
|
||||
Cohesion: 0.22
|
||||
Nodes (18): selectStyleByName(), activateStyleCatalogTab(), API, apiFetch(), copyText(), createThumbCard(), debounce(), initStyleCatalogTab() (+10 more)
|
||||
|
||||
### Community 45 - "add"
|
||||
Cohesion: 0.22
|
||||
Nodes (19): add(), fetchWorkflowRaw(), getCanvasDropPos(), handleInfoFile(), installCanvasDropHandler(), loadWorkflowOnCanvas(), placeClipTextEncodeNode(), placeLoraMgrNode() (+11 more)
|
||||
|
||||
### Community 46 - "comfyui-workflow.js"
|
||||
Cohesion: 0.20
|
||||
Nodes (13): _CONTROL_AFTER_GENERATE, _findInjectedWidgetIndex(), _flattenSubgraphs(), _getDynamicComboSubNames(), _getWidgetInputNames(), _getWidgetInputTypes(), _isExtraWidgetValue(), _isLinkedWidgetName() (+5 more)
|
||||
|
||||
### Community 51 - "gmic_routes.py"
|
||||
Cohesion: 0.21
|
||||
Nodes (14): _gmic_run_gui(), handle_open(), handle_result(), handle_status(), Application, Request, Response, G'MIC-Qt integration API routes. (+6 more)
|
||||
|
||||
### Community 52 - "ollama_routes.py"
|
||||
Cohesion: 0.23
|
||||
Nodes (14): _get_ollama_config(), handle_chat(), handle_models(), handle_test(), Application, Request, Response, Ollama API proxy routes. (+6 more)
|
||||
|
||||
### Community 54 - "analyze_workflow"
|
||||
Cohesion: 0.20
|
||||
Nodes (13): analyze_workflow(), _clip_type_from_ui_node(), _collect_all_ui_nodes(), _detect_model_type_from_name(), _model_name_from_api_node(), _model_name_from_ui_node(), Workflow analyzer - detects model types and counts input/output nodes., Analyze workflow JSON and return model types, input/output node counts. (+5 more)
|
||||
|
||||
### Community 56 - "i18n.js"
|
||||
Cohesion: 0.17
|
||||
Nodes (12): getLang(), getLanguageOptions(), getSummaryLang(), getSummaryLanguageOptions(), getSummaryPrompt(), LANGUAGE_OPTIONS, LANGUAGES, setLang() (+4 more)
|
||||
|
||||
### Community 57 - ".list_images"
|
||||
Cohesion: 0.17
|
||||
Nodes (6): os.scandir() で画像ファイルを1回のシステムコールで列挙。 キャッシュがあればそれを返す。 Returns: [(name,…, os.walk() でフォルダ配下を再帰的に列挙する（サブフォルダの画像も含む）。 ImagePrompt/Style…, 指定フォルダ内の画像一覧を返す（recursive=Trueならサブフォルダも含める）, 画像のプロンプトテキストを返す。 1. 画像と同名の .txt サイドカーがあればそれを使う 2. 無ければ ponyxlWildcardsVault 形式…, image_path の祖先を辿り、直下に thumbnails(_option2) を持つフォルダ…, ponyxlWildcardsVault形式のフォールバック解決。 thumbnails_option2 の "{leaf}.preview3.ext"…
|
||||
|
||||
### Community 60 - "gallery_feeder_extension.js"
|
||||
Cohesion: 0.36
|
||||
Nodes (11): beforeRegisterNodeDef(), fetchGroupImages(), getInputWidget(), getOrCreate(), _ns, setWidgetValue(), startLoop(), stopLoop() (+3 more)
|
||||
|
||||
### Community 61 - "createPanel"
|
||||
Cohesion: 0.24
|
||||
Nodes (12): applyTheme(), buildThemePanel(), createPanel(), fetchModelGroups(), fetchModelMetadata(), fetchPrompts(), fetchWorkflows(), loadModelsData() (+4 more)
|
||||
|
||||
### Community 62 - "util.js"
|
||||
Cohesion: 0.31
|
||||
Nodes (10): AI_BACKEND_DEFAULT_URLS, _bytesToDataUrl(), _dataUrlToBytes(), embedPngTextChunk(), getEagleSettings(), getSettings(), _joinBytes(), _n2b() (+2 more)
|
||||
|
||||
### Community 64 - "Workflow Analyzer Internals"
|
||||
Cohesion: 0.20
|
||||
Nodes (10): EXTRA_PNGINFO, last_link_id, last_node_id, links array, Position Index, PROMPT, widgets_values, workflow_analyzer.py (+2 more)
|
||||
|
||||
### Community 65 - "WFS_GalleryFeeder"
|
||||
Cohesion: 0.27
|
||||
Nodes (4): Path, WFS_GalleryFeeder – Feeds images from a gallery group into a workflow., Gallery グループ内の画像をインデックス順に1枚ずつ出力するノード。, WFS_GalleryFeeder
|
||||
|
||||
### Community 66 - "app.js"
|
||||
Cohesion: 0.22
|
||||
Nodes (7): applyI18nToHtml(), initModal(), _onHelpSearch(), initI18n(), applyJsonColors(), applyModelTabActiveColor(), applyTextareaFontSize()
|
||||
|
||||
### Community 69 - "fromWorkflow"
|
||||
Cohesion: 0.36
|
||||
Nodes (10): _collectAllNodes(), _collectUnique(), fromWorkflow(), _extractCheckpoints(), _extractDiffusionModels(), _extractLoRAs(), _extractMarkdownNoteModels(), addU() (+2 more)
|
||||
|
||||
### Community 70 - "_extractPromptsAPI"
|
||||
Cohesion: 0.33
|
||||
Nodes (10): _extractPrompts(), _extractPromptsAPI(), _extractPromptsFromNodeSet(), _extractPromptsLiteGraph(), _isPromptStylerNode(), _isSamplerNode(), _isTextEncoderNode(), _resolveEditPlusText() (+2 more)
|
||||
|
||||
### Community 71 - "generate_doc_index.py"
|
||||
Cohesion: 0.31
|
||||
Nodes (7): main(), PostToolUse hook: regenerate README.index.md / DEVLOG.index.md right after…, Path, generate_devlog_index(), generate_readme_index(), main(), Regenerate README.index.md / DEVLOG.index.md from README.md / DEVLOG.md.…
|
||||
|
||||
### Community 72 - "WS Inpaint Basic Workflow"
|
||||
Cohesion: 0.53
|
||||
Nodes (9): CLIP Text Encode (Prompt) - negative conditioning, CLIP Text Encode (Prompt) - positive conditioning, KSampler node (ddim sampler, 30 steps, cfg 4.0, denoise 1.00), Load Checkpoint node (hayochamixIDXL_v11+_bl checkpoint), Load Image node (clipspace painted mask input), Save Image node (ComfyUI filename prefix), VAE Decode node, VAE Encode (for Inpainting) node with grow_mask_by (+1 more)
|
||||
|
||||
### Community 73 - "._bg_index_folder"
|
||||
Cohesion: 0.25
|
||||
Nodes (5): Event, バックグラウンドスレッドで未キャッシュ画像のprompt_cacheを構築する。 10枚処理するたびに50msスリープしてメインスレッドへの影響を最小化。, PNG/JPEGからメタデータを抽出し、保存済みメタと合わせて返す, PNGのtEXtチャンクからメタデータを抽出, JPEGのEXIF/commentからメタデータを抽出（簡易）
|
||||
|
||||
### Community 74 - "SettingsService"
|
||||
Cohesion: 0.28
|
||||
Nodes (4): Return CivitAI API key. Env var CIVITAI_API_KEY takes priority over…, Merge updates into existing settings., Manages application settings (data/settings.json)., SettingsService
|
||||
|
||||
### Community 75 - "import_style_prompt_seed.py"
|
||||
Cohesion: 0.36
|
||||
Nodes (8): clean_tags(), import_category(), main(), parse_yaml_leaves(), Path, Style/Prompt ギャラリー用シードデータ投入スクリプト。 comfyui_prompt_gallery / prompt_builder_proto…, comfyui_prompt_gallery の parseYamlForImages と同じ簡易パーサ（PyYAML不要）。 「key:」行の直後が「-…, 1カテゴリ分を投入する。戻り値: (copied, images_without_prompt, leaves_total)
|
||||
|
||||
### Community 76 - "Image Edit (FireRed Image Edit 1.1) node"
|
||||
Cohesion: 0.32
|
||||
Nodes (8): 3D Pose Editor node, FireRed-Image-Edit-1.1_fp8mixed_comfy.safetensors, FireRed-Image-Edit-1.0-Lightning-8steps-v1.0.safetensors (LoRA), Image Edit (FireRed Image Edit 1.1) node, qwen_2.5_vl_7b_fp8_scaled.safetensors (CLIP), qwen_image_vae.safetensors (VAE), Save Image node, 3D Pose Editor to Image Edit (Fire Red) Workflow
|
||||
|
||||
### Community 77 - "Version 0.3.9"
|
||||
Cohesion: 0.25
|
||||
Nodes (8): Models Tab, Nodes Tab, Prompt Tab, Version 0.1.2, Version 0.1.7, Version 0.2.3, Version 0.3.9, Workflow Tab
|
||||
|
||||
### Community 78 - "DWPose Image Feeder Workflow"
|
||||
Cohesion: 0.52
|
||||
Nodes (7): DWPose Image Feeder Workflow, Apply ControlNet Node, DWPose Estimator Node (comfyui-controlnet-aux), Image Feeder Node (comfyui-image-feeder), KSampler Node, Load Checkpoint Node (animagineXLRealistic_v6), Load ControlNet Model Node (OpenPoseXL2)
|
||||
|
||||
### Community 79 - "civitai_service.py"
|
||||
Cohesion: 0.33
|
||||
Nodes (5): _get_ssl_context(), _make_ssl_context(), CivitAI API integration service., Return an SSL context with CA verification. 1. certifi CA bundle (available in…, Download an image from URL and save to save_path. Returns True on success.
|
||||
|
||||
### Community 80 - "Doc Index Automation"
|
||||
Cohesion: 0.40
|
||||
Nodes (6): on_doc_edit.py, DEVLOG.index.md, generate_doc_index.py, pre-commit, graphify, README.index.md
|
||||
|
||||
### Community 81 - "WFS_PromptText"
|
||||
Cohesion: 0.33
|
||||
Nodes (3): WFS_PromptText – Prompt preset node with positive/negative STRING outputs., Outputs positive and negative prompt strings., WFS_PromptText
|
||||
|
||||
### Community 82 - "workflow_service.py"
|
||||
Cohesion: 0.40
|
||||
Nodes (4): extract_png_workflow(), Extract ComfyUI workflow JSON from PNG metadata., Extract ComfyUI workflow JSON from PNG byte data. Returns dict or None., Workflow CRUD and metadata management service.
|
||||
|
||||
### Community 83 - "Project Directory Layout"
|
||||
Cohesion: 0.40
|
||||
Nodes (5): ComfyUI, SPA, static, templates, web/comfyui
|
||||
|
||||
### Community 84 - "config.py"
|
||||
Cohesion: 0.40
|
||||
Nodes (3): Resolve workflows dir from settings, falling back to default., _resolve_workflows_dir(), Settings management service.
|
||||
|
||||
### Community 85 - "Tagger Tab requirements.txt"
|
||||
Cohesion: 0.40
|
||||
Nodes (5): Tagger Tab requirements.txt, onnxruntime (WD Tagger / SwinV2), piexif (JPEG EXIF tag writing), python-dotenv (Unsloth .env loading), tensorflow (optional, DeepDanbooru)
|
||||
|
||||
### Community 86 - "Data Storage Files"
|
||||
Cohesion: 0.50
|
||||
Nodes (4): data directory, metadata.json, node_metadata.json, settings.json
|
||||
|
||||
### Community 87 - "AI Tool Version History"
|
||||
Cohesion: 0.67
|
||||
Nodes (3): AI TOOL Tab, Version 0.3.14, Version 0.3.99
|
||||
|
||||
### Community 88 - "Chinese Language Support"
|
||||
Cohesion: 1.00
|
||||
Nodes (3): Chinese Language Support, English Language Support, Japanese Language Support
|
||||
|
||||
### Community 89 - "Version 0.3.95"
|
||||
Cohesion: 0.67
|
||||
Nodes (3): ImagePrompt Gallery Subtab, Style_Catalog Subtab, Version 0.3.95
|
||||
|
||||
### Community 90 - "Windows Long Path Handling"
|
||||
Cohesion: 0.67
|
||||
Nodes (3): LongPathsEnabled, MAX_PATH, Windows
|
||||
|
||||
## Knowledge Gaps
|
||||
- **228 isolated node(s):** `state`, `groups`, `state`, `WF_RESERVED_GROUPS`, `AI_BACKEND_DEFAULT_URLS` (+223 more)
|
||||
These have ≤1 connection - possible missing edges or undocumented components.
|
||||
- **78 thin communities (<3 nodes) omitted from report** — run `graphify query` to explore isolated nodes.
|
||||
|
||||
## Suggested Questions
|
||||
_Questions this graph is uniquely positioned to answer:_
|
||||
|
||||
- **Why does `showToast()` connect `Generate & Export Utilities` to `models-tab.js`, `prompt-tab.js`, `generate-tab.js`, `lab-tab.js`, `gallery-tab.js`, `ImageEditTab`, `ai-tab.js`, `feeder-tab.js`, `workflow-tab.js`, `t`, `comfyui-editor.js`, `settings-tab.js`, `LayerManager`, `image-prompt-tab.js`, `ComfyUI Client & Mask Bridge`, `style-catalog-tab.js`, `BlurTool`, `BgRemove`, `app.js`, `InpaintI2IActions`, `Sam3Segmentation`?**
|
||||
_High betweenness centrality (0.062) - this node is a cross-community bridge._
|
||||
- **Why does `t()` connect `models-tab.js` to `prompt-tab.js`, `app.js`, `generate-tab.js`, `lab-tab.js`, `gallery-tab.js`, `ai-tab.js`, `Generate & Export Utilities`, `feeder-tab.js`, `image-prompt-tab.js`, `style-catalog-tab.js`, `metadata-tab.js`, `workflow-tab.js`, `t`, `comfyui-editor.js`, `i18n.js`, `settings-tab.js`?**
|
||||
_High betweenness centrality (0.048) - this node is a cross-community bridge._
|
||||
- **Why does `GalleryService` connect `GalleryService` to `gallery_routes.py`, `.get_image_prompt_root`, `gallery_service.py`, `.get_style_catalog_root`, `._bg_index_folder`, `.start_background_index`, `.list_images`, `GalleryMetadataStore`?**
|
||||
_High betweenness centrality (0.027) - this node is a cross-community bridge._
|
||||
- **What connects `state`, `groups`, `state` to the rest of the system?**
|
||||
_228 weakly-connected nodes found - possible documentation gaps or missing edges._
|
||||
- **Should `models-tab.js` be split into smaller, more focused modules?**
|
||||
_Cohesion score 0.06185919343814081 - nodes in this community are weakly interconnected._
|
||||
- **Should `prompt-tab.js` be split into smaller, more focused modules?**
|
||||
_Cohesion score 0.05572545022086307 - nodes in this community are weakly interconnected._
|
||||
- **Should `models_routes.py` be split into smaller, more focused modules?**
|
||||
_Cohesion score 0.0554954954954955 - nodes in this community are weakly interconnected._
|
||||
File diff suppressed because one or more lines are too long
+92009
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,866 @@
|
||||
{
|
||||
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|
||||
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|
||||
"semantic_hash": "b2c014309df240903aa90a26914c8c19"
|
||||
},
|
||||
".claude/settings.json": {
|
||||
"mtime": 1787279595.344592,
|
||||
"seen": 1787380810.2925637,
|
||||
"ast_hash": "cd2c4308b55c5d1aa1b647cdec814b79",
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"semantic_hash": "cd2c4308b55c5d1aa1b647cdec814b79"
|
||||
},
|
||||
"__init__.py": {
|
||||
"mtime": 1781939762.254596,
|
||||
"seen": 1787279199.0011544,
|
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"ast_hash": "72d9ef06026dff7be74da81ff98a306a",
|
||||
"semantic_hash": "72d9ef06026dff7be74da81ff98a306a"
|
||||
},
|
||||
"prestartup_script.py": {
|
||||
"mtime": 1787036267.332648,
|
||||
"seen": 1787279199.0011563,
|
||||
"ast_hash": "7b5d482ef21a480d1b38356e8d82970b",
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"semantic_hash": "7b5d482ef21a480d1b38356e8d82970b"
|
||||
},
|
||||
"py/__init__.py": {
|
||||
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|
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|
||||
},
|
||||
"py/config.py": {
|
||||
"mtime": 1786245317.3352194,
|
||||
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|
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|
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"py/nodes/__init__.py": {
|
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||||
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|
||||
},
|
||||
"py/nodes/gallery_feeder_node.py": {
|
||||
"mtime": 1781943269.5205867,
|
||||
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|
||||
"ast_hash": "d37c520b0f76e70e60ec7c72db904bf4",
|
||||
"semantic_hash": "d37c520b0f76e70e60ec7c72db904bf4"
|
||||
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|
||||
"py/nodes/prompt_text.py": {
|
||||
"mtime": 1774423045.6669831,
|
||||
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||||
"semantic_hash": "43cf16f182b9eda5fade2b52c2439372"
|
||||
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|
||||
"py/routes/__init__.py": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"py/routes/eagle_routes.py": {
|
||||
"mtime": 1785464911.9023821,
|
||||
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||||
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|
||||
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|
||||
"py/routes/gallery_routes.py": {
|
||||
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||||
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|
||||
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|
||||
"py/routes/gmic_routes.py": {
|
||||
"mtime": 1782544784.03979,
|
||||
"seen": 1787279199.0011659,
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"ast_hash": "b909c65af6590c0ca058136cb0b9234e",
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||||
"semantic_hash": "b909c65af6590c0ca058136cb0b9234e"
|
||||
},
|
||||
"py/routes/lab_routes.py": {
|
||||
"mtime": 1786322410.571655,
|
||||
"seen": 1787279199.0011666,
|
||||
"ast_hash": "48e94f71b11f2e494bb98bb7113e2baa",
|
||||
"semantic_hash": "48e94f71b11f2e494bb98bb7113e2baa"
|
||||
},
|
||||
"py/routes/models_routes.py": {
|
||||
"mtime": 1782289767.1073005,
|
||||
"seen": 1787279199.0011675,
|
||||
"ast_hash": "067aa9c89085fa3e077fdfacbb6be2cd",
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||||
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||||
"ast_hash": "02c572ab1c659d71dfd56e6b2b0334a5",
|
||||
"semantic_hash": "02c572ab1c659d71dfd56e6b2b0334a5"
|
||||
},
|
||||
"workflows/ws_3d_pose_editor_to_Image_Edit( Flux2 Klein ).png": {
|
||||
"mtime": 1779257864.2375357,
|
||||
"seen": 1787279199.001278,
|
||||
"ast_hash": "bb03ed86c7d4460a3568a4a8f90e5f40",
|
||||
"semantic_hash": "bb03ed86c7d4460a3568a4a8f90e5f40"
|
||||
},
|
||||
"workflows/ws_dwpose_image_feeder.png": {
|
||||
"mtime": 1779255324.1356418,
|
||||
"seen": 1787279199.0012789,
|
||||
"ast_hash": "e5db6889f59078e4ead6b89b8c07e91e",
|
||||
"semantic_hash": "e5db6889f59078e4ead6b89b8c07e91e"
|
||||
},
|
||||
"workflows/ws_inpaint_basic.png": {
|
||||
"mtime": 1784801838.7777002,
|
||||
"seen": 1787279199.00128,
|
||||
"ast_hash": "79321edb85aed7c0a8c777accc005486",
|
||||
"semantic_hash": "79321edb85aed7c0a8c777accc005486"
|
||||
},
|
||||
"workflows/ws_inpaint_mask_editor_one.png": {
|
||||
"mtime": 1784801946.027666,
|
||||
"seen": 1787279199.001281,
|
||||
"ast_hash": "bdd62facd6a8176c50e92264994dbbc2",
|
||||
"semantic_hash": "bdd62facd6a8176c50e92264994dbbc2"
|
||||
},
|
||||
"workflows/ws_sd15_t2i_basic.png": {
|
||||
"mtime": 1779253098.1897035,
|
||||
"seen": 1787279199.0012817,
|
||||
"ast_hash": "9582bb698439fff1a748315145538777",
|
||||
"semantic_hash": "9582bb698439fff1a748315145538777"
|
||||
},
|
||||
"workflows/ws_sdxl_2d_pose_editor_i2i.png": {
|
||||
"mtime": 1779256158.4621174,
|
||||
"seen": 1787279199.001283,
|
||||
"ast_hash": "bb7b9e26eae992abe62cd50299da9cb2",
|
||||
"semantic_hash": "bb7b9e26eae992abe62cd50299da9cb2"
|
||||
},
|
||||
"workflows/ws_sdxl_3d pose editor_dwpose.png": {
|
||||
"mtime": 1779255768.6191878,
|
||||
"seen": 1787279199.0012836,
|
||||
"ast_hash": "ac0fc8184ff7e42389fd0d562daa88c4",
|
||||
"semantic_hash": "ac0fc8184ff7e42389fd0d562daa88c4"
|
||||
},
|
||||
"workflows/ws_sdxl_dwpose.png": {
|
||||
"mtime": 1779255037.8606594,
|
||||
"seen": 1787279199.0012846,
|
||||
"ast_hash": "564f58d85a0b960154bd0a30ea111599",
|
||||
"semantic_hash": "564f58d85a0b960154bd0a30ea111599"
|
||||
},
|
||||
"workflows/ws_sdxl_from_metadata_t2i.png": {
|
||||
"mtime": 1779254360.9671092,
|
||||
"seen": 1787279199.0012853,
|
||||
"ast_hash": "98dfdb3ba374b0952c06995843f45171",
|
||||
"semantic_hash": "98dfdb3ba374b0952c06995843f45171"
|
||||
},
|
||||
"workflows/ws_sdxl_i2i_facedetailer.png": {
|
||||
"mtime": 1779253668.0007572,
|
||||
"seen": 1787279199.0012865,
|
||||
"ast_hash": "7285bb2a06aaf8dc7c5b37b84261be61",
|
||||
"semantic_hash": "7285bb2a06aaf8dc7c5b37b84261be61"
|
||||
},
|
||||
"workflows/ws_sdxl_image_feeder_i2i.png": {
|
||||
"mtime": 1779254714.0099926,
|
||||
"seen": 1787279199.0012872,
|
||||
"ast_hash": "13d4e7f87a87fd4c9a78427b38800cc6",
|
||||
"semantic_hash": "13d4e7f87a87fd4c9a78427b38800cc6"
|
||||
},
|
||||
"workflows/ws_sdxl_t2i_basic.png": {
|
||||
"mtime": 1779253180.3890488,
|
||||
"seen": 1787279199.0012882,
|
||||
"ast_hash": "9d8dda899602b8d74c6b23350e58a0d0",
|
||||
"semantic_hash": "9d8dda899602b8d74c6b23350e58a0d0"
|
||||
},
|
||||
"workflows/ws_sdxl_t2i_facedetaller.png": {
|
||||
"mtime": 1779253367.7925913,
|
||||
"seen": 1787279199.0012891,
|
||||
"ast_hash": "51cae742b39316023cf5332f1d794854",
|
||||
"semantic_hash": "51cae742b39316023cf5332f1d794854"
|
||||
},
|
||||
".claude/hooks/on_doc_edit.py": {
|
||||
"mtime": 1787279578.9362085,
|
||||
"seen": 1787380810.2925577,
|
||||
"ast_hash": "1a39a0e2ffaac441e8decccad0e1c42c",
|
||||
"semantic_hash": "1a39a0e2ffaac441e8decccad0e1c42c"
|
||||
},
|
||||
"tools/generate_doc_index.py": {
|
||||
"mtime": 1787279532.9148839,
|
||||
"seen": 1787380810.3572273,
|
||||
"ast_hash": "d0cdaf32b36fdef641a9396fd4ec403f",
|
||||
"semantic_hash": "d0cdaf32b36fdef641a9396fd4ec403f"
|
||||
},
|
||||
".github/workflows/publish.yml": {
|
||||
"mtime": 1778460176.5699222,
|
||||
"seen": 1787380810.3754551,
|
||||
"ast_hash": "a83dab23e8d09fdc8626689e96dfeb0e",
|
||||
"semantic_hash": "a83dab23e8d09fdc8626689e96dfeb0e"
|
||||
},
|
||||
"templates/index.html": {
|
||||
"mtime": 1787377487.6004047,
|
||||
"seen": 1787380810.3754609,
|
||||
"ast_hash": "639c4691eb3439d1707cd972bbed1402",
|
||||
"semantic_hash": "639c4691eb3439d1707cd972bbed1402"
|
||||
},
|
||||
"static/js/prompt-table.js": {
|
||||
"mtime": 1787376568.1601257,
|
||||
"seen": 1787380810.3524997,
|
||||
"ast_hash": "ca7fa878074a6fd12d5a478109c929d9",
|
||||
"semantic_hash": "ca7fa878074a6fd12d5a478109c929d9"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,24 @@
|
||||
"""Runs before ComfyUI imports any custom node — the only place early enough
|
||||
to populate os.environ for code that reads env vars at import/request time.
|
||||
|
||||
Loads a .env file (if present) from this plugin's root directory so the
|
||||
Unsloth backend's API key (UNSLOTH_API_KEY) can be set without editing
|
||||
settings.json or exposing it to the frontend.
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
PLUGIN_DIR = Path(__file__).resolve().parent
|
||||
ENV_FILE = PLUGIN_DIR / ".env"
|
||||
|
||||
if ENV_FILE.exists():
|
||||
try:
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv(ENV_FILE)
|
||||
except ImportError:
|
||||
print(
|
||||
"[WARNING] Workflow Studio: .env file found but python-dotenv is not installed "
|
||||
"(pip install -r requirements.txt). UNSLOTH_API_KEY will not be loaded from .env."
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"[WARNING] Workflow Studio: failed to load .env file: {e}")
|
||||
@@ -70,6 +70,7 @@ PROMPTS_FILE = DATA_DIR / "prompts.json"
|
||||
MODEL_METADATA_FILE = DATA_DIR / "model_metadata.json"
|
||||
WILDCARD_DIR = DATA_DIR / "wildcard"
|
||||
AI_SKILLS_DIR = DATA_DIR / "ai_skills"
|
||||
LAB_PLAN_DIR = DATA_DIR / "lab_plan"
|
||||
|
||||
# Tagger
|
||||
TAGGER_DB_FILE = DATA_DIR / "tagger.db"
|
||||
|
||||
+108
-35
@@ -2,11 +2,9 @@
|
||||
Gallery Routes - ギャラリータブ用APIエンドポイント
|
||||
"""
|
||||
import asyncio
|
||||
import base64
|
||||
import json
|
||||
import mimetypes
|
||||
import logging
|
||||
import re
|
||||
from pathlib import Path
|
||||
from aiohttp import web
|
||||
|
||||
@@ -43,6 +41,16 @@ def _init_allowed_root() -> None:
|
||||
except Exception as e:
|
||||
logger.warning("GalleryRoutes: failed to get ComfyUI output dir: %s", e)
|
||||
|
||||
# ImagePrompt/Style Catalogギャラリー用に実outputフォルダを常に別途保持する
|
||||
# (Settingsで_allowed_rootが別フォルダに変更されていても影響しないようにするため)
|
||||
try:
|
||||
import folder_paths # type: ignore
|
||||
output_dir = folder_paths.get_output_directory()
|
||||
if output_dir and Path(output_dir).is_dir():
|
||||
_service.set_comfy_output_root(output_dir)
|
||||
except Exception as e:
|
||||
logger.warning("GalleryRoutes: failed to get ComfyUI output dir for image-prompt root: %s", e)
|
||||
|
||||
|
||||
_init_allowed_root()
|
||||
|
||||
@@ -55,7 +63,12 @@ def setup_routes(app: web.Application):
|
||||
app.router.add_get("/wfm/gallery/image/serve", serve_image)
|
||||
app.router.add_get("/wfm/gallery/image/thumb", serve_thumb)
|
||||
app.router.add_post("/wfm/gallery/image/meta", save_image_meta)
|
||||
app.router.add_get("/wfm/gallery/image-prompt/root", get_image_prompt_root)
|
||||
app.router.add_post("/wfm/gallery/image/image-prompt", save_image_prompt)
|
||||
app.router.add_get("/wfm/gallery/style-catalog/root", get_style_catalog_root)
|
||||
app.router.add_post("/wfm/gallery/image/save", save_image_to_gallery)
|
||||
app.router.add_post("/wfm/gallery/image/save-to-folder", save_image_to_folder_route)
|
||||
app.router.add_post("/wfm/gallery/output-image/delete", delete_output_image)
|
||||
app.router.add_post("/wfm/gallery/image/favorite", toggle_favorite)
|
||||
app.router.add_get("/wfm/gallery/groups", list_groups)
|
||||
app.router.add_post("/wfm/gallery/groups", create_group)
|
||||
@@ -104,6 +117,7 @@ async def list_images(request: web.Request) -> web.Response:
|
||||
favorite_only = request.rel_url.query.get("favorite", "false") == "true"
|
||||
tag_filter = request.rel_url.query.get("tag", "")
|
||||
group_filter = request.rel_url.query.get("group", "")
|
||||
recursive = request.rel_url.query.get("recursive", "false") == "true"
|
||||
|
||||
if not folder:
|
||||
return web.json_response({"error": "folder parameter required"}, status=400)
|
||||
@@ -117,6 +131,7 @@ async def list_images(request: web.Request) -> web.Response:
|
||||
favorite_only=favorite_only,
|
||||
tag_filter=tag_filter,
|
||||
group_filter=group_filter,
|
||||
recursive=recursive,
|
||||
)
|
||||
# 検索なしのフォルダロード時にバックグラウンドインデックスを起動
|
||||
if not search:
|
||||
@@ -148,9 +163,15 @@ async def get_image_workflow(request: web.Request) -> web.Response:
|
||||
return web.json_response({"error": "path required"}, status=400)
|
||||
try:
|
||||
wf = _service.extract_workflow_from_metadata(path)
|
||||
if wf is None:
|
||||
return web.json_response({"workflow": None, "has_workflow": False})
|
||||
return web.json_response({"workflow": wf, "has_workflow": True})
|
||||
# prompt_workflow: プロンプト抽出専用（PNG[prompt]優先）。トップレベルとサブグラフに
|
||||
# 独立した複数系統を持つワークフローではUI形式(workflow)からの抽出がトップレベル
|
||||
# 系統しか拾えないことがあるため、Promptタブはこちらを使う(Metadataタブと揃える)。
|
||||
prompt_wf = _service.extract_prompt_workflow_from_metadata(path)
|
||||
return web.json_response({
|
||||
"workflow": wf,
|
||||
"has_workflow": wf is not None,
|
||||
"prompt_workflow": prompt_wf,
|
||||
})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
@@ -248,6 +269,36 @@ async def save_image_meta(request: web.Request) -> web.Response:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def get_image_prompt_root(request: web.Request) -> web.Response:
|
||||
"""GET /wfm/gallery/image-prompt/root — ImagePromptギャラリーのルートフォルダを返す"""
|
||||
root = _service.get_image_prompt_root()
|
||||
if root is None:
|
||||
return web.json_response({"error": "ComfyUI output directory not resolved"}, status=500)
|
||||
return web.json_response({"root": root})
|
||||
|
||||
|
||||
async def save_image_prompt(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/image/image-prompt — 画像の.txtサイドカーにプロンプトを保存"""
|
||||
try:
|
||||
body = await request.json()
|
||||
img_path = body.get("path", "")
|
||||
prompt = body.get("prompt", "")
|
||||
if not img_path:
|
||||
return web.json_response({"error": "path required"}, status=400)
|
||||
ok = _service.save_image_prompt(img_path, prompt)
|
||||
return web.json_response({"ok": ok})
|
||||
except Exception as e:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def get_style_catalog_root(request: web.Request) -> web.Response:
|
||||
"""GET /wfm/gallery/style-catalog/root — Style Catalogギャラリーのルートフォルダを返す"""
|
||||
root = _service.get_style_catalog_root()
|
||||
if root is None:
|
||||
return web.json_response({"error": "ComfyUI output directory not resolved"}, status=500)
|
||||
return web.json_response({"root": root})
|
||||
|
||||
|
||||
async def toggle_favorite(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
@@ -260,15 +311,6 @@ async def toggle_favorite(request: web.Request) -> web.Response:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
_SAVE_EXT_BY_MIME = {
|
||||
"image/png": ".png",
|
||||
"image/jpeg": ".jpg",
|
||||
"image/webp": ".webp",
|
||||
"image/gif": ".gif",
|
||||
"image/svg+xml": ".svg",
|
||||
}
|
||||
|
||||
|
||||
async def save_image_to_gallery(request: web.Request) -> web.Response:
|
||||
"""Image Edit Tab / AI Chat からの画像(PNG等)やSVGをギャラリーのルートフォルダに保存する。
|
||||
imageData は data URL (data:<mime>;base64,<data>) を想定。ヘッダがなければPNG扱い（旧互換）。"""
|
||||
@@ -283,33 +325,64 @@ async def save_image_to_gallery(request: web.Request) -> web.Response:
|
||||
if _service._allowed_root is None:
|
||||
return web.json_response({"error": "Gallery root not configured. Open Gallery tab and set output folder first."}, status=500)
|
||||
|
||||
# data URL から MIME タイプを判定し、拡張子を決定する
|
||||
raw = image_data
|
||||
mime = "image/png"
|
||||
if raw.startswith("data:"):
|
||||
header, _, raw = raw.partition(",")
|
||||
m = re.match(r"data:([^;]+)", header)
|
||||
if m:
|
||||
mime = m.group(1)
|
||||
ext = _SAVE_EXT_BY_MIME.get(mime, ".png")
|
||||
result = _service.save_image_to_folder(str(_service._allowed_root), filename, image_data)
|
||||
if not result["ok"]:
|
||||
return web.json_response({"error": result["error"]}, status=400)
|
||||
|
||||
# ファイル名サニタイズ（OSで使えない文字を除去）
|
||||
safe = re.sub(r'[<>:"/\\|?*\x00-\x1f]', "_", filename)
|
||||
if not safe.lower().endswith(ext):
|
||||
safe += ext
|
||||
|
||||
image_bytes = base64.b64decode(raw)
|
||||
|
||||
save_path = _service._allowed_root / safe
|
||||
save_path.write_bytes(image_bytes)
|
||||
|
||||
logger.info("save_image_to_gallery: saved %s (%d bytes)", save_path, len(image_bytes))
|
||||
return web.json_response({"ok": True, "path": str(save_path)})
|
||||
logger.info("save_image_to_gallery: saved %s", result["path"])
|
||||
return web.json_response({"ok": True, "path": result["path"]})
|
||||
except Exception as e:
|
||||
logger.error("save_image_to_gallery error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def save_image_to_folder_route(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/image/save-to-folder — 任意フォルダへ画像を保存（同名なら上書き）。
|
||||
Style Catalogのカタログ作成で、スタイル名をファイル名として使う際に利用する。"""
|
||||
try:
|
||||
body = await request.json()
|
||||
folder = body.get("folder", "").strip()
|
||||
filename = body.get("filename", "").strip()
|
||||
image_data = body.get("imageData", "")
|
||||
|
||||
if not folder or not filename or not image_data:
|
||||
return web.json_response({"error": "folder, filename and imageData required"}, status=400)
|
||||
|
||||
result = _service.save_image_to_folder(folder, filename, image_data)
|
||||
if not result["ok"]:
|
||||
return web.json_response({"error": result["error"]}, status=400)
|
||||
return web.json_response({"ok": True, "path": result["path"]})
|
||||
except Exception as e:
|
||||
logger.error("save_image_to_folder_route error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def delete_output_image(request: web.Request) -> web.Response:
|
||||
"""POST /wfm/gallery/output-image/delete — ComfyUI標準の画像参照(filename/subfolder/type)から
|
||||
実ファイルを削除する。Style Catalogのカタログ作成で「Outputフォルダに画像を残さない」
|
||||
オプションが有効な時、カタログへコピー保存した直後の元画像を消すために使う。
|
||||
type="output" 以外は拒否する（temp/inputの誤削除を防止）。"""
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = (body.get("filename") or "").strip()
|
||||
subfolder = body.get("subfolder") or ""
|
||||
img_type = body.get("type") or ""
|
||||
if not filename or img_type != "output":
|
||||
return web.json_response({"error": "filename required and type must be 'output'"}, status=400)
|
||||
|
||||
import folder_paths # type: ignore
|
||||
output_dir = Path(folder_paths.get_output_directory())
|
||||
target = output_dir / subfolder / filename
|
||||
|
||||
result = await asyncio.to_thread(_service.delete_images, [str(target)])
|
||||
if result["deleted"]:
|
||||
return web.json_response({"ok": True})
|
||||
return web.json_response({"error": "; ".join(result["errors"]) or "File not found"}, status=404)
|
||||
except Exception as e:
|
||||
logger.error("delete_output_image error: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# グループ管理
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
@@ -0,0 +1,85 @@
|
||||
"""Lab tab plan file management API routes."""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
from ..config import LAB_PLAN_DIR
|
||||
from ..services.lab_plan_service import LabPlanService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_service = LabPlanService(LAB_PLAN_DIR)
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
app.router.add_get("/api/wfm/lab/plans", handle_list)
|
||||
app.router.add_get("/api/wfm/lab/plans/content", handle_get_content)
|
||||
app.router.add_post("/api/wfm/lab/plans/save", handle_save)
|
||||
app.router.add_post("/api/wfm/lab/plans/delete", handle_delete)
|
||||
app.router.add_post("/api/wfm/lab/index-image/save-to-output", handle_save_index_to_output)
|
||||
|
||||
|
||||
async def handle_list(request: web.Request) -> web.Response:
|
||||
try:
|
||||
result = await asyncio.to_thread(_service.list_plans)
|
||||
return web.json_response(result)
|
||||
except Exception as e:
|
||||
logger.error("Error listing lab plans: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_get_content(request: web.Request) -> web.Response:
|
||||
filename = request.rel_url.query.get("filename", "")
|
||||
try:
|
||||
data = await asyncio.to_thread(_service.get_plan, filename)
|
||||
return web.json_response({"data": data})
|
||||
except FileNotFoundError:
|
||||
return web.json_response({"error": "not found"}, status=404)
|
||||
except ValueError as e:
|
||||
return web.json_response({"error": str(e)}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error getting lab plan content: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_save(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "")
|
||||
data = body.get("data", {})
|
||||
index_image_base64 = body.get("index_image_base64")
|
||||
result = await asyncio.to_thread(_service.save_plan, filename, data, index_image_base64)
|
||||
return web.json_response({"status": "ok", **result})
|
||||
except ValueError as e:
|
||||
return web.json_response({"error": str(e)}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error saving lab plan: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_delete(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "")
|
||||
await asyncio.to_thread(_service.delete_plan, filename)
|
||||
return web.json_response({"status": "ok"})
|
||||
except ValueError as e:
|
||||
return web.json_response({"error": str(e)}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error deleting lab plan: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_save_index_to_output(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
image_base64 = body.get("image_base64", "")
|
||||
if not image_base64:
|
||||
return web.json_response({"error": "image_base64 required"}, status=400)
|
||||
result = await asyncio.to_thread(_service.save_index_image_to_output, image_base64)
|
||||
return web.json_response({"status": "ok", **result})
|
||||
except Exception as e:
|
||||
logger.error("Error saving lab index image to output: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
@@ -1,8 +1,11 @@
|
||||
"""Settings API routes."""
|
||||
|
||||
import asyncio
|
||||
import io
|
||||
import json
|
||||
import logging
|
||||
import shutil
|
||||
import zipfile
|
||||
from pathlib import Path
|
||||
|
||||
from aiohttp import web
|
||||
@@ -24,6 +27,7 @@ _DATA_FILES = [
|
||||
"model_metadata.json",
|
||||
"gallery_metadata.json",
|
||||
"tagger_settings.json",
|
||||
"civitai_cache.json",
|
||||
]
|
||||
|
||||
|
||||
@@ -37,7 +41,12 @@ def setup_routes(app: web.Application):
|
||||
app.router.add_post("/api/wfm/settings/output-dir", handle_set_output_dir)
|
||||
app.router.add_get("/api/wfm/settings/export", handle_export)
|
||||
app.router.add_post("/api/wfm/settings/import", handle_import)
|
||||
app.router.add_get("/api/wfm/settings/export-full", handle_export_full)
|
||||
app.router.add_post("/api/wfm/settings/import-full", handle_import_full)
|
||||
app.router.add_get("/api/wfm/styles", handle_get_styles)
|
||||
app.router.add_post("/api/wfm/styles", handle_create_style)
|
||||
app.router.add_put("/api/wfm/styles/{name}", handle_update_style)
|
||||
app.router.add_delete("/api/wfm/styles/{name}", handle_delete_style)
|
||||
|
||||
|
||||
async def handle_get(request: web.Request) -> web.Response:
|
||||
@@ -245,18 +254,174 @@ async def handle_import(request: web.Request) -> web.Response:
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
def _load_styles() -> list:
|
||||
"""Load all style JSON files from DATA_DIR/style/ directory."""
|
||||
# Full-backup (ZIP) export/import: unlike the flat _DATA_FILES JSON bundle above, this
|
||||
# covers the whole DATA_DIR — directory-shaped data (ai_skills/, lab_plan/, style/) and
|
||||
# the SQLite tagger.db that the JSON bundle can't represent. Excludes transient/cache
|
||||
# dirs, and wildcard/ specifically because it's typically a symlink into a *different*
|
||||
# custom_node's folder (comfyui-impact-pack) rather than this plugin's own data.
|
||||
_FULL_BACKUP_EXCLUDE_NAMES = {"gmic_temp", "thumb_cache", "wildcard"}
|
||||
|
||||
|
||||
def _add_file_to_zip(zf: zipfile.ZipFile, path: Path, base: Path):
|
||||
rel = str(path.relative_to(base)).replace("\\", "/")
|
||||
if path.name == "settings.json":
|
||||
# Same API-key scrubbing as the JSON-bundle export — fall through to a raw
|
||||
# copy if the file turns out to be unreadable/corrupt rather than skipping it.
|
||||
try:
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
data = {k: v for k, v in data.items() if k not in _SETTINGS_EXPORT_EXCLUDE}
|
||||
zf.writestr(rel, json.dumps(data, ensure_ascii=False, indent=2))
|
||||
return
|
||||
except Exception:
|
||||
pass
|
||||
zf.write(path, rel)
|
||||
|
||||
|
||||
# Two folders this plugin doesn't own but users often want backed up alongside their
|
||||
# own data: ComfyUI's own default workflows dir, and the Impact Pack's wildcard
|
||||
# library (a separate custom_node — the plugin's own wildcard/ is usually just a
|
||||
# symlink into this same folder, see _FULL_BACKUP_EXCLUDE_NAMES above). Opt-in only
|
||||
# (see handle_export_full's include_workflows/include_wildcard query params), and
|
||||
# stored under an "_external/" prefix so _apply_full_backup_zip can recognize and
|
||||
# skip them on restore — see that function's docstring for why.
|
||||
def _default_workflows_dir() -> Path:
|
||||
from ..config import COMFYUI_ROOT
|
||||
return COMFYUI_ROOT / "user" / "default" / "workflows"
|
||||
|
||||
|
||||
def _impact_pack_wildcard_dir() -> Path:
|
||||
from ..config import COMFYUI_ROOT
|
||||
return COMFYUI_ROOT / "custom_nodes" / "comfyui-impact-pack" / "wildcards"
|
||||
|
||||
|
||||
def _add_external_dir_to_zip(zf: zipfile.ZipFile, src_dir: Path, zip_prefix: str):
|
||||
if not src_dir.is_dir() or src_dir.is_symlink():
|
||||
return
|
||||
for path in src_dir.rglob("*"):
|
||||
if path.is_dir() or path.is_symlink():
|
||||
continue
|
||||
rel = f"{zip_prefix}/{path.relative_to(src_dir)}".replace("\\", "/")
|
||||
zf.write(path, rel)
|
||||
|
||||
|
||||
def _build_full_backup_zip(include_workflows: bool = False, include_wildcard: bool = False) -> bytes:
|
||||
"""Zips everything under DATA_DIR except the excluded names above. Walks
|
||||
top-level entries first so an excluded symlinked directory (wildcard/) is never
|
||||
even opened, rather than filtering after a recursive walk has already followed it.
|
||||
Optionally also includes the two external folders above under "_external/"."""
|
||||
buf = io.BytesIO()
|
||||
with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as zf:
|
||||
for top in sorted(DATA_DIR.iterdir()):
|
||||
if top.name in _FULL_BACKUP_EXCLUDE_NAMES or top.is_symlink():
|
||||
continue
|
||||
if top.is_file():
|
||||
_add_file_to_zip(zf, top, DATA_DIR)
|
||||
elif top.is_dir():
|
||||
for path in top.rglob("*"):
|
||||
if path.is_dir() or path.is_symlink():
|
||||
continue
|
||||
_add_file_to_zip(zf, path, DATA_DIR)
|
||||
|
||||
if include_workflows:
|
||||
_add_external_dir_to_zip(zf, _default_workflows_dir(), "_external/default_workflows")
|
||||
if include_wildcard:
|
||||
_add_external_dir_to_zip(zf, _impact_pack_wildcard_dir(), "_external/wildcard")
|
||||
return buf.getvalue()
|
||||
|
||||
|
||||
def _apply_full_backup_zip(zip_bytes: bytes) -> dict:
|
||||
"""Extracts a full-backup ZIP into DATA_DIR, overwriting existing files. Guards
|
||||
against Zip Slip (entries whose path resolves outside DATA_DIR via '..' or an
|
||||
absolute path) by checking each entry before writing it.
|
||||
|
||||
Entries under "_external/" (default workflows dir, Impact Pack wildcards — see
|
||||
_build_full_backup_zip) are always skipped here rather than restored: they live
|
||||
outside DATA_DIR and are managed by ComfyUI core / a different custom_node, so
|
||||
auto-restoring them could silently overwrite files this plugin doesn't own. They
|
||||
stay in the ZIP for the user to copy back manually if they want them."""
|
||||
summary = {"extracted": [], "skipped": []}
|
||||
DATA_DIR.mkdir(parents=True, exist_ok=True)
|
||||
base = DATA_DIR.resolve()
|
||||
with zipfile.ZipFile(io.BytesIO(zip_bytes)) as zf:
|
||||
for info in zf.infolist():
|
||||
if info.is_dir():
|
||||
continue
|
||||
if info.filename.startswith("_external/"):
|
||||
summary["skipped"].append(info.filename)
|
||||
continue
|
||||
target = (base / info.filename).resolve()
|
||||
try:
|
||||
target.relative_to(base)
|
||||
except ValueError:
|
||||
summary["skipped"].append(info.filename)
|
||||
continue
|
||||
target.parent.mkdir(parents=True, exist_ok=True)
|
||||
with zf.open(info) as src, open(target, "wb") as dst:
|
||||
shutil.copyfileobj(src, dst)
|
||||
summary["extracted"].append(info.filename)
|
||||
return summary
|
||||
|
||||
|
||||
async def handle_export_full(request: web.Request) -> web.Response:
|
||||
"""GET /api/wfm/settings/export-full - Download a ZIP of the entire data
|
||||
directory, including data the flat JSON bundle (handle_export) can't represent.
|
||||
Optional query params include_workflows=1 / include_wildcard=1 additionally bundle
|
||||
ComfyUI's default workflows dir / the Impact Pack's wildcard dir (see
|
||||
_build_full_backup_zip) — both restore-excluded, export-only, manual-copy-back."""
|
||||
try:
|
||||
include_workflows = request.rel_url.query.get("include_workflows") == "1"
|
||||
include_wildcard = request.rel_url.query.get("include_wildcard") == "1"
|
||||
zip_bytes = await asyncio.to_thread(_build_full_backup_zip, include_workflows, include_wildcard)
|
||||
return web.Response(
|
||||
body=zip_bytes,
|
||||
content_type="application/zip",
|
||||
headers={"Content-Disposition": 'attachment; filename="wfm-full-backup.zip"'},
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error("Error building full backup: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_import_full(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/settings/import-full - Upload and restore a full-backup ZIP."""
|
||||
try:
|
||||
reader = await request.multipart()
|
||||
field = await reader.next()
|
||||
if field is None or field.name != "file":
|
||||
return web.json_response({"error": "file field required"}, status=400)
|
||||
zip_bytes = await field.read(decode=False)
|
||||
summary = await asyncio.to_thread(_apply_full_backup_zip, zip_bytes)
|
||||
return web.json_response({"status": "ok", **summary})
|
||||
except zipfile.BadZipFile:
|
||||
return web.json_response({"error": "Not a valid ZIP file"}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error importing full backup: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
def _style_files() -> list:
|
||||
style_dir = DATA_DIR / "style"
|
||||
styles = []
|
||||
if not style_dir.is_dir():
|
||||
return styles
|
||||
for json_file in sorted(style_dir.glob("*.json")):
|
||||
return []
|
||||
return sorted(style_dir.glob("*.json"))
|
||||
|
||||
|
||||
def _load_styles() -> list:
|
||||
"""Load all style JSON files from DATA_DIR/style/ directory.
|
||||
各エントリに定義元ファイル名を "file" として付与する（Promptタブの一覧表示・
|
||||
「このファイルへ追加」機能で使用）。"""
|
||||
styles = []
|
||||
for json_file in _style_files():
|
||||
try:
|
||||
with open(json_file, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
if isinstance(data, list):
|
||||
styles.extend(data)
|
||||
for entry in data:
|
||||
if isinstance(entry, dict):
|
||||
entry = dict(entry)
|
||||
entry["file"] = json_file.name
|
||||
styles.append(entry)
|
||||
except Exception as e:
|
||||
logger.warning("Failed to load style file %s: %s", json_file.name, e)
|
||||
return styles
|
||||
@@ -270,3 +435,139 @@ async def handle_get_styles(request: web.Request) -> web.Response:
|
||||
except Exception as e:
|
||||
logger.error("Error loading styles: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
# Promptタブ「Style」サブタブでの新規作成分の保存先。既存スタイルの編集は
|
||||
# 元々そのスタイルが記載されているファイルをそのまま書き換える（他ファイルへは移動しない）。
|
||||
_CUSTOM_STYLE_FILENAME = "custom.json"
|
||||
|
||||
|
||||
def _find_style(name: str):
|
||||
"""名前が一致する最初のスタイルを探し、(ファイルパス, そのファイルの全データ, インデックス) を返す。
|
||||
見つからなければ None。"""
|
||||
for json_file in _style_files():
|
||||
try:
|
||||
with open(json_file, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
except Exception as e:
|
||||
logger.warning("Failed to load style file %s: %s", json_file.name, e)
|
||||
continue
|
||||
if not isinstance(data, list):
|
||||
continue
|
||||
for i, entry in enumerate(data):
|
||||
if isinstance(entry, dict) and entry.get("name") == name:
|
||||
return json_file, data, i
|
||||
return None
|
||||
|
||||
|
||||
def _write_style_file(path: Path, data: list) -> None:
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
f.write("\n")
|
||||
|
||||
|
||||
async def handle_create_style(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/styles - 新規スタイルを作成する。
|
||||
body.file を指定すると既存のそのファイルへ追記する（Promptタブの「このファイルへ追加」用、
|
||||
ファイル名はstyleディレクトリに実在するもののみ許可しパストラバーサルを防ぐ）。
|
||||
未指定ならデフォルトのcustom.jsonへ追記する。"""
|
||||
try:
|
||||
body = await request.json()
|
||||
name = (body.get("name") or "").strip()
|
||||
prompt = body.get("prompt", "") or ""
|
||||
negative_prompt = body.get("negative_prompt", "") or ""
|
||||
target_file = (body.get("file") or "").strip()
|
||||
if not name:
|
||||
return web.json_response({"error": "name is required"}, status=400)
|
||||
|
||||
def _create():
|
||||
if _find_style(name) is not None:
|
||||
return "duplicate"
|
||||
style_dir = DATA_DIR / "style"
|
||||
style_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if target_file:
|
||||
path = next((f for f in _style_files() if f.name == target_file), None)
|
||||
if path is None:
|
||||
return "file_not_found"
|
||||
else:
|
||||
path = style_dir / _CUSTOM_STYLE_FILENAME
|
||||
|
||||
data = []
|
||||
if path.is_file():
|
||||
try:
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
loaded = json.load(f)
|
||||
if isinstance(loaded, list):
|
||||
data = loaded
|
||||
except Exception as e:
|
||||
logger.warning("Failed to load %s, recreating: %s", path.name, e)
|
||||
data.append({"name": name, "prompt": prompt, "negative_prompt": negative_prompt})
|
||||
_write_style_file(path, data)
|
||||
return "ok"
|
||||
|
||||
result = await asyncio.to_thread(_create)
|
||||
if result == "duplicate":
|
||||
return web.json_response({"error": f'Style "{name}" already exists'}, status=400)
|
||||
if result == "file_not_found":
|
||||
return web.json_response({"error": f'File "{target_file}" not found'}, status=400)
|
||||
return web.json_response({"ok": True})
|
||||
except Exception as e:
|
||||
logger.error("Error creating style: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_update_style(request: web.Request) -> web.Response:
|
||||
"""PUT /api/wfm/styles/{name} - 既存スタイルを更新する（記載元のファイルをそのまま書き換え、リネームも可）。"""
|
||||
try:
|
||||
original_name = request.match_info.get("name", "")
|
||||
body = await request.json()
|
||||
new_name = (body.get("name") or "").strip()
|
||||
prompt = body.get("prompt", "") or ""
|
||||
negative_prompt = body.get("negative_prompt", "") or ""
|
||||
if not new_name:
|
||||
return web.json_response({"error": "name is required"}, status=400)
|
||||
|
||||
def _update():
|
||||
found = _find_style(original_name)
|
||||
if found is None:
|
||||
return "not_found"
|
||||
path, data, idx = found
|
||||
if new_name != original_name and _find_style(new_name) is not None:
|
||||
return "duplicate"
|
||||
data[idx] = {"name": new_name, "prompt": prompt, "negative_prompt": negative_prompt}
|
||||
_write_style_file(path, data)
|
||||
return "ok"
|
||||
|
||||
result = await asyncio.to_thread(_update)
|
||||
if result == "not_found":
|
||||
return web.json_response({"error": "Style not found"}, status=404)
|
||||
if result == "duplicate":
|
||||
return web.json_response({"error": f'Style "{new_name}" already exists'}, status=400)
|
||||
return web.json_response({"ok": True})
|
||||
except Exception as e:
|
||||
logger.error("Error updating style: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_delete_style(request: web.Request) -> web.Response:
|
||||
"""DELETE /api/wfm/styles/{name} - スタイルを削除する（記載元のファイルから該当エントリのみ除去）。"""
|
||||
try:
|
||||
name = request.match_info.get("name", "")
|
||||
|
||||
def _delete():
|
||||
found = _find_style(name)
|
||||
if found is None:
|
||||
return False
|
||||
path, data, idx = found
|
||||
del data[idx]
|
||||
_write_style_file(path, data)
|
||||
return True
|
||||
|
||||
ok = await asyncio.to_thread(_delete)
|
||||
if not ok:
|
||||
return web.json_response({"error": "Style not found"}, status=404)
|
||||
return web.json_response({"ok": True})
|
||||
except Exception as e:
|
||||
logger.error("Error deleting style: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
@@ -0,0 +1,99 @@
|
||||
"""Unsloth API proxy routes.
|
||||
|
||||
Unlike Ollama/LM Studio/Lemonade, Unsloth Desktop requires an API key even
|
||||
for local access (Authorization: Bearer sk-unsloth-...). The key lives in
|
||||
UNSLOTH_API_KEY (loaded from a .env file by prestartup_script.py) and is
|
||||
never sent to the frontend — the frontend calls this proxy with the target
|
||||
path/method/payload, and the server attaches the Authorization header before
|
||||
relaying to Unsloth's OpenAI-compatible API.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import urllib.request
|
||||
from urllib.error import HTTPError, URLError
|
||||
from urllib.parse import urlparse
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
UNSLOTH_DEFAULT_URL = "http://localhost:8888"
|
||||
_ALLOWED_PATHS = {"/v1/models", "/v1/chat/completions"}
|
||||
# baseUrl is client-supplied (so a custom Unsloth port works), but the
|
||||
# Authorization header carries a real secret — restrict the host to loopback
|
||||
# so this proxy can't be used to exfiltrate UNSLOTH_API_KEY to an arbitrary
|
||||
# server (SSRF). Port is unrestricted.
|
||||
_ALLOWED_HOSTS = {"localhost", "127.0.0.1", "::1"}
|
||||
|
||||
|
||||
def _get_api_key():
|
||||
"""Return the Unsloth API key from the environment (.env), or None."""
|
||||
return os.environ.get("UNSLOTH_API_KEY", "").strip() or None
|
||||
|
||||
|
||||
def _is_allowed_base_url(base_url):
|
||||
"""Only relay (and attach the API key) to a loopback host."""
|
||||
try:
|
||||
parsed = urlparse(base_url)
|
||||
return parsed.scheme in ("http", "https") and parsed.hostname in _ALLOWED_HOSTS
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
"""Register Unsloth API routes."""
|
||||
app.router.add_post("/api/wfm/unsloth/proxy", handle_proxy)
|
||||
|
||||
|
||||
async def handle_proxy(request: web.Request) -> web.Response:
|
||||
"""POST /api/wfm/unsloth/proxy - Relay a request to Unsloth's OpenAI-compatible API.
|
||||
|
||||
Body: { baseUrl, path: "/v1/models" | "/v1/chat/completions", method: "GET" | "POST", payload }
|
||||
"""
|
||||
import asyncio
|
||||
try:
|
||||
body = await request.json()
|
||||
base_url = (body.get("baseUrl") or UNSLOTH_DEFAULT_URL).rstrip("/")
|
||||
path = body.get("path") or "/v1/models"
|
||||
method = (body.get("method") or "GET").upper()
|
||||
payload = body.get("payload")
|
||||
|
||||
if path not in _ALLOWED_PATHS or method not in ("GET", "POST"):
|
||||
return web.json_response({"message": "Unsupported proxy target"}, status=400)
|
||||
|
||||
if not _is_allowed_base_url(base_url):
|
||||
return web.json_response({
|
||||
"message": "Unsloth backend URL must point to localhost/127.0.0.1/::1",
|
||||
}, status=400)
|
||||
|
||||
api_key = _get_api_key()
|
||||
if not api_key:
|
||||
return web.json_response({
|
||||
"message": "UNSLOTH_API_KEY is not set. Copy .env.example to .env in the "
|
||||
"plugin folder, fill in the key, and restart ComfyUI.",
|
||||
}, status=401)
|
||||
|
||||
def _fetch():
|
||||
data = json.dumps(payload).encode("utf-8") if payload is not None else None
|
||||
headers = {"Authorization": f"Bearer {api_key}"}
|
||||
if data is not None:
|
||||
headers["Content-Type"] = "application/json"
|
||||
req = urllib.request.Request(f"{base_url}{path}", data=data, headers=headers, method=method)
|
||||
with urllib.request.urlopen(req, timeout=120) as resp:
|
||||
return json.loads(resp.read().decode("utf-8"))
|
||||
|
||||
data = await asyncio.to_thread(_fetch)
|
||||
return web.json_response(data)
|
||||
|
||||
except HTTPError as e:
|
||||
detail = e.read().decode("utf-8", errors="replace") if e.fp else str(e)
|
||||
logger.warning("Unsloth proxy HTTP error: %s %s", e.code, detail)
|
||||
return web.json_response({"message": f"Unsloth API error: HTTP {e.code}"}, status=e.code)
|
||||
except URLError as e:
|
||||
logger.warning("Unsloth proxy connection error: %s", e)
|
||||
return web.json_response({"message": f"Could not reach Unsloth: {e.reason}"}, status=502)
|
||||
except Exception as e:
|
||||
logger.error("Unsloth proxy error: %s", e)
|
||||
return web.json_response({"message": str(e)}, status=500)
|
||||
@@ -0,0 +1,59 @@
|
||||
"""Video tab utility API routes: last-frame capture, animated GIF conversion."""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from aiohttp import web
|
||||
|
||||
from ..services.video_service import VideoService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_service = VideoService()
|
||||
|
||||
|
||||
def setup_routes(app: web.Application):
|
||||
app.router.add_post("/api/wfm/video/frame/save-to-output", handle_save_frame)
|
||||
app.router.add_post("/api/wfm/video/to-gif", handle_to_gif)
|
||||
|
||||
|
||||
async def handle_save_frame(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
image_base64 = body.get("image_base64", "")
|
||||
if not image_base64:
|
||||
return web.json_response({"error": "image_base64 required"}, status=400)
|
||||
result = await asyncio.to_thread(_service.save_frame_to_output, image_base64)
|
||||
return web.json_response({"status": "ok", **result})
|
||||
except Exception as e:
|
||||
logger.error("Error saving video frame to output: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
|
||||
|
||||
async def handle_to_gif(request: web.Request) -> web.Response:
|
||||
try:
|
||||
body = await request.json()
|
||||
filename = body.get("filename", "")
|
||||
subfolder = body.get("subfolder", "")
|
||||
type_ = body.get("type", "output")
|
||||
start_time = float(body.get("start_time") or 0)
|
||||
end_time_raw = body.get("end_time")
|
||||
end_time = float(end_time_raw) if end_time_raw not in (None, "") else None
|
||||
fps = float(body.get("fps") or 10)
|
||||
max_width_raw = body.get("max_width")
|
||||
max_width = int(max_width_raw) if max_width_raw not in (None, "") else None
|
||||
|
||||
if not filename:
|
||||
return web.json_response({"error": "filename required"}, status=400)
|
||||
|
||||
result = await asyncio.to_thread(
|
||||
_service.convert_to_gif, filename, subfolder, type_, start_time, end_time, fps, max_width
|
||||
)
|
||||
return web.json_response({"status": "ok", **result})
|
||||
except FileNotFoundError as e:
|
||||
return web.json_response({"error": str(e)}, status=404)
|
||||
except ValueError as e:
|
||||
return web.json_response({"error": str(e)}, status=400)
|
||||
except Exception as e:
|
||||
logger.error("Error converting video to GIF: %s", e)
|
||||
return web.json_response({"error": str(e)}, status=500)
|
||||
+124
-82
@@ -3,6 +3,7 @@ Gallery Metadata Store - ギャラリー画像のメタデータ永続化
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
import threading
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -24,14 +25,24 @@ class GalleryMetadataStore:
|
||||
{"name": str}
|
||||
]
|
||||
}
|
||||
|
||||
self._data（特に "images" 辞書）は、バックグラウンドのフォルダインデックススレッド
|
||||
（gallery_service.pyの_bg_index_folder）と、リクエストハンドラを処理するメインスレッドの
|
||||
双方から並行に読み書きされる。ロックなしで辞書を反復（.items()/.values()/for key in ...や
|
||||
json.dumpによるシリアライズ）している最中に別スレッドがキーを追加/削除すると
|
||||
"dictionary changed size during iteration" が発生するため、_data へアクセスする
|
||||
全メソッドを self._lock で保護する（re-entrant: ensure_group()がcreate_group()を
|
||||
内部呼び出しするため、通常のLockではなくRLockを使う）。
|
||||
"""
|
||||
|
||||
def __init__(self, file_path: Path):
|
||||
self.file_path = file_path
|
||||
self._lock = threading.RLock()
|
||||
self._data: dict = {"images": {}, "groups": []}
|
||||
self._load()
|
||||
|
||||
def _load(self):
|
||||
# コンストラクタからのみ呼ばれ、この時点ではまだ他スレッドと共有されていないためロック不要
|
||||
if self.file_path.exists():
|
||||
try:
|
||||
with open(self.file_path, "r", encoding="utf-8") as f:
|
||||
@@ -44,6 +55,7 @@ class GalleryMetadataStore:
|
||||
logger.warning("GalleryMetadataStore: load error: %s", e)
|
||||
|
||||
def _save_to_disk(self):
|
||||
# 呼び出し元（save/create_group等）が既にself._lockを保持している前提の内部メソッド
|
||||
try:
|
||||
self.file_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(self.file_path, "w", encoding="utf-8") as f:
|
||||
@@ -58,129 +70,159 @@ class GalleryMetadataStore:
|
||||
|
||||
def get(self, image_path: str) -> dict:
|
||||
key = self._normalize_path(image_path)
|
||||
return dict(self._data["images"].get(key, {}))
|
||||
with self._lock:
|
||||
return dict(self._data["images"].get(key, {}))
|
||||
|
||||
def save(self, image_path: str, data: dict) -> bool:
|
||||
key = self._normalize_path(image_path)
|
||||
existing = self._data["images"].get(key, {})
|
||||
existing.update(data)
|
||||
allowed = {"favorite", "tags", "memo", "groups", "workflow"}
|
||||
existing = {k: v for k, v in existing.items() if k in allowed}
|
||||
self._data["images"][key] = existing
|
||||
return self._save_to_disk()
|
||||
with self._lock:
|
||||
existing = self._data["images"].get(key, {})
|
||||
existing.update(data)
|
||||
allowed = {"favorite", "tags", "memo", "groups", "workflow"}
|
||||
existing = {k: v for k, v in existing.items() if k in allowed}
|
||||
self._data["images"][key] = existing
|
||||
return self._save_to_disk()
|
||||
|
||||
# ── グループ管理 ──────────────────────────────────────────────
|
||||
|
||||
def list_groups(self) -> list[dict]:
|
||||
return list(self._data.get("groups", []))
|
||||
with self._lock:
|
||||
return list(self._data.get("groups", []))
|
||||
|
||||
def create_group(self, name: str) -> bool:
|
||||
groups = self._data.get("groups", [])
|
||||
if any(g["name"] == name for g in groups):
|
||||
return False
|
||||
groups.append({"name": name})
|
||||
self._data["groups"] = groups
|
||||
return self._save_to_disk()
|
||||
with self._lock:
|
||||
groups = self._data.get("groups", [])
|
||||
if any(g["name"] == name for g in groups):
|
||||
return False
|
||||
groups.append({"name": name})
|
||||
self._data["groups"] = groups
|
||||
return self._save_to_disk()
|
||||
|
||||
def rename_group(self, old_name: str, new_name: str) -> bool:
|
||||
"""グループ名を変更し、全画像のgroupsフィールドも更新する"""
|
||||
groups = self._data.get("groups", [])
|
||||
if not any(g["name"] == old_name for g in groups):
|
||||
return False
|
||||
if any(g["name"] == new_name for g in groups):
|
||||
return False # 新名前が既に存在
|
||||
# グループリスト更新
|
||||
for g in groups:
|
||||
if g["name"] == old_name:
|
||||
g["name"] = new_name
|
||||
break
|
||||
# 全画像のgroupsフィールドを更新
|
||||
for meta in self._data["images"].values():
|
||||
img_groups = meta.get("groups", [])
|
||||
if old_name in img_groups:
|
||||
meta["groups"] = [new_name if g == old_name else g for g in img_groups]
|
||||
return self._save_to_disk()
|
||||
with self._lock:
|
||||
groups = self._data.get("groups", [])
|
||||
if not any(g["name"] == old_name for g in groups):
|
||||
return False
|
||||
if any(g["name"] == new_name for g in groups):
|
||||
return False # 新名前が既に存在
|
||||
# グループリスト更新
|
||||
for g in groups:
|
||||
if g["name"] == old_name:
|
||||
g["name"] = new_name
|
||||
break
|
||||
# 全画像のgroupsフィールドを更新
|
||||
for meta in self._data["images"].values():
|
||||
img_groups = meta.get("groups", [])
|
||||
if old_name in img_groups:
|
||||
meta["groups"] = [new_name if g == old_name else g for g in img_groups]
|
||||
return self._save_to_disk()
|
||||
|
||||
def delete_group(self, name: str) -> bool:
|
||||
groups = [g for g in self._data.get("groups", []) if g["name"] != name]
|
||||
self._data["groups"] = groups
|
||||
# 画像のgroupsからも削除
|
||||
for meta in self._data["images"].values():
|
||||
if name in meta.get("groups", []):
|
||||
meta["groups"] = [g for g in meta["groups"] if g != name]
|
||||
return self._save_to_disk()
|
||||
with self._lock:
|
||||
groups = [g for g in self._data.get("groups", []) if g["name"] != name]
|
||||
self._data["groups"] = groups
|
||||
# 画像のgroupsからも削除
|
||||
for meta in self._data["images"].values():
|
||||
if name in meta.get("groups", []):
|
||||
meta["groups"] = [g for g in meta["groups"] if g != name]
|
||||
return self._save_to_disk()
|
||||
|
||||
def list_images_in_group(self, group_name: str) -> list[str]:
|
||||
result = []
|
||||
for key, meta in self._data["images"].items():
|
||||
if group_name in meta.get("groups", []):
|
||||
result.append(key)
|
||||
return result
|
||||
with self._lock:
|
||||
result = []
|
||||
for key, meta in self._data["images"].items():
|
||||
if group_name in meta.get("groups", []):
|
||||
result.append(key)
|
||||
return result
|
||||
|
||||
def remove_stale_paths_from_group(self, group_name: str, stale_paths: list) -> int:
|
||||
"""存在しないパスをグループから一括削除する（1回の保存で完結）"""
|
||||
stale_set = {self._normalize_path(p) for p in stale_paths}
|
||||
count = 0
|
||||
for key, meta in self._data["images"].items():
|
||||
if key in stale_set and group_name in meta.get("groups", []):
|
||||
meta["groups"] = [g for g in meta["groups"] if g != group_name]
|
||||
count += 1
|
||||
if count:
|
||||
self._save_to_disk()
|
||||
return count
|
||||
with self._lock:
|
||||
stale_set = {self._normalize_path(p) for p in stale_paths}
|
||||
count = 0
|
||||
for key, meta in self._data["images"].items():
|
||||
if key in stale_set and group_name in meta.get("groups", []):
|
||||
meta["groups"] = [g for g in meta["groups"] if g != group_name]
|
||||
count += 1
|
||||
if count:
|
||||
self._save_to_disk()
|
||||
return count
|
||||
|
||||
def get_group_member_set(self, group_name: str) -> set:
|
||||
"""グループメンバーのパスをsetで返す（高速フィルタ用）"""
|
||||
return {
|
||||
key for key, meta in self._data["images"].items()
|
||||
if group_name in meta.get("groups", [])
|
||||
}
|
||||
with self._lock:
|
||||
return {
|
||||
key for key, meta in self._data["images"].items()
|
||||
if group_name in meta.get("groups", [])
|
||||
}
|
||||
|
||||
def clear_group(self, group_name: str) -> bool:
|
||||
"""グループ内の全画像を除外する（グループ自体は残す）"""
|
||||
for meta in self._data["images"].values():
|
||||
if group_name in meta.get("groups", []):
|
||||
meta["groups"] = [g for g in meta["groups"] if g != group_name]
|
||||
return self._save_to_disk()
|
||||
with self._lock:
|
||||
for meta in self._data["images"].values():
|
||||
if group_name in meta.get("groups", []):
|
||||
meta["groups"] = [g for g in meta["groups"] if g != group_name]
|
||||
return self._save_to_disk()
|
||||
|
||||
def ensure_group(self, name: str) -> bool:
|
||||
"""グループが存在しない場合のみ作成する"""
|
||||
if any(g["name"] == name for g in self._data.get("groups", [])):
|
||||
return True
|
||||
return self.create_group(name)
|
||||
with self._lock:
|
||||
if any(g["name"] == name for g in self._data.get("groups", [])):
|
||||
return True
|
||||
return self.create_group(name)
|
||||
|
||||
def delete(self, image_path: str) -> bool:
|
||||
"""画像のメタデータエントリを削除する"""
|
||||
key = self._normalize_path(image_path)
|
||||
if key in self._data["images"]:
|
||||
del self._data["images"][key]
|
||||
return self._save_to_disk()
|
||||
return True
|
||||
with self._lock:
|
||||
if key in self._data["images"]:
|
||||
del self._data["images"][key]
|
||||
return self._save_to_disk()
|
||||
return True
|
||||
|
||||
def rename_path(self, old_path: str, new_path: str) -> bool:
|
||||
"""画像のパスキーを変更する（ファイル移動後のメタデータ引継ぎ）"""
|
||||
old_key = self._normalize_path(old_path)
|
||||
new_key = self._normalize_path(new_path)
|
||||
if old_key in self._data["images"]:
|
||||
self._data["images"][new_key] = self._data["images"].pop(old_key)
|
||||
return self._save_to_disk()
|
||||
return True
|
||||
with self._lock:
|
||||
if old_key in self._data["images"]:
|
||||
self._data["images"][new_key] = self._data["images"].pop(old_key)
|
||||
return self._save_to_disk()
|
||||
return True
|
||||
|
||||
def cleanup_stale_images(self, folder_path: str, existing_paths: set) -> int:
|
||||
def cleanup_stale_images(self, folder_path: str, existing_paths: set, recursive: bool = True) -> int:
|
||||
"""folder_path 配下の存在しないファイルのメタデータを削除する。
|
||||
existing_paths: そのフォルダの現在のファイルパス集合（_normalize_path 済み）
|
||||
recursive: False の場合、folder_path 直下のファイルのみを対象にする。
|
||||
呼び出し元(list_images)が非recursiveスキャン(フォルダ直下のみ列挙)の
|
||||
結果を existing_paths として渡してくる場合、サブフォルダ内のファイルは
|
||||
existing_paths に含まれない。それにもかかわらず対象範囲を「folder_path配下
|
||||
全階層」のままにすると、サブフォルダ内画像のメタデータ(tags/memo等)を
|
||||
"存在しない"と誤判定して全削除してしまう(実際に発生した不具合: サブフォルダ
|
||||
に動画を保存する運用で、ルートフォルダを非recursiveで開くたびに
|
||||
サブフォルダ内の全メタデータが失われていた)。
|
||||
"""
|
||||
folder_key = self._normalize_path(folder_path)
|
||||
if not folder_key.endswith("/"):
|
||||
folder_key += "/"
|
||||
stale = [
|
||||
key for key in self._data["images"]
|
||||
if key.startswith(folder_key) and key not in existing_paths
|
||||
]
|
||||
if not stale:
|
||||
return 0
|
||||
for key in stale:
|
||||
del self._data["images"][key]
|
||||
self._save_to_disk()
|
||||
logger.info("Cleaned up %d stale metadata entries from %s", len(stale), folder_path)
|
||||
return len(stale)
|
||||
|
||||
def _in_scope(key: str) -> bool:
|
||||
if not key.startswith(folder_key):
|
||||
return False
|
||||
if recursive:
|
||||
return True
|
||||
return "/" not in key[len(folder_key):]
|
||||
|
||||
with self._lock:
|
||||
stale = [
|
||||
key for key in self._data["images"]
|
||||
if _in_scope(key) and key not in existing_paths
|
||||
]
|
||||
if not stale:
|
||||
return 0
|
||||
for key in stale:
|
||||
del self._data["images"][key]
|
||||
self._save_to_disk()
|
||||
logger.info("Cleaned up %d stale metadata entries from %s", len(stale), folder_path)
|
||||
return len(stale)
|
||||
|
||||
+377
-38
@@ -1,6 +1,7 @@
|
||||
"""
|
||||
Gallery Service - outputフォルダの画像管理、メタデータ閲覧
|
||||
"""
|
||||
import base64
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
@@ -15,8 +16,39 @@ from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# サポートする画像拡張子
|
||||
IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp", ".gif", ".svg"}
|
||||
# サポートする画像拡張子（.mp4は動画だがGalleryでは静止画と同じ一覧・配信経路を共有する）
|
||||
IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp", ".gif", ".svg", ".mp4"}
|
||||
|
||||
# data URL の MIME タイプ -> 拡張子（save_image_to_gallery / save_image_to_folder共用）
|
||||
_SAVE_EXT_BY_MIME = {
|
||||
"image/png": ".png",
|
||||
"image/jpeg": ".jpg",
|
||||
"image/webp": ".webp",
|
||||
"image/gif": ".gif",
|
||||
"image/svg+xml": ".svg",
|
||||
}
|
||||
|
||||
|
||||
def _decode_image_data_url(image_data: str) -> tuple[bytes, str]:
|
||||
"""data URL (data:<mime>;base64,<data>) をデコードし (バイト列, 拡張子) を返す。
|
||||
ヘッダが無ければPNG扱い（旧互換）。"""
|
||||
raw = image_data
|
||||
mime = "image/png"
|
||||
if raw.startswith("data:"):
|
||||
header, _, raw = raw.partition(",")
|
||||
m = re.match(r"data:([^;]+)", header)
|
||||
if m:
|
||||
mime = m.group(1)
|
||||
ext = _SAVE_EXT_BY_MIME.get(mime, ".png")
|
||||
return base64.b64decode(raw), ext
|
||||
|
||||
|
||||
def _sanitize_save_filename(filename: str, ext: str) -> str:
|
||||
"""OSで使えない文字を除去し、拡張子が無ければ付与する"""
|
||||
safe = re.sub(r'[<>:"/\\|?*\x00-\x1f]', "_", filename)
|
||||
if not safe.lower().endswith(ext):
|
||||
safe += ext
|
||||
return safe
|
||||
|
||||
# メタデータ保存ファイル (gallery専用)
|
||||
from .gallery_metadata import GalleryMetadataStore
|
||||
@@ -30,6 +62,54 @@ _JPEG_READ_MAX = 64 * 1024 * 1024
|
||||
# フォルダキャッシュのTTL秒（フォルダmtimeが同じでも念のため）
|
||||
_CACHE_TTL = 60.0
|
||||
|
||||
# ImagePromptギャラリー: 画像と同名の.txtサイドカーに保存されたプロンプトテキストの最大読み込みサイズ
|
||||
_SIDECAR_PROMPT_MAX = 20_000
|
||||
|
||||
# ImagePromptギャラリーの管理フォルダ名（ComfyUI実outputフォルダ直下）
|
||||
IMAGE_PROMPT_FOLDER_NAME = "ws_image_prompt"
|
||||
|
||||
# Style Catalogギャラリーの管理フォルダ名（ComfyUI実outputフォルダ直下）
|
||||
STYLE_CATALOG_FOLDER_NAME = "ws_style_catalog"
|
||||
|
||||
# ponyxlWildcardsVault形式（.yaml + thumbnails/）を検出するための、画像の祖先フォルダ探索上限
|
||||
_VAULT_ROOT_SEARCH_DEPTH = 8
|
||||
_VAULT_THUMB_DIR_NAMES = ("thumbnails", "thumbnails_option2")
|
||||
|
||||
|
||||
def _clean_vault_tags(text: str) -> str:
|
||||
"""タグ文字列の前後の空白・カンマを整理する（tools/import_style_prompt_seed.py と同じ処理）"""
|
||||
text = text.strip()
|
||||
text = re.sub(r"^[,\s]+|[,\s]+$", "", text)
|
||||
text = re.sub(r"\s*,\s*", ", ", text)
|
||||
return text
|
||||
|
||||
|
||||
def _parse_vault_yaml_leaves(yaml_path: Path) -> dict[str, str]:
|
||||
"""comfyui_prompt_gallery の parseYamlForImages と同じ簡易パーサ（PyYAML不要）。
|
||||
「key:」行の直後が「- タグ文字列」で始まる行なら key をリーフとみなす。
|
||||
戻り値: {leaf_name(小文字): タグ文字列}
|
||||
"""
|
||||
leaves: dict[str, str] = {}
|
||||
try:
|
||||
lines = yaml_path.read_text(encoding="utf-8", errors="replace").split("\n")
|
||||
except OSError:
|
||||
return leaves
|
||||
for i, line in enumerate(lines):
|
||||
trimmed = line.strip()
|
||||
if not trimmed or trimmed.startswith("#") or not trimmed.endswith(":"):
|
||||
continue
|
||||
if i + 1 >= len(lines):
|
||||
continue
|
||||
next_trimmed = lines[i + 1].strip()
|
||||
if not next_trimmed.startswith("-"):
|
||||
continue
|
||||
key = trimmed[:-1].strip()
|
||||
tags = next_trimmed[1:].strip()
|
||||
if not tags or key.lower() == "skip":
|
||||
continue
|
||||
leaves[key.lower()] = _clean_vault_tags(tags)
|
||||
return leaves
|
||||
|
||||
|
||||
class _FolderCache:
|
||||
"""フォルダ単位の画像スキャン結果キャッシュ。
|
||||
@@ -73,15 +153,26 @@ class GalleryService:
|
||||
self.data_dir = data_dir
|
||||
self.metadata_store = GalleryMetadataStore(data_dir / "gallery_metadata.json")
|
||||
self._allowed_root: Path | None = None
|
||||
# ComfyUI実outputフォルダの不変ルート。Settingsでユーザーが_allowed_rootを
|
||||
# 別フォルダに変更しても、ImagePrompt/Style Catalogギャラリー(常に実output配下)への
|
||||
# アクセスが塞がれないようにするため_allowed_rootとは別管理にする
|
||||
self._comfy_output_root: Path | None = None
|
||||
self._folder_cache = _FolderCache()
|
||||
self._bg_cancel = threading.Event()
|
||||
self._bg_cancel.set() # 初期状態: キャンセル済み
|
||||
# ponyxlWildcardsVault形式フォールバック用キャッシュ: category_root文字列 -> (yamlシグネチャ, {leaf: tags})
|
||||
self._vault_leaf_cache: dict[str, tuple[tuple, dict]] = {}
|
||||
|
||||
def update_output_root(self, root_path: str) -> None:
|
||||
"""許可するルートパスを更新する（Settings変更時に呼ぶ）"""
|
||||
p = Path(root_path).resolve() if root_path else None
|
||||
self._allowed_root = p
|
||||
|
||||
def set_comfy_output_root(self, root_path: str) -> None:
|
||||
"""ComfyUI実outputフォルダの不変ルートを設定する（起動時に一度だけ呼ぶ）"""
|
||||
p = Path(root_path).resolve() if root_path else None
|
||||
self._comfy_output_root = p
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# バックグラウンドインデックス
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
@@ -147,14 +238,23 @@ class GalleryService:
|
||||
logger.debug("BG index error: %s", e)
|
||||
|
||||
def _check_path_allowed(self, path: Path) -> bool:
|
||||
"""パスが許可ルート配下かチェック（パストラバーサル防止）"""
|
||||
if self._allowed_root is None:
|
||||
return False
|
||||
try:
|
||||
path.resolve().relative_to(self._allowed_root)
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
"""パスが許可ルート配下かチェック（パストラバーサル防止）。
|
||||
Output ギャラリーの _allowed_root、またはComfyUI実outputフォルダの
|
||||
_comfy_output_root のいずれか配下であれば許可する。"""
|
||||
resolved = path.resolve()
|
||||
if self._allowed_root is not None:
|
||||
try:
|
||||
resolved.relative_to(self._allowed_root)
|
||||
return True
|
||||
except ValueError:
|
||||
pass
|
||||
if self._comfy_output_root is not None:
|
||||
try:
|
||||
resolved.relative_to(self._comfy_output_root)
|
||||
return True
|
||||
except ValueError:
|
||||
pass
|
||||
return False
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# フォルダツリー
|
||||
@@ -183,11 +283,13 @@ class GalleryService:
|
||||
except PermissionError:
|
||||
pass
|
||||
children_nodes = [build_tree(path / name, rel_base) for name in sorted(children)]
|
||||
total_count = image_count + sum(c["image_count_total"] for c in children_nodes)
|
||||
return {
|
||||
"name": path.name,
|
||||
"path": rel if rel != "." else "",
|
||||
"abs_path": str(path).replace("\\", "/"),
|
||||
"image_count": image_count,
|
||||
"image_count_total": total_count,
|
||||
"children": children_nodes,
|
||||
}
|
||||
|
||||
@@ -230,6 +332,33 @@ class GalleryService:
|
||||
self._folder_cache.set(folder, entries)
|
||||
return entries
|
||||
|
||||
def _scan_folder_recursive(self, folder: Path) -> list[tuple[str, str, int, float]]:
|
||||
"""os.walk() でフォルダ配下を再帰的に列挙する（サブフォルダの画像も含む）。
|
||||
ImagePrompt/Style Catalogギャラリーのように深い階層に画像が置かれるケース用。
|
||||
キャッシュはしない（フォルダ数が少なく、都度スキャンしても軽い想定）。
|
||||
"""
|
||||
entries = []
|
||||
try:
|
||||
for root, dirs, files in os.walk(folder):
|
||||
dirs[:] = [d for d in dirs if not d.startswith(".")]
|
||||
for name in files:
|
||||
if Path(name).suffix.lower() not in IMAGE_EXTENSIONS:
|
||||
continue
|
||||
full = Path(root) / name
|
||||
try:
|
||||
st = full.stat()
|
||||
entries.append((
|
||||
name,
|
||||
str(full.resolve()).replace("\\", "/"),
|
||||
st.st_size,
|
||||
st.st_mtime,
|
||||
))
|
||||
except OSError:
|
||||
continue
|
||||
except PermissionError:
|
||||
pass
|
||||
return entries
|
||||
|
||||
def list_images(
|
||||
self,
|
||||
folder_path: str,
|
||||
@@ -238,8 +367,9 @@ class GalleryService:
|
||||
favorite_only: bool = False,
|
||||
tag_filter: str = "",
|
||||
group_filter: str = "",
|
||||
recursive: bool = False,
|
||||
) -> list[dict]:
|
||||
"""指定フォルダ内の画像一覧を返す（サブフォルダなし）"""
|
||||
"""指定フォルダ内の画像一覧を返す（recursive=Trueならサブフォルダも含める）"""
|
||||
folder = Path(folder_path).resolve()
|
||||
if not folder.is_dir():
|
||||
return []
|
||||
@@ -253,11 +383,14 @@ class GalleryService:
|
||||
group_member_set = self.metadata_store.get_group_member_set(group_filter)
|
||||
|
||||
# os.scandir() でファイル情報を一括取得（stat()の個別呼び出しを排除）
|
||||
raw_entries = self._scan_folder(folder)
|
||||
raw_entries = self._scan_folder_recursive(folder) if recursive else self._scan_folder(folder)
|
||||
|
||||
# 孤立メタデータを自動クリーンアップ（移動・削除されたファイルの残骸を除去）
|
||||
# 孤立メタデータを自動クリーンアップ（移動・削除されたファイルの残骸を除去）。
|
||||
# recursive=False（非再帰スキャン）の場合、existing_pathsにはfolder直下の
|
||||
# ファイルしか含まれないため、cleanup側にもrecursive=Falseを伝えて対象範囲を
|
||||
# 直下のみに限定する（でないとサブフォルダ内ファイルのメタデータを誤削除する）。
|
||||
existing_paths = {abs_path for _, abs_path, _, _ in raw_entries}
|
||||
self.metadata_store.cleanup_stale_images(str(folder), existing_paths)
|
||||
self.metadata_store.cleanup_stale_images(str(folder), existing_paths, recursive=recursive)
|
||||
|
||||
results = []
|
||||
for name, abs_path, size, mtime in raw_entries:
|
||||
@@ -289,14 +422,16 @@ class GalleryService:
|
||||
"groups": meta.get("groups", []),
|
||||
}
|
||||
|
||||
# 検索フィルタ（ファイル名・メモ・タグ・キャッシュ済みプロンプト）
|
||||
# 検索フィルタ（ファイル名・メモ・タグ・キャッシュ済みプロンプト・.txtサイドカー）
|
||||
if search:
|
||||
s = search.lower()
|
||||
sidecar_prompt = self._read_sidecar_prompt(Path(abs_path)) or ""
|
||||
if not (
|
||||
s in name.lower()
|
||||
or s in item["memo"].lower()
|
||||
or any(s in t.lower() for t in tags)
|
||||
or s in meta.get("prompt_cache", "").lower()
|
||||
or s in sidecar_prompt.lower()
|
||||
):
|
||||
continue
|
||||
|
||||
@@ -314,6 +449,126 @@ class GalleryService:
|
||||
|
||||
return results
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# ImagePromptギャラリー: .txtサイドカーによるプロンプト保存
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def get_image_prompt_root(self) -> str | None:
|
||||
"""ImagePromptギャラリーのルートフォルダ(ComfyUI実output/ws_image_prompt)を
|
||||
返す。無ければ作成する。"""
|
||||
if self._comfy_output_root is None:
|
||||
return None
|
||||
root = self._comfy_output_root / IMAGE_PROMPT_FOLDER_NAME
|
||||
root.mkdir(parents=True, exist_ok=True)
|
||||
return str(root).replace("\\", "/")
|
||||
|
||||
def get_style_catalog_root(self) -> str | None:
|
||||
"""Style Catalogギャラリーのルートフォルダ(ComfyUI実output/ws_style_catalog)を
|
||||
返す。無ければ作成する。"""
|
||||
if self._comfy_output_root is None:
|
||||
return None
|
||||
root = self._comfy_output_root / STYLE_CATALOG_FOLDER_NAME
|
||||
root.mkdir(parents=True, exist_ok=True)
|
||||
return str(root).replace("\\", "/")
|
||||
|
||||
def save_image_to_folder(self, folder_path: str, filename: str, image_data: str) -> dict:
|
||||
"""任意フォルダへ画像を保存する（data URL想定、同名なら上書き）。
|
||||
Style Catalogのカタログ作成（スタイル名でのファイル保存）で使用。"""
|
||||
folder = Path(folder_path).resolve()
|
||||
if not folder.is_dir():
|
||||
return {"ok": False, "error": "Destination folder not found"}
|
||||
if not self._check_path_allowed(folder):
|
||||
return {"ok": False, "error": "Access denied"}
|
||||
try:
|
||||
image_bytes, ext = _decode_image_data_url(image_data)
|
||||
except Exception as e:
|
||||
return {"ok": False, "error": f"Invalid imageData: {e}"}
|
||||
safe = _sanitize_save_filename(filename, ext)
|
||||
if not safe:
|
||||
return {"ok": False, "error": "Invalid filename"}
|
||||
save_path = folder / safe
|
||||
try:
|
||||
save_path.write_bytes(image_bytes)
|
||||
except OSError as e:
|
||||
return {"ok": False, "error": str(e)}
|
||||
return {"ok": True, "path": str(save_path).replace("\\", "/")}
|
||||
|
||||
def _sidecar_path(self, image_path: Path) -> Path:
|
||||
return image_path.with_suffix(".txt")
|
||||
|
||||
def _read_sidecar_prompt(self, image_path: Path) -> str | None:
|
||||
"""画像のプロンプトテキストを返す。
|
||||
1. 画像と同名の .txt サイドカーがあればそれを使う
|
||||
2. 無ければ ponyxlWildcardsVault 形式 (*.yaml + thumbnails/) をフォールバックとして
|
||||
その場で解決する（インポート未実施のフォルダをそのまま置いた場合に対応するため）
|
||||
"""
|
||||
sidecar = self._sidecar_path(image_path)
|
||||
try:
|
||||
if sidecar.is_file() and sidecar.stat().st_size <= _SIDECAR_PROMPT_MAX:
|
||||
text = sidecar.read_text(encoding="utf-8", errors="replace").strip()
|
||||
if text:
|
||||
return text
|
||||
except OSError:
|
||||
pass
|
||||
return self._read_vault_prompt(image_path)
|
||||
|
||||
def _find_vault_category_root(self, image_path: Path) -> Path | None:
|
||||
"""image_path の祖先を辿り、直下に thumbnails(_option2) を持つフォルダ
|
||||
(=ponyxlWildcardsVaultのカテゴリルート) を探す"""
|
||||
cur = image_path.parent
|
||||
for _ in range(_VAULT_ROOT_SEARCH_DEPTH):
|
||||
if any((cur / name).is_dir() for name in _VAULT_THUMB_DIR_NAMES):
|
||||
return cur
|
||||
if cur.parent == cur:
|
||||
break
|
||||
cur = cur.parent
|
||||
return None
|
||||
|
||||
def _load_vault_leaves(self, category_root: Path) -> dict[str, str]:
|
||||
yaml_files = sorted(category_root.glob("*.yaml")) + sorted(category_root.glob("*.yml"))
|
||||
if not yaml_files:
|
||||
return {}
|
||||
try:
|
||||
signature = tuple(sorted((str(f), f.stat().st_mtime) for f in yaml_files))
|
||||
except OSError:
|
||||
signature = ()
|
||||
cache_key = str(category_root)
|
||||
cached = self._vault_leaf_cache.get(cache_key)
|
||||
if cached is not None and cached[0] == signature:
|
||||
return cached[1]
|
||||
leaves: dict[str, str] = {}
|
||||
for yf in yaml_files:
|
||||
leaves.update(_parse_vault_yaml_leaves(yf))
|
||||
self._vault_leaf_cache[cache_key] = (signature, leaves)
|
||||
return leaves
|
||||
|
||||
def _read_vault_prompt(self, image_path: Path) -> str | None:
|
||||
"""ponyxlWildcardsVault形式のフォールバック解決。
|
||||
thumbnails_option2 の "{leaf}.preview3.ext" のような二重拡張子にも対応するため、
|
||||
ファイル名の最初の "." より前をリーフ名候補として使う。"""
|
||||
category_root = self._find_vault_category_root(image_path)
|
||||
if category_root is None:
|
||||
return None
|
||||
leaves = self._load_vault_leaves(category_root)
|
||||
if not leaves:
|
||||
return None
|
||||
leaf_candidate = image_path.name.split(".")[0].lower()
|
||||
return leaves.get(leaf_candidate)
|
||||
|
||||
def save_image_prompt(self, image_path: str, text: str) -> bool:
|
||||
"""画像と同名の.txtサイドカーにプロンプトテキストを保存する"""
|
||||
p = Path(image_path).resolve()
|
||||
if not p.is_file():
|
||||
return False
|
||||
if not self._check_path_allowed(p):
|
||||
return False
|
||||
try:
|
||||
self._sidecar_path(p).write_text((text or "").strip(), encoding="utf-8")
|
||||
return True
|
||||
except OSError as e:
|
||||
logger.warning("save_image_prompt: failed for %s: %s", p, e)
|
||||
return False
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# 画像メタデータ
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
@@ -332,7 +587,10 @@ class GalleryService:
|
||||
embedded = self._read_png_metadata(path)
|
||||
elif ext in {".jpg", ".jpeg"}:
|
||||
embedded = self._read_jpeg_metadata(path)
|
||||
elif ext == ".mp4":
|
||||
embedded = self._read_mp4_metadata(path)
|
||||
|
||||
dims = self._read_media_dimensions(path, ext)
|
||||
saved = self.metadata_store.get(str(path))
|
||||
|
||||
# workflow キーを除く文字列フィールドをプロンプトキャッシュとして保存（検索用）
|
||||
@@ -351,10 +609,14 @@ class GalleryService:
|
||||
"mtime": stat.st_mtime,
|
||||
"ext": ext,
|
||||
"embedded": embedded,
|
||||
"width": dims["width"],
|
||||
"height": dims["height"],
|
||||
"duration": dims["duration"],
|
||||
"favorite": saved.get("favorite", False),
|
||||
"tags": saved.get("tags", []),
|
||||
"memo": saved.get("memo", ""),
|
||||
"groups": saved.get("groups", []),
|
||||
"image_prompt": self._read_sidecar_prompt(path),
|
||||
}
|
||||
|
||||
def _read_png_metadata(self, path: Path) -> dict:
|
||||
@@ -429,45 +691,102 @@ class GalleryService:
|
||||
logger.debug("JPEG metadata read error: %s", e)
|
||||
return result
|
||||
|
||||
def extract_workflow_from_metadata(self, image_path: str) -> dict | None:
|
||||
"""ワークフローを抽出する。
|
||||
優先順位: PNG[workflow] > PNG[prompt] > gallery_metadata.json[workflow]
|
||||
def _read_mp4_metadata(self, path: Path) -> dict:
|
||||
"""MP4コンテナのformat-levelメタデータタグ（workflow/prompt等）をPyAVで抽出する。
|
||||
ComfyUIのSaveVideoはPNGのtEXtチャンクと同じキー名（workflow=UI形式, prompt=API形式）で
|
||||
moov/udta/meta（mdta）にJSON文字列を書き込む。"""
|
||||
result = {}
|
||||
try:
|
||||
import av
|
||||
with av.open(str(path)) as container:
|
||||
for k, v in (container.metadata or {}).items():
|
||||
result[k] = v
|
||||
except Exception as e:
|
||||
logger.debug("MP4 metadata read error: %s", e)
|
||||
return result
|
||||
|
||||
def _read_media_dimensions(self, path: Path, ext: str) -> dict:
|
||||
"""画像は幅高さ、動画は幅高さ+再生時間（秒）を返す。取得できない項目はNone。"""
|
||||
info: dict = {"width": None, "height": None, "duration": None}
|
||||
try:
|
||||
if ext == ".mp4":
|
||||
import av
|
||||
with av.open(str(path)) as container:
|
||||
if container.streams.video:
|
||||
stream = container.streams.video[0]
|
||||
info["width"] = stream.width
|
||||
info["height"] = stream.height
|
||||
if container.duration:
|
||||
info["duration"] = container.duration / 1_000_000
|
||||
elif ext != ".svg":
|
||||
from PIL import Image
|
||||
with Image.open(path) as img:
|
||||
info["width"], info["height"] = img.size
|
||||
except Exception as e:
|
||||
logger.debug("dimension read error for %s: %s", path, e)
|
||||
return info
|
||||
|
||||
def _extract_embedded_workflow(self, image_path: str, prefer: str = "workflow") -> dict | None:
|
||||
"""埋め込みワークフローを抽出する（PNG/MP4の[workflow]/[prompt]の優先順位を選べる）。
|
||||
|
||||
prefer="workflow": UI形式(ノード位置・グループ等を保持)優先。GenerateUIへの
|
||||
ロードやJSON表示などエディタ復元が必要な用途向け。
|
||||
prefer="prompt": API形式(サブグラフ展開済みでフラット)優先。トップレベルと
|
||||
サブグラフに独立した複数系統を持つワークフロー(例: Krea2)ではUI形式の
|
||||
extractPromptsLiteGraphがトップレベル系統しか拾えないことがあるため、
|
||||
プロンプト抽出専用にAPI形式を優先する（Metadataタブのプロンプト抽出と同じ優先順位）。
|
||||
"""
|
||||
path = Path(image_path).resolve()
|
||||
if not self._check_path_allowed(path):
|
||||
return None
|
||||
|
||||
if path.suffix.lower() == ".png":
|
||||
ext = path.suffix.lower()
|
||||
embedded = None
|
||||
if ext == ".png":
|
||||
embedded = self._read_png_metadata(path)
|
||||
# 1. workflow キー (ComfyUI UI形式)
|
||||
for key in ("workflow", "Workflow"):
|
||||
s = embedded.get(key)
|
||||
if s:
|
||||
try:
|
||||
return json.loads(s)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
# 2. prompt キー (ComfyUI API形式 — 大多数の生成画像)
|
||||
for key in ("prompt", "Prompt"):
|
||||
s = embedded.get(key)
|
||||
if s:
|
||||
try:
|
||||
return json.loads(s)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
elif ext == ".mp4":
|
||||
embedded = self._read_mp4_metadata(path)
|
||||
|
||||
# 3. gallery_metadata.json に保存されたworkflow
|
||||
if embedded:
|
||||
key_groups = (("workflow", "Workflow"), ("prompt", "Prompt"))
|
||||
if prefer == "prompt":
|
||||
key_groups = tuple(reversed(key_groups))
|
||||
for keys in key_groups:
|
||||
for key in keys:
|
||||
s = embedded.get(key)
|
||||
if s:
|
||||
try:
|
||||
return json.loads(s)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# gallery_metadata.json に保存されたworkflow
|
||||
saved = self.metadata_store.get(str(path))
|
||||
return saved.get("workflow") or None
|
||||
|
||||
def extract_workflow_from_metadata(self, image_path: str) -> dict | None:
|
||||
"""ワークフローを抽出する。
|
||||
優先順位: PNG[workflow] > PNG[prompt] > gallery_metadata.json[workflow]
|
||||
"""
|
||||
return self._extract_embedded_workflow(image_path, prefer="workflow")
|
||||
|
||||
def extract_prompt_workflow_from_metadata(self, image_path: str) -> dict | None:
|
||||
"""プロンプト抽出専用: PNG[prompt] > PNG[workflow] > gallery_metadata.json[workflow]"""
|
||||
return self._extract_embedded_workflow(image_path, prefer="prompt")
|
||||
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# メタデータ保存
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
|
||||
def save_image_meta(self, image_path: str, data: dict) -> bool:
|
||||
if not self._check_path_allowed(Path(image_path).resolve()):
|
||||
resolved = Path(image_path).resolve()
|
||||
if not self._check_path_allowed(resolved):
|
||||
logger.warning("save_image_meta: path not allowed: %s", resolved)
|
||||
return False
|
||||
return self.metadata_store.save(image_path, data)
|
||||
# get_image_metadata() 側は resolve() 済みパスをキーにして取得するため、
|
||||
# ここでも resolve() 後の文字列で統一して保存する（キー不一致による
|
||||
# 「保存はできるが表示されない」というサイレントな食い違いを防ぐ）。
|
||||
return self.metadata_store.save(str(resolved), data)
|
||||
|
||||
def toggle_favorite(self, image_path: str) -> bool:
|
||||
if not self._check_path_allowed(Path(image_path).resolve()):
|
||||
@@ -670,6 +989,8 @@ class GalleryService:
|
||||
ディスクキャッシュがあればそれを返し、なければPillowで生成して保存する。
|
||||
GIFはアニメーション保持のため元ファイルをそのまま返す。
|
||||
Pillowが使えない場合は元ファイルにフォールバック。
|
||||
MP4はPillowで開けないためPyAVで先頭フレームを抽出しJPEGとしてキャッシュする
|
||||
（元ファイルへのフォールバックはできないため、失敗時はNoneを返す）。
|
||||
"""
|
||||
p = Path(image_path).resolve()
|
||||
if not p.is_file():
|
||||
@@ -702,6 +1023,24 @@ class GalleryService:
|
||||
if thumb_path.exists():
|
||||
return thumb_path
|
||||
|
||||
# MP4は動画のためPillowで開けない。PyAV(av)で先頭フレームを抽出しJPEGとしてキャッシュする。
|
||||
# avが未導入/デコード失敗の場合、元mp4ファイルは<img>的な経路に渡せないため
|
||||
# (GIF/SVGと違い元ファイルへフォールバックできない) Noneを返しプレースホルダー表示に委ねる。
|
||||
if p.suffix.lower() == ".mp4":
|
||||
try:
|
||||
import av
|
||||
from PIL import Image
|
||||
with av.open(str(p)) as container:
|
||||
stream = container.streams.video[0]
|
||||
frame = next(container.decode(stream))
|
||||
img = frame.to_image().convert("RGB")
|
||||
img.thumbnail((width, width), Image.LANCZOS)
|
||||
img.save(thumb_path, "JPEG", quality=85, optimize=True)
|
||||
return thumb_path
|
||||
except Exception as e:
|
||||
logger.warning("serve_thumbnail: mp4 frame extraction failed for %s: %s", p, e)
|
||||
return None
|
||||
|
||||
try:
|
||||
from PIL import Image
|
||||
with Image.open(p) as img:
|
||||
|
||||
@@ -0,0 +1,138 @@
|
||||
"""Lab tab plan file management service (JSON plan + optional PNG index thumbnail)."""
|
||||
|
||||
import base64
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LabPlanService:
|
||||
def __init__(self, plan_dir: Path):
|
||||
self.plan_dir = plan_dir
|
||||
self.plan_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Path safety
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _safe_path(self, filename: str, ext: str = ".json") -> Path | None:
|
||||
if not filename or not filename.strip():
|
||||
return None
|
||||
name = filename.strip()
|
||||
if not name.endswith(ext):
|
||||
name = name + ext
|
||||
if "/" in name or "\\" in name or name in (".", "..") or "\x00" in name:
|
||||
return None
|
||||
resolved = (self.plan_dir / name).resolve()
|
||||
plan_dir_resolved = self.plan_dir.resolve()
|
||||
if resolved != plan_dir_resolved and not str(resolved).startswith(str(plan_dir_resolved) + "\\") and not str(resolved).startswith(str(plan_dir_resolved) + "/"):
|
||||
return None
|
||||
return resolved
|
||||
|
||||
def _now_iso(self) -> str:
|
||||
return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# CRUD
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def list_plans(self) -> list[dict]:
|
||||
if not self.plan_dir.is_dir():
|
||||
return []
|
||||
result = []
|
||||
for path in sorted(self.plan_dir.glob("*.json")):
|
||||
try:
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
except Exception as e:
|
||||
logger.warning("Skipping unreadable lab plan %s: %s", path.name, e)
|
||||
continue
|
||||
|
||||
stem = path.stem
|
||||
thumbnail = None
|
||||
thumb_path = self.plan_dir / f"{stem}.png"
|
||||
if thumb_path.is_file():
|
||||
thumbnail = f"/wfm_data/lab_plan/{stem}.png"
|
||||
|
||||
result.append({
|
||||
"filename": path.name,
|
||||
"name": data.get("name", stem),
|
||||
"note": data.get("note", ""),
|
||||
"batch_count": data.get("batch_count", 1),
|
||||
"updated_at": data.get("updated_at", ""),
|
||||
"thumbnail": thumbnail,
|
||||
})
|
||||
result.sort(key=lambda p: p.get("updated_at", ""), reverse=True)
|
||||
return result
|
||||
|
||||
def get_plan(self, filename: str) -> dict:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
if not path.is_file():
|
||||
raise FileNotFoundError(f"Plan not found: {filename}")
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
def save_plan(self, filename: str, data: dict, index_image_base64: str | None = None) -> dict:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
|
||||
data = dict(data)
|
||||
data["updated_at"] = self._now_iso()
|
||||
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
if index_image_base64:
|
||||
self.save_index_image(filename, index_image_base64)
|
||||
|
||||
return {"filename": path.name, "updated_at": data["updated_at"]}
|
||||
|
||||
def save_index_image(self, filename: str, image_base64: str) -> None:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
stem = path.stem
|
||||
thumb_path = self.plan_dir / f"{stem}.png"
|
||||
|
||||
# Accept both raw base64 and data: URL forms
|
||||
b64 = image_base64
|
||||
if "," in b64 and b64.strip().lower().startswith("data:"):
|
||||
b64 = b64.split(",", 1)[1]
|
||||
|
||||
image_bytes = base64.b64decode(b64)
|
||||
with open(thumb_path, "wb") as f:
|
||||
f.write(image_bytes)
|
||||
|
||||
def save_index_image_to_output(self, image_base64: str, filename_prefix: str = "Lab_index") -> dict:
|
||||
"""Saves the contact-sheet index image into ComfyUI's own output folder,
|
||||
alongside the images generated during the run (unlike save_index_image,
|
||||
which saves it next to the plan file in plan_dir)."""
|
||||
b64 = image_base64
|
||||
if "," in b64 and b64.strip().lower().startswith("data:"):
|
||||
b64 = b64.split(",", 1)[1]
|
||||
image_bytes = base64.b64decode(b64)
|
||||
|
||||
import folder_paths # type: ignore
|
||||
output_dir = folder_paths.get_output_directory()
|
||||
full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(filename_prefix, output_dir)
|
||||
save_name = f"{filename}_{counter:05}_.png"
|
||||
save_path = Path(full_output_folder) / save_name
|
||||
save_path.write_bytes(image_bytes)
|
||||
return {"filename": save_name, "subfolder": subfolder}
|
||||
|
||||
def delete_plan(self, filename: str) -> None:
|
||||
path = self._safe_path(filename)
|
||||
if path is None:
|
||||
raise ValueError(f"Invalid filename: {filename}")
|
||||
if path.is_file():
|
||||
path.unlink()
|
||||
thumb_path = self.plan_dir / f"{path.stem}.png"
|
||||
if thumb_path.is_file():
|
||||
thumb_path.unlink()
|
||||
@@ -0,0 +1,127 @@
|
||||
"""Video tab utilities: last-frame capture and animated GIF conversion.
|
||||
|
||||
Runs entirely on dependencies ComfyUI itself already requires — PyAV for
|
||||
decoding (ComfyUI core already needs av>=17.0.0) and Pillow for GIF encoding
|
||||
— so no ffmpeg binary and no new pip installs, unlike a typical ffmpeg-based
|
||||
implementation (e.g. Eagle's video2gif/video-to-frame plugins).
|
||||
"""
|
||||
|
||||
import base64
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
try:
|
||||
from PIL import Image
|
||||
_RESAMPLE = getattr(getattr(Image, "Resampling", Image), "LANCZOS")
|
||||
except Exception: # pragma: no cover - Pillow is a hard ComfyUI dependency
|
||||
_RESAMPLE = None
|
||||
|
||||
|
||||
class VideoService:
|
||||
def _resolve_media_path(self, filename: str, subfolder: str, type_: str) -> Path:
|
||||
"""Resolves a ComfyUI-style {filename, subfolder, type} reference to an
|
||||
absolute path, guarding against path traversal outside the matching
|
||||
input/output/temp directory."""
|
||||
import folder_paths # type: ignore
|
||||
|
||||
base_dir = folder_paths.get_directory_by_type(type_)
|
||||
if not base_dir:
|
||||
raise ValueError(f"Invalid type: {type_}")
|
||||
base = Path(base_dir).resolve()
|
||||
target = (base / (subfolder or "") / filename).resolve()
|
||||
try:
|
||||
target.relative_to(base)
|
||||
except ValueError:
|
||||
raise ValueError("Access denied: path outside allowed directory")
|
||||
if not target.is_file():
|
||||
raise FileNotFoundError(f"File not found: {filename}")
|
||||
return target
|
||||
|
||||
def save_frame_to_output(self, image_base64: str, filename_prefix: str = "video/Frame") -> dict:
|
||||
"""Saves a client-captured video frame (PNG data URL) into ComfyUI's own
|
||||
output folder — same auto-numbered pattern as Lab's index-image save
|
||||
(see LabPlanService.save_index_image_to_output)."""
|
||||
import folder_paths # type: ignore
|
||||
|
||||
b64 = image_base64
|
||||
if "," in b64 and b64.strip().lower().startswith("data:"):
|
||||
b64 = b64.split(",", 1)[1]
|
||||
image_bytes = base64.b64decode(b64)
|
||||
|
||||
output_dir = folder_paths.get_output_directory()
|
||||
full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(filename_prefix, output_dir)
|
||||
save_name = f"{filename}_{counter:05}_.png"
|
||||
save_path = Path(full_output_folder) / save_name
|
||||
save_path.write_bytes(image_bytes)
|
||||
return {"filename": save_name, "subfolder": subfolder}
|
||||
|
||||
def convert_to_gif(
|
||||
self,
|
||||
filename: str,
|
||||
subfolder: str,
|
||||
type_: str,
|
||||
start_time: float = 0.0,
|
||||
end_time=None,
|
||||
fps: float = 10.0,
|
||||
max_width=None,
|
||||
filename_prefix: str = "video/GIF",
|
||||
) -> dict:
|
||||
"""Decodes the given video (PyAV), samples it down to `fps`, optionally
|
||||
downscales to `max_width`, and encodes the result as an animated GIF
|
||||
(Pillow) into ComfyUI's own output folder."""
|
||||
import av # type: ignore
|
||||
import folder_paths # type: ignore
|
||||
|
||||
if _RESAMPLE is None:
|
||||
raise RuntimeError("Pillow is not available")
|
||||
|
||||
src_path = self._resolve_media_path(filename, subfolder, type_)
|
||||
fps = max(0.1, float(fps))
|
||||
|
||||
container = av.open(str(src_path))
|
||||
try:
|
||||
stream = container.streams.video[0]
|
||||
source_fps = float(stream.average_rate) if stream.average_rate else 24.0
|
||||
step = max(1, round(source_fps / fps))
|
||||
|
||||
pil_frames = []
|
||||
idx = 0
|
||||
for frame in container.decode(stream):
|
||||
t = float(frame.pts * stream.time_base) if frame.pts is not None else None
|
||||
if t is not None:
|
||||
if t < start_time:
|
||||
idx += 1
|
||||
continue
|
||||
if end_time is not None and t > end_time:
|
||||
break
|
||||
if idx % step == 0:
|
||||
im = frame.to_image()
|
||||
if max_width and im.width > max_width:
|
||||
ratio = max_width / im.width
|
||||
im = im.resize((max_width, max(1, round(im.height * ratio))), _RESAMPLE)
|
||||
pil_frames.append(im.convert("RGB"))
|
||||
idx += 1
|
||||
finally:
|
||||
container.close()
|
||||
|
||||
if not pil_frames:
|
||||
raise ValueError("No frames found in the specified range")
|
||||
|
||||
output_dir = folder_paths.get_output_directory()
|
||||
full_output_folder, out_filename, counter, out_subfolder, _ = folder_paths.get_save_image_path(filename_prefix, output_dir)
|
||||
save_name = f"{out_filename}_{counter:05}_.gif"
|
||||
save_path = Path(full_output_folder) / save_name
|
||||
|
||||
duration_ms = max(20, round(1000 / fps))
|
||||
pil_frames[0].save(
|
||||
save_path,
|
||||
format="GIF",
|
||||
save_all=True,
|
||||
append_images=pil_frames[1:],
|
||||
duration=duration_ms,
|
||||
loop=0,
|
||||
optimize=True,
|
||||
)
|
||||
return {"filename": save_name, "subfolder": out_subfolder, "frame_count": len(pil_frames)}
|
||||
@@ -4,7 +4,7 @@ import jinja2
|
||||
from aiohttp import web
|
||||
from pathlib import Path
|
||||
|
||||
from .config import TEMPLATES_DIR, STATIC_DIR, DATA_DIR, WORKFLOWS_DIR
|
||||
from .config import TEMPLATES_DIR, STATIC_DIR, DATA_DIR, WORKFLOWS_DIR, LAB_PLAN_DIR
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -41,6 +41,22 @@ async def serve_workflow_file(request: web.Request) -> web.Response:
|
||||
})
|
||||
|
||||
|
||||
async def serve_lab_plan_file(request: web.Request) -> web.Response:
|
||||
"""Serve files (index thumbnails) from the Lab plan directory."""
|
||||
filename = request.match_info.get("filename", "")
|
||||
if not filename or ".." in filename or "/" in filename or "\\" in filename:
|
||||
return web.Response(status=404)
|
||||
|
||||
file_path = LAB_PLAN_DIR / filename
|
||||
if not file_path.is_file():
|
||||
return web.Response(status=404)
|
||||
|
||||
content_type, _ = mimetypes.guess_type(str(file_path))
|
||||
return web.FileResponse(file_path, headers={
|
||||
"Content-Type": content_type or "application/octet-stream",
|
||||
})
|
||||
|
||||
|
||||
class WorkflowStudio:
|
||||
"""Main entry point for Workflow Studio plugin."""
|
||||
|
||||
@@ -57,15 +73,19 @@ class WorkflowStudio:
|
||||
# Dynamic workflow file serving (thumbnails etc.)
|
||||
app.router.add_get("/wfm_data/workflows/{filename}", serve_workflow_file)
|
||||
|
||||
# Dynamic Lab plan file serving (index thumbnails)
|
||||
app.router.add_get("/wfm_data/lab_plan/{filename}", serve_lab_plan_file)
|
||||
|
||||
# Main page
|
||||
app.router.add_get("/wfm", serve_index_page)
|
||||
|
||||
# API routes
|
||||
from .routes import workflow_routes, settings_routes, ollama_routes, eagle_routes, nodes_routes, prompts_routes, models_routes, gallery_routes, wildcard_routes, tagger_routes, gmic_routes, skill_routes
|
||||
from .routes import workflow_routes, settings_routes, ollama_routes, unsloth_routes, eagle_routes, nodes_routes, prompts_routes, models_routes, gallery_routes, wildcard_routes, tagger_routes, gmic_routes, skill_routes, lab_routes, video_routes
|
||||
|
||||
workflow_routes.setup_routes(app)
|
||||
settings_routes.setup_routes(app)
|
||||
ollama_routes.setup_routes(app)
|
||||
unsloth_routes.setup_routes(app)
|
||||
eagle_routes.setup_routes(app)
|
||||
nodes_routes.setup_routes(app)
|
||||
prompts_routes.setup_routes(app)
|
||||
@@ -75,5 +95,7 @@ class WorkflowStudio:
|
||||
tagger_routes.setup_routes(app)
|
||||
gmic_routes.setup_routes(app)
|
||||
skill_routes.setup_routes(app)
|
||||
lab_routes.setup_routes(app)
|
||||
video_routes.setup_routes(app)
|
||||
|
||||
logger.info("Workflow Studio: Routes registered successfully")
|
||||
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
[project]
|
||||
name = "comfyui-workflow-studio"
|
||||
description = "A comprehensive workflow, asset management, and generation UI plugin for ComfyUI. Manage workflows, models, images, and prompts; batch generation and Image Feeder in the GenerateUI tab; library side panel with canvas drag & drop and AI tools."
|
||||
version = "0.3.79"
|
||||
version = "0.5.2"
|
||||
license = "MIT"
|
||||
requires-python = ">= 3.10"
|
||||
dependencies = ["jinja2"]
|
||||
|
||||
@@ -19,3 +19,13 @@ piexif>=1.1.3
|
||||
# Omit if you do not use DeepDanbooru models.
|
||||
# TensorFlow is large (~600MB) and conflicts with some CUDA setups.
|
||||
# tensorflow>=2.10
|
||||
|
||||
# -- Required to load UNSLOTH_API_KEY from a local .env file (Unsloth backend, AI TOOL tab) --
|
||||
python-dotenv>=1.0,<2.0
|
||||
|
||||
# -- MP4 thumbnail generation (Gallery tab) --
|
||||
# Uses PyAV (av) to decode the first frame. ComfyUI core already requires av>=17.0.0,
|
||||
# so it is normally pre-installed in python_embeded and no action is needed here.
|
||||
# If missing, mp4 thumbnail generation simply fails and falls back to a placeholder
|
||||
# (Gallery itself keeps working). Not pinned here on purpose — see torch note above.
|
||||
# av>=17.0.0
|
||||
|
||||
+188
-38
@@ -2,11 +2,45 @@
|
||||
Gallery Tab Styles
|
||||
============================================================ */
|
||||
|
||||
/* ── サブタブ (Output / Style-Prompt) ──────────────────────── */
|
||||
|
||||
.wfm-gallery-subtab-nav {
|
||||
display: flex;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-gallery-subtab-btn {
|
||||
padding: 6px 16px;
|
||||
font-size: 12px;
|
||||
background: transparent;
|
||||
border: none;
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: pointer;
|
||||
border-bottom: 2px solid transparent;
|
||||
margin-bottom: -1px;
|
||||
transition: color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-gallery-subtab-btn:hover { color: var(--wfm-text-primary); }
|
||||
.wfm-gallery-subtab-btn.active { color: var(--wfm-primary); border-bottom-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-gallery-panel {
|
||||
display: none;
|
||||
flex-direction: column;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-gallery-panel.active { display: flex; }
|
||||
|
||||
/* ── 3カラムレイアウト ──────────────────────────────────────── */
|
||||
|
||||
.wfm-gallery-layout {
|
||||
display: flex;
|
||||
height: calc(100vh - var(--wfm-header-height, 96px));
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
gap: 0;
|
||||
}
|
||||
@@ -256,6 +290,24 @@
|
||||
color: #38bdf8;
|
||||
}
|
||||
|
||||
/* 動画ファイル（mp4）の再生アイコンバッジ（サムネイル中央） */
|
||||
.wfm-gallery-thumb-video-badge {
|
||||
position: absolute;
|
||||
top: 50%;
|
||||
left: 50%;
|
||||
transform: translate(-50%, -50%);
|
||||
width: 36px;
|
||||
height: 36px;
|
||||
border-radius: 50%;
|
||||
background: rgba(0,0,0,0.55);
|
||||
color: #fff;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
font-size: 14px;
|
||||
pointer-events: none;
|
||||
}
|
||||
|
||||
/* テーブルビューのお気に入りボタン */
|
||||
.wfm-gallery-table-fav-btn {
|
||||
background: none;
|
||||
@@ -431,43 +483,6 @@
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.wfm-gallery-preview-overlay {
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
right: 0;
|
||||
bottom: 0;
|
||||
background: rgba(0,0,0,0.5);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
opacity: 0;
|
||||
transition: opacity 0.2s;
|
||||
}
|
||||
|
||||
.wfm-gallery-preview-wrapper:hover .wfm-gallery-preview-overlay {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.wfm-gallery-download-btn {
|
||||
background: var(--wfm-accent, #4f8ef7);
|
||||
border: none;
|
||||
border-radius: 50%;
|
||||
width: 40px;
|
||||
height: 40px;
|
||||
font-size: 20px;
|
||||
cursor: pointer;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
color: #fff;
|
||||
transition: background 0.2s;
|
||||
}
|
||||
|
||||
.wfm-gallery-download-btn:hover {
|
||||
background: var(--wfm-accent-hover, #3f7fe7);
|
||||
}
|
||||
|
||||
/* タブコンテンツ */
|
||||
.wfm-gallery-detail-tab-content {
|
||||
flex: 1;
|
||||
@@ -499,6 +514,20 @@
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-gallery-image-prompt-text {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-primary);
|
||||
line-height: 1.5;
|
||||
white-space: pre-wrap;
|
||||
word-break: break-word;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
padding: 6px 8px;
|
||||
max-height: 160px;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.wfm-gallery-tags-display {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
@@ -788,6 +817,16 @@
|
||||
border-color: #6a4de0;
|
||||
}
|
||||
|
||||
.wfm-gallery-action-btn-orange {
|
||||
background: #f59e0b;
|
||||
border-color: #f59e0b;
|
||||
color: #1a1a2e;
|
||||
}
|
||||
.wfm-gallery-action-btn-orange:hover {
|
||||
background: #d97f06;
|
||||
border-color: #d97f06;
|
||||
}
|
||||
|
||||
/* ── ファイル操作ボタン (詳細パネル) ─────────────────────────── */
|
||||
|
||||
.wfm-gallery-file-actions {
|
||||
@@ -833,3 +872,114 @@
|
||||
gap: 8px;
|
||||
justify-content: flex-end;
|
||||
}
|
||||
|
||||
/* ============================================================
|
||||
ImagePrompt / Style_Catalog ギャラリー (右ペイン)
|
||||
============================================================ */
|
||||
|
||||
.wfm-imageprompt-detail-panel {
|
||||
width: 520px;
|
||||
min-width: 520px;
|
||||
max-width: 520px;
|
||||
flex-direction: row;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-detail-col {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-detail-col-left {
|
||||
width: 280px;
|
||||
min-width: 280px;
|
||||
max-width: 280px;
|
||||
overflow-y: auto;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
padding-bottom: 8px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-detail-col-right {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
padding: 8px 10px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-section {
|
||||
padding: 8px 10px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-prompt-box {
|
||||
width: 100%;
|
||||
resize: vertical;
|
||||
font-size: 12px;
|
||||
padding: 6px 8px;
|
||||
background: var(--wfm-bg-primary, #16213e);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
color: var(--wfm-text-primary);
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-btn-row {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
margin-top: 6px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-btn-row .wfm-btn {
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chips {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 4px;
|
||||
margin-top: 6px;
|
||||
min-height: 24px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chip {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 4px;
|
||||
max-width: 100%;
|
||||
background: rgba(79,142,247,0.15);
|
||||
color: var(--wfm-accent, #4f8ef7);
|
||||
border: 1px solid rgba(79,142,247,0.3);
|
||||
border-radius: 10px;
|
||||
padding: 2px 4px 2px 8px;
|
||||
font-size: 11px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chip-text {
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
max-width: 220px;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chip-remove {
|
||||
background: none;
|
||||
border: none;
|
||||
color: inherit;
|
||||
cursor: pointer;
|
||||
padding: 0 2px;
|
||||
font-size: 13px;
|
||||
line-height: 1;
|
||||
opacity: 0.7;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-chip-remove:hover { opacity: 1; }
|
||||
|
||||
.wfm-imageprompt-final-section {
|
||||
height: 100%;
|
||||
}
|
||||
|
||||
.wfm-imageprompt-final-textarea {
|
||||
margin-top: 4px;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
resize: none;
|
||||
}
|
||||
|
||||
@@ -0,0 +1,413 @@
|
||||
/* Lab tab — experimental I2I batch generation with per-column keyframes */
|
||||
|
||||
/* .wfm-gen-subtab-content.active defaults to flex-direction:row (main.css);
|
||||
Lab needs its Setting/Results nav stacked above the panel body. */
|
||||
#wfm-gen-subtab-lab.active {
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.wfm-lab-subtab-nav {
|
||||
display: flex;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-subtab-btn {
|
||||
padding: 6px 16px;
|
||||
font-size: 12px;
|
||||
background: transparent;
|
||||
border: none;
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: pointer;
|
||||
border-bottom: 2px solid transparent;
|
||||
margin-bottom: -1px;
|
||||
transition: color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-lab-subtab-btn:hover { color: var(--wfm-text-primary); }
|
||||
.wfm-lab-subtab-btn.active { color: var(--wfm-primary); border-bottom-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-lab-panel {
|
||||
display: none;
|
||||
flex-direction: column;
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
height: calc(100% - 33px);
|
||||
}
|
||||
|
||||
.wfm-lab-panel.active { display: flex; }
|
||||
|
||||
.wfm-lab-planjson-toolbar {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
padding: 8px 10px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-pane-header {
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text-secondary);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.05em;
|
||||
padding: 4px 0 6px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
/* ---------------- Setting layout: left | center (4 columns) | right ---------------- */
|
||||
|
||||
.wfm-lab-setting-layout {
|
||||
display: flex;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
gap: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-pane {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
overflow: hidden;
|
||||
min-width: 0;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
padding: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-pane:last-child { border-right: none; }
|
||||
|
||||
.wfm-lab-left { width: 220px; flex-shrink: 0; }
|
||||
.wfm-lab-center { flex: 1; padding: 0; }
|
||||
.wfm-lab-right { width: 260px; flex-shrink: 0; overflow-y: auto; }
|
||||
|
||||
.wfm-lab-side-btns {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 6px;
|
||||
margin-top: 12px;
|
||||
}
|
||||
|
||||
.wfm-lab-plan-workflow-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
margin-top: 8px;
|
||||
padding: 6px 8px;
|
||||
background: var(--wfm-bg-secondary);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 4px;
|
||||
}
|
||||
|
||||
.wfm-lab-plan-workflow-name {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
font-size: 11px;
|
||||
color: var(--wfm-text-secondary);
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-lab-field-row {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-field-row label {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.wfm-lab-field-row input {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-note {
|
||||
width: 100%;
|
||||
resize: vertical;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.wfm-lab-checkbox-label {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
margin-top: 8px;
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
/* ---------------- Keyframe columns ---------------- */
|
||||
|
||||
.wfm-lab-columns {
|
||||
display: flex;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-lab-column {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-lab-column:last-child { border-right: none; }
|
||||
|
||||
.wfm-lab-col-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
flex-wrap: wrap;
|
||||
justify-content: space-between;
|
||||
gap: 4px;
|
||||
row-gap: 4px;
|
||||
padding: 6px 8px;
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
color: var(--wfm-text-secondary);
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-col-btns { display: flex; gap: 2px; }
|
||||
|
||||
.wfm-lab-col-header-left {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
/* Model column header: C/L/V toggle that switches which single field its cells
|
||||
summarize (see _modelViewMode in lab-tab.js) — not part of the Plan data. */
|
||||
.wfm-lab-model-view-toggle { display: flex; gap: 2px; }
|
||||
|
||||
.wfm-lab-model-view-btn {
|
||||
width: 20px;
|
||||
height: 20px;
|
||||
padding: 0;
|
||||
font-size: 10px;
|
||||
font-weight: 700;
|
||||
line-height: 1;
|
||||
background: var(--wfm-bg-tertiary);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: var(--wfm-radius-sm);
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: pointer;
|
||||
transition: color 0.15s, border-color 0.15s, background 0.15s;
|
||||
}
|
||||
|
||||
.wfm-lab-model-view-btn:hover { color: var(--wfm-text-primary); border-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-lab-model-view-btn.active {
|
||||
color: #fff;
|
||||
background: var(--wfm-primary);
|
||||
border-color: var(--wfm-primary);
|
||||
}
|
||||
|
||||
.wfm-lab-col-cells {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
padding: 6px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.wfm-lab-cell {
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: var(--wfm-radius-sm);
|
||||
padding: 6px 8px;
|
||||
font-size: 11px;
|
||||
cursor: pointer;
|
||||
background: var(--wfm-bg-tertiary);
|
||||
transition: border-color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-lab-cell:hover { border-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-lab-cell-index {
|
||||
font-weight: 600;
|
||||
color: var(--wfm-primary);
|
||||
margin-right: 4px;
|
||||
}
|
||||
|
||||
.wfm-lab-cell-value {
|
||||
color: var(--wfm-text-primary);
|
||||
word-break: break-all;
|
||||
}
|
||||
|
||||
.wfm-lab-cell.wfm-lab-cell-revert .wfm-lab-cell-value {
|
||||
color: var(--wfm-text-secondary);
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
/* Setting 1, not yet explicitly overridden: showing the live GenerateUI value instead of
|
||||
its own stored one (see _isLiveDisplay in lab-tab.js). Dashed border distinguishes it
|
||||
from a saved override (solid border) at a glance. */
|
||||
.wfm-lab-cell.wfm-lab-cell-live {
|
||||
border-style: dashed;
|
||||
}
|
||||
|
||||
.wfm-lab-cell.wfm-lab-cell-live .wfm-lab-cell-value {
|
||||
color: var(--wfm-text-secondary);
|
||||
}
|
||||
|
||||
/* This keyframe row is skipped entirely (see _resolveKeyframe's bypassed handling) —
|
||||
amber to read as "temporarily disabled", distinct from the neutral gray used for
|
||||
revert/live. Takes priority over both of those in _cellHtml. */
|
||||
.wfm-lab-cell.wfm-lab-cell-bypassed {
|
||||
border-color: var(--wfm-warning);
|
||||
opacity: 0.6;
|
||||
}
|
||||
|
||||
.wfm-lab-cell.wfm-lab-cell-bypassed .wfm-lab-cell-value {
|
||||
color: var(--wfm-warning);
|
||||
}
|
||||
|
||||
/* LoRA node-bypass (strength forced to 0 at apply-time) — red to distinguish from the
|
||||
amber row-level bypassed above; the row itself still applies, only the LoRA doesn't. */
|
||||
.wfm-lab-cell.wfm-lab-cell-node-bypassed {
|
||||
border-color: var(--wfm-danger);
|
||||
}
|
||||
|
||||
.wfm-lab-cell.wfm-lab-cell-node-bypassed .wfm-lab-cell-value {
|
||||
color: var(--wfm-danger);
|
||||
}
|
||||
|
||||
/* Single-click selection target for the column header's up/down reorder buttons —
|
||||
layered as a box-shadow (not border-color) so it never conflicts with the
|
||||
revert/live/bypassed/node-bypassed border-color signaling above. */
|
||||
.wfm-lab-cell.wfm-lab-cell-selected {
|
||||
box-shadow: 0 0 0 2px var(--wfm-primary);
|
||||
}
|
||||
|
||||
.wfm-lab-cell-applied {
|
||||
margin-top: 3px;
|
||||
padding-top: 3px;
|
||||
border-top: 1px dashed var(--wfm-border);
|
||||
font-size: 10px;
|
||||
color: var(--wfm-text-secondary);
|
||||
}
|
||||
|
||||
/* Shown instead of a keyframe list when the loaded workflow has no node this Model
|
||||
sub-column (checkpoint/lora/vae) could override — see _hasNodeForColumn in lab-tab.js. */
|
||||
.wfm-lab-cell-empty {
|
||||
padding: 6px 8px;
|
||||
font-size: 11px;
|
||||
font-style: italic;
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: default;
|
||||
}
|
||||
|
||||
.wfm-lab-cell-prompt-line {
|
||||
display: block;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
max-width: 100%;
|
||||
}
|
||||
|
||||
/* ---------------- Modal form (checkpoint/vae/prompt/ksampler edit) ---------------- */
|
||||
|
||||
.wfm-lab-modal-row {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 4px;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-modal-row label {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
}
|
||||
|
||||
.wfm-lab-modal-inline {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.wfm-lab-modal-btnrow {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 6px;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-modal-hint {
|
||||
font-size: 11px;
|
||||
color: var(--wfm-text-secondary);
|
||||
font-style: italic;
|
||||
margin: 0 0 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-modal-inline > div {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.wfm-lab-modal-checkbox {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
font-size: 12px;
|
||||
margin-top: 4px;
|
||||
}
|
||||
|
||||
/* ---------------- Results layout ---------------- */
|
||||
|
||||
.wfm-lab-results-layout {
|
||||
display: flex;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-lab-results-left {
|
||||
width: 260px;
|
||||
flex-shrink: 0;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
padding: 10px;
|
||||
}
|
||||
|
||||
.wfm-lab-results-center {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
padding: 10px;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.wfm-lab-results-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fill, minmax(140px, 1fr));
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.wfm-lab-result-thumb {
|
||||
width: 100%;
|
||||
aspect-ratio: 1 / 1;
|
||||
object-fit: cover;
|
||||
border-radius: var(--wfm-radius-sm);
|
||||
border: 1px solid var(--wfm-border);
|
||||
cursor: pointer;
|
||||
transition: border-color 0.15s;
|
||||
}
|
||||
|
||||
.wfm-lab-result-thumb:hover { border-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-lab-result-item { position: relative; }
|
||||
|
||||
.wfm-lab-result-index {
|
||||
position: absolute;
|
||||
top: 2px;
|
||||
left: 2px;
|
||||
background: rgba(0, 0, 0, 0.6);
|
||||
color: #fff;
|
||||
font-size: 10px;
|
||||
padding: 1px 5px;
|
||||
border-radius: var(--wfm-radius-sm);
|
||||
}
|
||||
+319
-6
@@ -653,8 +653,8 @@ body {
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.wfm-lora-bypass-btn,
|
||||
.wfm-lora-refresh-btn {
|
||||
margin-left: auto;
|
||||
font-size: 13px;
|
||||
line-height: 1;
|
||||
padding: 2px 5px;
|
||||
@@ -662,8 +662,21 @@ body {
|
||||
text-transform: none;
|
||||
letter-spacing: 0;
|
||||
}
|
||||
.wfm-lora-bypass-btn {
|
||||
margin-left: auto;
|
||||
}
|
||||
.wfm-lora-bypass-btn:hover,
|
||||
.wfm-lora-refresh-btn:hover { opacity: 1; }
|
||||
|
||||
/* Bypass is active for the current LoRA target node: strength forced to 0 (see
|
||||
comfyui-editor.js renderLoraPane / _loraNodeBypass). */
|
||||
.wfm-lora-bypass-btn.active {
|
||||
opacity: 1;
|
||||
background: var(--wfm-danger);
|
||||
border-color: var(--wfm-danger);
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.wfm-gen-lora-col-body {
|
||||
flex: 1;
|
||||
overflow-y: auto;
|
||||
@@ -2342,6 +2355,280 @@ body {
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
/* Col3 (Wildcard/Style) タブ切り替え */
|
||||
.wfm-prompt-col3-tabs {
|
||||
display: flex;
|
||||
background: var(--wfm-surface);
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.wfm-prompt-col3-tab {
|
||||
flex: 1;
|
||||
padding: 8px 4px;
|
||||
background: none;
|
||||
border: none;
|
||||
border-bottom: 2px solid transparent;
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 13px;
|
||||
font-weight: bold;
|
||||
cursor: pointer;
|
||||
transition: all 0.15s;
|
||||
}
|
||||
.wfm-prompt-col3-tab:hover { color: var(--wfm-text); }
|
||||
.wfm-prompt-col3-tab.active {
|
||||
color: var(--wfm-text);
|
||||
border-bottom-color: var(--wfm-primary);
|
||||
}
|
||||
.wfm-prompt-col3-tab-content {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
/* Prompt タブ: Form / Table 切り替え */
|
||||
.wfm-prompt-toplevel-nav {
|
||||
display: flex;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.wfm-prompt-toplevel-tab {
|
||||
padding: 6px 16px;
|
||||
font-size: 12px;
|
||||
background: transparent;
|
||||
border: none;
|
||||
color: var(--wfm-text-secondary);
|
||||
cursor: pointer;
|
||||
border-bottom: 2px solid transparent;
|
||||
margin-bottom: -1px;
|
||||
transition: color 0.15s;
|
||||
}
|
||||
.wfm-prompt-toplevel-tab:hover { color: var(--wfm-text-primary); }
|
||||
.wfm-prompt-toplevel-tab.active { color: var(--wfm-primary); border-bottom-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-prompt-panel {
|
||||
display: none;
|
||||
flex-direction: column;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
.wfm-prompt-panel.active { display: flex; }
|
||||
|
||||
/* Table パネル内サブタブ (Presets / Wildcards / Style) */
|
||||
.wfm-prompt-table-subnav {
|
||||
display: flex;
|
||||
background: var(--wfm-surface);
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.wfm-prompt-table-subtab {
|
||||
padding: 8px 16px;
|
||||
background: none;
|
||||
border: none;
|
||||
border-bottom: 2px solid transparent;
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 13px;
|
||||
font-weight: bold;
|
||||
cursor: pointer;
|
||||
transition: all 0.15s;
|
||||
}
|
||||
.wfm-prompt-table-subtab:hover { color: var(--wfm-text); }
|
||||
.wfm-prompt-table-subtab.active { color: var(--wfm-text); border-bottom-color: var(--wfm-primary); }
|
||||
|
||||
.wfm-prompt-table-pane {
|
||||
display: none;
|
||||
flex-direction: column;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
.wfm-prompt-table-pane.active { display: flex; }
|
||||
|
||||
.wfm-prompt-table-toolbar {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 6px;
|
||||
align-items: center;
|
||||
padding: 8px;
|
||||
flex-shrink: 0;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
}
|
||||
|
||||
.wfm-prompt-table-scroll {
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: auto;
|
||||
}
|
||||
|
||||
/* 編集可能テーブル (Presets / Wildcards / Style 共通) */
|
||||
.wfm-prompt-edit-table {
|
||||
width: 100%;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
.wfm-prompt-edit-table th,
|
||||
.wfm-prompt-edit-table td {
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
padding: 4px 6px;
|
||||
vertical-align: top;
|
||||
}
|
||||
.wfm-prompt-edit-table th {
|
||||
position: sticky;
|
||||
top: 0;
|
||||
background: var(--wfm-surface);
|
||||
text-align: left;
|
||||
font-size: 11px;
|
||||
color: var(--wfm-text-secondary);
|
||||
z-index: 1;
|
||||
}
|
||||
.wfm-prompt-edit-table tbody tr:hover td {
|
||||
background: var(--wfm-surface);
|
||||
}
|
||||
.wfm-prompt-edit-input,
|
||||
.wfm-prompt-edit-textarea {
|
||||
width: 100%;
|
||||
box-sizing: border-box;
|
||||
background: transparent;
|
||||
border: 1px solid transparent;
|
||||
border-radius: 3px;
|
||||
color: var(--wfm-text);
|
||||
font-size: 12px;
|
||||
padding: 3px 5px;
|
||||
font-family: inherit;
|
||||
}
|
||||
.wfm-prompt-edit-textarea {
|
||||
min-height: 40px;
|
||||
resize: vertical;
|
||||
}
|
||||
.wfm-prompt-edit-input:hover,
|
||||
.wfm-prompt-edit-textarea:hover {
|
||||
border-color: var(--wfm-border);
|
||||
}
|
||||
.wfm-prompt-edit-input:focus,
|
||||
.wfm-prompt-edit-textarea:focus {
|
||||
outline: none;
|
||||
border-color: var(--wfm-primary);
|
||||
background: var(--wfm-bg);
|
||||
}
|
||||
@keyframes wfm-prompt-cell-flash {
|
||||
0% { background: rgba(74, 158, 255, 0.3); }
|
||||
100% { background: transparent; }
|
||||
}
|
||||
.wfm-prompt-edit-cell-saved {
|
||||
animation: wfm-prompt-cell-flash 0.7s ease;
|
||||
}
|
||||
.wfm-prompt-edit-fav-btn {
|
||||
background: none;
|
||||
border: none;
|
||||
cursor: pointer;
|
||||
font-size: 14px;
|
||||
color: var(--wfm-text-secondary);
|
||||
padding: 2px;
|
||||
line-height: 1;
|
||||
}
|
||||
.wfm-prompt-edit-fav-btn.active { color: #ffd700; }
|
||||
.wfm-prompt-edit-batch-btn.active { color: #f0c040; }
|
||||
.wfm-prompt-edit-row-new td { background: rgba(74, 158, 255, 0.06); }
|
||||
.wfm-prompt-edit-file-badge {
|
||||
font-size: 10px;
|
||||
color: var(--wfm-text-secondary);
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
display: block;
|
||||
}
|
||||
.wfm-prompt-edit-empty {
|
||||
padding: 24px;
|
||||
text-align: center;
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
/* ダブルクリックで開くプロンプト編集モーダル (Presets / Wildcards / Style 共通) */
|
||||
.wfm-prompt-modal-toggle {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
}
|
||||
.wfm-prompt-modal-textarea {
|
||||
width: 100%;
|
||||
box-sizing: border-box;
|
||||
min-height: 320px;
|
||||
resize: vertical;
|
||||
margin-top: 8px;
|
||||
}
|
||||
.wfm-prompt-modal-actions {
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
justify-content: flex-end;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
/* #(自動採番)列: クリックで行選択 → 上部の Delete ボタンで一括削除 */
|
||||
.wfm-prompt-edit-num-cell {
|
||||
text-align: center;
|
||||
cursor: pointer;
|
||||
-webkit-user-select: none;
|
||||
user-select: none;
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 11px;
|
||||
font-variant-numeric: tabular-nums;
|
||||
white-space: nowrap;
|
||||
}
|
||||
.wfm-prompt-edit-num-cell:hover {
|
||||
color: var(--wfm-text);
|
||||
background: var(--wfm-surface-2, var(--wfm-surface));
|
||||
}
|
||||
.wfm-prompt-edit-table tbody tr.wfm-prompt-edit-row-selected td {
|
||||
background: rgba(255, 69, 58, 0.14);
|
||||
}
|
||||
.wfm-prompt-edit-table tbody tr.wfm-prompt-edit-row-selected .wfm-prompt-edit-num-cell {
|
||||
color: var(--wfm-danger);
|
||||
font-weight: bold;
|
||||
}
|
||||
.wfm-prompt-edit-num-cell-new {
|
||||
display: flex;
|
||||
gap: 2px;
|
||||
justify-content: center;
|
||||
}
|
||||
.wfm-prompt-edit-inline-add-btn,
|
||||
.wfm-prompt-edit-inline-cancel-btn {
|
||||
background: none;
|
||||
border: none;
|
||||
cursor: pointer;
|
||||
font-size: 12px;
|
||||
padding: 2px 3px;
|
||||
line-height: 1;
|
||||
border-radius: 3px;
|
||||
}
|
||||
.wfm-prompt-edit-inline-add-btn { color: var(--wfm-primary); }
|
||||
.wfm-prompt-edit-inline-add-btn:hover { background: var(--wfm-primary); color: #fff; }
|
||||
.wfm-prompt-edit-inline-cancel-btn { color: var(--wfm-text-secondary); }
|
||||
.wfm-prompt-edit-inline-cancel-btn:hover { color: var(--wfm-danger); }
|
||||
|
||||
/* Presets Group テーブル: プリセットバッジ */
|
||||
.wfm-prompt-edit-preset-badge {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 3px;
|
||||
background: var(--wfm-surface);
|
||||
border: 1px solid var(--wfm-border);
|
||||
border-radius: 10px;
|
||||
padding: 2px 4px 2px 8px;
|
||||
font-size: 11px;
|
||||
margin: 2px 3px 2px 0;
|
||||
}
|
||||
.wfm-prompt-edit-badge-remove {
|
||||
background: none;
|
||||
border: none;
|
||||
cursor: pointer;
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 10px;
|
||||
padding: 1px 3px;
|
||||
line-height: 1;
|
||||
}
|
||||
.wfm-prompt-edit-badge-remove:hover { color: var(--wfm-danger); }
|
||||
|
||||
/* Ollama Assistant */
|
||||
.wfm-ollama-assistant {
|
||||
display: flex;
|
||||
@@ -2885,6 +3172,29 @@ body {
|
||||
Image Input (I2I) Slots
|
||||
============================================ */
|
||||
|
||||
/* Container/header styling comes from .wfm-settings-section / .wfm-settings-summary
|
||||
(shared details/summary accordion used by the Settings tab) — collapsed by default
|
||||
since this is a low-frequency feature. */
|
||||
|
||||
.wfm-i2i-placeholder-mode {
|
||||
display: flex;
|
||||
gap: 16px;
|
||||
font-size: 12px;
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
|
||||
.wfm-i2i-placeholder-mode label {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 4px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.wfm-i2i-placeholder-fields input[type="color"] {
|
||||
padding: 2px;
|
||||
height: 30px;
|
||||
}
|
||||
|
||||
.wfm-i2i-grid {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
@@ -2948,6 +3258,12 @@ body {
|
||||
background: rgba(74, 158, 255, 0.08);
|
||||
}
|
||||
|
||||
.wfm-i2i-drop-zone.wfm-disabled {
|
||||
opacity: 0.4;
|
||||
pointer-events: none;
|
||||
cursor: default;
|
||||
}
|
||||
|
||||
.wfm-i2i-drop-label {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
@@ -4957,15 +5273,12 @@ body {
|
||||
Metadata Tab
|
||||
============================================ */
|
||||
|
||||
#wfm-tab-metadata {
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-metadata-layout {
|
||||
display: grid;
|
||||
grid-template-columns: 220px 1fr 1fr;
|
||||
gap: 0;
|
||||
height: 100%;
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,185 @@
|
||||
/* =============================================
|
||||
Video Tab (MiniMax H3専用・GenerateUIタブとは独立)
|
||||
3ペイン構成: 左=フォーム / 中央=プレビュー+編集 / 右=プロパティ
|
||||
各ペインは独立したカラムとして高さ100%を占め、内部でスクロールする。
|
||||
中央ペインのコンテンツ量(生成結果の有無・動画の縦横比)が変わっても
|
||||
左右のペインの位置が動かないよう、意図的に高さを共有させない構造にしている。
|
||||
============================================= */
|
||||
|
||||
#wfm-tab-video {
|
||||
padding: 0;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.wfm-video-layout {
|
||||
display: flex;
|
||||
flex-direction: row;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
/* ── 左ペイン: フォーム ── */
|
||||
.wfm-video-form-panel {
|
||||
width: 380px;
|
||||
flex: none;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 10px;
|
||||
padding: 16px;
|
||||
overflow-y: auto;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
}
|
||||
|
||||
.wfm-video-form-panel label {
|
||||
display: block;
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
.wfm-video-header {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 2px;
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
.wfm-video-header h3 {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.wfm-video-wf-name {
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.wfm-video-frame-slots {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 12px;
|
||||
}
|
||||
|
||||
.wfm-video-frame-slot {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.wfm-video-resolution-row {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr 1fr;
|
||||
gap: 10px;
|
||||
align-items: end;
|
||||
}
|
||||
|
||||
.wfm-video-seed-randomize-wrap {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
padding-bottom: 6px;
|
||||
}
|
||||
|
||||
/* ── 中央ペイン: プレビュー + 編集 ── */
|
||||
.wfm-video-center-panel {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
overflow-y: auto;
|
||||
padding: 16px;
|
||||
gap: 16px;
|
||||
border-right: 1px solid var(--wfm-border);
|
||||
}
|
||||
|
||||
/* 生成結果 or ソース選択エリアで読み込んだ動画を横並びで表示する行。
|
||||
プレビュー枠が主役、ソースパネルは選択UIのみの固定幅。 */
|
||||
.wfm-video-preview-row {
|
||||
flex: none;
|
||||
display: flex;
|
||||
flex-direction: row;
|
||||
gap: 16px;
|
||||
}
|
||||
|
||||
.wfm-video-preview-frame {
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
min-height: 260px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
border: 1px dashed var(--wfm-border);
|
||||
border-radius: 6px;
|
||||
background: var(--wfm-bg-hover, #111);
|
||||
}
|
||||
|
||||
.wfm-video-preview-el {
|
||||
max-width: 100%;
|
||||
max-height: 60vh;
|
||||
border-radius: 6px;
|
||||
}
|
||||
|
||||
.wfm-video-source-panel {
|
||||
width: 220px;
|
||||
flex: none;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.wfm-video-pane-header {
|
||||
font-weight: 600;
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
.wfm-video-prop-hint {
|
||||
font-size: 12px;
|
||||
color: var(--wfm-text-secondary);
|
||||
margin: 0 0 8px 0;
|
||||
}
|
||||
|
||||
/* 将来の動画編集機能(トリミング等)用のプレースホルダー領域 */
|
||||
.wfm-video-edit-panel {
|
||||
flex: 1;
|
||||
min-height: 160px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
border: 1px dashed var(--wfm-border);
|
||||
border-radius: 6px;
|
||||
color: var(--wfm-text-secondary);
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
/* ── 右ペイン: プロパティ (Frame抽出 / GIF変換タブ) ── */
|
||||
.wfm-video-properties-panel {
|
||||
width: 240px;
|
||||
flex: none;
|
||||
padding: 16px;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.wfm-video-prop-tabnav {
|
||||
display: flex;
|
||||
gap: 4px;
|
||||
margin-bottom: 12px;
|
||||
border-bottom: 1px solid var(--wfm-border);
|
||||
}
|
||||
|
||||
.wfm-video-prop-tab {
|
||||
flex: 1;
|
||||
background: none;
|
||||
border: none;
|
||||
padding: 6px 4px;
|
||||
cursor: pointer;
|
||||
color: var(--wfm-text-secondary);
|
||||
border-bottom: 2px solid transparent;
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.wfm-video-prop-tab.active {
|
||||
color: var(--wfm-text);
|
||||
border-bottom-color: var(--wfm-primary);
|
||||
}
|
||||
|
||||
.wfm-video-prop-content {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 6px;
|
||||
}
|
||||
+205
-37
@@ -50,33 +50,102 @@ function fileToBase64(file) {
|
||||
});
|
||||
}
|
||||
|
||||
async function callVLM(url, backend, model, prompt, base64Image, mimeType) {
|
||||
// ============================================
|
||||
// Thinking mode / Max tokens (shared by callLLM / callChat / callVLM)
|
||||
// ============================================
|
||||
|
||||
// Ollamaは `think` (bool) と `options.num_predict` で制御できるが、
|
||||
// LM Studio/LemonadeはOpenAI互換APIのため max_tokens のみ標準対応。
|
||||
// thinking mode切替は非対応バックエンド／モデル向けに、出力からの
|
||||
// <think>タグ除去でも担保する（stripThinkingTagsを参照）。
|
||||
function _applyGenOptions(body, backend, settings) {
|
||||
const maxTokens = parseInt(settings?.maxTokens, 10);
|
||||
if (backend === "ollama") {
|
||||
body.think = !!settings?.thinkingMode;
|
||||
if (maxTokens > 0) body.options = { ...(body.options || {}), num_predict: maxTokens };
|
||||
} else if (maxTokens > 0) {
|
||||
body.max_tokens = maxTokens;
|
||||
}
|
||||
return body;
|
||||
}
|
||||
|
||||
function stripThinkingTags(text) {
|
||||
return (text || "")
|
||||
.replace(/<think>[\s\S]*?<\/think>/gi, "")
|
||||
.replace(/<thinking>[\s\S]*?<\/thinking>/gi, "")
|
||||
.trim();
|
||||
}
|
||||
|
||||
async function callVLM(url, backend, model, prompt, base64Image, mimeType, settings = {}) {
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/generate`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, prompt, images: [base64Image], stream: false }),
|
||||
body: JSON.stringify(_applyGenOptions({ model, prompt, images: [base64Image], stream: false }, backend, settings)),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).response || "";
|
||||
const text = (await res.json()).response || "";
|
||||
return settings.thinkingMode ? text : stripThinkingTags(text);
|
||||
} else if (backend === "unsloth") {
|
||||
const body = _applyGenOptions({
|
||||
model,
|
||||
messages: [{ role: "user", content: [
|
||||
{ type: "text", text: prompt },
|
||||
{ type: "image_url", image_url: { url: `data:${mimeType};base64,${base64Image}` } },
|
||||
]}],
|
||||
stream: false,
|
||||
}, backend, settings);
|
||||
const data = await unslothProxy(url, "/v1/chat/completions", "POST", body);
|
||||
const text = _unslothContent(data.choices?.[0]?.message);
|
||||
return settings.thinkingMode ? text : stripThinkingTags(text);
|
||||
} else {
|
||||
const res = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
body: JSON.stringify(_applyGenOptions({
|
||||
model,
|
||||
messages: [{ role: "user", content: [
|
||||
{ type: "text", text: prompt },
|
||||
{ type: "image_url", image_url: { url: `data:${mimeType};base64,${base64Image}` } },
|
||||
]}],
|
||||
stream: false,
|
||||
}),
|
||||
}, backend, settings)),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).choices?.[0]?.message?.content || "";
|
||||
const text = (await res.json()).choices?.[0]?.message?.content || "";
|
||||
return settings.thinkingMode ? text : stripThinkingTags(text);
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Unsloth proxy (server relays the request, attaching the API key from .env
|
||||
// so it never reaches the frontend — see py/routes/unsloth_routes.py)
|
||||
// ============================================
|
||||
|
||||
async function unslothProxy(baseUrl, path, method, payload) {
|
||||
const res = await fetch("/api/wfm/unsloth/proxy", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ baseUrl, path, method, payload }),
|
||||
});
|
||||
const data = await res.json().catch(() => ({}));
|
||||
if (!res.ok) throw new Error(data.message || `HTTP ${res.status}`);
|
||||
return data;
|
||||
}
|
||||
|
||||
// Unlike Ollama/LM Studio/Lemonade (which inline reasoning as <think> tags
|
||||
// inside content), Unsloth's OpenAI-compatible API returns it in a separate
|
||||
// `reasoning_content` field. Fold it back into a <think> block so the
|
||||
// existing Thinking-mode show/strip logic (_applyGenOptions/stripThinkingTags)
|
||||
// still applies uniformly. Without this, a low max_tokens can make the model
|
||||
// spend its whole budget reasoning and return an empty `content` with no
|
||||
// visible explanation.
|
||||
function _unslothContent(message) {
|
||||
const reasoning = message?.reasoning_content;
|
||||
const content = message?.content || "";
|
||||
return reasoning ? `<think>${reasoning}</think>${content}` : content;
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// URL validation
|
||||
// ============================================
|
||||
@@ -100,6 +169,9 @@ async function fetchModels(url, backend) {
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const data = await res.json();
|
||||
return (data.models || []).map((m) => m.name);
|
||||
} else if (backend === "unsloth") {
|
||||
const data = await unslothProxy(url, "/v1/models", "GET");
|
||||
return (data.data || []).map((m) => m.id);
|
||||
} else {
|
||||
// LM Studio (OpenAI-compatible)
|
||||
const res = await fetch(`${url}/v1/models`);
|
||||
@@ -114,30 +186,37 @@ async function testConnection(url, backend) {
|
||||
return models.length;
|
||||
}
|
||||
|
||||
async function callLLM(url, backend, model, prompt) {
|
||||
async function callLLM(url, backend, model, prompt, settings = {}) {
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/generate`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, prompt, stream: false }),
|
||||
body: JSON.stringify(_applyGenOptions({ model, prompt, stream: false }, backend, settings)),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const data = await res.json();
|
||||
return data.response || "";
|
||||
const text = data.response || "";
|
||||
return settings.thinkingMode ? text : stripThinkingTags(text);
|
||||
} else if (backend === "unsloth") {
|
||||
const body = _applyGenOptions({ model, messages: [{ role: "user", content: prompt }], stream: false }, backend, settings);
|
||||
const data = await unslothProxy(url, "/v1/chat/completions", "POST", body);
|
||||
const text = _unslothContent(data.choices?.[0]?.message);
|
||||
return settings.thinkingMode ? text : stripThinkingTags(text);
|
||||
} else {
|
||||
// LM Studio (OpenAI-compatible)
|
||||
const res = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
body: JSON.stringify(_applyGenOptions({
|
||||
model,
|
||||
messages: [{ role: "user", content: prompt }],
|
||||
stream: false,
|
||||
}),
|
||||
}, backend, settings)),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const data = await res.json();
|
||||
return data.choices?.[0]?.message?.content || "";
|
||||
const text = data.choices?.[0]?.message?.content || "";
|
||||
return settings.thinkingMode ? text : stripThinkingTags(text);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -145,11 +224,41 @@ async function callLLM(url, backend, model, prompt) {
|
||||
// Translation prompt builder
|
||||
// ============================================
|
||||
|
||||
function buildTranslationPrompt(text, srcLang, dstLang, settings) {
|
||||
const srcName = srcLang === "free" ? (settings.freeSrcLang || "Auto") : LANG_NAMES[srcLang];
|
||||
const dstName = dstLang === "free" ? (settings.freeDstLang || "English") : LANG_NAMES[dstLang];
|
||||
const fromPart = srcLang === "free" ? "" : `from ${srcName} `;
|
||||
return `Translate the following text ${fromPart}to ${dstName}. Output only the translated text, nothing else.\n\n${text}`;
|
||||
// Uses a system role (via callChat) rather than a single raw prompt string —
|
||||
// local models follow "translate only, no commentary" far more reliably
|
||||
// when it's a system instruction instead of buried inside the user text.
|
||||
function buildTranslationMessages(text, srcLang, dstLang, settings) {
|
||||
const srcName = srcLang === "free" ? (settings.freeSrcLang || "").trim() : LANG_NAMES[srcLang];
|
||||
const dstName = dstLang === "free" ? (settings.freeDstLang || "English").trim() || "English" : LANG_NAMES[dstLang];
|
||||
const fromPart = srcName ? ` from ${srcName}` : "";
|
||||
const system = `You are a professional translator. Translate the text the user sends${fromPart} into ${dstName}. `
|
||||
+ `Reply with ONLY the translated text and nothing else: no explanations, no notes, no preamble like "Here is the translation:", `
|
||||
+ `no surrounding quotes, and do not repeat or echo the original text.`;
|
||||
return [
|
||||
{ role: "system", content: system },
|
||||
{ role: "user", content: text },
|
||||
];
|
||||
}
|
||||
|
||||
// Strips common LLM wrapping/preamble artifacts that survive despite the system prompt.
|
||||
function cleanTranslationOutput(raw) {
|
||||
let s = (raw || "").trim();
|
||||
s = s.replace(/^(sure[,.]?\s*)?(here('|)s|here is)?\s*(the\s+)?translation\s*:?\s*/i, "");
|
||||
s = s.replace(/^(翻訳|译文|翻译)\s*[::]\s*/, "");
|
||||
if (s.length >= 2) {
|
||||
const first = s[0], last = s[s.length - 1];
|
||||
const pairs = { '"': '"', "'": "'", "“": "”", "「": "」" };
|
||||
if (pairs[first] === last) s = s.slice(1, -1).trim();
|
||||
}
|
||||
return s;
|
||||
}
|
||||
|
||||
// Heuristic: if the cleaned output is (near-)identical to the input, the
|
||||
// model most likely echoed the source instead of translating it.
|
||||
function looksUntranslated(input, output) {
|
||||
const normalize = (s) => s.trim().toLowerCase().replace(/[\s.,!?"'。、！？「」]/g, "");
|
||||
const a = normalize(input), b = normalize(output);
|
||||
return a.length > 0 && a === b;
|
||||
}
|
||||
|
||||
// ============================================
|
||||
@@ -217,11 +326,19 @@ function initTranslateTab() {
|
||||
outputEl.value = "";
|
||||
|
||||
try {
|
||||
const prompt = buildTranslationPrompt(text, srcLangSel.value, dstLangSel.value, settings);
|
||||
const result = await callLLM(url, backend, model, prompt);
|
||||
outputEl.value = result.trim();
|
||||
statusEl.textContent = t("aiStatusDone");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-ok";
|
||||
const messages = buildTranslationMessages(text, srcLangSel.value, dstLangSel.value, settings);
|
||||
const { content } = await callChat(url, backend, model, messages, undefined, settings);
|
||||
const cleaned = cleanTranslationOutput(content);
|
||||
outputEl.value = cleaned;
|
||||
|
||||
if (srcLangSel.value !== dstLangSel.value && looksUntranslated(text, cleaned)) {
|
||||
statusEl.textContent = t("aiStatusDone");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-error";
|
||||
showToast(t("aiToastMaybeNotTranslated"), "error");
|
||||
} else {
|
||||
statusEl.textContent = t("aiStatusDone");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-ok";
|
||||
}
|
||||
} catch (err) {
|
||||
statusEl.textContent = `${t("aiStatusConnectFail")}${err.message}`;
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-error";
|
||||
@@ -288,6 +405,11 @@ function initSettingsTab() {
|
||||
const urlInput = document.getElementById("wfm-ai-backend-url");
|
||||
if (urlInput && saved.backendUrl) urlInput.value = saved.backendUrl;
|
||||
|
||||
const thinkingModeCheckbox = document.getElementById("wfm-ai-thinking-mode-checkbox");
|
||||
if (thinkingModeCheckbox) thinkingModeCheckbox.checked = !!saved.thinkingMode;
|
||||
const maxTokensInput = document.getElementById("wfm-ai-max-tokens");
|
||||
if (maxTokensInput && saved.maxTokens) maxTokensInput.value = saved.maxTokens;
|
||||
|
||||
// Switch URL to the new backend's default when the user changes backend
|
||||
backendRadios.forEach((r) => {
|
||||
r.addEventListener("change", () => {
|
||||
@@ -395,6 +517,8 @@ function initSettingsTab() {
|
||||
const chatGenDedicatedFilename = chatGenSelect?.value || "";
|
||||
const chatGenI2IDedicatedEnabled = !!chatGenI2ICheckbox?.checked;
|
||||
const chatGenI2IDedicatedFilename = chatGenI2ISelect?.value || "";
|
||||
const thinkingMode = !!thinkingModeCheckbox?.checked;
|
||||
const maxTokens = Math.max(0, parseInt(maxTokensInput?.value, 10) || 0);
|
||||
|
||||
if (url && !isValidBackendUrl(url)) {
|
||||
showToast(t("aiToastInvalidUrl"), "error");
|
||||
@@ -403,6 +527,7 @@ function initSettingsTab() {
|
||||
|
||||
saveAiSettings({
|
||||
backend, backendUrl: url, model, freeSrcLang, freeDstLang,
|
||||
thinkingMode, maxTokens,
|
||||
chatGenDedicatedEnabled, chatGenDedicatedFilename,
|
||||
chatGenI2IDedicatedEnabled, chatGenI2IDedicatedFilename,
|
||||
});
|
||||
@@ -423,12 +548,22 @@ const IMAGE_GEN_TOOLS = [{
|
||||
type: "function",
|
||||
function: {
|
||||
name: "generate_image",
|
||||
description: "Generate an image using the currently loaded ComfyUI workflow. If an image is currently attached to the conversation, this performs image-to-image generation using that image as the base; otherwise it performs text-to-image. Call this when the user asks to create, draw, paint, edit, or transform an image.",
|
||||
description: "Generate an image using the currently loaded ComfyUI workflow. If an image is currently attached to the conversation, this performs image-to-image generation using that image as the base; otherwise it performs text-to-image. Call this when the user asks to create, draw, paint, edit, or transform an image. All parameters besides prompt are optional — omit any the user didn't specify and the workflow's own current setting is kept.",
|
||||
parameters: {
|
||||
type: "object",
|
||||
properties: {
|
||||
prompt: { type: "string", description: "Positive prompt describing the desired image." },
|
||||
negative_prompt: { type: "string", description: "What to avoid in the image. Optional." },
|
||||
steps: { type: "integer", description: "Sampling steps. Optional." },
|
||||
cfg: { type: "number", description: "Classifier-free guidance scale. Optional." },
|
||||
sampler_name: { type: "string", description: "ComfyUI sampler name (e.g. euler, dpmpp_2m, ddim). Optional." },
|
||||
scheduler: { type: "string", description: "ComfyUI scheduler name (e.g. normal, karras, simple). Optional." },
|
||||
denoise: { type: "number", description: "Denoise strength 0-1 (mainly relevant for image-to-image). Optional." },
|
||||
seed: { type: "integer", description: "Fixed seed. Omit for a random seed each time. Optional." },
|
||||
width: { type: "integer", description: "Output image width in pixels. Ignored if the workflow has a Resolution Selector node — use aspect_ratio instead for those. Optional." },
|
||||
height: { type: "integer", description: "Output image height in pixels. Ignored if the workflow has a Resolution Selector node — use aspect_ratio instead for those. Optional." },
|
||||
aspect_ratio: { type: "string", enum: ["1:1", "2:3", "3:2", "3:4", "4:3", "9:16", "16:9", "21:9"], description: "Aspect ratio preset. Only applied if the workflow has a Resolution Selector node (feeding width/height from a ratio + megapixel target); ignored otherwise. Optional." },
|
||||
batch_size: { type: "integer", description: "Number of images to generate in this single call. Optional." },
|
||||
},
|
||||
required: ["prompt"],
|
||||
},
|
||||
@@ -453,10 +588,10 @@ function _formatMessagesForBackend(messages, backend) {
|
||||
});
|
||||
}
|
||||
|
||||
async function callChat(url, backend, model, messages, tools) {
|
||||
async function callChat(url, backend, model, messages, tools, settings = {}) {
|
||||
const formattedMessages = _formatMessagesForBackend(messages, backend);
|
||||
if (backend === "ollama") {
|
||||
const body = { model, messages: formattedMessages, stream: false };
|
||||
const body = _applyGenOptions({ model, messages: formattedMessages, stream: false }, backend, settings);
|
||||
if (tools) body.tools = tools;
|
||||
const res = await fetch(`${url}/api/chat`, {
|
||||
method: "POST",
|
||||
@@ -465,9 +600,17 @@ async function callChat(url, backend, model, messages, tools) {
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const message = (await res.json()).message || {};
|
||||
return { content: message.content || "", toolCalls: message.tool_calls || null };
|
||||
const content = message.content || "";
|
||||
return { content: settings.thinkingMode ? content : stripThinkingTags(content), toolCalls: message.tool_calls || null };
|
||||
} else if (backend === "unsloth") {
|
||||
const body = _applyGenOptions({ model, messages: formattedMessages, stream: false }, backend, settings);
|
||||
if (tools) { body.tools = tools; body.tool_choice = "auto"; }
|
||||
const data = await unslothProxy(url, "/v1/chat/completions", "POST", body);
|
||||
const message = data.choices?.[0]?.message || {};
|
||||
const content = _unslothContent(message);
|
||||
return { content: settings.thinkingMode ? content : stripThinkingTags(content), toolCalls: message.tool_calls || null };
|
||||
} else {
|
||||
const body = { model, messages: formattedMessages, stream: false };
|
||||
const body = _applyGenOptions({ model, messages: formattedMessages, stream: false }, backend, settings);
|
||||
if (tools) { body.tools = tools; body.tool_choice = "auto"; }
|
||||
const res = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST",
|
||||
@@ -476,7 +619,8 @@ async function callChat(url, backend, model, messages, tools) {
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const message = (await res.json()).choices?.[0]?.message || {};
|
||||
return { content: message.content || "", toolCalls: message.tool_calls || null };
|
||||
const content = message.content || "";
|
||||
return { content: settings.thinkingMode ? content : stripThinkingTags(content), toolCalls: message.tool_calls || null };
|
||||
}
|
||||
}
|
||||
|
||||
@@ -501,7 +645,7 @@ async function _loadDedicatedChatGenWorkflow(filename) {
|
||||
return { workflow, analysis: comfyWorkflow.analyzeWorkflow(workflow) };
|
||||
}
|
||||
|
||||
async function generateImageFromChat(prompt, negativePrompt) {
|
||||
async function generateImageFromChat(prompt, negativePrompt, params = {}) {
|
||||
if (!window._wfmGenerateTab?.generate) throw new Error(t("aiChatGenerateNotReady"));
|
||||
|
||||
const settings = loadAiSettings();
|
||||
@@ -526,12 +670,24 @@ async function generateImageFromChat(prompt, negativePrompt) {
|
||||
comfyEditor.setPromptText("positive", prompt || "", { workflow, analysis });
|
||||
if (negativePrompt) comfyEditor.setPromptText("negative", negativePrompt, { workflow, analysis });
|
||||
|
||||
const result = await window._wfmGenerateTab.generate(workflow);
|
||||
const { steps, cfg, sampler_name, scheduler, denoise, seed, width, height, batch_size, aspect_ratio } = params;
|
||||
let resolvedAspectRatio = null;
|
||||
const resSelector = analysis.resolution_selector_nodes?.[0];
|
||||
if (aspect_ratio && resSelector) {
|
||||
resolvedAspectRatio = await comfyEditor.resolveAspectRatioOption(resSelector.type, aspect_ratio);
|
||||
}
|
||||
comfyEditor.setGenerationParams({
|
||||
steps, cfg, sampler_name, scheduler, denoise, seed, width, height, batch_size,
|
||||
resolvedAspectRatio, workflow, analysis,
|
||||
});
|
||||
|
||||
const result = await window._wfmGenerateTab.generate(workflow, seed != null ? { seedValue: seed } : {});
|
||||
const images = result?.images || [];
|
||||
if (images.length === 0) throw new Error(t("aiChatNoImageProduced"));
|
||||
|
||||
const img = images[0];
|
||||
return `/view?filename=${encodeURIComponent(img.filename)}&subfolder=${encodeURIComponent(img.subfolder || "")}&type=${encodeURIComponent(img.type || "output")}`;
|
||||
return images.map((img) =>
|
||||
`/view?filename=${encodeURIComponent(img.filename)}&subfolder=${encodeURIComponent(img.subfolder || "")}&type=${encodeURIComponent(img.type || "output")}`
|
||||
);
|
||||
}
|
||||
|
||||
// アシスタント応答からSVGコードを抽出する（```svg フェンス or 生の <svg>...</svg>）
|
||||
@@ -832,6 +988,14 @@ function appendChatBubble(messagesEl, role, content, opts = {}) {
|
||||
img.className = "wfm-ai-chat-img";
|
||||
div.appendChild(img);
|
||||
}
|
||||
if (opts.imageUrls?.length) {
|
||||
opts.imageUrls.forEach((url) => {
|
||||
const img = document.createElement("img");
|
||||
img.src = url;
|
||||
img.className = "wfm-ai-chat-img";
|
||||
div.appendChild(img);
|
||||
});
|
||||
}
|
||||
if (opts.attachThumb) {
|
||||
const img = document.createElement("img");
|
||||
img.src = opts.attachThumb;
|
||||
@@ -909,7 +1073,7 @@ function initChatTab() {
|
||||
const messagesToSend = skillPrompt
|
||||
? [{ role: "system", content: skillPrompt }, ...chatHistory]
|
||||
: chatHistory;
|
||||
const reply = await callChat(url, backend, model, messagesToSend, tools);
|
||||
const reply = await callChat(url, backend, model, messagesToSend, tools, settings);
|
||||
const toolCall = reply.toolCalls?.find(
|
||||
(tc) => (tc.function?.name || tc.name) === "generate_image"
|
||||
);
|
||||
@@ -921,8 +1085,8 @@ function initChatTab() {
|
||||
statusEl.textContent = t("aiStatusGeneratingImage");
|
||||
const args = _parseToolArgs(toolCall);
|
||||
try {
|
||||
const imageUrl = await generateImageFromChat(args.prompt, args.negative_prompt);
|
||||
appendChatBubble(messagesEl, "assistant", "", { imageUrl });
|
||||
const imageUrls = await generateImageFromChat(args.prompt, args.negative_prompt, args);
|
||||
appendChatBubble(messagesEl, "assistant", "", { imageUrls });
|
||||
} catch (genErr) {
|
||||
appendChatBubble(messagesEl, "assistant", `${t("aiToastImageGenFailed")}${genErr.message}`);
|
||||
showToast(t("aiToastImageGenFailed") + genErr.message, "error");
|
||||
@@ -1069,7 +1233,7 @@ function initVlmTab() {
|
||||
const prompt = `Generate ${count} wildcard entries for the category "${name}". Output only plain text in English, one entry per line, no numbers, no markdown, no asterisks, no bold, nothing else.`;
|
||||
|
||||
try {
|
||||
const result = await callLLM(url, backend, model, prompt);
|
||||
const result = await callLLM(url, backend, model, prompt, settings);
|
||||
resultEl.value = result.trim()
|
||||
.split("\n")
|
||||
.map(l => l.replace(/\*\*/g, "").replace(/^\*\s*/, "").replace(/^\d+\.\s*/, "").trim())
|
||||
@@ -1095,7 +1259,7 @@ function initVlmTab() {
|
||||
resultEl.value = "";
|
||||
|
||||
try {
|
||||
const result = await callVLM(url, backend, model, VLM_PROMPTS[task], _toolsImage.base64, _toolsImage.mimeType);
|
||||
const result = await callVLM(url, backend, model, VLM_PROMPTS[task], _toolsImage.base64, _toolsImage.mimeType, settings);
|
||||
resultEl.value = result.trim();
|
||||
statusEl.textContent = t("aiStatusDone");
|
||||
statusEl.className = "wfm-ai-trans-status wfm-ai-status-ok";
|
||||
@@ -1125,3 +1289,7 @@ export function initAiTab() {
|
||||
initVlmTab();
|
||||
initSettingsTab();
|
||||
}
|
||||
|
||||
// gallery-tab.js の外部（Comic Creator）向けブリッジ（半自動マンガ作成の画像プロンプト
|
||||
// 下書き機能）が、設定タブと同じAI接続設定・LLM呼び出しロジックを再利用するためのexport。
|
||||
export { callLLM, loadAiSettings, isValidBackendUrl };
|
||||
|
||||
+175
-6
@@ -42,11 +42,32 @@ export function showToast(message, type = "info", duration = 3000) {
|
||||
|
||||
export function openModal(title, contentHtml) {
|
||||
const overlay = document.getElementById("wfm-modal-overlay");
|
||||
const titleEl = document.getElementById("wfm-modal-title");
|
||||
let titleEl = document.getElementById("wfm-modal-title");
|
||||
const bodyEl = document.getElementById("wfm-modal-body");
|
||||
const titleInput = document.getElementById("wfm-modal-title-input");
|
||||
|
||||
if (!overlay || !titleEl || !bodyEl) return;
|
||||
|
||||
// This modal shell is shared by every caller (Workflow/Models/Nodes detail
|
||||
// modals, Lab's keyframe editor, etc.). Some callers (e.g. workflow-tab.js's
|
||||
// openDetailModal) inject extra header chrome after calling openModal() — a
|
||||
// favorite star button, and a click-to-rename listener + tooltip/cursor on the
|
||||
// title — but nothing ever tore that down again. The next caller to reuse this
|
||||
// same DOM inherited it as stale leftovers: a Lab cell modal could show a
|
||||
// favorite star wired to the PREVIOUS caller's workflow object, and clicking it
|
||||
// silently toggled that unrelated workflow's favorite state. Clean slate every
|
||||
// time, before any caller-specific chrome is added back on top.
|
||||
titleEl.parentElement?.querySelectorAll(".wfm-fav-btn").forEach((el) => el.remove());
|
||||
const freshTitleEl = titleEl.cloneNode(false); // drops any addEventListener from a prior caller
|
||||
freshTitleEl.removeAttribute("title");
|
||||
freshTitleEl.removeAttribute("style"); // also clears any leftover display:none from an interrupted rename
|
||||
titleEl.replaceWith(freshTitleEl);
|
||||
titleEl = freshTitleEl;
|
||||
if (titleInput) {
|
||||
titleInput.style.display = "none";
|
||||
titleInput.value = "";
|
||||
}
|
||||
|
||||
titleEl.textContent = title;
|
||||
bodyEl.innerHTML = contentHtml;
|
||||
overlay.style.display = "flex";
|
||||
@@ -91,7 +112,7 @@ function initTabs() {
|
||||
|
||||
function applyI18nToHtml() {
|
||||
// Tab labels
|
||||
const tabMap = { workflow: "tabWorkflow", nodes: "tabNodes", models: "tabModels", generate: "tabGenerate", prompt: "tabPrompt", metadata: "tabMetadata", gallery: "tabGallery", "image-edit": "tabImageEdit", settings: "tabSettings", help: "tabHelp", ai: "tabAi", tagger: "tabTagger" };
|
||||
const tabMap = { workflow: "tabWorkflow", nodes: "tabNodes", models: "tabModels", generate: "tabGenerate", prompt: "tabPrompt", gallery: "tabGallery", "image-edit": "tabImageEdit", video: "tabVideo", settings: "tabSettings", help: "tabHelp", ai: "tabAi", tagger: "tabTagger" };
|
||||
document.querySelectorAll(".wfm-tab").forEach((tab) => {
|
||||
const key = tabMap[tab.dataset.tab];
|
||||
if (key) tab.textContent = t(key);
|
||||
@@ -113,6 +134,12 @@ function applyI18nToHtml() {
|
||||
if (listLoadBtn.disabled) listLoadBtn.title = t("selectCardFirst");
|
||||
}
|
||||
|
||||
const listLoadVideoBtn = document.getElementById("wfm-list-load-video-btn");
|
||||
if (listLoadVideoBtn) {
|
||||
listLoadVideoBtn.textContent = t("loadInVideo");
|
||||
if (listLoadVideoBtn.disabled) listLoadVideoBtn.title = t("selectCardFirst");
|
||||
}
|
||||
|
||||
const listOpenComfyBtn = document.getElementById("wfm-list-open-comfyui-btn");
|
||||
if (listOpenComfyBtn) {
|
||||
listOpenComfyBtn.textContent = t("sendToCanvas");
|
||||
@@ -195,6 +222,7 @@ function applyI18nToHtml() {
|
||||
if (btn.dataset.detailTab === "info") btn.textContent = t("modelsSideInfo");
|
||||
if (btn.dataset.detailTab === "meta") btn.textContent = t("gallerySideMetadata");
|
||||
if (btn.dataset.detailTab === "group") btn.textContent = t("modelsSideGroup");
|
||||
if (btn.dataset.detailTab === "prompt") btn.textContent = t("gallerySidePrompt");
|
||||
});
|
||||
const galleryBulkDeselect = document.getElementById("wfm-gallery-bulk-deselect");
|
||||
if (galleryBulkDeselect) galleryBulkDeselect.textContent = t("galleryBulkDeselectAll");
|
||||
@@ -222,6 +250,70 @@ function applyI18nToHtml() {
|
||||
const galleryOpenTaggerBtn = document.getElementById("wfm-gallery-open-tagger-btn");
|
||||
if (galleryOpenTaggerBtn) galleryOpenTaggerBtn.textContent = t("tabTagger");
|
||||
|
||||
// Gallery サブタブ (Output / ImagePrompt / Style_Catalog)
|
||||
const galleryOutputSubtab = document.getElementById("wfm-gallery-subtab-output");
|
||||
if (galleryOutputSubtab) galleryOutputSubtab.textContent = t("galleryTabOutput");
|
||||
const galleryImagePromptSubtab = document.getElementById("wfm-gallery-subtab-imageprompt");
|
||||
if (galleryImagePromptSubtab) galleryImagePromptSubtab.textContent = t("galleryTabImagePrompt");
|
||||
const galleryStyleCatalogSubtab = document.getElementById("wfm-gallery-subtab-styleCatalog");
|
||||
if (galleryStyleCatalogSubtab) galleryStyleCatalogSubtab.textContent = t("galleryTabStyleCatalog");
|
||||
const galleryMetadataSubtab = document.getElementById("wfm-gallery-subtab-metadata");
|
||||
if (galleryMetadataSubtab) galleryMetadataSubtab.textContent = t("galleryTabMetadata");
|
||||
|
||||
// ImagePrompt ギャラリーパネル
|
||||
const imagePromptSearch = document.getElementById("wfm-imageprompt-search");
|
||||
if (imagePromptSearch) imagePromptSearch.placeholder = t("gallerySearchPlaceholder");
|
||||
const imagePromptRefreshBtn = document.getElementById("wfm-imageprompt-refresh-btn");
|
||||
if (imagePromptRefreshBtn) imagePromptRefreshBtn.textContent = t("refresh");
|
||||
const imagePromptTreeTitle = document.getElementById("wfm-imageprompt-tree-title");
|
||||
if (imagePromptTreeTitle) imagePromptTreeTitle.textContent = t("galleryFolders");
|
||||
const imagePromptListPlaceholder = document.getElementById("wfm-imageprompt-list-placeholder");
|
||||
if (imagePromptListPlaceholder) imagePromptListPlaceholder.textContent = t("gallerySelectFolder");
|
||||
const imagePromptNoSelection = document.getElementById("wfm-imageprompt-no-selection");
|
||||
if (imagePromptNoSelection) imagePromptNoSelection.textContent = t("galleryNoSelection");
|
||||
const imagePromptSelectedPromptTitle = document.getElementById("wfm-imageprompt-selected-prompt-title");
|
||||
if (imagePromptSelectedPromptTitle) imagePromptSelectedPromptTitle.textContent = t("imagePromptSelectedPrompt");
|
||||
const imagePromptSelectedPrompt = document.getElementById("wfm-imageprompt-selected-prompt");
|
||||
if (imagePromptSelectedPrompt) imagePromptSelectedPrompt.placeholder = t("imagePromptNoPrompt");
|
||||
const imagePromptSavePromptBtn = document.getElementById("wfm-imageprompt-save-prompt-btn");
|
||||
if (imagePromptSavePromptBtn) imagePromptSavePromptBtn.textContent = t("save");
|
||||
const imagePromptAddBtn = document.getElementById("wfm-imageprompt-add-btn");
|
||||
if (imagePromptAddBtn) imagePromptAddBtn.textContent = t("imagePromptAddBtn");
|
||||
const imagePromptChipsTitle = document.getElementById("wfm-imageprompt-chips-title");
|
||||
if (imagePromptChipsTitle) imagePromptChipsTitle.textContent = t("imagePromptChipsTitle");
|
||||
const imagePromptClearBtn = document.getElementById("wfm-imageprompt-clear-btn");
|
||||
if (imagePromptClearBtn) imagePromptClearBtn.textContent = t("imagePromptClearBtn");
|
||||
const imagePromptFinalPromptTitle = document.getElementById("wfm-imageprompt-final-prompt-title");
|
||||
if (imagePromptFinalPromptTitle) imagePromptFinalPromptTitle.textContent = t("imagePromptFinalPromptTitle");
|
||||
const imagePromptCopyBtn = document.getElementById("wfm-imageprompt-copy-btn");
|
||||
if (imagePromptCopyBtn) imagePromptCopyBtn.textContent = t("copy");
|
||||
|
||||
// Style_Catalog ギャラリーパネル
|
||||
const scSearch = document.getElementById("wfm-stylecatalog-search");
|
||||
if (scSearch) scSearch.placeholder = t("stylecatalogSearchPlaceholder");
|
||||
const scRefreshBtn = document.getElementById("wfm-stylecatalog-refresh-btn");
|
||||
if (scRefreshBtn) scRefreshBtn.textContent = t("refresh");
|
||||
const scTreeTitle = document.getElementById("wfm-stylecatalog-tree-title");
|
||||
if (scTreeTitle) scTreeTitle.textContent = t("galleryFolders");
|
||||
const scListPlaceholder = document.getElementById("wfm-stylecatalog-list-placeholder");
|
||||
if (scListPlaceholder) scListPlaceholder.textContent = t("gallerySelectFolder");
|
||||
const scNoSelection = document.getElementById("wfm-stylecatalog-no-selection");
|
||||
if (scNoSelection) scNoSelection.textContent = t("galleryNoSelection");
|
||||
const scPositiveTitle = document.getElementById("wfm-stylecatalog-positive-title");
|
||||
if (scPositiveTitle) scPositiveTitle.textContent = t("stylecatalogPositive");
|
||||
const scCopyPositiveBtn = document.getElementById("wfm-stylecatalog-copy-positive-btn");
|
||||
if (scCopyPositiveBtn) scCopyPositiveBtn.textContent = t("copy");
|
||||
const scNegativeTitle = document.getElementById("wfm-stylecatalog-negative-title");
|
||||
if (scNegativeTitle) scNegativeTitle.textContent = t("stylecatalogNegative");
|
||||
const scCopyNegativeBtn = document.getElementById("wfm-stylecatalog-copy-negative-btn");
|
||||
if (scCopyNegativeBtn) scCopyNegativeBtn.textContent = t("copy");
|
||||
const scSelectStyleBtn = document.getElementById("wfm-stylecatalog-select-style-btn");
|
||||
if (scSelectStyleBtn) scSelectStyleBtn.textContent = t("stylecatalogSelectStyleBtn");
|
||||
const scLoadGenuiBtn = document.getElementById("wfm-stylecatalog-load-genui-btn");
|
||||
if (scLoadGenuiBtn) scLoadGenuiBtn.textContent = t("stylecatalogLoadGenuiBtn");
|
||||
const scOpenMetadataBtn = document.getElementById("wfm-stylecatalog-open-metadata-btn");
|
||||
if (scOpenMetadataBtn) scOpenMetadataBtn.textContent = t("stylecatalogOpenMetadataBtn");
|
||||
|
||||
// Prompt tab
|
||||
const assistantHeader = document.querySelector(".wfm-prompt-split-left .wfm-prompt-split-header");
|
||||
if (assistantHeader) assistantHeader.textContent = t("assistantSubtab");
|
||||
@@ -302,6 +394,14 @@ function applyI18nToHtml() {
|
||||
}
|
||||
const aiModelRefreshBtn = document.getElementById("wfm-ai-model-refresh-btn");
|
||||
if (aiModelRefreshBtn) aiModelRefreshBtn.textContent = t("aiSettingsRefreshBtn");
|
||||
const aiGenerationTitle = document.getElementById("wfm-ai-settings-generation-title");
|
||||
if (aiGenerationTitle) aiGenerationTitle.textContent = t("aiSettingsGenerationTitle");
|
||||
const aiThinkingModeLabel = document.getElementById("wfm-ai-thinking-mode-label");
|
||||
if (aiThinkingModeLabel) aiThinkingModeLabel.textContent = t("aiSettingsThinkingMode");
|
||||
const aiMaxTokensLabel = document.getElementById("wfm-ai-max-tokens-label");
|
||||
if (aiMaxTokensLabel) aiMaxTokensLabel.textContent = t("aiSettingsMaxTokens");
|
||||
const aiMaxTokensInput = document.getElementById("wfm-ai-max-tokens");
|
||||
if (aiMaxTokensInput) aiMaxTokensInput.placeholder = t("aiSettingsMaxTokensPlaceholder");
|
||||
const aiFreeLangTitle = document.getElementById("wfm-ai-settings-freelang-title");
|
||||
if (aiFreeLangTitle) aiFreeLangTitle.textContent = t("aiSettingsFreeLang");
|
||||
const aiFreeSrcLabel = document.getElementById("wfm-ai-free-src-label");
|
||||
@@ -380,6 +480,13 @@ function applyI18nToHtml() {
|
||||
"wfm-help-gen-15": "helpGen15", "wfm-help-gen-16": "helpGen16",
|
||||
"wfm-help-gen-17": "helpGen17", "wfm-help-gen-18": "helpGen18",
|
||||
"wfm-help-gen-19": "helpGen19",
|
||||
"wfm-help-gen-20": "helpGen20", "wfm-help-gen-21": "helpGen21",
|
||||
"wfm-help-gen-22": "helpGen22", "wfm-help-gen-23": "helpGen23",
|
||||
"wfm-help-gen-24": "helpGen24", "wfm-help-gen-25": "helpGen25",
|
||||
"wfm-help-gen-26": "helpGen26", "wfm-help-gen-27": "helpGen27",
|
||||
"wfm-help-gen-28": "helpGen28",
|
||||
"wfm-help-gen-29": "helpGen29",
|
||||
"wfm-help-gen-30": "helpGen30",
|
||||
"wfm-help-feeder-title": "helpFeederTitle",
|
||||
"wfm-help-feeder-desc": "helpFeederDesc",
|
||||
"wfm-help-feeder-imgloop-title": "helpFeederImgloopTitle",
|
||||
@@ -394,18 +501,55 @@ function applyI18nToHtml() {
|
||||
"wfm-help-feeder-gal-1": "helpFeederGal1", "wfm-help-feeder-gal-2": "helpFeederGal2",
|
||||
"wfm-help-feeder-gal-3": "helpFeederGal3", "wfm-help-feeder-gal-4": "helpFeederGal4",
|
||||
"wfm-help-feeder-gal-5": "helpFeederGal5", "wfm-help-feeder-gal-6": "helpFeederGal6",
|
||||
"wfm-help-lab-title": "helpLabTitle",
|
||||
"wfm-help-lab-desc": "helpLabDesc",
|
||||
"wfm-help-lab-card-columns-title": "helpLabCardColumnsTitle",
|
||||
"wfm-help-lab-1": "helpLab1", "wfm-help-lab-2": "helpLab2",
|
||||
"wfm-help-lab-3": "helpLab3", "wfm-help-lab-4": "helpLab4",
|
||||
"wfm-help-lab-5": "helpLab5", "wfm-help-lab-6": "helpLab6",
|
||||
"wfm-help-lab-7": "helpLab7", "wfm-help-lab-8": "helpLab8",
|
||||
"wfm-help-lab-9": "helpLab9", "wfm-help-lab-10": "helpLab10",
|
||||
"wfm-help-lab-11": "helpLab11", "wfm-help-lab-12": "helpLab12",
|
||||
"wfm-help-lab-13": "helpLab13", "wfm-help-lab-14": "helpLab14",
|
||||
"wfm-help-lab-15": "helpLab15",
|
||||
"wfm-help-lab-16": "helpLab16", "wfm-help-lab-17": "helpLab17",
|
||||
"wfm-help-lab-18": "helpLab18", "wfm-help-lab-19": "helpLab19",
|
||||
"wfm-help-lab-card-plan-title": "helpLabCardPlanTitle",
|
||||
"wfm-help-lab-plan-1": "helpLabPlan1", "wfm-help-lab-plan-2": "helpLabPlan2",
|
||||
"wfm-help-lab-plan-3": "helpLabPlan3", "wfm-help-lab-plan-4": "helpLabPlan4",
|
||||
"wfm-help-lab-plan-5": "helpLabPlan5", "wfm-help-lab-plan-6": "helpLabPlan6",
|
||||
"wfm-help-lab-plan-7": "helpLabPlan7", "wfm-help-lab-plan-8": "helpLabPlan8",
|
||||
"wfm-help-lab-plan-9": "helpLabPlan9", "wfm-help-lab-plan-10": "helpLabPlan10",
|
||||
"wfm-help-lab-plan-11": "helpLabPlan11",
|
||||
"wfm-help-lab-card-results-title": "helpLabCardResultsTitle",
|
||||
"wfm-help-lab-results-1": "helpLabResults1", "wfm-help-lab-results-2": "helpLabResults2",
|
||||
"wfm-help-lab-results-3": "helpLabResults3",
|
||||
"wfm-help-prompt-1": "helpPrompt1", "wfm-help-prompt-2": "helpPrompt2",
|
||||
"wfm-help-prompt-3": "helpPrompt3", "wfm-help-prompt-4": "helpPrompt4",
|
||||
"wfm-help-prompt-5": "helpPrompt5", "wfm-help-prompt-6": "helpPrompt6",
|
||||
"wfm-help-prompt-7": "helpPrompt7", "wfm-help-prompt-8": "helpPrompt8",
|
||||
"wfm-help-prompt-9": "helpPrompt9",
|
||||
"wfm-help-prompt-card-style-title": "helpPromptCardStyleTitle",
|
||||
"wfm-help-prompt-10": "helpPrompt10", "wfm-help-prompt-11": "helpPrompt11",
|
||||
"wfm-help-prompt-12": "helpPrompt12", "wfm-help-prompt-13": "helpPrompt13",
|
||||
"wfm-help-prompt-14": "helpPrompt14",
|
||||
"wfm-help-prompt-card-toplevel-title": "helpPromptCardToplevel",
|
||||
"wfm-help-prompt-15": "helpPrompt15",
|
||||
"wfm-help-prompt-card-table-title": "helpPromptCardTable",
|
||||
"wfm-help-prompt-16": "helpPrompt16", "wfm-help-prompt-17": "helpPrompt17",
|
||||
"wfm-help-prompt-18": "helpPrompt18", "wfm-help-prompt-19": "helpPrompt19",
|
||||
"wfm-help-prompt-20": "helpPrompt20", "wfm-help-prompt-21": "helpPrompt21",
|
||||
"wfm-help-prompt-22": "helpPrompt22", "wfm-help-prompt-23": "helpPrompt23",
|
||||
"wfm-help-prompt-24": "helpPrompt24", "wfm-help-prompt-25": "helpPrompt25",
|
||||
"wfm-help-prompt-26": "helpPrompt26",
|
||||
"wfm-help-settings-1": "helpSettings1", "wfm-help-settings-2": "helpSettings2",
|
||||
"wfm-help-settings-3": "helpSettings3", "wfm-help-settings-4": "helpSettings4",
|
||||
"wfm-help-settings-5": "helpSettings5", "wfm-help-settings-6": "helpSettings6",
|
||||
"wfm-help-settings-7": "helpSettings7", "wfm-help-settings-8": "helpSettings8",
|
||||
"wfm-help-settings-9": "helpSettings9", "wfm-help-settings-10": "helpSettings10",
|
||||
"wfm-help-settings-11": "helpSettings11", "wfm-help-settings-12": "helpSettings12",
|
||||
"wfm-help-settings-13": "helpSettings13",
|
||||
"wfm-help-settings-13": "helpSettings13", "wfm-help-settings-14": "helpSettings14",
|
||||
"wfm-help-settings-15": "helpSettings15", "wfm-help-settings-16": "helpSettings16",
|
||||
"wfm-help-nodes-1": "helpNodes1", "wfm-help-nodes-2": "helpNodes2",
|
||||
"wfm-help-nodes-3": "helpNodes3", "wfm-help-nodes-4": "helpNodes4",
|
||||
"wfm-help-nodes-5": "helpNodes5", "wfm-help-nodes-6": "helpNodes6",
|
||||
@@ -425,7 +569,14 @@ function applyI18nToHtml() {
|
||||
"wfm-help-gallery-11": "helpGallery11", "wfm-help-gallery-12": "helpGallery12",
|
||||
"wfm-help-gallery-13": "helpGallery13", "wfm-help-gallery-14": "helpGallery14",
|
||||
"wfm-help-gallery-15": "helpGallery15", "wfm-help-gallery-16": "helpGallery16",
|
||||
"wfm-help-gallery-17": "helpGallery17",
|
||||
"wfm-help-gallery-17": "helpGallery17", "wfm-help-gallery-27": "helpGallery27",
|
||||
"wfm-help-gallery-18": "helpGallery18", "wfm-help-gallery-19": "helpGallery19",
|
||||
"wfm-help-gallery-20": "helpGallery20", "wfm-help-gallery-21": "helpGallery21",
|
||||
"wfm-help-gallery-22": "helpGallery22", "wfm-help-gallery-23": "helpGallery23",
|
||||
"wfm-help-gallery-24": "helpGallery24", "wfm-help-gallery-25": "helpGallery25",
|
||||
"wfm-help-gallery-26": "helpGallery26",
|
||||
"wfm-help-gallery-card-styleprompt-title": "helpGalleryCardStyleprompt",
|
||||
"wfm-help-gallery-card-stylecatalog-title": "helpGalleryCardStylecatalog",
|
||||
"wfm-help-tagger-title": "helpTaggerTitle",
|
||||
"wfm-help-tagger-1": "helpTagger1", "wfm-help-tagger-2": "helpTagger2",
|
||||
"wfm-help-tagger-3": "helpTagger3", "wfm-help-tagger-4": "helpTagger4",
|
||||
@@ -471,7 +622,8 @@ function applyI18nToHtml() {
|
||||
"wfm-help-imageedit-12": "helpImageEdit12", "wfm-help-imageedit-13": "helpImageEdit13",
|
||||
"wfm-help-imageedit-14": "helpImageEdit14", "wfm-help-imageedit-15": "helpImageEdit15",
|
||||
"wfm-help-imageedit-16": "helpImageEdit16", "wfm-help-imageedit-17": "helpImageEdit17",
|
||||
"wfm-help-imageedit-18": "helpImageEdit18", "wfm-help-imageedit-19": "helpImageEdit19",
|
||||
"wfm-help-imageedit-18": "helpImageEdit18",
|
||||
"wfm-help-imageedit-19b": "helpImageEdit19b",
|
||||
"wfm-help-imageedit-20": "helpImageEdit20", "wfm-help-imageedit-21": "helpImageEdit21",
|
||||
"wfm-help-imageedit-22": "helpImageEdit22", "wfm-help-imageedit-23": "helpImageEdit23",
|
||||
"wfm-help-imageedit-24": "helpImageEdit24", "wfm-help-imageedit-25": "helpImageEdit25",
|
||||
@@ -497,6 +649,7 @@ function applyI18nToHtml() {
|
||||
"wfm-help-imageedit-mask-9": "helpImageEditMask9", "wfm-help-imageedit-mask-10": "helpImageEditMask10",
|
||||
"wfm-help-imageedit-mask-11": "helpImageEditMask11", "wfm-help-imageedit-mask-12": "helpImageEditMask12",
|
||||
"wfm-help-imageedit-mask-13": "helpImageEditMask13",
|
||||
"wfm-help-imageedit-mask-14": "helpImageEditMask14",
|
||||
"wfm-help-imageedit-gmic-title": "helpImageEditGmicTitle",
|
||||
"wfm-help-imageedit-gmic-1": "helpImageEditGmic1", "wfm-help-imageedit-gmic-2": "helpImageEditGmic2",
|
||||
"wfm-help-imageedit-gmic-3": "helpImageEditGmic3", "wfm-help-imageedit-gmic-4": "helpImageEditGmic4",
|
||||
@@ -517,6 +670,7 @@ function applyI18nToHtml() {
|
||||
"wfm-help-trouble-3": "helpTrouble3", "wfm-help-trouble-4": "helpTrouble4",
|
||||
"wfm-help-trouble-5": "helpTrouble5", "wfm-help-trouble-6": "helpTrouble6",
|
||||
"wfm-help-trouble-7": "helpTrouble7", "wfm-help-trouble-8": "helpTrouble8",
|
||||
"wfm-help-trouble-9": "helpTrouble9",
|
||||
"wfm-help-support-title": "helpSupportTitle",
|
||||
"wfm-help-support-desc": "helpSupportDesc",
|
||||
"wfm-help-github-desc": "helpGithubDesc",
|
||||
@@ -534,6 +688,7 @@ function applyI18nToHtml() {
|
||||
"wfm-help-gen-card-seed-title": "helpGenCardSeed",
|
||||
"wfm-help-gen-card-batchsetup-title": "helpGenCardBatchSetup",
|
||||
"wfm-help-gen-card-batchtypes-title": "helpGenCardBatchTypes",
|
||||
"wfm-help-gen-card-lora-title": "helpGenCardLora",
|
||||
"wfm-help-prompt-title": "helpPromptTitle",
|
||||
"wfm-help-prompt-card-ai-title": "helpPromptCardAi",
|
||||
"wfm-help-prompt-card-presets-title": "helpPromptCardPresets",
|
||||
@@ -576,6 +731,12 @@ function applyI18nToHtml() {
|
||||
"wfm-help-trouble-card-connection-title": "helpTroubleCardConnection",
|
||||
"wfm-help-trouble-card-workflow-title": "helpTroubleCardWorkflow",
|
||||
"wfm-help-trouble-card-tagger-title": "helpTroubleCardTagger",
|
||||
"wfm-help-video-title": "helpVideoTitle",
|
||||
"wfm-help-video-desc": "helpVideoDesc",
|
||||
"wfm-help-video-1": "helpVideo1", "wfm-help-video-2": "helpVideo2",
|
||||
"wfm-help-video-3": "helpVideo3", "wfm-help-video-4": "helpVideo4",
|
||||
"wfm-help-video-5": "helpVideo5", "wfm-help-video-6": "helpVideo6",
|
||||
"wfm-help-video-7": "helpVideo7",
|
||||
};
|
||||
for (const [id, key] of Object.entries(helpIdMap)) {
|
||||
const el = document.getElementById(id);
|
||||
@@ -658,13 +819,15 @@ import { initWorkflowTab } from "./workflow-tab.js";
|
||||
import { initNodesTab } from "./nodes-tab.js";
|
||||
import { initGenerateTab } from "./generate-tab.js";
|
||||
import { initPromptTab } from "./prompt-tab.js";
|
||||
import { initSettingsTab, applyTheme, getSavedTheme, applyTextareaFontSize, applyJsonColors } from "./settings-tab.js";
|
||||
import { initPromptTableTab } from "./prompt-table.js";
|
||||
import { initSettingsTab, applyTheme, getSavedTheme, applyTextareaFontSize, applyJsonColors, applyModelTabActiveColor } from "./settings-tab.js";
|
||||
import { initModelsTab } from "./models-tab.js";
|
||||
import { initGalleryTab } from "./gallery-tab.js";
|
||||
import { initMetadataTab } from "./metadata-tab.js";
|
||||
import { initAiTab } from "./ai-tab.js";
|
||||
import { initTaggerTab } from "./tagger-tab.js";
|
||||
import { imageEditTab } from "./image-edit-tab.js";
|
||||
import { initVideoTab } from "./video-tab.js";
|
||||
|
||||
// Apply saved theme immediately to prevent flash of default theme
|
||||
applyTheme(getSavedTheme());
|
||||
@@ -674,6 +837,7 @@ try {
|
||||
const _s = getSettings();
|
||||
if (_s.textareaFontSize) applyTextareaFontSize(_s.textareaFontSize);
|
||||
applyJsonColors(_s.jsonColors);
|
||||
applyModelTabActiveColor(_s.modelTabActiveColor);
|
||||
} catch {}
|
||||
|
||||
function initHelpTab() {
|
||||
@@ -691,6 +855,9 @@ function initHelpTab() {
|
||||
document.querySelectorAll(".wfm-help-jump-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
document.querySelector(`.wfm-tab[data-tab="${btn.dataset.jumpTab}"]`)?.click();
|
||||
if (btn.dataset.jumpSubtab) {
|
||||
document.querySelector(`.wfm-gallery-subtab-btn[data-gallery-subtab="${btn.dataset.jumpSubtab}"]`)?.click();
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
@@ -706,11 +873,13 @@ document.addEventListener("DOMContentLoaded", () => {
|
||||
initModelsTab();
|
||||
initGenerateTab();
|
||||
initPromptTab();
|
||||
initPromptTableTab();
|
||||
initMetadataTab();
|
||||
initGalleryTab();
|
||||
initAiTab();
|
||||
initTaggerTab();
|
||||
imageEditTab.init();
|
||||
initVideoTab();
|
||||
|
||||
console.log("Workflow Studio: App initialized");
|
||||
});
|
||||
|
||||
@@ -18,8 +18,15 @@ export const comfyUI = {
|
||||
connected: false,
|
||||
generating: false,
|
||||
currentPromptId: null,
|
||||
// trackProgress() 実行中の Promise を promptId -> reject関数 で保持。
|
||||
// WebSocket切断時に、待機中のPromiseを解決させずに放置してバッチループが
|
||||
// 無言のまま停止する（例: カタログ作成バッチが特定件数で止まる）のを防ぐ。
|
||||
_pendingTrackers: new Map(),
|
||||
currentWorkflow: null,
|
||||
currentAnalysis: null,
|
||||
// ワークフロー読み込み時点のLora Loader (LoraManager)ノードの loras/text 状態。
|
||||
// Stackグループチェックボックスのトグル時に、OFF側の「読み込み時の状態へ戻す」用に使う。
|
||||
loraManagerSnapshots: {},
|
||||
|
||||
updateUrl(url) {
|
||||
if (!url) {
|
||||
@@ -66,10 +73,18 @@ export const comfyUI = {
|
||||
this.socket = new WebSocket(url);
|
||||
this.socket.onopen = () => resolve(true);
|
||||
this.socket.onerror = () => resolve(false);
|
||||
this.socket.onclose = () => { this.socket = null; };
|
||||
this.socket.onclose = () => {
|
||||
this.socket = null;
|
||||
this._rejectPendingTrackers(new Error("WebSocket disconnected"));
|
||||
};
|
||||
});
|
||||
},
|
||||
|
||||
_rejectPendingTrackers(err) {
|
||||
for (const rejectFn of this._pendingTrackers.values()) rejectFn(err);
|
||||
this._pendingTrackers.clear();
|
||||
},
|
||||
|
||||
// Node info fetching
|
||||
async fetchObjectInfo(nodeClass) {
|
||||
const res = await fetch(`${this.baseUrl}/object_info/${nodeClass}`);
|
||||
@@ -196,12 +211,29 @@ export const comfyUI = {
|
||||
return await res.json();
|
||||
},
|
||||
|
||||
trackProgress(promptId, progressCallback) {
|
||||
// timeoutMs: この時間内に完了/エラー/中断通知が来なければ強制的にreject。
|
||||
// WebSocketの取りこぼしやexecution_interrupted未処理でPromiseが永久に
|
||||
// ぶら下がり続け、バッチループが無言で停止するのを防ぐための安全弁。
|
||||
trackProgress(promptId, progressCallback, timeoutMs = 10 * 60 * 1000) {
|
||||
return new Promise((resolve, reject) => {
|
||||
if (!this.socket || this.socket.readyState !== WebSocket.OPEN) {
|
||||
reject(new Error("WebSocket not connected"));
|
||||
return;
|
||||
}
|
||||
let settled = false;
|
||||
const finish = (isResolve, value) => {
|
||||
if (settled) return;
|
||||
settled = true;
|
||||
this.socket?.removeEventListener("message", handler);
|
||||
clearTimeout(timer);
|
||||
this._pendingTrackers.delete(promptId);
|
||||
if (isResolve) resolve(value); else reject(value);
|
||||
};
|
||||
|
||||
const timer = setTimeout(() => {
|
||||
finish(false, new Error(`Generation timed out after ${Math.round(timeoutMs / 1000)}s (prompt_id=${promptId})`));
|
||||
}, timeoutMs);
|
||||
|
||||
const handler = (event) => {
|
||||
try {
|
||||
const msg = JSON.parse(event.data);
|
||||
@@ -211,17 +243,19 @@ export const comfyUI = {
|
||||
}
|
||||
if (msg.type === "executing" && msg.data?.prompt_id === promptId) {
|
||||
if (msg.data.node === null) {
|
||||
this.socket.removeEventListener("message", handler);
|
||||
resolve();
|
||||
finish(true);
|
||||
}
|
||||
}
|
||||
if (msg.type === "execution_error" && msg.data?.prompt_id === promptId) {
|
||||
this.socket.removeEventListener("message", handler);
|
||||
reject(new Error(msg.data.exception_message || "Execution error"));
|
||||
finish(false, new Error(msg.data.exception_message || "Execution error"));
|
||||
}
|
||||
if (msg.type === "execution_interrupted" && msg.data?.prompt_id === promptId) {
|
||||
finish(false, new Error("Execution interrupted"));
|
||||
}
|
||||
} catch {}
|
||||
};
|
||||
this.socket.addEventListener("message", handler);
|
||||
this._pendingTrackers.set(promptId, (err) => finish(false, err));
|
||||
});
|
||||
},
|
||||
|
||||
@@ -276,7 +310,7 @@ export const comfyUI = {
|
||||
|
||||
// High-level generate
|
||||
async generate(workflow, options = {}) {
|
||||
const { seedMode = "random", seedValue = -1, onProgress, onComplete, onError } = options;
|
||||
const { seedMode = "random", seedValue = -1, onProgress, onComplete, onError, timeoutMs } = options;
|
||||
|
||||
this.generating = true;
|
||||
try {
|
||||
@@ -298,7 +332,7 @@ export const comfyUI = {
|
||||
this.currentPromptId = result.prompt_id;
|
||||
|
||||
// Track progress
|
||||
await this.trackProgress(result.prompt_id, onProgress);
|
||||
await this.trackProgress(result.prompt_id, onProgress, timeoutMs);
|
||||
|
||||
// Get history/results
|
||||
const history = await this.getHistory(result.prompt_id);
|
||||
|
||||
+609
-85
@@ -5,7 +5,8 @@
|
||||
import { comfyUI } from "./comfyui-client.js";
|
||||
import { syncJsonHighlight } from "./json-highlight.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { escapeHtml } from "./util.js";
|
||||
import { escapeHtml, setupSearchClearBtn } from "./util.js";
|
||||
import { showToast } from "./app.js";
|
||||
|
||||
// ── Latent Image preset state ─────────────────────────────
|
||||
const _LATENT_PRESET_KEY = "wfm_latent_presets";
|
||||
@@ -27,6 +28,114 @@ function _saveLatentPresets(list) {
|
||||
localStorage.setItem(_LATENT_PRESET_KEY, JSON.stringify(list));
|
||||
}
|
||||
|
||||
// ── I2I placeholder image (Input tab / Image sub-panel) ──────────────────
|
||||
// One shared default (color-generated or a fixed uploaded image), remembered across
|
||||
// sessions. Each LoadImage slot's own "Placeholder" button applies this same default
|
||||
// to just that slot — see renderImageTab.
|
||||
const _I2I_PLACEHOLDER_KEY = "wfm_i2i_placeholder";
|
||||
const _I2I_PLACEHOLDER_DEFAULT = { mode: "color", color: "#808080", width: 512, height: 512, imageFilename: "" };
|
||||
|
||||
function _loadI2IPlaceholderConfig() {
|
||||
try {
|
||||
const raw = localStorage.getItem(_I2I_PLACEHOLDER_KEY);
|
||||
return raw ? { ..._I2I_PLACEHOLDER_DEFAULT, ...JSON.parse(raw) } : { ..._I2I_PLACEHOLDER_DEFAULT };
|
||||
} catch { return { ..._I2I_PLACEHOLDER_DEFAULT }; }
|
||||
}
|
||||
|
||||
function _saveI2IPlaceholderConfig(cfg) {
|
||||
localStorage.setItem(_I2I_PLACEHOLDER_KEY, JSON.stringify(cfg));
|
||||
}
|
||||
|
||||
// Renders a solid-color PNG on an offscreen canvas and returns it as a File, ready to
|
||||
// upload through comfyUI.uploadImage() the same way a picked/dropped file would be.
|
||||
function _generateColorImageFile(width, height, color) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const canvas = document.createElement("canvas");
|
||||
canvas.width = Math.max(1, width || 512);
|
||||
canvas.height = Math.max(1, height || 512);
|
||||
const ctx = canvas.getContext("2d");
|
||||
ctx.fillStyle = color || "#808080";
|
||||
ctx.fillRect(0, 0, canvas.width, canvas.height);
|
||||
canvas.toBlob((blob) => {
|
||||
if (!blob) { reject(new Error("Failed to render placeholder image")); return; }
|
||||
resolve(new File([blob], `placeholder_${canvas.width}x${canvas.height}.png`, { type: "image/png" }));
|
||||
}, "image/png");
|
||||
});
|
||||
}
|
||||
|
||||
// Wires the placeholder config panel at the top of the Image sub-panel (mode switch,
|
||||
// color/size fields, default-image drop zone). Every change is persisted immediately via
|
||||
// _saveI2IPlaceholderConfig so it's remembered next time the panel is rendered — this
|
||||
// panel is display-only config, it doesn't touch any workflow node itself; only each
|
||||
// slot's own Placeholder button (see renderImageTab) does that.
|
||||
function _wireI2IPlaceholderConfig(scopeEl) {
|
||||
const colorModeRadio = scopeEl.querySelector("#wfm-i2i-ph-mode-color");
|
||||
const imageModeRadio = scopeEl.querySelector("#wfm-i2i-ph-mode-image");
|
||||
const colorFields = scopeEl.querySelector("#wfm-i2i-ph-color-fields");
|
||||
const imageFields = scopeEl.querySelector("#wfm-i2i-ph-image-fields");
|
||||
const widthInput = scopeEl.querySelector("#wfm-i2i-ph-width");
|
||||
const heightInput = scopeEl.querySelector("#wfm-i2i-ph-height");
|
||||
const colorInput = scopeEl.querySelector("#wfm-i2i-ph-color");
|
||||
const dropZone = scopeEl.querySelector("#wfm-i2i-ph-drop");
|
||||
const fileInput = scopeEl.querySelector("#wfm-i2i-ph-file");
|
||||
const previewWrap = scopeEl.querySelector("#wfm-i2i-ph-preview-wrap");
|
||||
const previewImg = scopeEl.querySelector("#wfm-i2i-ph-preview-img");
|
||||
const statusEl = scopeEl.querySelector("#wfm-i2i-ph-status");
|
||||
if (!colorModeRadio) return; // no LoadImage slots rendered — panel doesn't exist
|
||||
|
||||
const setMode = (mode) => {
|
||||
if (colorFields) colorFields.style.display = mode === "color" ? "" : "none";
|
||||
if (imageFields) imageFields.style.display = mode === "image" ? "" : "none";
|
||||
_saveI2IPlaceholderConfig({ ..._loadI2IPlaceholderConfig(), mode });
|
||||
};
|
||||
colorModeRadio.addEventListener("change", () => { if (colorModeRadio.checked) setMode("color"); });
|
||||
imageModeRadio?.addEventListener("change", () => { if (imageModeRadio.checked) setMode("image"); });
|
||||
|
||||
widthInput?.addEventListener("change", () => {
|
||||
const width = Math.max(1, parseInt(widthInput.value, 10) || 512);
|
||||
widthInput.value = width;
|
||||
_saveI2IPlaceholderConfig({ ..._loadI2IPlaceholderConfig(), width });
|
||||
});
|
||||
heightInput?.addEventListener("change", () => {
|
||||
const height = Math.max(1, parseInt(heightInput.value, 10) || 512);
|
||||
heightInput.value = height;
|
||||
_saveI2IPlaceholderConfig({ ..._loadI2IPlaceholderConfig(), height });
|
||||
});
|
||||
colorInput?.addEventListener("change", () => {
|
||||
_saveI2IPlaceholderConfig({ ..._loadI2IPlaceholderConfig(), color: colorInput.value });
|
||||
});
|
||||
|
||||
// Default image: uploaded to ComfyUI immediately (its filename is what actually gets
|
||||
// reused later, not the raw file) so every slot's Placeholder button can apply it
|
||||
// without re-uploading.
|
||||
const applyDefaultImageFile = async (file) => {
|
||||
if (!file || !file.type.startsWith("image/")) return;
|
||||
if (statusEl) statusEl.textContent = t("i2iPlaceholderUploading");
|
||||
try {
|
||||
const result = await comfyUI.uploadImage(file, file.name);
|
||||
if (!result.name) throw new Error("Upload returned no filename");
|
||||
_saveI2IPlaceholderConfig({ ..._loadI2IPlaceholderConfig(), imageFilename: result.name });
|
||||
if (previewImg) previewImg.src = `/view?filename=${encodeURIComponent(result.name)}&type=input`;
|
||||
if (previewWrap) previewWrap.style.display = "";
|
||||
if (statusEl) { statusEl.textContent = `✓ ${result.name}`; statusEl.style.color = "var(--wfm-success)"; }
|
||||
} catch (err) {
|
||||
if (statusEl) { statusEl.textContent = `✗ ${err.message}`; statusEl.style.color = "var(--wfm-danger)"; }
|
||||
}
|
||||
};
|
||||
fileInput?.addEventListener("change", () => {
|
||||
if (fileInput.files.length > 0) applyDefaultImageFile(fileInput.files[0]);
|
||||
});
|
||||
if (dropZone) {
|
||||
dropZone.addEventListener("dragover", (e) => { e.preventDefault(); dropZone.classList.add("drag-over"); });
|
||||
dropZone.addEventListener("dragleave", () => dropZone.classList.remove("drag-over"));
|
||||
dropZone.addEventListener("drop", (e) => {
|
||||
e.preventDefault();
|
||||
dropZone.classList.remove("drag-over");
|
||||
if (e.dataTransfer.files.length > 0) applyDefaultImageFile(e.dataTransfer.files[0]);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
function _buildPresetOptions(customPresets) {
|
||||
const customs = customPresets.map((p) =>
|
||||
`<option value="${p.w}x${p.h}">${p.w}x${p.h}</option>`
|
||||
@@ -48,6 +157,16 @@ function _refreshPresetSelect(customPresets) {
|
||||
let _stackStrengths = {};
|
||||
// { modelFullPath: boolean } — true = active (default)
|
||||
let _stackActive = {};
|
||||
// ── LoRA node-bypass state (GenerateUI Model tab header toggle) ───────────
|
||||
// Force the LoRA node's strength to 0 without touching the actual ComfyUI node's mode —
|
||||
// currentWorkflow is already-converted API format (see comfyui-workflow.js
|
||||
// convertUiToApi), where mode has no effect at execution time, so a real Bypass(mode:4)
|
||||
// can't be reproduced here; zeroing strength is the behavioral equivalent.
|
||||
// { nodeId: boolean } — true = currently bypassed
|
||||
let _loraNodeBypass = {};
|
||||
// { nodeId: { loraName, strModel, strClip } | { loras, text } } — pre-bypass values to
|
||||
// restore when toggled back off. Shape depends on isLoraManager at bypass-time.
|
||||
let _loraBypassSnapshot = {};
|
||||
// { id: string|null, selStart: number, selEnd: number }
|
||||
let _lastPromptFocus = { id: null, selStart: 0, selEnd: 0 };
|
||||
|
||||
@@ -344,11 +463,11 @@ export const comfyEditor = {
|
||||
return found || name;
|
||||
},
|
||||
|
||||
renderAll(analysis, workflow) {
|
||||
renderAll(analysis, workflow, opts = {}) {
|
||||
this.renderPromptTab(analysis, "wfm-gen-prompt-fields");
|
||||
this.renderImageTab(analysis, "wfm-gen-image-fields");
|
||||
this.renderModelTab(analysis, "wfm-gen-model-fields");
|
||||
this.renderLoraPane(analysis, "wfm-gen-lora-fields"); // async, fires independently
|
||||
this.renderLoraPane(analysis, "wfm-gen-lora-fields", opts); // async, fires independently
|
||||
this.renderSettingsTab(analysis, "wfm-gen-settings-fields");
|
||||
_syncRawJson();
|
||||
},
|
||||
@@ -367,7 +486,7 @@ export const comfyEditor = {
|
||||
<label>Positive Prompt</label>
|
||||
<div style="display:flex;gap:8px;margin-bottom:6px;">
|
||||
<select id="wfm-prompt-pos-target" class="wfm-select" style="width:auto;flex:1;min-width:0;">
|
||||
${positiveNodes.map((n) => `<option value="${n.id}" data-text-key="${n.textKey || "text"}" selected>ID:${n.id} (${n.title})</option>`).join("")}
|
||||
${positiveNodes.map((n, i) => `<option value="${n.id}" data-text-key="${n.textKey || "text"}" ${i === 0 ? "selected" : ""}>ID:${n.id} (${n.title})</option>`).join("")}
|
||||
${nodeOpts}
|
||||
</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-prompt-pos-revert" title="${t("revertPromptTitle")}">↺</button>
|
||||
@@ -379,7 +498,7 @@ export const comfyEditor = {
|
||||
<label>Negative Prompt</label>
|
||||
<div style="display:flex;gap:8px;margin-bottom:6px;">
|
||||
<select id="wfm-prompt-neg-target" class="wfm-select" style="width:auto;flex:1;min-width:0;">
|
||||
${negativeNodes.map((n) => `<option value="${n.id}" data-text-key="${n.textKey || "text"}" selected>ID:${n.id} (${n.title})</option>`).join("")}
|
||||
${negativeNodes.map((n, i) => `<option value="${n.id}" data-text-key="${n.textKey || "text"}" ${i === 0 ? "selected" : ""}>ID:${n.id} (${n.title})</option>`).join("")}
|
||||
${nodeOpts}
|
||||
</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-prompt-neg-revert" title="${t("revertPromptTitle")}">↺</button>
|
||||
@@ -508,10 +627,14 @@ export const comfyEditor = {
|
||||
const el = document.getElementById(containerId);
|
||||
if (!el) return;
|
||||
|
||||
const sections = [
|
||||
// 使用頻度の高い順: Checkpoint/VAE/Diffusion Model/Text Encoder は常時表示。
|
||||
// ControlNet/Hypernetwork は意図的に使う場面が限られるため折りたたみにする。
|
||||
const mainSections = [
|
||||
{ label: "Checkpoint", key: "checkpoints", nodes: analysis.checkpoint_nodes, inputKey: "ckpt_name" },
|
||||
{ label: "VAE", key: "vaes", nodes: analysis.vae_nodes, inputKey: "vae_name" },
|
||||
{ label: "Diffusion Model", key: "diffusionModels", nodes: analysis.diffusion_model_nodes, inputKey: "unet_name" },
|
||||
];
|
||||
const collapsibleSections = [
|
||||
{ label: "ControlNet", key: "controlNets", nodes: analysis.controlnet_nodes, inputKey: "control_net_name" },
|
||||
{
|
||||
label: "Hypernetwork", key: "hypernetworks", nodes: analysis.hypernetwork_nodes, inputKey: "hypernetwork_name",
|
||||
@@ -519,48 +642,70 @@ export const comfyEditor = {
|
||||
},
|
||||
];
|
||||
|
||||
el.innerHTML = sections
|
||||
.map((s) => {
|
||||
const models = this.models[s.key] || [];
|
||||
const currentVal = s.nodes?.[0]?.[s.inputKey] || "";
|
||||
const isMissing = !!currentVal && !models.includes(currentVal);
|
||||
const targetOpts = s.nodes
|
||||
.map((n) => `<option value="${n.id}">ID:${n.id} (${n.title})</option>`)
|
||||
.join("");
|
||||
const extrasHtml = (s.extras || []).map((ex) => {
|
||||
const curVal = s.nodes?.[0]?.[ex.inputKey] ?? ex.defaultVal;
|
||||
return `<div style="display:flex;gap:6px;align-items:center;margin-top:4px;">
|
||||
<label style="font-size:12px;white-space:nowrap;color:var(--wfm-text-secondary);">${ex.label}</label>
|
||||
<input type="${ex.type}" class="wfm-input wfm-model-extra" id="wfm-model-${s.key}-${ex.inputKey}"
|
||||
data-key="${s.key}" data-input-key="${ex.inputKey}"
|
||||
value="${curVal}" step="${ex.step}" min="${ex.min}" max="${ex.max}"
|
||||
style="width:80px;">
|
||||
</div>`;
|
||||
}).join("");
|
||||
const renderSectionBody = (s) => {
|
||||
const models = this.models[s.key] || [];
|
||||
const currentVal = s.nodes?.[0]?.[s.inputKey] || "";
|
||||
const isMissing = !!currentVal && !models.includes(currentVal);
|
||||
const targetOpts = s.nodes
|
||||
.map((n) => `<option value="${n.id}">ID:${n.id} (${n.title})</option>`)
|
||||
.join("");
|
||||
const extrasHtml = (s.extras || []).map((ex) => {
|
||||
const curVal = s.nodes?.[0]?.[ex.inputKey] ?? ex.defaultVal;
|
||||
return `<div style="display:flex;gap:6px;align-items:center;margin-top:4px;">
|
||||
<label style="font-size:12px;white-space:nowrap;color:var(--wfm-text-secondary);">${ex.label}</label>
|
||||
<input type="${ex.type}" class="wfm-input wfm-model-extra" id="wfm-model-${s.key}-${ex.inputKey}"
|
||||
data-key="${s.key}" data-input-key="${ex.inputKey}"
|
||||
value="${curVal}" step="${ex.step}" min="${ex.min}" max="${ex.max}"
|
||||
style="width:80px;">
|
||||
</div>`;
|
||||
}).join("");
|
||||
|
||||
return `
|
||||
<div class="wfm-form-group" style="border-bottom:1px solid var(--wfm-border);padding-bottom:12px;">
|
||||
<label>${s.label}</label>
|
||||
<input type="text" class="wfm-input wfm-model-filter" placeholder="Filter..." data-target="wfm-model-${s.key}" style="margin-bottom:4px;">
|
||||
<select class="wfm-select ${isMissing ? "wfm-select-missing" : ""}" id="wfm-model-${s.key}" style="margin-bottom:4px;">
|
||||
${isMissing ? `<option value="${escapeHtml(currentVal)}" selected>⚠ ${escapeHtml(currentVal)} (${t("modelNotFound")})</option>` : ""}
|
||||
${models.map((m) => `<option value="${m}" ${m === currentVal ? "selected" : ""}>${m}</option>`).join("")}
|
||||
</select>
|
||||
${isMissing ? `<div class="wfm-model-missing-hint">${t("modelNotFoundHint")}</div>` : ""}
|
||||
${extrasHtml}
|
||||
<div style="display:flex;gap:8px;align-items:center;margin-top:4px;">
|
||||
<select class="wfm-select" id="wfm-model-${s.key}-target" style="flex:1;">${targetOpts}</select>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-model-apply" data-key="${s.key}" data-input="${s.inputKey}" title="Apply (Alt+Click: Apply & Generate)">Apply</button>
|
||||
</div>
|
||||
return `
|
||||
<div class="wfm-search-wrap" style="margin-bottom:4px;width:100%;">
|
||||
<input type="text" class="wfm-input wfm-search-input wfm-model-filter" id="wfm-model-${s.key}-filter" placeholder="Filter..." data-target="wfm-model-${s.key}">
|
||||
<button type="button" class="wfm-search-clear-btn" id="wfm-model-${s.key}-filter-clear" title="Clear search">✕</button>
|
||||
</div>
|
||||
<select class="wfm-select ${isMissing ? "wfm-select-missing" : ""}" id="wfm-model-${s.key}" style="margin-bottom:4px;">
|
||||
${isMissing ? `<option value="${escapeHtml(currentVal)}" selected>⚠ ${escapeHtml(currentVal)} (${t("modelNotFound")})</option>` : ""}
|
||||
${models.map((m) => `<option value="${m}" ${m === currentVal ? "selected" : ""}>${m}</option>`).join("")}
|
||||
</select>
|
||||
${isMissing ? `<div class="wfm-model-missing-hint">${t("modelNotFoundHint")}</div>` : ""}
|
||||
${extrasHtml}
|
||||
<div style="display:flex;gap:8px;align-items:center;margin-top:4px;">
|
||||
<select class="wfm-select" id="wfm-model-${s.key}-target" style="flex:1;">${targetOpts}</select>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-model-apply" data-key="${s.key}" data-input="${s.inputKey}" title="Apply (Alt+Click: Apply & Generate)">Apply</button>
|
||||
</div>
|
||||
`;
|
||||
})
|
||||
.join("") + '<div class="wfm-form-group" id="wfm-te-section" style="border-bottom:1px solid var(--wfm-border);padding-bottom:12px;"><label>Text Encoder</label></div>';
|
||||
};
|
||||
|
||||
const mainHtml = mainSections.map((s) => {
|
||||
const hasNodes = (s.nodes?.length || 0) > 0;
|
||||
return `
|
||||
<div class="wfm-form-group" style="border-bottom:1px solid var(--wfm-border);padding-bottom:12px;">
|
||||
<label class="${hasNodes ? "wfm-model-label-active" : ""}">${s.label}</label>
|
||||
${renderSectionBody(s)}
|
||||
</div>`;
|
||||
}).join("");
|
||||
|
||||
const teHtml = '<div class="wfm-form-group" id="wfm-te-section" style="border-bottom:1px solid var(--wfm-border);padding-bottom:12px;"><label>Text Encoder</label></div>';
|
||||
|
||||
const collapsibleHtml = collapsibleSections.map((s) => {
|
||||
const hasNodes = (s.nodes?.length || 0) > 0;
|
||||
return `
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary ${hasNodes ? "wfm-model-label-active" : ""}">${s.label}</summary>
|
||||
<div class="wfm-form-group" style="margin-bottom:0;">
|
||||
${renderSectionBody(s)}
|
||||
</div>
|
||||
</details>`;
|
||||
}).join("");
|
||||
|
||||
el.innerHTML = mainHtml + teHtml + collapsibleHtml;
|
||||
|
||||
// Filter inputs
|
||||
el.querySelectorAll(".wfm-model-filter").forEach((input) => {
|
||||
input.addEventListener("input", () => {
|
||||
const targetId = input.dataset.target;
|
||||
const targetId = input.dataset.target;
|
||||
const rebuild = () => {
|
||||
const select = document.getElementById(targetId);
|
||||
if (!select) return;
|
||||
const filter = input.value.toLowerCase();
|
||||
@@ -570,7 +715,9 @@ export const comfyEditor = {
|
||||
.filter((m) => m.toLowerCase().includes(filter))
|
||||
.map((m) => `<option value="${m}">${m}</option>`)
|
||||
.join("");
|
||||
});
|
||||
};
|
||||
input.addEventListener("input", rebuild);
|
||||
setupSearchClearBtn(input.id, `${input.id}-clear`, rebuild);
|
||||
});
|
||||
|
||||
// Apply buttons
|
||||
@@ -606,13 +753,104 @@ export const comfyEditor = {
|
||||
_initTextEncoderSection(analysis, this.models.textEncoders || [], el).catch(() => {});
|
||||
},
|
||||
|
||||
async renderLoraPane(analysis, containerId) {
|
||||
// Toggles the GenerateUI Model tab's LoRA column-header bypass button: forces the
|
||||
// current target LoRA node's strength to 0 (both tabs' strength fields, whichever
|
||||
// is currently in currentWorkflow), or restores whatever was there before. Targets
|
||||
// the same node Single/Stack Apply default to (first LoraManager, else first LoRA
|
||||
// node) so one button covers both tabs without needing its own node picker.
|
||||
toggleLoraNodeBypass(analysis) {
|
||||
const loraNodes = analysis?.lora_nodes || [];
|
||||
let nodeId = (loraNodes.find((n) => n.is_lora_manager) || loraNodes[0])?.id;
|
||||
let isLoraManager = !!loraNodes.find((n) => String(n.id) === String(nodeId))?.is_lora_manager;
|
||||
if (nodeId == null) {
|
||||
nodeId = Object.keys(comfyUI.currentWorkflow || {}).find((id) => comfyUI.currentWorkflow[id]?.inputs && "lora_name" in comfyUI.currentWorkflow[id].inputs);
|
||||
isLoraManager = false;
|
||||
}
|
||||
const wfNode = nodeId != null ? comfyUI.currentWorkflow?.[nodeId] : null;
|
||||
if (!wfNode) {
|
||||
showToast(t("modelsGenUINoNode", "LoRA"), "warning");
|
||||
return;
|
||||
}
|
||||
|
||||
if (_loraNodeBypass[nodeId]) {
|
||||
// Restore whatever was set before bypassing
|
||||
const snap = _loraBypassSnapshot[nodeId];
|
||||
if (snap) {
|
||||
if (isLoraManager) {
|
||||
wfNode.inputs.loras = { __value__: JSON.parse(JSON.stringify(snap.loras || [])) };
|
||||
wfNode.inputs.text = snap.text ?? "";
|
||||
} else {
|
||||
wfNode.inputs.lora_name = snap.lora_name;
|
||||
wfNode.inputs.strength_model = snap.strength_model;
|
||||
wfNode.inputs.strength_clip = snap.strength_clip;
|
||||
}
|
||||
}
|
||||
delete _loraNodeBypass[nodeId];
|
||||
delete _loraBypassSnapshot[nodeId];
|
||||
} else {
|
||||
// Snapshot current values, then zero the strength (LoraManager: every
|
||||
// entry in its loras list, keeping the list itself intact so un-bypassing
|
||||
// restores the exact same Stack — not just a single-LoRA fallback).
|
||||
if (isLoraManager) {
|
||||
const loras = Array.isArray(wfNode.inputs.loras?.__value__) ? wfNode.inputs.loras.__value__ : [];
|
||||
_loraBypassSnapshot[nodeId] = { loras: JSON.parse(JSON.stringify(loras)), text: wfNode.inputs.text ?? "" };
|
||||
wfNode.inputs.loras = { __value__: loras.map((l) => ({ ...l, strength: 0, clipStrength: 0 })) };
|
||||
wfNode.inputs.text = "";
|
||||
} else {
|
||||
_loraBypassSnapshot[nodeId] = {
|
||||
lora_name: wfNode.inputs.lora_name,
|
||||
strength_model: wfNode.inputs.strength_model,
|
||||
strength_clip: wfNode.inputs.strength_clip,
|
||||
};
|
||||
wfNode.inputs.strength_model = 0;
|
||||
wfNode.inputs.strength_clip = 0;
|
||||
}
|
||||
_loraNodeBypass[nodeId] = true;
|
||||
}
|
||||
_syncRawJson();
|
||||
},
|
||||
|
||||
async renderLoraPane(analysis, containerId, opts = {}) {
|
||||
const el = document.getElementById(containerId);
|
||||
if (!el) return;
|
||||
|
||||
const loraNodes = analysis.lora_nodes || [];
|
||||
document.getElementById("wfm-gen-lora-col-title")?.classList.toggle("wfm-model-label-active", loraNodes.length > 0);
|
||||
const loras = this.models.loras || [];
|
||||
const defaultStackTarget = (loraNodes.find((n) => n.is_lora_manager) || loraNodes[0])?.id;
|
||||
const hasLoraManager = loraNodes.some((n) => n.is_lora_manager);
|
||||
|
||||
// Sync the column-header bypass button (lives outside this container's
|
||||
// innerHTML, in the static wfm-gen-lora-col-header) to whatever the target
|
||||
// node's current bypass state is.
|
||||
const bypassBtn = document.getElementById("wfm-lora-bypass-toggle");
|
||||
if (bypassBtn) {
|
||||
bypassBtn.classList.toggle("active", defaultStackTarget != null && !!_loraNodeBypass[defaultStackTarget]);
|
||||
bypassBtn.disabled = loraNodes.length === 0;
|
||||
}
|
||||
// Use Stack Group チェックボックスの現在状態を先読み(Stack表示の取得元判定に使う)。
|
||||
// resetStackMode(新規読み込み)時は常にOFF扱い。
|
||||
const stackModeChecked = hasLoraManager && !opts.resetStackMode && (el.querySelector("#wfm-lora-stack-mode-toggle")?.checked ?? false);
|
||||
|
||||
// ワークフロー新規読み込み時のみ: LoraManagerノードのloras/text状態をスナップショットし、
|
||||
// Stackグループ使用チェックボックスをOFFへリセットする(既存のGenUI状態への影響を避けるため
|
||||
// タブ切替やStack再読込などの単なる再描画では行わない)。
|
||||
if (opts.resetStackMode) {
|
||||
// New workflow loaded — bypass state/snapshots belonged to the previous
|
||||
// workflow's node IDs, so they can't be meaningfully carried forward.
|
||||
_loraNodeBypass = {};
|
||||
_loraBypassSnapshot = {};
|
||||
comfyUI.loraManagerSnapshots = {};
|
||||
for (const n of loraNodes) {
|
||||
if (!n.is_lora_manager) continue;
|
||||
const wfNode = comfyUI.currentWorkflow?.[n.id];
|
||||
if (!wfNode?.inputs) continue;
|
||||
comfyUI.loraManagerSnapshots[n.id] = {
|
||||
loras: Array.isArray(wfNode.inputs.loras?.__value__) ? JSON.parse(JSON.stringify(wfNode.inputs.loras.__value__)) : [],
|
||||
text: wfNode.inputs.text ?? "",
|
||||
};
|
||||
}
|
||||
}
|
||||
const nodeOpts = loraNodes
|
||||
.map((n) => `<option value="${n.id}">ID:${n.id} (${n.title})</option>`)
|
||||
.join("");
|
||||
@@ -637,26 +875,53 @@ export const comfyEditor = {
|
||||
}
|
||||
}
|
||||
|
||||
// Fetch Stack group, metadata, CivitAI cache in parallel
|
||||
// Stack表示の取得元: ON(Use Stack Group)ならModelsタブの登録済みStackグループ、
|
||||
// OFF(既定)ならLoraManagerノードに実際に設定されているLoRAをそのまま表示する。
|
||||
let stackModels = [];
|
||||
let metadata = {};
|
||||
let civitaiCache = {};
|
||||
const useWorkflowLoraState = hasLoraManager && !stackModeChecked;
|
||||
try {
|
||||
const [grpRes, metaRes, civRes] = await Promise.all([
|
||||
fetch("/api/wfm/models/groups?type=lora"),
|
||||
const [metaRes, civRes] = await Promise.all([
|
||||
fetch("/api/wfm/models/metadata"),
|
||||
fetch("/api/wfm/models/civitai/cache"),
|
||||
]);
|
||||
const groups = grpRes.ok ? await grpRes.json() : {};
|
||||
stackModels = groups["Stack"] || [];
|
||||
metadata = metaRes.ok ? await metaRes.json() : {};
|
||||
civitaiCache = civRes.ok ? await civRes.json() : {};
|
||||
} catch { /* ignore */ }
|
||||
|
||||
stackModels.forEach((m) => {
|
||||
if (!_stackStrengths[m]) _stackStrengths[m] = { m: 1.0, c: 1.0 };
|
||||
if (_stackActive[m] === undefined) _stackActive[m] = true;
|
||||
});
|
||||
if (stackModeChecked) {
|
||||
try {
|
||||
const grpRes = await fetch("/api/wfm/models/groups?type=lora");
|
||||
const groups = grpRes.ok ? await grpRes.json() : {};
|
||||
stackModels = groups["Stack"] || [];
|
||||
} catch { /* ignore */ }
|
||||
stackModels.forEach((m) => {
|
||||
if (!_stackStrengths[m]) _stackStrengths[m] = { m: 1.0, c: 1.0 };
|
||||
if (_stackActive[m] === undefined) _stackActive[m] = true;
|
||||
});
|
||||
} else if (useWorkflowLoraState) {
|
||||
const wfNode = comfyUI.currentWorkflow?.[defaultStackTarget];
|
||||
const wfLoras = wfNode?.inputs?.loras?.__value__;
|
||||
// resetStackMode(新規読み込み)/syncFromWorkflow(チェックボックスOFF切替直後)の時だけ
|
||||
// ワークフローの実データで強制上書きする。タブ切替等の単なる再描画では、
|
||||
// 既にメモリ上に値があればそれを維持し、Stackパネルでの一時的なON/OFF・強度編集を
|
||||
// 再描画のたびに消してしまわないようにする。
|
||||
const shouldSync = opts.resetStackMode || opts.syncFromWorkflow;
|
||||
if (Array.isArray(wfLoras)) {
|
||||
stackModels = wfLoras.map((l) => {
|
||||
const found = (this.models.loras || []).find((m) => _loraBasename(m) === l.name);
|
||||
const full = found || l.name;
|
||||
if (shouldSync || _stackStrengths[full] === undefined) {
|
||||
_stackStrengths[full] = { m: parseFloat(l.strength) || 1.0, c: parseFloat(l.clipStrength ?? l.strength) || 1.0 };
|
||||
}
|
||||
if (shouldSync || _stackActive[full] === undefined) {
|
||||
_stackActive[full] = l.active !== false;
|
||||
}
|
||||
return full;
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Build Stack trigger words
|
||||
const activeTriggerWords = [];
|
||||
@@ -673,7 +938,9 @@ export const comfyEditor = {
|
||||
? activeTriggerWords.map((w) => `<span class="wfm-lora-trigger-word">${w}</span>`).join(" ")
|
||||
: `<span style="color:var(--wfm-text-secondary);font-size:12px;">—</span>`;
|
||||
const stackLoraSyntax = _buildLoraSyntax(stackModels);
|
||||
const allActive = stackModels.every((m) => _stackActive[m] !== false);
|
||||
// stackModelsが空だと every() が空配列に対し true を返す(空虚な真)ため、
|
||||
// 何も無いのに「トグルオール」チェックボックスがON表示になるのを防ぐ
|
||||
const allActive = stackModels.length > 0 && stackModels.every((m) => _stackActive[m] !== false);
|
||||
const anyActive = stackModels.some((m) => _stackActive[m] !== false);
|
||||
|
||||
const stackModelRows = stackModels.map((m) => {
|
||||
@@ -691,7 +958,10 @@ export const comfyEditor = {
|
||||
</div>`;
|
||||
}).join("");
|
||||
|
||||
const _prevActiveTab = el.querySelector(".wfm-lora-tab-btn.active")?.dataset?.tab || "single";
|
||||
let _prevActiveTab = el.querySelector(".wfm-lora-tab-btn.active")?.dataset?.tab || "single";
|
||||
// 新規読み込み時、LoraManagerノードに実際に複数LoRAが設定されている場合は
|
||||
// Single(1件しか表示できない)ではなくStackタブを初期表示にする
|
||||
if (opts.resetStackMode && useWorkflowLoraState && stackModels.length > 1) _prevActiveTab = "stack";
|
||||
// applyToGenUIで設定したSingle表示の状態を保存（再描画後に復元）
|
||||
const _prevSingleSyntax = document.getElementById("wfm-lora-single-syntax")?.textContent || "";
|
||||
const _prevSingleTriggers = document.getElementById("wfm-lora-single-triggers")?.innerHTML || "";
|
||||
@@ -700,11 +970,19 @@ export const comfyEditor = {
|
||||
<div class="wfm-lora-tab-header">
|
||||
<button class="wfm-lora-tab-btn active" data-tab="single">Single</button>
|
||||
<button class="wfm-lora-tab-btn" data-tab="stack">Stack</button>
|
||||
${hasLoraManager ? `
|
||||
<label class="wfm-lora-stack-mode-label" style="margin-left:auto;display:flex;align-items:center;gap:4px;font-size:12px;white-space:nowrap;" title="ON: 登録済みStackグループを読み込む / OFF: ワークフローに実際に設定されているLoRAを表示・編集する">
|
||||
<input type="checkbox" id="wfm-lora-stack-mode-toggle" ${stackModeChecked ? "checked" : ""}>
|
||||
Use Stack Group
|
||||
</label>` : ""}
|
||||
</div>
|
||||
|
||||
<!-- Single tab -->
|
||||
<div class="wfm-lora-tab-content" id="wfm-lora-panel-single">
|
||||
<input type="text" class="wfm-input" id="wfm-lora-filter" placeholder="Filter...">
|
||||
<div class="wfm-search-wrap" style="width:100%;">
|
||||
<input type="text" class="wfm-input wfm-search-input" id="wfm-lora-filter" placeholder="Filter...">
|
||||
<button type="button" class="wfm-search-clear-btn" id="wfm-lora-filter-clear" title="Clear search">✕</button>
|
||||
</div>
|
||||
<select class="wfm-select" id="wfm-lora-select">
|
||||
${loras.map((m) => `<option value="${m}" ${m === currentVal ? "selected" : ""}>${m}</option>`).join("")}
|
||||
</select>
|
||||
@@ -731,6 +1009,7 @@ export const comfyEditor = {
|
||||
|
||||
<!-- Stack tab -->
|
||||
<div class="wfm-lora-tab-content" id="wfm-lora-panel-stack" style="display:none;">
|
||||
${hasLoraManager ? `<div style="font-size:11px;color:var(--wfm-text-secondary);margin-bottom:6px;">${useWorkflowLoraState ? "Showing: LoRAs currently in workflow" : "Showing: registered Stack group"}</div>` : ""}
|
||||
<div class="wfm-lora-stack-header">
|
||||
<select class="wfm-select" id="wfm-lora-stack-target" style="flex:1;min-width:0;">${stackTargetOpts}</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-lora-stack-apply" title="Apply Stack to node and sync to Positive prompt">Apply</button>
|
||||
@@ -765,7 +1044,7 @@ export const comfyEditor = {
|
||||
<div id="wfm-lora-stack-triggers" class="wfm-lora-stack-triggers">${stackTriggerHtml}</div>
|
||||
</div>
|
||||
<div class="wfm-lora-stack-models">
|
||||
${stackModelRows || `<p class="wfm-placeholder">No models in Stack group</p>`}
|
||||
${stackModelRows || `<p class="wfm-placeholder">${useWorkflowLoraState ? "No LoRAs set on this node" : "No models in Stack group"}</p>`}
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
@@ -803,9 +1082,63 @@ export const comfyEditor = {
|
||||
document.getElementById("wfm-lora-panel-stack").style.display = "";
|
||||
}
|
||||
|
||||
// ── Stackグループ使用チェックボックス ─────────────────
|
||||
// ON: LoraManagerノードへStackグループを（クリアしてから）読み込む(既存のStack Applyを流用)
|
||||
// OFF: ワークフロー読み込み時点のloras/text状態へ（クリアしてから）戻す
|
||||
document.getElementById("wfm-lora-stack-mode-toggle")?.addEventListener("change", async (e) => {
|
||||
const nodeId = document.getElementById("wfm-lora-stack-target")?.value || defaultStackTarget;
|
||||
if (!nodeId || !comfyUI.currentWorkflow?.[nodeId]) return;
|
||||
|
||||
if (e.target.checked) {
|
||||
// stackModelsはこの時点では「ワークフロー state」を指したままのため、
|
||||
// 先に再描画してStackグループを取得させてからApplyする
|
||||
await this.renderLoraPane(analysis, containerId);
|
||||
document.getElementById("wfm-lora-stack-apply")?.click();
|
||||
return;
|
||||
}
|
||||
|
||||
const snap = comfyUI.loraManagerSnapshots?.[nodeId] || { loras: [], text: "" };
|
||||
comfyUI.currentWorkflow[nodeId].inputs.loras = { __value__: JSON.parse(JSON.stringify(snap.loras)) };
|
||||
comfyUI.currentWorkflow[nodeId].inputs.text = snap.text;
|
||||
_syncRawJson();
|
||||
|
||||
// ON時にPositiveプロンプトへ挿入されたStack由来の<lora:...>構文とトリガーワードを除去する
|
||||
// (新規に追加はしない。ワークフロー本来のプロンプトへ戻すだけ)
|
||||
const posTextarea = document.getElementById("wfm-prompt-pos-text");
|
||||
if (posTextarea && stackModels.length > 0) {
|
||||
let cleaned = posTextarea.value;
|
||||
for (const m of stackModels) {
|
||||
const stem = _loraBasename(m).replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
|
||||
cleaned = cleaned.replace(new RegExp(`,?\\s*<lora:${stem}:[^>]*>`, "gi"), "");
|
||||
}
|
||||
const stackWords = new Set();
|
||||
stackModels.forEach((m) => {
|
||||
const sha = (metadata[m] || {}).sha256;
|
||||
const civInfo = sha && civitaiCache[sha];
|
||||
civInfo?.trainedWords?.forEach((w) => stackWords.add(w.trim().toLowerCase()));
|
||||
});
|
||||
if (stackWords.size > 0) {
|
||||
cleaned = cleaned.split(",").map((p) => p.trim()).filter((p) => p && !stackWords.has(p.toLowerCase())).join(", ");
|
||||
}
|
||||
posTextarea.value = cleaned.replace(/,\s*$/, "").trim();
|
||||
|
||||
const posNodeId = document.getElementById("wfm-prompt-pos-target")?.value;
|
||||
if (posNodeId && comfyUI.currentWorkflow?.[posNodeId]) {
|
||||
const promptNode = comfyUI.currentAnalysis?.prompt_nodes?.find((n) => String(n.id) === String(posNodeId));
|
||||
comfyUI.currentWorkflow[posNodeId].inputs[promptNode?.textKey || "text"] = posTextarea.value;
|
||||
_syncRawJson();
|
||||
}
|
||||
}
|
||||
|
||||
// syncFromWorkflow: OFFへ戻した直後は、たった今書き戻したloras/text状態と
|
||||
// Stackパネルの強度/有効状態表示を一致させるため強制同期する
|
||||
this.renderLoraPane(analysis, containerId, { syncFromWorkflow: true });
|
||||
});
|
||||
|
||||
// ── Single: filter ───────────────────────────────────
|
||||
document.getElementById("wfm-lora-filter")?.addEventListener("input", (e) => {
|
||||
const filter = e.target.value.toLowerCase();
|
||||
const loraFilterInput = document.getElementById("wfm-lora-filter");
|
||||
const rebuildLoraSingleList = () => {
|
||||
const filter = loraFilterInput?.value.toLowerCase() || "";
|
||||
const select = document.getElementById("wfm-lora-select");
|
||||
if (!select) return;
|
||||
select.innerHTML = loras
|
||||
@@ -813,7 +1146,9 @@ export const comfyEditor = {
|
||||
.map((m) => `<option value="${m}">${m}</option>`)
|
||||
.join("");
|
||||
_refreshLoraSingleDynamic(metadata, civitaiCache);
|
||||
});
|
||||
};
|
||||
loraFilterInput?.addEventListener("input", rebuildLoraSingleList);
|
||||
setupSearchClearBtn("wfm-lora-filter", "wfm-lora-filter-clear", rebuildLoraSingleList);
|
||||
|
||||
// ── Single: update SYNTAX/TRIGGERS on model or strength change ──
|
||||
document.getElementById("wfm-lora-select")?.addEventListener("change", () => _refreshLoraSingleDynamic(metadata, civitaiCache));
|
||||
@@ -830,6 +1165,11 @@ export const comfyEditor = {
|
||||
const strModel = parseFloat(document.getElementById("wfm-lora-str-model")?.value) || 1.0;
|
||||
const strClip = parseFloat(document.getElementById("wfm-lora-str-clip")?.value) || 1.0;
|
||||
_applyLoraToNode(nodeId, select.value, strModel, strClip, node?.is_lora_manager);
|
||||
// An explicit Apply overwrites whatever bypass had zeroed — clear it so the
|
||||
// header button (and a later un-bypass) don't stomp this new value.
|
||||
delete _loraNodeBypass[nodeId];
|
||||
delete _loraBypassSnapshot[nodeId];
|
||||
document.getElementById("wfm-lora-bypass-toggle")?.classList.remove("active");
|
||||
_syncRawJson();
|
||||
|
||||
// Sync to Positive prompt
|
||||
@@ -962,6 +1302,11 @@ export const comfyEditor = {
|
||||
comfyUI.currentWorkflow[nodeId].inputs.strength_clip = _stackStrengths[first]?.c ?? 1.0;
|
||||
}
|
||||
}
|
||||
// An explicit Apply overwrites whatever bypass had zeroed — clear it so the
|
||||
// header button (and a later un-bypass) don't stomp this new value.
|
||||
delete _loraNodeBypass[nodeId];
|
||||
delete _loraBypassSnapshot[nodeId];
|
||||
document.getElementById("wfm-lora-bypass-toggle")?.classList.remove("active");
|
||||
_syncRawJson();
|
||||
|
||||
// Sync Stack LORA SYNTAX + TRIGGER WORDS to Positive prompt
|
||||
@@ -1030,23 +1375,39 @@ export const comfyEditor = {
|
||||
const sampler = analysis.sampler_nodes?.[0];
|
||||
const latent = analysis.latent_nodes?.[0];
|
||||
|
||||
// "Advanced Sampling" workflows (Flux.1/Flux.2, SD3.5, Chroma...) spread seed/steps/cfg/
|
||||
// sampler/scheduler/denoise across RandomNoise + CFGGuider + KSamplerSelect + a *Scheduler
|
||||
// node instead of one KSampler. sampler.*NodeId (set in analyzeWorkflow) says which real
|
||||
// node each field writes back to; null/"" means that workflow has no such control at all
|
||||
// (e.g. Flux2Scheduler has no named "scheduler" combo) so the field is disabled here.
|
||||
const schedulerAvailable = !!sampler && sampler.schedulerNodeId != null;
|
||||
const denoiseAvailable = !!sampler && sampler.denoiseNodeId != null;
|
||||
|
||||
el.innerHTML = `
|
||||
<div style="display:flex;flex-direction:row;gap:0;align-items:flex-start;">
|
||||
<div style="flex:1;min-width:0;padding-right:14px;border-right:1px solid var(--wfm-border);">
|
||||
<h3 style="font-size:13px;font-weight:600;text-transform:uppercase;letter-spacing:0.5px;color:var(--wfm-text-secondary);margin:0 0 12px;">KSampler</h3>
|
||||
<h3 style="font-size:13px;font-weight:600;text-transform:uppercase;letter-spacing:0.5px;color:var(--wfm-text-secondary);margin:0 0 12px;">${sampler?.advanced ? "Sampler (Advanced)" : "KSampler"}</h3>
|
||||
${sampler ? `
|
||||
<input type="hidden" id="wfm-settings-sampler-id" value="${sampler.id}" data-seed-key="${sampler.seedKey || "seed"}">
|
||||
<input type="hidden" id="wfm-settings-sampler-id"
|
||||
data-seed-key="${sampler.seedKey || "seed"}"
|
||||
data-seed-node-id="${sampler.seedNodeId ?? sampler.id}"
|
||||
data-steps-node-id="${sampler.stepsNodeId ?? sampler.id}"
|
||||
data-cfg-key="${sampler.cfgKey || "cfg"}"
|
||||
data-cfg-node-id="${sampler.cfgNodeId ?? sampler.id}"
|
||||
data-sampler-node-id="${sampler.samplerNodeId ?? sampler.id}"
|
||||
data-scheduler-node-id="${sampler.schedulerNodeId ?? ""}"
|
||||
data-denoise-node-id="${sampler.denoiseNodeId ?? ""}">
|
||||
<div class="wfm-form-group">
|
||||
<label>Seed</label>
|
||||
<input type="number" class="wfm-input" id="wfm-settings-seed" value="${sampler.seed ?? -1}">
|
||||
<input type="number" class="wfm-input" id="wfm-settings-seed" value="${sampler.seed ?? ""}" placeholder="${sampler.seed === undefined ? "linked" : ""}">
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>Steps</label>
|
||||
<input type="number" class="wfm-input" id="wfm-settings-steps" value="${sampler.steps ?? 20}" min="1" max="200">
|
||||
<input type="number" class="wfm-input" id="wfm-settings-steps" value="${sampler.steps ?? ""}" placeholder="${sampler.steps === undefined ? "linked" : ""}" min="1" max="200">
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>CFG</label>
|
||||
<input type="number" class="wfm-input" id="wfm-settings-cfg" value="${sampler.cfg ?? 7}" step="0.5" min="0">
|
||||
<input type="number" class="wfm-input" id="wfm-settings-cfg" value="${sampler.cfg ?? ""}" placeholder="${sampler.cfg === undefined ? "linked" : ""}" step="0.5" min="0">
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>Sampler</label>
|
||||
@@ -1056,13 +1417,15 @@ export const comfyEditor = {
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>Scheduler</label>
|
||||
<select class="wfm-select" id="wfm-settings-scheduler">
|
||||
${this.models.schedulers.map((s) => `<option value="${s}" ${s === sampler.scheduler ? "selected" : ""}>${s}</option>`).join("")}
|
||||
<select class="wfm-select" id="wfm-settings-scheduler" ${schedulerAvailable ? "" : "disabled"}>
|
||||
${schedulerAvailable
|
||||
? this.models.schedulers.map((s) => `<option value="${s}" ${s === sampler.scheduler ? "selected" : ""}>${s}</option>`).join("")
|
||||
: `<option value="">N/A</option>`}
|
||||
</select>
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>Denoise</label>
|
||||
<input type="number" class="wfm-input" id="wfm-settings-denoise" value="${sampler.denoise ?? 1.0}" step="0.05" min="0" max="1">
|
||||
<input type="number" class="wfm-input" id="wfm-settings-denoise" value="${sampler.denoise ?? ""}" placeholder="${denoiseAvailable && sampler.denoise === undefined ? "linked" : ""}" step="0.05" min="0" max="1" ${denoiseAvailable ? "" : "disabled"}>
|
||||
</div>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-sampler-apply" title="Apply (Alt+Click: Apply & Generate)">Apply</button>
|
||||
` : "<p class='wfm-placeholder'>No KSampler node found</p>"}
|
||||
@@ -1073,17 +1436,18 @@ export const comfyEditor = {
|
||||
<input type="hidden" id="wfm-settings-latent-id" value="${latent.id}">
|
||||
<div class="wfm-form-group">
|
||||
<label>Width</label>
|
||||
<input type="number" class="wfm-input" id="wfm-settings-width" value="${latent.width ?? 512}" step="8" min="64">
|
||||
<input type="number" class="wfm-input" id="wfm-settings-width" value="${latent.width ?? ""}" placeholder="${latent.width === undefined ? "linked" : ""}" step="8" min="64">
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>Height</label>
|
||||
<input type="number" class="wfm-input" id="wfm-settings-height" value="${latent.height ?? 512}" step="8" min="64">
|
||||
<input type="number" class="wfm-input" id="wfm-settings-height" value="${latent.height ?? ""}" placeholder="${latent.height === undefined ? "linked" : ""}" step="8" min="64">
|
||||
</div>
|
||||
<div class="wfm-form-group">
|
||||
<label>Batch Size</label>
|
||||
<input type="number" class="wfm-input" id="wfm-settings-batch" value="${latent.batch_size ?? 1}" min="1" max="64">
|
||||
</div>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-latent-apply">Apply</button>
|
||||
${latent.width === undefined || latent.height === undefined ? `<p class="wfm-placeholder" style="margin-top:6px;">Width/Height is linked to another node (e.g. auto-follows the input image) — leave blank to keep it linked.</p>` : ""}
|
||||
<div style="margin-top:10px;border-top:1px solid var(--wfm-border);padding-top:10px;">
|
||||
<div style="display:flex;flex-wrap:wrap;gap:4px;margin-bottom:8px;">
|
||||
${[512, 768, 1024, 2048].map((s) => `<button class="wfm-btn wfm-btn-sm wfm-latent-sq-btn" data-size="${s}">${s}</button>`).join("")}
|
||||
@@ -1109,17 +1473,21 @@ export const comfyEditor = {
|
||||
`;
|
||||
|
||||
document.getElementById("wfm-settings-sampler-apply")?.addEventListener("click", (e) => {
|
||||
const nodeId = document.getElementById("wfm-settings-sampler-id")?.value;
|
||||
if (!nodeId || !comfyUI.currentWorkflow?.[nodeId]) return;
|
||||
const inputs = comfyUI.currentWorkflow[nodeId].inputs;
|
||||
// KSamplerAdvanced uses noise_seed; fall back to seed
|
||||
const seedKey = document.getElementById("wfm-settings-sampler-id")?.dataset?.seedKey || "seed";
|
||||
inputs[seedKey] = parseInt(document.getElementById("wfm-settings-seed")?.value) || -1;
|
||||
inputs.steps = parseInt(document.getElementById("wfm-settings-steps")?.value) || 20;
|
||||
inputs.cfg = parseFloat(document.getElementById("wfm-settings-cfg")?.value) || 7;
|
||||
inputs.sampler_name = document.getElementById("wfm-settings-sampler-name")?.value;
|
||||
inputs.scheduler = document.getElementById("wfm-settings-scheduler")?.value;
|
||||
inputs.denoise = parseFloat(document.getElementById("wfm-settings-denoise")?.value) || 1.0;
|
||||
const ds = document.getElementById("wfm-settings-sampler-id")?.dataset || {};
|
||||
const write = (nodeId, key, value) => {
|
||||
if (!nodeId || !comfyUI.currentWorkflow?.[nodeId]) return;
|
||||
comfyUI.currentWorkflow[nodeId].inputs[key] = value;
|
||||
};
|
||||
const seedEl = document.getElementById("wfm-settings-seed");
|
||||
if (seedEl?.value !== "") write(ds.seedNodeId, ds.seedKey || "seed", parseInt(seedEl.value) || -1);
|
||||
const stepsEl = document.getElementById("wfm-settings-steps");
|
||||
if (stepsEl?.value !== "") write(ds.stepsNodeId, "steps", parseInt(stepsEl.value) || 20);
|
||||
const cfgEl = document.getElementById("wfm-settings-cfg");
|
||||
if (cfgEl?.value !== "") write(ds.cfgNodeId, ds.cfgKey || "cfg", parseFloat(cfgEl.value) || 7);
|
||||
write(ds.samplerNodeId, "sampler_name", document.getElementById("wfm-settings-sampler-name")?.value);
|
||||
if (ds.schedulerNodeId) write(ds.schedulerNodeId, "scheduler", document.getElementById("wfm-settings-scheduler")?.value);
|
||||
const denoiseEl = document.getElementById("wfm-settings-denoise");
|
||||
if (ds.denoiseNodeId && denoiseEl?.value !== "") write(ds.denoiseNodeId, "denoise", parseFloat(denoiseEl.value) || 1.0);
|
||||
_syncRawJson();
|
||||
if (e.altKey) document.dispatchEvent(new CustomEvent("wfm:apply-and-generate"));
|
||||
});
|
||||
@@ -1128,8 +1496,11 @@ export const comfyEditor = {
|
||||
const nodeId = document.getElementById("wfm-settings-latent-id")?.value;
|
||||
if (!nodeId || !comfyUI.currentWorkflow?.[nodeId]) return;
|
||||
const inputs = comfyUI.currentWorkflow[nodeId].inputs;
|
||||
inputs.width = parseInt(document.getElementById("wfm-settings-width")?.value) || 512;
|
||||
inputs.height = parseInt(document.getElementById("wfm-settings-height")?.value) || 512;
|
||||
const wEl = document.getElementById("wfm-settings-width");
|
||||
const hEl = document.getElementById("wfm-settings-height");
|
||||
// Blank = leave the (linked) width/height alone instead of clobbering it with a default
|
||||
if (wEl?.value !== "") inputs.width = parseInt(wEl.value) || 512;
|
||||
if (hEl?.value !== "") inputs.height = parseInt(hEl.value) || 512;
|
||||
inputs.batch_size = parseInt(document.getElementById("wfm-settings-batch")?.value) || 1;
|
||||
_syncRawJson();
|
||||
});
|
||||
@@ -1212,8 +1583,34 @@ export const comfyEditor = {
|
||||
const slots = loadNodes.slice(0, 4);
|
||||
// Mask Editor One ノード（画像+マスクを内包、常に両方必須）— 別枠で最大2件表示
|
||||
const meoSlots = meoNodes.slice(0, 2);
|
||||
const phCfg = _loadI2IPlaceholderConfig();
|
||||
const isColorMode = phCfg.mode !== "image";
|
||||
el.innerHTML = `
|
||||
${slots.length === 0 ? "" : `
|
||||
<details class="wfm-settings-section wfm-i2i-placeholder-config">
|
||||
<summary class="wfm-settings-summary">${t("i2iPlaceholderTitle")}</summary>
|
||||
<div class="wfm-i2i-placeholder-mode">
|
||||
<label><input type="radio" name="wfm-i2i-ph-mode" id="wfm-i2i-ph-mode-color" value="color" ${isColorMode ? "checked" : ""}> ${t("i2iPlaceholderColor")}</label>
|
||||
<label><input type="radio" name="wfm-i2i-ph-mode" id="wfm-i2i-ph-mode-image" value="image" ${isColorMode ? "" : "checked"}> ${t("i2iPlaceholderImage")}</label>
|
||||
</div>
|
||||
<div id="wfm-i2i-ph-color-fields" class="wfm-i2i-placeholder-fields wfm-lab-modal-inline" style="${isColorMode ? "" : "display:none;"}">
|
||||
<div><label>${t("i2iPlaceholderWidth")}</label><input type="number" min="1" id="wfm-i2i-ph-width" class="wfm-input" value="${Number(phCfg.width) || 512}"></div>
|
||||
<div><label>${t("i2iPlaceholderHeight")}</label><input type="number" min="1" id="wfm-i2i-ph-height" class="wfm-input" value="${Number(phCfg.height) || 512}"></div>
|
||||
<div><label>${t("i2iPlaceholderColorLabel")}</label><input type="color" id="wfm-i2i-ph-color" class="wfm-input" value="${escapeHtml(/^#[0-9a-fA-F]{6}$/.test(phCfg.color) ? phCfg.color : "#808080")}"></div>
|
||||
</div>
|
||||
<div id="wfm-i2i-ph-image-fields" class="wfm-i2i-placeholder-fields" style="${isColorMode ? "display:none;" : ""}">
|
||||
<div class="wfm-i2i-preview-wrap" id="wfm-i2i-ph-preview-wrap" style="${phCfg.imageFilename ? "" : "display:none;"}">
|
||||
<img class="wfm-i2i-preview-img" id="wfm-i2i-ph-preview-img" src="${phCfg.imageFilename ? `/view?filename=${encodeURIComponent(phCfg.imageFilename)}&type=input` : ""}">
|
||||
</div>
|
||||
<div class="wfm-i2i-drop-zone" id="wfm-i2i-ph-drop">
|
||||
<label class="wfm-i2i-drop-label">
|
||||
${t("i2iPlaceholderDropImage")}
|
||||
<input type="file" accept="image/*" id="wfm-i2i-ph-file" style="display:none;">
|
||||
</label>
|
||||
</div>
|
||||
<span class="wfm-i2i-status" id="wfm-i2i-ph-status"></span>
|
||||
</div>
|
||||
</details>
|
||||
<div class="wfm-i2i-grid">
|
||||
${slots.map((node, i) => `
|
||||
<div class="wfm-i2i-slot" data-slot="${i}" data-node-id="${node.id}">
|
||||
@@ -1247,8 +1644,10 @@ export const comfyEditor = {
|
||||
</div>
|
||||
</div>
|
||||
` : ""}
|
||||
<div style="display:flex;gap:6px;margin-top:6px;">
|
||||
<div style="display:flex;gap:6px;margin-top:6px;flex-wrap:wrap;">
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary wfm-i2i-apply" data-slot="${i}" disabled>Apply</button>
|
||||
<button type="button" class="wfm-btn wfm-btn-sm wfm-i2i-placeholder-btn" data-slot="${i}">${t("i2iPlaceholderApply")}</button>
|
||||
<button type="button" class="wfm-btn wfm-btn-sm wfm-i2i-clear" data-slot="${i}">${t("clear")}</button>
|
||||
<span class="wfm-i2i-status" id="wfm-i2i-status-${i}"></span>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1409,8 +1808,64 @@ export const comfyEditor = {
|
||||
applyBtn.disabled = false;
|
||||
}
|
||||
});
|
||||
|
||||
// Clear: reset this slot's staged/preview state back to empty. Doesn't touch
|
||||
// the workflow node itself — LoadImage always needs *some* valid filename, so
|
||||
// there's nothing meaningful to "unset" there; this just lets the user cancel
|
||||
// a pending pick and start over.
|
||||
const clearBtn = el.querySelector(`.wfm-i2i-clear[data-slot="${i}"]`);
|
||||
clearBtn?.addEventListener("click", () => {
|
||||
pendingFile = null;
|
||||
previewImg.src = "";
|
||||
previewWrap.style.display = "none";
|
||||
filenameEl.textContent = "";
|
||||
if (fileInput) fileInput.value = "";
|
||||
applyBtn.disabled = !pendingMaskFile;
|
||||
statusEl.textContent = "";
|
||||
});
|
||||
|
||||
// Placeholder: generate (color mode) or reuse (image mode) the shared default
|
||||
// configured at the top of the panel, and replace this slot's image
|
||||
// immediately — no separate Apply click needed, matching the "one button,
|
||||
// one slot" behavior described for this feature.
|
||||
const placeholderBtn = el.querySelector(`.wfm-i2i-placeholder-btn[data-slot="${i}"]`);
|
||||
placeholderBtn?.addEventListener("click", async () => {
|
||||
const cfg = _loadI2IPlaceholderConfig();
|
||||
placeholderBtn.disabled = true;
|
||||
statusEl.textContent = t("i2iPlaceholderGenerating");
|
||||
try {
|
||||
let filename;
|
||||
if (cfg.mode === "image") {
|
||||
if (!cfg.imageFilename) throw new Error(t("i2iPlaceholderNoDefaultImage"));
|
||||
filename = cfg.imageFilename;
|
||||
} else {
|
||||
const file = await _generateColorImageFile(cfg.width, cfg.height, cfg.color);
|
||||
const result = await comfyUI.uploadImage(file, file.name);
|
||||
if (!result.name) throw new Error("Upload returned no filename");
|
||||
filename = result.name;
|
||||
}
|
||||
if (comfyUI.currentWorkflow?.[node.id]) {
|
||||
comfyUI.currentWorkflow[node.id].inputs.image = filename;
|
||||
}
|
||||
pendingFile = null;
|
||||
previewImg.src = `/view?filename=${encodeURIComponent(filename)}&type=input`;
|
||||
previewWrap.style.display = "";
|
||||
filenameEl.textContent = filename;
|
||||
applyBtn.disabled = !pendingMaskFile;
|
||||
statusEl.textContent = `✓ ${filename}`;
|
||||
statusEl.style.color = "var(--wfm-success)";
|
||||
_syncRawJson();
|
||||
} catch (err) {
|
||||
statusEl.textContent = `✗ ${err.message}`;
|
||||
statusEl.style.color = "var(--wfm-danger)";
|
||||
} finally {
|
||||
placeholderBtn.disabled = false;
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
_wireI2IPlaceholderConfig(el);
|
||||
|
||||
// Initialize each Mask Editor One slot（画像+マスク両方が揃って初めてApply可能）
|
||||
meoSlots.forEach((node, i) => {
|
||||
const fileInput = document.getElementById(`wfm-meo-file-${i}`);
|
||||
@@ -1626,6 +2081,75 @@ export const comfyEditor = {
|
||||
if (!workflow) _syncRawJson();
|
||||
},
|
||||
|
||||
// Resolves a user/LLM-supplied aspect ratio (e.g. "3:2" or the full COMBO label
|
||||
// "3:2 (Photo)") against a ResolutionSelector-type node's actual options via
|
||||
// /object_info — the workflow analysis only knows the node's CURRENT value, not its
|
||||
// full option list, so a bare "3:2" needs matching against whatever labeled string
|
||||
// that node's aspect_ratio COMBO actually accepts. Falls back to the raw input if
|
||||
// the node type can't be queried or nothing matches, so ComfyUI's own validation
|
||||
// surfaces a clear error instead of this silently doing nothing.
|
||||
async resolveAspectRatioOption(nodeType, requested) {
|
||||
if (!requested) return null;
|
||||
const ratioMatch = String(requested).match(/(\d+)\s*:\s*(\d+)/);
|
||||
const wantedRatio = ratioMatch ? `${ratioMatch[1]}:${ratioMatch[2]}` : String(requested).trim();
|
||||
try {
|
||||
const info = await comfyUI.fetchObjectInfo(nodeType);
|
||||
const options = info?.[nodeType]?.input?.required?.aspect_ratio?.[1]?.options || [];
|
||||
if (options.includes(requested)) return requested;
|
||||
return options.find((o) => o.startsWith(wantedRatio)) || requested;
|
||||
} catch {
|
||||
return requested;
|
||||
}
|
||||
},
|
||||
|
||||
// AI TOOL Chat's generate_image tool call — writes whichever of KSampler
|
||||
// (steps/cfg/sampler_name/scheduler/denoise/seed), Latent Image (width/height/
|
||||
// batch_size), and Resolution Selector (resolvedAspectRatio) fields the LLM
|
||||
// supplied onto the given workflow/analysis (or comfyUI.currentWorkflow/
|
||||
// currentAnalysis by default). Mirrors lab-tab.js's _buildWorkflowForIteration
|
||||
// KSampler branch — same per-field node-id indirection (stepsNodeId/cfgNodeId/...
|
||||
// falling back to sampler.id) for Advanced-Sampling workflows (Flux.1/2, SD3.5,
|
||||
// Chroma...) that spread these across several nodes.
|
||||
// seed is returned rather than written directly — the caller passes it through to
|
||||
// comfyUI.generate()'s seedMode/seedValue instead, same as every other seed source.
|
||||
// resolvedAspectRatio must already be one of the node's actual option strings (see
|
||||
// resolveAspectRatioOption above) — passed in rather than resolved here because
|
||||
// that lookup is async and this stays a plain synchronous writer.
|
||||
setGenerationParams({ steps, cfg, sampler_name, scheduler, denoise, seed, width, height, batch_size, resolvedAspectRatio, workflow, analysis } = {}) {
|
||||
const wf = workflow || comfyUI.currentWorkflow;
|
||||
const an = analysis || comfyUI.currentAnalysis;
|
||||
if (!wf || !an) return { seed: null };
|
||||
|
||||
const sampler = an.sampler_nodes?.[0];
|
||||
if (sampler) {
|
||||
if (steps != null) { const id = sampler.stepsNodeId ?? sampler.id; if (wf[id]) wf[id].inputs.steps = steps; }
|
||||
if (cfg != null) { const id = sampler.cfgNodeId ?? sampler.id; if (wf[id]) wf[id].inputs.cfg = cfg; }
|
||||
if (sampler_name) { const id = sampler.samplerNodeId ?? sampler.id; if (wf[id]) wf[id].inputs.sampler_name = sampler_name; }
|
||||
if (scheduler && sampler.schedulerNodeId != null && wf[sampler.schedulerNodeId]) {
|
||||
wf[sampler.schedulerNodeId].inputs.scheduler = scheduler;
|
||||
}
|
||||
if (denoise != null && sampler.denoiseNodeId != null && wf[sampler.denoiseNodeId]) {
|
||||
wf[sampler.denoiseNodeId].inputs.denoise = denoise;
|
||||
}
|
||||
}
|
||||
|
||||
// Resolution Selector takes priority over direct width/height — writing width/
|
||||
// height straight onto the EmptyLatentImage would just clobber whatever the
|
||||
// selector computed, silently ignoring the requested aspect ratio.
|
||||
const resSelector = an.resolution_selector_nodes?.[0];
|
||||
const latent = an.latent_nodes?.[0];
|
||||
if (resolvedAspectRatio && resSelector && wf[resSelector.id]) {
|
||||
wf[resSelector.id].inputs.aspect_ratio = resolvedAspectRatio;
|
||||
} else if (latent && wf[latent.id]) {
|
||||
if (width != null) wf[latent.id].inputs.width = width;
|
||||
if (height != null) wf[latent.id].inputs.height = height;
|
||||
}
|
||||
if (latent && wf[latent.id] && batch_size != null) wf[latent.id].inputs.batch_size = batch_size;
|
||||
|
||||
if (!workflow) _syncRawJson();
|
||||
return { seed: seed ?? null };
|
||||
},
|
||||
|
||||
syncToWorkflow() {
|
||||
// Sync prompt texts before generation
|
||||
const posSelect = document.getElementById("wfm-prompt-pos-target");
|
||||
@@ -1752,7 +2276,7 @@ async function _initTextEncoderSection(analysis, textEncoders, containerEl) {
|
||||
const deviceOpts = ["default", "cpu"].map(d => `<option value="${d}" ${d === currentDevice ? "selected" : ""}>${d}</option>`).join("");
|
||||
|
||||
section.innerHTML = `
|
||||
<label>Text Encoder</label>
|
||||
<label class="wfm-model-label-active">Text Encoder</label>
|
||||
<input type="text" class="wfm-input" id="wfm-te-filter" placeholder="Filter..." style="margin-bottom:4px;">
|
||||
<select class="wfm-select" id="wfm-te-clip1" style="margin-bottom:4px;">${clip1Opts}</select>
|
||||
${isDual ? `<select class="wfm-select" id="wfm-te-clip2" style="margin-bottom:4px;">${clip2Opts}</select>` : ""}
|
||||
|
||||
+506
-86
@@ -18,31 +18,42 @@ let _lastBypassedNodes = [];
|
||||
let _lastMutedNodes = [];
|
||||
|
||||
/**
|
||||
* Trace a link's source through any Bypass-mode (mode 4) nodes, matching each bypassed
|
||||
* node's output slot to an input slot of the same type and following the connection
|
||||
* upstream (recursively, for chained bypasses). Mirrors ComfyUI's own Bypass behavior,
|
||||
* where a bypassed node is removed but its wires are reconnected end-to-end.
|
||||
* Trace a link's source through any Bypass-mode (mode 4) nodes AND Reroute nodes, matching each
|
||||
* one's output slot to an input slot of the same type and following the connection upstream
|
||||
* (recursively, for chains of either/both). Mirrors ComfyUI's own Bypass behavior, where a
|
||||
* bypassed node is removed but its wires are reconnected end-to-end — Reroute needs the identical
|
||||
* treatment because it's a UI-only passthrough node with no backend node class at all: leaving it
|
||||
* in the API payload makes ComfyUI reject the whole prompt with "missing_node_type" (Reroute
|
||||
* always has to be resolved through, regardless of its mode).
|
||||
* Returns null when no pass-through source exists (the matching input is unconnected),
|
||||
* in which case the caller should treat the input as unlinked.
|
||||
*/
|
||||
function _resolveBypassSource(nodeById, linkMap, nodeId, slot, visited) {
|
||||
const idStr = String(nodeId);
|
||||
const node = nodeById[idStr];
|
||||
if (!node || node.mode !== 4) return [idStr, slot];
|
||||
if (visited.has(idStr)) return null; // circular bypass chain guard
|
||||
if (!node || (node.mode !== 4 && node.type !== "Reroute")) return [idStr, slot];
|
||||
if (visited.has(idStr)) return null; // circular bypass/reroute chain guard
|
||||
|
||||
visited.add(idStr);
|
||||
const outputs = node.outputs || [];
|
||||
const inputs = node.inputs || [];
|
||||
const outType = outputs[slot]?.type;
|
||||
|
||||
let inIdx = -1;
|
||||
if (inputs[slot]?.type === outType && linkMap[idStr]?.[slot]) {
|
||||
inIdx = slot;
|
||||
let inIdx;
|
||||
if (node.type === "Reroute") {
|
||||
// Reroute is always exactly one input -> one output. Its stored input type is commonly
|
||||
// the generic "*" even when the output shows the propagated concrete type (e.g. MODEL),
|
||||
// so the type-match search used for Bypass below would fail to find it — just take the
|
||||
// sole input.
|
||||
inIdx = 0;
|
||||
} else {
|
||||
inIdx = inputs.findIndex((inp, i) => inp.type === outType && linkMap[idStr]?.[i]);
|
||||
const outType = outputs[slot]?.type;
|
||||
if (inputs[slot]?.type === outType && linkMap[idStr]?.[slot]) {
|
||||
inIdx = slot;
|
||||
} else {
|
||||
inIdx = inputs.findIndex((inp, i) => inp.type === outType && linkMap[idStr]?.[i]);
|
||||
}
|
||||
}
|
||||
if (inIdx === -1) return null;
|
||||
if (inIdx === -1 || inIdx === undefined) return null;
|
||||
|
||||
const upstream = linkMap[idStr][inIdx];
|
||||
if (!upstream) return null;
|
||||
@@ -74,6 +85,9 @@ function _getWidgetInputNames(objectInfo, classType) {
|
||||
// Required inputs: skip types that are linked (have uppercase first letter = node output types)
|
||||
for (const [name, spec] of Object.entries(required)) {
|
||||
const type = Array.isArray(spec) ? spec[0] : spec;
|
||||
// forceInput: true — link-only socket even though its type is a widget type
|
||||
// (e.g. CLIPTextEncodeEditPlus's text1/text2). Never has a widgets_values entry.
|
||||
if (Array.isArray(spec) && spec[1]?.forceInput) continue;
|
||||
// Link types are strings like "MODEL", "CLIP", "CONDITIONING", "LATENT", "IMAGE", etc.
|
||||
// Widget types are: "INT", "FLOAT", "STRING", "BOOLEAN", or an array of choices
|
||||
if (Array.isArray(type)) {
|
||||
@@ -91,6 +105,7 @@ function _getWidgetInputNames(objectInfo, classType) {
|
||||
// Optional inputs that are widgets
|
||||
for (const [name, spec] of Object.entries(optional)) {
|
||||
const type = Array.isArray(spec) ? spec[0] : spec;
|
||||
if (Array.isArray(spec) && spec[1]?.forceInput) continue;
|
||||
if (Array.isArray(type)) {
|
||||
names.push(name);
|
||||
} else if (typeof type === "string") {
|
||||
@@ -117,6 +132,7 @@ function _getWidgetInputTypes(objectInfo, classType) {
|
||||
|
||||
for (const [name, spec] of Object.entries(required)) {
|
||||
const type = Array.isArray(spec) ? spec[0] : spec;
|
||||
if (Array.isArray(spec) && spec[1]?.forceInput) continue;
|
||||
if (Array.isArray(type)) {
|
||||
types.push("COMBO");
|
||||
} else if (typeof type === "string") {
|
||||
@@ -128,6 +144,7 @@ function _getWidgetInputTypes(objectInfo, classType) {
|
||||
}
|
||||
for (const [name, spec] of Object.entries(optional)) {
|
||||
const type = Array.isArray(spec) ? spec[0] : spec;
|
||||
if (Array.isArray(spec) && spec[1]?.forceInput) continue;
|
||||
if (Array.isArray(type)) {
|
||||
types.push("COMBO");
|
||||
} else if (typeof type === "string") {
|
||||
@@ -143,6 +160,13 @@ function _getWidgetInputTypes(objectInfo, classType) {
|
||||
/**
|
||||
* Resolve the sub-input names for a DynamicCombo's currently-selected option.
|
||||
* objectInfo spec shape: ["COMFY_DYNAMICCOMBO_V3", { options: [{ key, inputs: { required: {...} } }, ...] }]
|
||||
*
|
||||
* Includes both required AND optional sub-fields: ComfyUI serializes every sub-field of the
|
||||
* selected option into widgets_values regardless of which bucket it's declared in (e.g.
|
||||
* TextGenerate's "on" option puts temperature/top_k/.../seed under required but
|
||||
* presence_penalty under optional — all 7 still occupy a widgets_values slot). Dropping the
|
||||
* optional ones here under-counts how many slots the combo actually consumed, misaligning
|
||||
* every widget declared after it in the node's schema.
|
||||
*/
|
||||
function _getDynamicComboSubNames(objectInfo, classType, widgetName, selectedKey) {
|
||||
const info = objectInfo[classType];
|
||||
@@ -150,7 +174,10 @@ function _getDynamicComboSubNames(objectInfo, classType, widgetName, selectedKey
|
||||
const options = spec?.[1]?.options;
|
||||
if (!Array.isArray(options)) return [];
|
||||
const opt = options.find((o) => o.key === selectedKey) || options[0];
|
||||
return Object.keys(opt?.inputs?.required || {});
|
||||
return [
|
||||
...Object.keys(opt?.inputs?.required || {}),
|
||||
...Object.keys(opt?.inputs?.optional || {}),
|
||||
];
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -170,6 +197,140 @@ function _isExtraWidgetValue(val, expectedType) {
|
||||
return false;
|
||||
}
|
||||
|
||||
/**
|
||||
* True when a subgraph-template node's widget input (by name) is actually wired end-to-end —
|
||||
* either redirected from an outer parent-graph link, or wired to another internal node within the
|
||||
* same subgraph template (boundary links from the subgraph's own input ports, origin_id === -10,
|
||||
* don't count: those resolve to a plain injected widget value instead, handled separately).
|
||||
*/
|
||||
function _isLinkedWidgetName(remappedNode, sgDef, origInternalId, redirectedTargets, name) {
|
||||
const inputDef = (remappedNode.inputs || []).find((i) => i.widget && i.name === name);
|
||||
if (!inputDef) return false;
|
||||
const slotIdx = remappedNode.inputs.indexOf(inputDef);
|
||||
if (redirectedTargets.has(`${remappedNode.id}:${slotIdx}`)) return true;
|
||||
return inputDef.link != null && (sgDef.links || []).some(
|
||||
(l) => l.id === inputDef.link && String(l.target_id) === origInternalId && l.target_slot === slotIdx && l.origin_id !== -10
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Simulate consuming widgets_values against object_info's widget name/type order, accounting for
|
||||
* frontend-only "control_after_generate" extras (e.g. "fixed" after a seed) that occupy one extra
|
||||
* slot and shift every later index by one. When skipLinked is true, widgets the caller's
|
||||
* isLinkedName() flags as linked are treated as consuming zero widgets_values entries (the
|
||||
* "modern" ComfyUI format); with it false, every widget is assumed present (the "legacy full"
|
||||
* format some subgraph templates store). Returns each widget's actual widgets_values index
|
||||
* (or -1 if the array ran out), plus consumedIndices — every index a widget's own turn advanced
|
||||
* past (normally just its value, but two when a control_after_generate extra was skipped) — and
|
||||
* how many entries were consumed in total (the latter is how callers detect which of the two
|
||||
* formats a given widgets_values array actually follows).
|
||||
*
|
||||
* consumedIndices matters because convertUiToApi's own widget-mapping loop only skips a
|
||||
* control_after_generate extra while advancing past the *next* widget's turn (see its
|
||||
* `!linkedSlotNames.has(name)` branch) — if that next widget turns out to be linked, the loop
|
||||
* `continue`s before ever looking at the extra, leaving it unconsumed and silently misread as the
|
||||
* value of whatever non-linked widget comes after. Stripping a linked widget's position alone
|
||||
* (without its swallowed extra) reproduces exactly that bug one level up, in the injection step.
|
||||
*
|
||||
* comboExpander(name, selectedKey) — when a widget's type is COMFY_DYNAMICCOMBO_V3, its own turn
|
||||
* only consumes the *selector* slot (the chosen option's key); the selected option's sub-fields
|
||||
* (e.g. TextGenerate's sampling_mode → temperature/top_k/.../seed/presence_penalty) each occupy
|
||||
* one more widgets_values slot right after it. Without expanding those here too, every widget
|
||||
* declared later in the node's schema (e.g. TextGenerate's "thinking") gets matched against a
|
||||
* slot that's actually still inside the combo's sub-field span, corrupting both values.
|
||||
*/
|
||||
function _simulateWidgetValues(widgetNames, widgetTypes, widgetsValues, isLinkedName, skipLinked, comboExpander) {
|
||||
let idx = 0;
|
||||
const positions = {};
|
||||
const consumedIndices = {};
|
||||
for (let nIdx = 0; nIdx < widgetNames.length; nIdx++) {
|
||||
const name = widgetNames[nIdx];
|
||||
if (skipLinked && isLinkedName(name)) {
|
||||
positions[name] = -1;
|
||||
consumedIndices[name] = [];
|
||||
continue;
|
||||
}
|
||||
if (idx >= widgetsValues.length) {
|
||||
positions[name] = -1;
|
||||
consumedIndices[name] = [];
|
||||
continue;
|
||||
}
|
||||
const consumed = [idx];
|
||||
let p = idx;
|
||||
idx++;
|
||||
if (_isExtraWidgetValue(widgetsValues[p], widgetTypes[nIdx]) && idx < widgetsValues.length) {
|
||||
consumed.push(idx);
|
||||
p = idx;
|
||||
idx++;
|
||||
}
|
||||
if (widgetTypes[nIdx] === "COMFY_DYNAMICCOMBO_V3" && comboExpander) {
|
||||
const subNames = comboExpander(name, widgetsValues[p]);
|
||||
for (let s = 0; s < subNames.length && idx < widgetsValues.length; s++) {
|
||||
consumed.push(idx);
|
||||
idx++;
|
||||
}
|
||||
}
|
||||
positions[name] = p;
|
||||
consumedIndices[name] = consumed;
|
||||
}
|
||||
return { positions, consumedIndices, consumed: idx };
|
||||
}
|
||||
|
||||
/**
|
||||
* Strip a subgraph-template node's widgets_values entries belonging to widget slots that are
|
||||
* actually linked (see _isLinkedWidgetName), so convertUiToApi's own widget-mapping loop — which
|
||||
* assumes the "modern" format where linked widgets contribute zero widgets_values entries — maps
|
||||
* every remaining widget to the right value instead of drifting by however many linked entries
|
||||
* were left in place. No-op when widgets_values already follows the modern format (nothing to
|
||||
* strip) or the node has no widget inputs at all.
|
||||
*/
|
||||
function _stripLegacyLinkedWidgetValues(objectInfo, remappedNode, sgDef, origInternalId, redirectedTargets) {
|
||||
const widgetsValues = remappedNode.widgets_values;
|
||||
const allWidgetInputs = (remappedNode.inputs || []).filter((i) => i.widget);
|
||||
if (!widgetsValues || allWidgetInputs.length === 0) return;
|
||||
|
||||
const widgetNames = _getWidgetInputNames(objectInfo, remappedNode.type);
|
||||
const widgetTypes = _getWidgetInputTypes(objectInfo, remappedNode.type);
|
||||
const isLinkedName = (name) => _isLinkedWidgetName(remappedNode, sgDef, origInternalId, redirectedTargets, name);
|
||||
const comboExpander = (name, selectedKey) => _getDynamicComboSubNames(objectInfo, remappedNode.type, name, selectedKey);
|
||||
|
||||
// If skipping linked widgets already accounts for every entry, widgets_values is already in
|
||||
// the modern (no dangling linked entries) format — nothing to strip.
|
||||
const skipSim = _simulateWidgetValues(widgetNames, widgetTypes, widgetsValues, isLinkedName, true, comboExpander);
|
||||
if (skipSim.consumed === widgetsValues.length) return;
|
||||
|
||||
// Otherwise it's "legacy full": recompute with every widget present to find exactly which
|
||||
// indices the linked ones occupy — including any control_after_generate extra a linked
|
||||
// widget's own turn would have consumed, which must be dropped too (see _simulateWidgetValues).
|
||||
const fullSim = _simulateWidgetValues(widgetNames, widgetTypes, widgetsValues, isLinkedName, false, comboExpander);
|
||||
const linkedIdx = new Set();
|
||||
for (const name of widgetNames) {
|
||||
if (isLinkedName(name)) {
|
||||
for (const i of fullSim.consumedIndices[name] || []) linkedIdx.add(i);
|
||||
}
|
||||
}
|
||||
if (linkedIdx.size > 0) {
|
||||
remappedNode.widgets_values = widgetsValues.filter((_, i) => !linkedIdx.has(i));
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Find the widgets_values array index that corresponds to a given widget input name on a
|
||||
* subgraph-template node, for injecting an outer subgraph-node's un-linked widget value into it.
|
||||
* Assumes _stripLegacyLinkedWidgetValues has already normalized widgets_values to the modern
|
||||
* format (linked widgets contribute zero entries), matching what convertUiToApi's own
|
||||
* widget-mapping loop expects.
|
||||
*/
|
||||
function _findInjectedWidgetIndex(objectInfo, remappedNode, sgDef, origInternalId, redirectedTargets, targetName) {
|
||||
const widgetNames = _getWidgetInputNames(objectInfo, remappedNode.type);
|
||||
const widgetTypes = _getWidgetInputTypes(objectInfo, remappedNode.type);
|
||||
const widgetsValues = remappedNode.widgets_values || [];
|
||||
const isLinkedName = (name) => _isLinkedWidgetName(remappedNode, sgDef, origInternalId, redirectedTargets, name);
|
||||
const comboExpander = (name, selectedKey) => _getDynamicComboSubNames(objectInfo, remappedNode.type, name, selectedKey);
|
||||
const sim = _simulateWidgetValues(widgetNames, widgetTypes, widgetsValues, isLinkedName, true, comboExpander);
|
||||
return sim.positions[targetName] ?? -1;
|
||||
}
|
||||
|
||||
/**
|
||||
* Flatten a subgraph-containing UI workflow into a plain UI workflow.
|
||||
*
|
||||
@@ -226,16 +387,20 @@ function _flattenSubgraphs(workflow, objectInfo) {
|
||||
}
|
||||
|
||||
// === Map subgraph input ports → internal target {nodeId, slot} ===
|
||||
// A single boundary port can fan out to MULTIPLE internal targets (e.g. a "width" pin
|
||||
// feeding both EmptyFlux2LatentImage.width as a widget AND a PreviewAny.source wildcard
|
||||
// link purely for canvas display) — collect all of them, not just the last one seen.
|
||||
const inputPortTargets = {};
|
||||
for (let portIdx = 0; portIdx < (sgDef.inputs || []).length; portIdx++) {
|
||||
const port = sgDef.inputs[portIdx];
|
||||
for (const linkId of (port.linkIds || [])) {
|
||||
const iLink = (sgDef.links || []).find(l => l.id === linkId);
|
||||
if (iLink && iLink.origin_id === -10) {
|
||||
inputPortTargets[portIdx] = {
|
||||
if (!inputPortTargets[portIdx]) inputPortTargets[portIdx] = [];
|
||||
inputPortTargets[portIdx].push({
|
||||
targetNodeId: nodeIdRemap[iLink.target_id],
|
||||
targetSlot: iLink.target_slot,
|
||||
};
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -270,7 +435,11 @@ function _flattenSubgraphs(workflow, objectInfo) {
|
||||
? (sgDef.inputs || []).findIndex(p => p.name === inputName)
|
||||
: -1;
|
||||
if (portIdx === -1) portIdx = dstSlot;
|
||||
const target = inputPortTargets[portIdx];
|
||||
// Only the port's first fanned-out target is redirected here (a real outer link
|
||||
// feeding a fanned-out port is rare — not seen in any known template so far).
|
||||
// The far more common case (an outer *widget value*, no real link, fanning out to
|
||||
// multiple internal targets) is handled fully below, for every target.
|
||||
const target = inputPortTargets[portIdx]?.[0];
|
||||
if (target) {
|
||||
link[3] = target.targetNodeId;
|
||||
link[4] = target.targetSlot;
|
||||
@@ -289,80 +458,102 @@ function _flattenSubgraphs(workflow, objectInfo) {
|
||||
|
||||
// === Add internal nodes (remapped IDs) ===
|
||||
const remappedByOrigId = {};
|
||||
const normalizedNodeIds = new Set();
|
||||
for (const iNode of sgDef.nodes) {
|
||||
const remapped = JSON.parse(JSON.stringify(iNode));
|
||||
remapped.id = nodeIdRemap[iNode.id];
|
||||
|
||||
// Some subgraph-template nodes store widgets_values in the "legacy full" format
|
||||
// (one entry per widget input, including linked ones), while convertUiToApi's
|
||||
// widget-mapping loop assumes the "modern" format (linked widget inputs have no
|
||||
// entry at all). Detect the full format by comparing lengths and strip the
|
||||
// entries belonging to inputs that are *actually* linked end-to-end (per
|
||||
// redirectedTargets above) so downstream mapping stays index-aligned. Boundary
|
||||
// links that terminate in a plain widget value (e.g. prompt text) are NOT
|
||||
// stripped — their value lives in the outer node's widgets_values instead and
|
||||
// is injected below.
|
||||
const allWidgetInputs = (remapped.inputs || []).filter((i) => i.widget);
|
||||
if (remapped.widgets_values && remapped.widgets_values.length === allWidgetInputs.length) {
|
||||
const linkedIdx = new Set();
|
||||
allWidgetInputs.forEach((inp, i) => {
|
||||
const slotIdx = remapped.inputs.indexOf(inp);
|
||||
if (redirectedTargets.has(`${remapped.id}:${slotIdx}`)) linkedIdx.add(i);
|
||||
});
|
||||
if (linkedIdx.size > 0) {
|
||||
remapped.widgets_values = remapped.widgets_values.filter((_, i) => !linkedIdx.has(i));
|
||||
normalizedNodeIds.add(remapped.id);
|
||||
}
|
||||
// Propagate the outer subgraph-node's Mute/Bypass mode onto every internal node.
|
||||
// Without this, muting or bypassing a whole subgraph instance (e.g. disabling one of
|
||||
// several parallel Flux.2 sampling pipelines via Ctrl-B) leaves its internal nodes at
|
||||
// their template-authored mode (normally 0/enabled), so convertUiToApi's mode check
|
||||
// below never excludes them — they end up in the API payload and can be picked up by
|
||||
// node-existence-based UI logic (e.g. "first matching node found") ahead of the nodes
|
||||
// actually driving the enabled pipeline.
|
||||
if (node.mode === 2 || node.mode === 4) {
|
||||
remapped.mode = node.mode;
|
||||
}
|
||||
|
||||
// Some subgraph-template nodes store widgets_values in the "legacy full" format (one
|
||||
// entry per widget input, including ones that are actually linked — either redirected
|
||||
// from an outer parent-graph link, or wired to another internal node within the same
|
||||
// subgraph template, e.g. EmptyFlux2LatentImage.width <- GetImageSize). Strip those
|
||||
// entries so convertUiToApi's widget-mapping loop (which assumes the "modern" format:
|
||||
// linked widgets contribute zero entries) doesn't drift every subsequent widget by
|
||||
// however many linked entries were left in place — see _stripLegacyLinkedWidgetValues.
|
||||
// Boundary links that terminate in a plain widget value (e.g. prompt text) are NOT
|
||||
// treated as linked here — those resolve via the outer-value injection step below.
|
||||
_stripLegacyLinkedWidgetValues(objectInfo, remapped, sgDef, String(iNode.id), redirectedTargets);
|
||||
|
||||
addedNodes.push(remapped);
|
||||
remappedByOrigId[iNode.id] = remapped;
|
||||
}
|
||||
|
||||
// === Inject outer subgraph-node widget values into internal nodes ===
|
||||
// Only un-linked widget inputs — linked ones are already redirected above.
|
||||
// Only un-linked widget inputs — linked ones are already redirected above. A port can fan
|
||||
// out to several internal targets (see inputPortTargets collection above) — apply the same
|
||||
// value to every one of them, not just the first. Iterating per PORT (rather than per
|
||||
// outer widget) matters for unfilled templates whose outer widgets_values is empty ([]) —
|
||||
// there is then no outer override to apply, but the fan-out's widget-type target (e.g.
|
||||
// EmptyFlux2LatentImage.width) still holds its own template-authored default, and that
|
||||
// same value is what a sibling non-widget target (e.g. a PreviewAny used to render the
|
||||
// value into a text template) needs too.
|
||||
const outerWidgetInputs = (node.inputs || []).filter((inp) => inp.widget);
|
||||
outerWidgetInputs.forEach((inp, widgetIdx) => {
|
||||
if (inp.link != null) return;
|
||||
if (widgetIdx >= (node.widgets_values || []).length) return;
|
||||
const outerValue = node.widgets_values[widgetIdx];
|
||||
for (let portIdx = 0; portIdx < (sgDef.inputs || []).length; portIdx++) {
|
||||
const targets = inputPortTargets[portIdx];
|
||||
if (!targets || targets.length === 0) continue;
|
||||
|
||||
const portIdx = (sgDef.inputs || []).findIndex((p) => p.name === inp.name);
|
||||
if (portIdx === -1) return;
|
||||
const target = inputPortTargets[portIdx];
|
||||
if (!target) return;
|
||||
const port = sgDef.inputs[portIdx];
|
||||
const outerInp = outerWidgetInputs.find((inp) => inp.name === port.name);
|
||||
const outerWidgetIdx = outerInp ? outerWidgetInputs.indexOf(outerInp) : -1;
|
||||
const hasOuterOverride = !!outerInp && outerInp.link == null && outerWidgetIdx !== -1
|
||||
&& outerWidgetIdx < (node.widgets_values || []).length;
|
||||
const outerValue = hasOuterOverride ? node.widgets_values[outerWidgetIdx] : undefined;
|
||||
|
||||
const origInternalId = String(target.targetNodeId).split(":").pop();
|
||||
const remappedNode = remappedByOrigId[origInternalId];
|
||||
if (!remappedNode) return;
|
||||
// Value to apply to non-widget (wildcard) fan-out targets: the outer override if
|
||||
// present, otherwise whatever a sibling widget-type target ends up holding (its own
|
||||
// unmodified template default, when there is no outer override).
|
||||
let fallbackLiteral = outerValue;
|
||||
|
||||
const targetInputDef = (remappedNode.inputs || [])[target.targetSlot];
|
||||
if (!targetInputDef?.widget) return;
|
||||
for (const target of targets) {
|
||||
const origInternalId = String(target.targetNodeId).split(":").pop();
|
||||
const remappedNode = remappedByOrigId[origInternalId];
|
||||
if (!remappedNode) continue;
|
||||
const targetInputDef = (remappedNode.inputs || [])[target.targetSlot];
|
||||
if (!targetInputDef?.widget) continue;
|
||||
|
||||
// When the internal node's widgets_values was normalized above (stripped of
|
||||
// linked-slot entries), the index must be computed against that same reduced
|
||||
// set of widget names — object_info's full name order (which still includes
|
||||
// e.g. width/height) would be off by however many entries were stripped.
|
||||
let iWidgetIdx;
|
||||
if (normalizedNodeIds.has(remappedNode.id)) {
|
||||
const remainingNames = (remappedNode.inputs || [])
|
||||
.filter((i) => i.widget)
|
||||
.filter((i) => {
|
||||
const slotIdx = remappedNode.inputs.indexOf(i);
|
||||
return !redirectedTargets.has(`${remappedNode.id}:${slotIdx}`);
|
||||
})
|
||||
.map((i) => i.name);
|
||||
iWidgetIdx = remainingNames.indexOf(targetInputDef.name);
|
||||
} else {
|
||||
const widgetNames = _getWidgetInputNames(objectInfo, remappedNode.type);
|
||||
iWidgetIdx = widgetNames.indexOf(targetInputDef.name);
|
||||
// Compute where in widgets_values this widget's value actually lives — accounting
|
||||
// for linked slots (which consume no entry) and control_after_generate-style
|
||||
// extras (which shift later indices by one). See _findInjectedWidgetIndex for why
|
||||
// a plain name-index lookup silently corrupts sibling widgets here.
|
||||
const iWidgetIdx = _findInjectedWidgetIndex(
|
||||
objectInfo, remappedNode, sgDef, origInternalId, redirectedTargets, targetInputDef.name
|
||||
);
|
||||
if (iWidgetIdx === -1) continue;
|
||||
|
||||
if (!remappedNode.widgets_values) remappedNode.widgets_values = [];
|
||||
if (hasOuterOverride) remappedNode.widgets_values[iWidgetIdx] = outerValue;
|
||||
if (fallbackLiteral === undefined) fallbackLiteral = remappedNode.widgets_values[iWidgetIdx];
|
||||
}
|
||||
if (iWidgetIdx === -1) return;
|
||||
|
||||
if (!remappedNode.widgets_values) remappedNode.widgets_values = [];
|
||||
remappedNode.widgets_values[iWidgetIdx] = outerValue;
|
||||
});
|
||||
if (fallbackLiteral === undefined) continue;
|
||||
|
||||
for (const target of targets) {
|
||||
const origInternalId = String(target.targetNodeId).split(":").pop();
|
||||
const remappedNode = remappedByOrigId[origInternalId];
|
||||
if (!remappedNode) continue;
|
||||
const targetInputDef = (remappedNode.inputs || [])[target.targetSlot];
|
||||
if (!targetInputDef || targetInputDef.widget) continue;
|
||||
|
||||
// Not a widget-representable input — typically a required wildcard ("*") link slot
|
||||
// with no widget UI (e.g. a "Preview as Text" node used to render a width/height
|
||||
// value into a prompt template string). There is no upstream node to link it to
|
||||
// once flattened (the value only ever existed as this outer widget or a sibling
|
||||
// target's own default), so stash it for convertUiToApi() to inject as a plain
|
||||
// literal value instead of a link.
|
||||
if (!remappedNode._wfmLiteralInputs) remappedNode._wfmLiteralInputs = {};
|
||||
remappedNode._wfmLiteralInputs[targetInputDef.name] = fallbackLiteral;
|
||||
}
|
||||
}
|
||||
|
||||
// === Add internal links (object format → array format) ===
|
||||
for (const iLink of (sgDef.links || [])) {
|
||||
@@ -459,6 +650,10 @@ export const comfyWorkflow = {
|
||||
_lastBypassedNodes.push({ nodeId: String(node.id), title: node.title || node.type });
|
||||
continue;
|
||||
}
|
||||
// Reroute: UI-only passthrough node, no backend node class — including it in the API
|
||||
// payload triggers a "missing_node_type" 400 from ComfyUI. Its wires are already
|
||||
// resolved through via _resolveBypassSource() below, same as a Bypassed node's.
|
||||
if (node.type === "Reroute") continue;
|
||||
// Skip note/display-only nodes that are unknown to ComfyUI backend
|
||||
if (!objectInfo[node.type] && _isDisplayOnlyNode(node)) continue;
|
||||
const nodeId = String(node.id);
|
||||
@@ -606,6 +801,15 @@ export const comfyWorkflow = {
|
||||
inputs["wildcard_text"] = widgets[0];
|
||||
}
|
||||
|
||||
// Literal values for non-widget (e.g. wildcard "*") inputs that a subgraph boundary
|
||||
// widget fanned out to — see the injection step in _flattenSubgraphs for why these
|
||||
// can't be represented as widgets_values entries or resolved as a link.
|
||||
if (node._wfmLiteralInputs) {
|
||||
for (const [k, v] of Object.entries(node._wfmLiteralInputs)) {
|
||||
if (!(k in inputs)) inputs[k] = v;
|
||||
}
|
||||
}
|
||||
|
||||
api[nodeId] = {
|
||||
class_type: node.type,
|
||||
inputs,
|
||||
@@ -682,6 +886,7 @@ export const comfyWorkflow = {
|
||||
prompt_nodes: [],
|
||||
sampler_nodes: [],
|
||||
latent_nodes: [],
|
||||
resolution_selector_nodes: [],
|
||||
lora_nodes: [],
|
||||
vae_nodes: [],
|
||||
save_nodes: [],
|
||||
@@ -715,15 +920,48 @@ export const comfyWorkflow = {
|
||||
|
||||
// KSamplerAdvanced uses noise_seed instead of seed
|
||||
const seedKey = "seed" in inputs ? "seed" : "noise_seed";
|
||||
const seedVal = inputs.seed ?? inputs.noise_seed;
|
||||
// seed/steps/cfg/denoise are sometimes wired to Primitive nodes (e.g. a shared
|
||||
// "steps" value switched between two LoRA presets) instead of holding a direct
|
||||
// number — leave those undefined so the settings-tab UI shows "linked" instead of
|
||||
// a link-reference array, and Apply doesn't clobber the link with a literal value.
|
||||
result.sampler_nodes.push({
|
||||
id, type: ct, title,
|
||||
seed: inputs.seed ?? inputs.noise_seed,
|
||||
seedKey,
|
||||
steps: inputs.steps, cfg: inputs.cfg,
|
||||
sampler_name: inputs.sampler_name, scheduler: inputs.scheduler,
|
||||
denoise: inputs.denoise,
|
||||
seed: typeof seedVal === "number" ? seedVal : undefined,
|
||||
seedKey, seedNodeId: id,
|
||||
steps: typeof inputs.steps === "number" ? inputs.steps : undefined, stepsNodeId: id,
|
||||
cfg: typeof inputs.cfg === "number" ? inputs.cfg : undefined, cfgNodeId: id,
|
||||
sampler_name: inputs.sampler_name, samplerNodeId: id,
|
||||
scheduler: inputs.scheduler, schedulerNodeId: id,
|
||||
denoise: typeof inputs.denoise === "number" ? inputs.denoise : undefined, denoiseNodeId: id,
|
||||
});
|
||||
}
|
||||
|
||||
// CFGGuider / BasicGuider / DualCFGGuider — used by "Advanced Sampling" workflows
|
||||
// (Flux.1/Flux.2, SD3.5, Chroma, HiDream E1, etc.) in place of KSampler's
|
||||
// positive/negative inputs. These feed SamplerCustomAdvanced rather than driving a
|
||||
// sampler themselves, so role propagation must start here too, or CLIPTextEncode
|
||||
// nodes downstream stay "unknown" role.
|
||||
if (ct === "CFGGuider") {
|
||||
if (Array.isArray(inputs.positive)) samplerPositiveRef[inputs.positive[0]] = true;
|
||||
if (Array.isArray(inputs.negative)) samplerNegativeRef[inputs.negative[0]] = true;
|
||||
}
|
||||
if (ct === "BasicGuider") {
|
||||
if (Array.isArray(inputs.conditioning)) samplerPositiveRef[inputs.conditioning[0]] = true;
|
||||
}
|
||||
// DualCFGGuider (HiDream E1 instruct-pix2pix style edit): cond1/cond2 both derive from
|
||||
// the positive text (often via InstructPixToPixConditioning), negative is separate —
|
||||
// cond2 is left unmarked since it doesn't cleanly map to either role.
|
||||
if (ct === "DualCFGGuider") {
|
||||
if (Array.isArray(inputs.cond1)) samplerPositiveRef[inputs.cond1[0]] = true;
|
||||
if (Array.isArray(inputs.negative)) samplerNegativeRef[inputs.negative[0]] = true;
|
||||
}
|
||||
// SamplerCustom (older/simpler counterpart to SamplerCustomAdvanced): holds
|
||||
// positive/negative directly instead of going through a separate Guider node.
|
||||
if (ct === "SamplerCustom") {
|
||||
if (Array.isArray(inputs.positive)) samplerPositiveRef[inputs.positive[0]] = true;
|
||||
if (Array.isArray(inputs.negative)) samplerNegativeRef[inputs.negative[0]] = true;
|
||||
}
|
||||
}
|
||||
|
||||
// === Pass 1b: Expand refs through CONDITIONING graph (BFS, 5 iterations) ===
|
||||
@@ -737,6 +975,7 @@ export const comfyWorkflow = {
|
||||
"ControlNetApply", "ControlNetApplyAdvanced",
|
||||
"IPAdapterApply", "IPAdapterApplyFaceID",
|
||||
"StyleModelApply",
|
||||
"FluxGuidance", "FluxKontextMultiReferenceLatentMethod",
|
||||
]);
|
||||
|
||||
for (let iter = 0; iter < 5; iter++) {
|
||||
@@ -791,6 +1030,60 @@ export const comfyWorkflow = {
|
||||
}
|
||||
}
|
||||
}
|
||||
// InstructPixToPixConditioning (HiDream E1 edit) — unlike COND_PASSTHROUGH nodes,
|
||||
// this has *separate* positive/negative inputs feeding separate outputs, so each
|
||||
// role must route only to its own input (not both) or the negative CLIPTextEncode
|
||||
// would incorrectly get marked positive too via the positive-output branch.
|
||||
if (ct === "InstructPixToPixConditioning") {
|
||||
if (isPos && Array.isArray(inputs.positive)) samplerPositiveRef[inputs.positive[0]] = true;
|
||||
if (isNeg && Array.isArray(inputs.negative)) samplerNegativeRef[inputs.negative[0]] = true;
|
||||
}
|
||||
// ComfySwitchNode ("If/Else Switch", on_false/on_true/switch) — used by Ernie
|
||||
// Image's prompt-enhancement toggle to pick between the raw PrimitiveStringMultiline
|
||||
// text and an LLM-generated (TextGenerate) rewrite. The rewrite can't be known
|
||||
// statically, so propagate the role through both branches — only the branch that
|
||||
// resolves to a literal string source (e.g. PrimitiveStringMultiline) will end up
|
||||
// producing a prompt_nodes entry in Pass 2.
|
||||
if (ct === "ComfySwitchNode") {
|
||||
for (const key of ["on_false", "on_true"]) {
|
||||
const tv = inputs[key];
|
||||
if (Array.isArray(tv)) {
|
||||
if (isPos) samplerPositiveRef[tv[0]] = true;
|
||||
if (isNeg) samplerNegativeRef[tv[0]] = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
// TextEncodeQwenImageEditPlus/TextEncodeQwenImageEdit — propagate role through its
|
||||
// linked "prompt" input when prompt-enhancement wiring feeds it via a switch chain
|
||||
// instead of a literal string (e.g. Krea-2's image-reference workflow).
|
||||
if (ct === "TextEncodeQwenImageEditPlus" || ct === "TextEncodeQwenImageEdit") {
|
||||
const tv = inputs.prompt;
|
||||
if (Array.isArray(tv)) {
|
||||
if (isPos) samplerPositiveRef[tv[0]] = true;
|
||||
if (isNeg) samplerNegativeRef[tv[0]] = true;
|
||||
}
|
||||
}
|
||||
// PreviewAny — generic value tap/relay used mid-chain in prompt-enhancement wiring
|
||||
// (e.g. Krea-2's "Refine Prompt?" switch output tap); pass role through its
|
||||
// "source" input unchanged.
|
||||
if (ct === "PreviewAny") {
|
||||
const sv = inputs.source;
|
||||
if (Array.isArray(sv)) {
|
||||
if (isPos) samplerPositiveRef[sv[0]] = true;
|
||||
if (isNeg) samplerNegativeRef[sv[0]] = true;
|
||||
}
|
||||
}
|
||||
// StringConcatenate — appends e.g. a LoRA trigger word onto the prompt text
|
||||
// (Krea-2); propagate role through both operands so the upstream literal is found.
|
||||
if (ct === "StringConcatenate") {
|
||||
for (const key of ["string_a", "string_b"]) {
|
||||
const tv = inputs[key];
|
||||
if (Array.isArray(tv)) {
|
||||
if (isPos) samplerPositiveRef[tv[0]] = true;
|
||||
if (isNeg) samplerNegativeRef[tv[0]] = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -812,6 +1105,72 @@ export const comfyWorkflow = {
|
||||
return "unknown";
|
||||
};
|
||||
|
||||
// --- Advanced Sampler: SamplerCustomAdvanced ---
|
||||
// "Advanced Sampling" pattern used by Flux.1/Flux.2, SD3.5, Chroma, HiDream E1, etc.:
|
||||
// instead of one KSampler node holding seed/steps/cfg/sampler/scheduler, those values
|
||||
// are spread across RandomNoise (noise_seed) → a Guider node (cfg) → KSamplerSelect
|
||||
// (sampler_name) → a *Scheduler node (steps/denoise), all feeding SamplerCustomAdvanced.
|
||||
// Collect them into one composite entry so Settings-tab editing still works; each
|
||||
// value's *NodeId tracks which real node to write back to (null = no such control
|
||||
// exists on this workflow, e.g. Flux2Scheduler has no named "scheduler" combo).
|
||||
if (ct === "SamplerCustomAdvanced") {
|
||||
const noiseId = Array.isArray(inputs.noise) ? String(inputs.noise[0]) : null;
|
||||
const guiderId = Array.isArray(inputs.guider) ? String(inputs.guider[0]) : null;
|
||||
const samplerSelId = Array.isArray(inputs.sampler) ? String(inputs.sampler[0]) : null;
|
||||
const schedulerId = Array.isArray(inputs.sigmas) ? String(inputs.sigmas[0]) : null;
|
||||
|
||||
const noiseInputs = (noiseId && workflow[noiseId]?.inputs) || {};
|
||||
const guiderNode = guiderId ? workflow[guiderId] : null;
|
||||
const guiderInputs = guiderNode?.inputs || {};
|
||||
// DualCFGGuider (HiDream E1 instruct-pix2pix edit) has no plain "cfg" input — its
|
||||
// two CFG knobs are cfg_conds/cfg_cond2_negative. Use cfg_conds as the
|
||||
// representative value so Settings still shows/edits *something* meaningful.
|
||||
const guiderCfgKey = guiderNode?.class_type === "DualCFGGuider" ? "cfg_conds" : "cfg";
|
||||
const samplerSelInputs = (samplerSelId && workflow[samplerSelId]?.inputs) || {};
|
||||
const schedulerInputs = (schedulerId && workflow[schedulerId]?.inputs) || {};
|
||||
|
||||
result.sampler_nodes.push({
|
||||
id, type: ct, title, advanced: true,
|
||||
seed: typeof noiseInputs.noise_seed === "number" ? noiseInputs.noise_seed : undefined,
|
||||
seedKey: "noise_seed", seedNodeId: noiseId,
|
||||
steps: typeof schedulerInputs.steps === "number" ? schedulerInputs.steps : undefined,
|
||||
stepsNodeId: schedulerId,
|
||||
cfg: typeof guiderInputs[guiderCfgKey] === "number" ? guiderInputs[guiderCfgKey] : undefined,
|
||||
cfgNodeId: guiderCfgKey in guiderInputs ? guiderId : null,
|
||||
cfgKey: guiderCfgKey,
|
||||
sampler_name: samplerSelInputs.sampler_name, samplerNodeId: samplerSelId,
|
||||
scheduler: undefined, schedulerNodeId: "scheduler" in schedulerInputs ? schedulerId : null,
|
||||
denoise: typeof schedulerInputs.denoise === "number" ? schedulerInputs.denoise : undefined,
|
||||
denoiseNodeId: "denoise" in schedulerInputs ? schedulerId : null,
|
||||
});
|
||||
}
|
||||
|
||||
// --- Advanced Sampler: SamplerCustom ---
|
||||
// Older/simpler counterpart to SamplerCustomAdvanced: no RandomNoise/Guider
|
||||
// indirection — noise_seed and cfg are its own widgets, and positive/negative feed it
|
||||
// directly. sampler_name/steps/scheduler/denoise still come from separate
|
||||
// KSamplerSelect/*Scheduler nodes via its sampler/sigmas inputs.
|
||||
if (ct === "SamplerCustom") {
|
||||
const samplerSelId = Array.isArray(inputs.sampler) ? String(inputs.sampler[0]) : null;
|
||||
const schedulerId = Array.isArray(inputs.sigmas) ? String(inputs.sigmas[0]) : null;
|
||||
const samplerSelInputs = (samplerSelId && workflow[samplerSelId]?.inputs) || {};
|
||||
const schedulerInputs = (schedulerId && workflow[schedulerId]?.inputs) || {};
|
||||
|
||||
result.sampler_nodes.push({
|
||||
id, type: ct, title, advanced: true,
|
||||
seed: typeof inputs.noise_seed === "number" ? inputs.noise_seed : undefined,
|
||||
seedKey: "noise_seed", seedNodeId: id,
|
||||
steps: typeof schedulerInputs.steps === "number" ? schedulerInputs.steps : undefined,
|
||||
stepsNodeId: schedulerId,
|
||||
cfg: typeof inputs.cfg === "number" ? inputs.cfg : undefined,
|
||||
cfgNodeId: id,
|
||||
sampler_name: samplerSelInputs.sampler_name, samplerNodeId: samplerSelId,
|
||||
scheduler: schedulerInputs.scheduler, schedulerNodeId: "scheduler" in schedulerInputs ? schedulerId : null,
|
||||
denoise: typeof schedulerInputs.denoise === "number" ? schedulerInputs.denoise : undefined,
|
||||
denoiseNodeId: "denoise" in schedulerInputs ? schedulerId : null,
|
||||
});
|
||||
}
|
||||
|
||||
// --- Checkpoint nodes ---
|
||||
// Matches: CheckpointLoaderSimple, CheckpointLoader, "Checkpoint Loader" (WAS), etc.
|
||||
if (ct.includes("CheckpointLoader") || ct === "Checkpoint Loader") {
|
||||
@@ -878,13 +1237,34 @@ export const comfyWorkflow = {
|
||||
}
|
||||
}
|
||||
|
||||
// Qwen-based image+text encoder
|
||||
if (ct === "TextEncodeQwenImageEditPlus") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title, role: getRole(),
|
||||
text: inputs.prompt || "",
|
||||
textKey: "prompt",
|
||||
});
|
||||
// Qwen-based image+text encoder (Plus variant takes up to 3 reference images;
|
||||
// the base variant takes one — both share the same "prompt" widget). Only add when
|
||||
// "prompt" is a direct string; if linked (e.g. Krea-2's prompt-enhancement wiring),
|
||||
// the upstream literal node is resolved instead via the role propagation above.
|
||||
if (ct === "TextEncodeQwenImageEditPlus" || ct === "TextEncodeQwenImageEdit") {
|
||||
const textVal = inputs.prompt;
|
||||
if (typeof textVal === "string") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title, role: getRole(),
|
||||
text: textVal, textKey: "prompt",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// TextEncodeBooguEdit (Boogu image-edit model) — prompt + negative_prompt in one node
|
||||
if (ct === "TextEncodeBooguEdit") {
|
||||
if (typeof inputs.prompt === "string") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title: `${title} [positive]`, role: "positive",
|
||||
text: inputs.prompt, textKey: "prompt",
|
||||
});
|
||||
}
|
||||
if (typeof inputs.negative_prompt === "string") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title: `${title} [negative]`, role: "negative",
|
||||
text: inputs.negative_prompt, textKey: "negative_prompt",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Mage-Flow text encoder — prompt + negative_prompt in one node (fixed roles,
|
||||
@@ -956,15 +1336,47 @@ export const comfyWorkflow = {
|
||||
});
|
||||
}
|
||||
|
||||
// Video all-in-one nodes (e.g. MiniMaxH3ImageToVideo) — hold their own "prompt"
|
||||
// widget directly instead of going through a separate CLIPTextEncode, and have
|
||||
// no negative counterpart. Matched generically by class name (ImageToVideo /
|
||||
// TextToVideo) rather than a fixed list so future models sharing this pattern
|
||||
// (WAN, HunyuanVideo, etc.) work without a code change — same detection metadata-tab.js
|
||||
// and node_sets_menu.js already use for prompt extraction (see extractPromptsAPI).
|
||||
if ((ct.includes("ImageToVideo") || ct.includes("TextToVideo")) && typeof inputs.prompt === "string") {
|
||||
result.prompt_nodes.push({
|
||||
id, type: ct, title, role: "positive",
|
||||
text: inputs.prompt, textKey: "prompt",
|
||||
});
|
||||
}
|
||||
|
||||
// --- Latent nodes ---
|
||||
if (ct === "EmptyLatentImage" || ct === "EmptySD3LatentImage") {
|
||||
// EmptyFlux2LatentImage (Flux.2 templates) often has width/height wired to a
|
||||
// GetImageSize node (auto-follows the reference image) rather than a direct number —
|
||||
// leave those as undefined, same treatment as TextEncodeMageFlowEdit below, so the
|
||||
// settings-tab UI shows "linked" instead of a stale default and Apply doesn't clobber it.
|
||||
if (ct === "EmptyLatentImage" || ct === "EmptySD3LatentImage" || ct === "EmptyFlux2LatentImage") {
|
||||
result.latent_nodes.push({
|
||||
id, type: ct, title,
|
||||
width: inputs.width, height: inputs.height,
|
||||
width: typeof inputs.width === "number" ? inputs.width : undefined,
|
||||
height: typeof inputs.height === "number" ? inputs.height : undefined,
|
||||
batch_size: inputs.batch_size,
|
||||
});
|
||||
}
|
||||
|
||||
// ResolutionSelector (ComfyUI core, comfy_extras.nodes_resolution): typically
|
||||
// feeds an EmptyLatentImage's width/height rather than being a latent node
|
||||
// itself. aspect_ratio is a fixed-choice COMBO (e.g. "3:2 (Photo)") — its exact
|
||||
// option strings aren't known here, only the node's current value, so callers
|
||||
// that want to set a new one (AI TOOL Chat's generate_image) must resolve
|
||||
// against /object_info at call time (see comfyui-editor.js resolveAspectRatioOption).
|
||||
if (ct === "ResolutionSelector") {
|
||||
result.resolution_selector_nodes.push({
|
||||
id, type: ct, title,
|
||||
aspect_ratio: inputs.aspect_ratio,
|
||||
megapixels: inputs.megapixels,
|
||||
});
|
||||
}
|
||||
|
||||
// TextEncodeMageFlowEdit also carries width/height/batch_size directly
|
||||
// (it builds the latent internally instead of a separate EmptyLatentImage node).
|
||||
// width/height are commonly wired to a ResolutionSelector-style helper node
|
||||
@@ -1120,6 +1532,14 @@ export const comfyWorkflow = {
|
||||
return result;
|
||||
},
|
||||
|
||||
// True if this analysis found a video all-in-one node (see analyzeWorkflow's
|
||||
// ImageToVideo/TextToVideo prompt_nodes branch). Callers use this to pick a longer
|
||||
// generation timeout (video generation runs far longer than a still image) — see
|
||||
// generate-tab.js _coreGenerate and lab-tab.js _runLabBatch.
|
||||
isVideoWorkflow(analysis) {
|
||||
return (analysis?.prompt_nodes || []).some((n) => /ImageToVideo|TextToVideo/.test(n.type));
|
||||
},
|
||||
|
||||
applyParams(workflow, params) {
|
||||
for (const [key, value] of Object.entries(params)) {
|
||||
// Parse key format: nodeId.paramName
|
||||
|
||||
+275
-19
@@ -5,12 +5,15 @@
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { loadFileIntoMetadataTab } from "./metadata-tab.js";
|
||||
import { loadFileIntoMetadataTab, extractPrompts, buildPromptItem } from "./metadata-tab.js";
|
||||
import { loadWorkflowIntoEditor } from "./generate-tab.js";
|
||||
import { escapeHtml, setupSearchClearBtn } from "./util.js";
|
||||
import { escapeHtml, setupSearchClearBtn, getAiBackendDefaultUrl } from "./util.js";
|
||||
import { comfyWorkflow } from "./comfyui-workflow.js";
|
||||
import { comfyUI } from "./comfyui-client.js";
|
||||
import { comfyEditor } from "./comfyui-editor.js";
|
||||
import { callLLM, loadAiSettings, isValidBackendUrl } from "./ai-tab.js";
|
||||
import { initImagePromptTab, activateImagePromptTab } from "./image-prompt-tab.js";
|
||||
import { initStyleCatalogTab, activateStyleCatalogTab } from "./style-catalog-tab.js";
|
||||
|
||||
// ── 定数 ─────────────────────────────────────────────────────
|
||||
|
||||
@@ -43,6 +46,13 @@ const API = {
|
||||
|
||||
export const FEEDER_GROUP = "__Feeder__";
|
||||
|
||||
// 拡張子から動画ファイル（mp4）かどうかを判定する。
|
||||
// 一覧アイテムは path/filename、詳細メタ(get_image_metadata)は ext フィールドを持つため両対応。
|
||||
function isVideoFile(img) {
|
||||
const source = img?.ext || img?.filename || img?.path || "";
|
||||
return source.toLowerCase().endsWith(".mp4");
|
||||
}
|
||||
|
||||
// ComfyUI Comic Creater からiframe越しに画像を受け取り、Generate UIのImage入力スロットへ直接セットする（I2I連携）。
|
||||
// Comic Creater側の「I2Iへ送る」ボタンから
|
||||
// iframe.contentWindow._wfmReceiveImageForI2I(blob, name, workflowData?, workflowFilename?) として呼ばれる。
|
||||
@@ -126,6 +136,87 @@ window._wfmReceiveI2IRunRequest = async (imageBlob, params, workflowData, workfl
|
||||
}
|
||||
};
|
||||
|
||||
// ComfyUI Comic Creater からiframe越しにコマの設定（シーン・要素・セリフ等）を受け取り、
|
||||
// AIタブの設定タブと同じLLM接続設定（Ollama/LM Studio）で画像生成プロンプトの下書きを
|
||||
// 生成して返す（半自動マンガ作成の「L」ボタン連携用）。
|
||||
// Comic Creater側のスクリプトタブ「L」ボタンから
|
||||
// iframe.contentWindow._wfmReceiveLLMPromptRequest(context) として呼ばれる。
|
||||
// context: { scene, elements, dialogues, existingPrompt }（いずれも文字列、空文字可）
|
||||
window._wfmReceiveLLMPromptRequest = async (context) => {
|
||||
try {
|
||||
const settings = loadAiSettings();
|
||||
const { backend = "ollama", backendUrl, model } = settings;
|
||||
const url = backendUrl || getAiBackendDefaultUrl(backend);
|
||||
if (!isValidBackendUrl(url)) return { ok: false, message: "AI settings: invalid backend URL (configure it in the Settings tab)" };
|
||||
if (!model) return { ok: false, message: "AI settings: no model selected (configure it in the Settings tab)" };
|
||||
|
||||
const { scene = "", elements = "", dialogues = "", existingPrompt = "" } = context || {};
|
||||
const prompt = [
|
||||
"Create a concise Stable Diffusion image generation prompt (English, comma-separated tags/phrases describing scene, composition, character appearance and action) for one manga panel, based on the following context.",
|
||||
"Output only the prompt text, nothing else.",
|
||||
scene ? `Scene: ${scene}` : null,
|
||||
elements ? `Characters/elements: ${elements}` : null,
|
||||
dialogues ? `Dialogue: ${dialogues}` : null,
|
||||
existingPrompt ? `Current draft prompt (revise/improve it): ${existingPrompt}` : null,
|
||||
].filter(Boolean).join("\n");
|
||||
|
||||
const result = await callLLM(url, backend, model, prompt);
|
||||
const text = (result || "").trim();
|
||||
if (!text) return { ok: false, message: "LLM returned an empty response" };
|
||||
return { ok: true, text };
|
||||
} catch (e) {
|
||||
return { ok: false, message: e.message };
|
||||
}
|
||||
};
|
||||
|
||||
// ComfyUI Comic Creater からiframe越しに画像プロンプト・サイズを受け取り、Generate UIで
|
||||
// テキストから画像を生成して結果URLを返す（半自動マンガ作成のコマ単位バッチ画像生成用）。
|
||||
// Comic Creater側のスクリプトタブ「画像を一括生成」から
|
||||
// iframe.contentWindow._wfmReceiveGenerateRequest(prompt, width, height, negative, workflowData?, workflowFilename?)
|
||||
// として呼ばれる。I2I/Inpaintと異なり画像アップロードは行わない（txt2img）。
|
||||
// workflowData が渡された場合（Comic Creater側のT2I設定でデフォルトワークフローが有効な時）は、
|
||||
// 実行前にそのワークフローを読み込む。渡されない場合はGenerate UIに現在ロード中のワークフローをそのまま使う。
|
||||
window._wfmReceiveGenerateRequest = async (prompt, width, height, negative, workflowData, workflowFilename) => {
|
||||
try {
|
||||
if (!window._wfmGenerateTab?.generate) return { ok: false, message: "Generate UI is not ready yet" };
|
||||
|
||||
if (workflowData) {
|
||||
try {
|
||||
if (!comfyUI.connected) {
|
||||
await comfyUI.checkConnection();
|
||||
}
|
||||
if (comfyUI.connected && (!comfyEditor.models.checkpoints || comfyEditor.models.checkpoints.length === 0)) {
|
||||
await comfyEditor.loadModelLists();
|
||||
}
|
||||
await loadWorkflowIntoEditor(workflowData, workflowFilename || "workflow.json");
|
||||
} catch (e) {
|
||||
console.warn("[T2I] failed to load default workflow:", e);
|
||||
}
|
||||
}
|
||||
|
||||
const analysis = comfyUI.currentAnalysis;
|
||||
if (!comfyUI.currentWorkflow || !analysis) return { ok: false, message: "No workflow loaded in Generate UI" };
|
||||
|
||||
const workflow = JSON.parse(JSON.stringify(comfyUI.currentWorkflow));
|
||||
const latent = analysis.latent_nodes?.[0];
|
||||
if (latent && width && height && workflow[latent.id]) {
|
||||
workflow[latent.id].inputs.width = width;
|
||||
workflow[latent.id].inputs.height = height;
|
||||
}
|
||||
|
||||
comfyEditor.setPromptText("positive", prompt || "", { workflow, analysis });
|
||||
if (negative) comfyEditor.setPromptText("negative", negative, { workflow, analysis });
|
||||
|
||||
await window._wfmGenerateTab.generate(workflow);
|
||||
|
||||
const resultUrl = document.getElementById("wfm-gen-result-img")?.src;
|
||||
if (!resultUrl) return { ok: false, message: "No result image produced" };
|
||||
return { ok: true, url: resultUrl };
|
||||
} catch (e) {
|
||||
return { ok: false, message: e.message };
|
||||
}
|
||||
};
|
||||
|
||||
// ── ページング ────────────────────────────────────────────────
|
||||
const PAGE_SIZE = 50;
|
||||
let _renderedCount = 0;
|
||||
@@ -169,6 +260,13 @@ function formatDate(mtime) {
|
||||
return new Date(mtime * 1000).toLocaleString();
|
||||
}
|
||||
|
||||
function formatDuration(seconds) {
|
||||
const total = Math.round(seconds);
|
||||
const m = Math.floor(total / 60);
|
||||
const s = total % 60;
|
||||
return `${m}:${String(s).padStart(2, "0")}`;
|
||||
}
|
||||
|
||||
async function openImageInMetadataTab(img) {
|
||||
try {
|
||||
const res = await fetch(API.serveImage(img.path));
|
||||
@@ -319,7 +417,7 @@ function renderTreeNode(node, container, depth, isRoot) {
|
||||
// フォルダ名
|
||||
const label = document.createElement("span");
|
||||
label.className = "wfm-gallery-tree-label";
|
||||
label.textContent = isRoot ? `[root] ${node.name}` : node.name;
|
||||
label.textContent = isRoot ? "[root]" : node.name;
|
||||
if (node.image_count > 0) {
|
||||
const badge = document.createElement("span");
|
||||
badge.className = "wfm-gallery-tree-count";
|
||||
@@ -494,6 +592,14 @@ function createThumbCard(img) {
|
||||
};
|
||||
card.appendChild(imgEl);
|
||||
|
||||
// 動画ファイルは再生アイコンバッジを重ねて表示（サムネイル自体は先頭フレームのJPEG）
|
||||
if (isVideoFile(img)) {
|
||||
const playBadge = document.createElement("div");
|
||||
playBadge.className = "wfm-gallery-thumb-video-badge";
|
||||
playBadge.textContent = "▶";
|
||||
card.appendChild(playBadge);
|
||||
}
|
||||
|
||||
// お気に入りトグルボタン（カード右上）
|
||||
const favBtn = document.createElement("button");
|
||||
favBtn.className = `wfm-gallery-thumb-fav-btn${img.favorite ? " active" : ""}`;
|
||||
@@ -793,18 +899,18 @@ async function loadImageDetail(img) {
|
||||
if (moveBtn) moveBtn.disabled = false;
|
||||
if (delBtn) delBtn.disabled = false;
|
||||
|
||||
// プレビュー
|
||||
// プレビュー（mp4は<video controls>、それ以外は<img>）
|
||||
const preview = document.getElementById("wfm-gallery-detail-preview");
|
||||
const isVideo = isVideoFile(img);
|
||||
const mediaHtml = isVideo
|
||||
? `<video src="${API.serveImage(img.path)}" class="wfm-gallery-detail-img" controls title="Double-click to enlarge"></video>`
|
||||
: `<img src="${API.serveImage(img.path)}" class="wfm-gallery-detail-img" alt="${escapeHtml(img.filename)}" title="Double-click to enlarge">`;
|
||||
preview.innerHTML = `
|
||||
<div class="wfm-gallery-preview-wrapper">
|
||||
<img src="${API.serveImage(img.path)}" class="wfm-gallery-detail-img" alt="${escapeHtml(img.filename)}" title="Double-click to enlarge">
|
||||
<div class="wfm-gallery-preview-overlay">
|
||||
<button class="wfm-gallery-download-btn" title="Download image">⬇</button>
|
||||
</div>
|
||||
${mediaHtml}
|
||||
</div>
|
||||
`;
|
||||
preview.querySelector("img").addEventListener("dblclick", () => openLightbox(img));
|
||||
preview.querySelector(".wfm-gallery-download-btn").addEventListener("click", () => downloadImage(img));
|
||||
preview.querySelector(isVideo ? "video" : "img").addEventListener("dblclick", () => openLightbox(img));
|
||||
|
||||
// ファイル名
|
||||
document.getElementById("wfm-gallery-detail-filename").textContent = img.filename;
|
||||
@@ -829,15 +935,45 @@ async function loadImageDetail(img) {
|
||||
state.embeddedWorkflow = wfRes.has_workflow ? wfRes.workflow : null;
|
||||
renderWorkflowJson(state.embeddedWorkflow);
|
||||
_updateCopyCanvasBtn();
|
||||
renderImagePromptSection(metaRes.image_prompt);
|
||||
renderDimensionInfo(metaRes);
|
||||
// Promptタブは prompt_workflow (API形式優先) を使う。トップレベルとサブグラフに
|
||||
// 独立した複数系統を持つワークフローでは workflow (UI形式) からの抽出だと
|
||||
// トップレベル系統しか拾えないことがあるため（Metadataタブと同じ優先順位に揃える）。
|
||||
renderPromptTab(wfRes.prompt_workflow ?? state.embeddedWorkflow);
|
||||
} catch (e) {
|
||||
renderWorkflowJson(null);
|
||||
_updateCopyCanvasBtn();
|
||||
renderImagePromptSection(null);
|
||||
renderDimensionInfo(null);
|
||||
renderPromptTab(null);
|
||||
}
|
||||
|
||||
// グループタブ更新
|
||||
renderDetailGroup(img);
|
||||
}
|
||||
|
||||
function renderDimensionInfo(metaRes) {
|
||||
const el = document.getElementById("wfm-gallery-info-dim");
|
||||
if (!el) return;
|
||||
const parts = [];
|
||||
if (metaRes?.width && metaRes?.height) parts.push(`${metaRes.width}×${metaRes.height}`);
|
||||
if (metaRes?.duration) parts.push(formatDuration(metaRes.duration));
|
||||
el.textContent = parts.length ? parts.join(" · ") : "";
|
||||
}
|
||||
|
||||
function renderImagePromptSection(imagePrompt) {
|
||||
const section = document.getElementById("wfm-gallery-image-prompt-section");
|
||||
const text = document.getElementById("wfm-gallery-image-prompt-text");
|
||||
if (!section || !text) return;
|
||||
if (imagePrompt) {
|
||||
text.textContent = imagePrompt;
|
||||
section.style.display = "";
|
||||
} else {
|
||||
section.style.display = "none";
|
||||
}
|
||||
}
|
||||
|
||||
function renderTagsDisplay(tags) {
|
||||
const container = document.getElementById("wfm-gallery-tags-display");
|
||||
container.innerHTML = "";
|
||||
@@ -866,6 +1002,44 @@ function renderWorkflowJson(workflow) {
|
||||
}
|
||||
}
|
||||
|
||||
// 埋め込みワークフローからpositive/negativeプロンプトを抽出してPromptタブに表示する。
|
||||
// 複数候補があるケース(PromptStyler等で正負が複数生成される場合)に対応するため、
|
||||
// Metadataタブと同じ POS/NEGリスト → クリックで全文表示のUI(buildPromptItem)をそのまま
|
||||
// 再利用する。選択画像を切り替えるたびにマウス操作無しで読めるよう、先頭項目を自動選択する。
|
||||
function renderPromptTab(workflow) {
|
||||
const listEl = document.getElementById("wfm-gallery-prompt-list");
|
||||
const fullArea = document.getElementById("wfm-gallery-prompt-full");
|
||||
const fullLabel = document.getElementById("wfm-gallery-prompt-full-label");
|
||||
if (!listEl || !fullArea || !fullLabel) return;
|
||||
|
||||
listEl.innerHTML = "";
|
||||
fullArea.value = "";
|
||||
fullLabel.textContent = "";
|
||||
|
||||
if (!workflow) {
|
||||
listEl.innerHTML = `<div class="wfm-meta-item" style="opacity:0.6;">No workflow embedded in this image.</div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
const { positives, negatives, texts } = extractPrompts(workflow);
|
||||
const allPrompts = [
|
||||
...positives.map(p => ({ type: "positive", text: p })),
|
||||
...negatives.map(p => ({ type: "negative", text: p })),
|
||||
...(texts ?? []).map(p => ({ type: "text", text: p })),
|
||||
];
|
||||
|
||||
if (allPrompts.length === 0) {
|
||||
listEl.innerHTML = `<div class="wfm-meta-item" style="opacity:0.6;">No prompt found.</div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
for (const { type, text } of allPrompts) {
|
||||
listEl.appendChild(buildPromptItem(text, type, text, fullArea, fullLabel, listEl));
|
||||
}
|
||||
// Auto-select first item (positive prompts come first in allPrompts)
|
||||
listEl.querySelector(".wfm-meta-item-clickable")?.click();
|
||||
}
|
||||
|
||||
// ── タグ操作 ─────────────────────────────────────────────────
|
||||
|
||||
async function addTag(tag) {
|
||||
@@ -873,7 +1047,8 @@ async function addTag(tag) {
|
||||
const tags = [...(state.selectedImage.tags || [])];
|
||||
if (tags.includes(tag.trim())) return;
|
||||
tags.push(tag.trim());
|
||||
await saveMetaField({ tags });
|
||||
const ok = await saveMetaField({ tags });
|
||||
if (!ok) return;
|
||||
state.selectedImage.tags = tags;
|
||||
renderTagsDisplay(tags);
|
||||
updateTagFilter(state.images);
|
||||
@@ -882,24 +1057,38 @@ async function addTag(tag) {
|
||||
async function removeTag(tag) {
|
||||
if (!state.selectedImage) return;
|
||||
const tags = (state.selectedImage.tags || []).filter(t => t !== tag);
|
||||
await saveMetaField({ tags });
|
||||
const ok = await saveMetaField({ tags });
|
||||
if (!ok) return;
|
||||
state.selectedImage.tags = tags;
|
||||
renderTagsDisplay(tags);
|
||||
updateTagFilter(state.images);
|
||||
}
|
||||
|
||||
// 戻り値: 保存が実際にサーバーへ永続化されたか(true/false)。呼び出し元はこれを見て
|
||||
// ローカル状態(state.selectedImage等)の更新可否を判断すること — でないと、サーバーが
|
||||
// 保存を拒否した場合にもUI上は成功したように見えてしまう(リロードすると消える不具合の原因)。
|
||||
async function saveMetaField(fields) {
|
||||
if (!state.selectedImage) return;
|
||||
if (!state.selectedImage) return false;
|
||||
try {
|
||||
await fetch(API.saveImageMeta, {
|
||||
const res = await fetch(API.saveImageMeta, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ path: state.selectedImage.path, ...fields }),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const json = await res.json();
|
||||
// サーバー側は常にHTTP 200を返し、成否は body.ok (bool) で表す設計
|
||||
// (gallery_routes.py save_image_meta)。ここをチェックしないと、
|
||||
// 許可パス外などでサーバーが保存を拒否してもUI上は成功したように見えてしまう。
|
||||
if (json.error || json.ok === false) {
|
||||
throw new Error(json.error || "Save rejected by server");
|
||||
}
|
||||
const idx = state.images.findIndex(i => i.path === state.selectedImage.path);
|
||||
if (idx >= 0) Object.assign(state.images[idx], fields);
|
||||
return true;
|
||||
} catch (e) {
|
||||
showToast(t("saveFailed", e.message), "error");
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1442,9 +1631,12 @@ async function bulkSetFavorite(favoriteValue) {
|
||||
function openLightbox(img) {
|
||||
const overlay = document.createElement("div");
|
||||
overlay.className = "wfm-gallery-lightbox";
|
||||
const mediaHtml = isVideoFile(img)
|
||||
? `<video src="${API.serveImage(img.path)}" class="wfm-gallery-lightbox-img" controls autoplay></video>`
|
||||
: `<img src="${API.serveImage(img.path)}" class="wfm-gallery-lightbox-img" alt="${escapeHtml(img.filename)}">`;
|
||||
overlay.innerHTML = `
|
||||
<div class="wfm-gallery-lightbox-inner">
|
||||
<img src="${API.serveImage(img.path)}" class="wfm-gallery-lightbox-img" alt="${escapeHtml(img.filename)}">
|
||||
${mediaHtml}
|
||||
<div class="wfm-gallery-lightbox-footer">${escapeHtml(img.filename)}</div>
|
||||
<button class="wfm-gallery-lightbox-close">×</button>
|
||||
</div>
|
||||
@@ -1466,12 +1658,17 @@ function openCompare(paths) {
|
||||
const overlay = document.createElement("div");
|
||||
overlay.className = "wfm-gallery-lightbox wfm-lightbox-compare";
|
||||
|
||||
const itemsHtml = imgs.map(img => `
|
||||
const itemsHtml = imgs.map(img => {
|
||||
const mediaHtml = isVideoFile(img)
|
||||
? `<video src="${API.serveImage(img.path)}" class="wfm-lightbox-compare-img" controls></video>`
|
||||
: `<img src="${API.serveImage(img.path)}" class="wfm-lightbox-compare-img" alt="${escapeHtml(img.filename)}" loading="lazy">`;
|
||||
return `
|
||||
<div class="wfm-lightbox-compare-item">
|
||||
<img src="${API.serveImage(img.path)}" class="wfm-lightbox-compare-img" alt="${escapeHtml(img.filename)}" loading="lazy">
|
||||
${mediaHtml}
|
||||
<div class="wfm-lightbox-compare-label">${escapeHtml(img.filename)}</div>
|
||||
</div>
|
||||
`).join("");
|
||||
`;
|
||||
}).join("");
|
||||
|
||||
overlay.innerHTML = `
|
||||
<div class="wfm-lightbox-compare-inner">
|
||||
@@ -1524,6 +1721,9 @@ export function initGalleryTab() {
|
||||
}
|
||||
|
||||
bindEvents();
|
||||
_initGallerySubtabToggle();
|
||||
initImagePromptTab();
|
||||
initStyleCatalogTab();
|
||||
}
|
||||
|
||||
let _initialized = false;
|
||||
@@ -1535,6 +1735,24 @@ function onGalleryTabActivated() {
|
||||
loadGroups();
|
||||
}
|
||||
|
||||
// ── サブタブ切替 (Output / ImagePrompt / Style_Catalog / Metadata) ────────────
|
||||
|
||||
function _initGallerySubtabToggle() {
|
||||
document.querySelectorAll(".wfm-gallery-subtab-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const target = btn.dataset.gallerySubtab;
|
||||
document.querySelectorAll(".wfm-gallery-subtab-btn").forEach((b) => b.classList.remove("active"));
|
||||
btn.classList.add("active");
|
||||
document.getElementById("wfm-gallery-panel-output")?.classList.toggle("active", target === "output");
|
||||
document.getElementById("wfm-gallery-panel-imageprompt")?.classList.toggle("active", target === "imageprompt");
|
||||
document.getElementById("wfm-gallery-panel-styleCatalog")?.classList.toggle("active", target === "styleCatalog");
|
||||
document.getElementById("wfm-gallery-panel-metadata")?.classList.toggle("active", target === "metadata");
|
||||
if (target === "imageprompt") activateImagePromptTab();
|
||||
if (target === "styleCatalog") activateStyleCatalogTab();
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
function bindEvents() {
|
||||
// リフレッシュ
|
||||
document.getElementById("wfm-gallery-refresh-btn")?.addEventListener("click", () => {
|
||||
@@ -1589,6 +1807,34 @@ function bindEvents() {
|
||||
}
|
||||
});
|
||||
|
||||
// 選択画像をComfyUIキャンバス上で選択中のノード（LoadImage系）のimageウィジェットへ送信。
|
||||
// Image Editタブの「Send to LI node」と同じ橋渡し(window.opener.wfmSendImageToSelectedNode、
|
||||
// node_sets_menu.js側で定義)を使う。Galleryの画像はGalleryルート配下の任意フォルダにあり得るため、
|
||||
// GenUI送信と違いLoadImageノードの有無に依存しない — /upload/imageでinputフォルダへ
|
||||
// アップロードしてから、そのファイル名を書き込む。
|
||||
document.getElementById("wfm-gallery-send-li-node-btn")?.addEventListener("click", async () => {
|
||||
if (!state.selectedImage) {
|
||||
showToast(t("gallerySelectImageFirst"), "info");
|
||||
return;
|
||||
}
|
||||
if (!window.opener || typeof window.opener.wfmSendImageToSelectedNode !== "function") {
|
||||
showToast(t("sendToLiNodeNoOpener"), "error");
|
||||
return;
|
||||
}
|
||||
try {
|
||||
const res = await fetch(API.serveImage(state.selectedImage.path));
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const blob = await res.blob();
|
||||
const file = new File([blob], state.selectedImage.filename || "gallery-image.png", { type: blob.type || "image/png" });
|
||||
const result = await comfyUI.uploadImage(file, file.name);
|
||||
const filename = result.subfolder ? `${result.subfolder}/${result.name}` : result.name;
|
||||
window.opener.wfmSendImageToSelectedNode(filename);
|
||||
showToast(t("sendToLiNodeSuccess", filename), "success");
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
}
|
||||
});
|
||||
|
||||
// 選択画像を ComfyUI Comic Creator の選択コマ／オーバーレイへ送信
|
||||
// （ComfyUI Comic CreaterからこのGalleryタブがiframe埋め込みされている場合のみ表示・動作する）
|
||||
const sendCcBtn = document.getElementById("wfm-gallery-send-cc-btn");
|
||||
@@ -1722,7 +1968,7 @@ function bindEvents() {
|
||||
// メモ保存
|
||||
document.getElementById("wfm-gallery-memo-save-btn")?.addEventListener("click", () => {
|
||||
const memo = document.getElementById("wfm-gallery-memo").value;
|
||||
saveMetaField({ memo }).then(() => showToast(t("memoSaved"), "success"));
|
||||
saveMetaField({ memo }).then((ok) => { if (ok) showToast(t("memoSaved"), "success"); });
|
||||
});
|
||||
|
||||
// ワークフローコピー＆キャンバスへ送る
|
||||
@@ -1855,6 +2101,16 @@ function bindEvents() {
|
||||
openImageInTaggerTab(state.selectedImage);
|
||||
});
|
||||
|
||||
// Downloadボタン: プレビューのオーバーレイだと動画の再生コントロール操作を妨げるため、
|
||||
// アクションボタン行に常時表示のボタンとして配置している
|
||||
document.getElementById("wfm-gallery-download-btn")?.addEventListener("click", () => {
|
||||
if (!state.selectedImage) {
|
||||
showToast(t("gallerySelectImageFirst"), "error");
|
||||
return;
|
||||
}
|
||||
downloadImage(state.selectedImage);
|
||||
});
|
||||
|
||||
// Load in GenerateUI ボタン: 埋め込みワークフローをGenerateUIタブに読み込む
|
||||
document.getElementById("wfm-gallery-load-genui-btn")?.addEventListener("click", async () => {
|
||||
if (!state.selectedImage) {
|
||||
|
||||
+280
-65
@@ -9,50 +9,9 @@ import { comfyEditor } from "./comfyui-editor.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { syncJsonHighlight, syncScroll } from "./json-highlight.js";
|
||||
import { initFeederTab, refreshFeederNodeList } from "./feeder-tab.js";
|
||||
import { getSettings, readJsonStorage, escapeHtml } from "./util.js";
|
||||
|
||||
// ============================================
|
||||
// Eagle Auto-Save
|
||||
// ============================================
|
||||
|
||||
function getEagleSettings() {
|
||||
const s = getSettings();
|
||||
return {
|
||||
url: s.eagleUrl || "http://localhost:41595",
|
||||
autoSave: !!s.eagleAutoSave,
|
||||
};
|
||||
}
|
||||
|
||||
async function saveToEagle(imageUrl, name, tags = [], fileInfo = null) {
|
||||
const eagle = getEagleSettings();
|
||||
if (!eagle.autoSave) return;
|
||||
try {
|
||||
const res = await fetch("/api/wfm/eagle/add", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
eagleUrl: eagle.url,
|
||||
url: imageUrl,
|
||||
name,
|
||||
tags: ["wfm-comfyui", ...tags],
|
||||
// SVG はサーバー側でローカルパス解決(addFromPath)するために必要
|
||||
filename: fileInfo?.filename || "",
|
||||
subfolder: fileInfo?.subfolder || "",
|
||||
type: fileInfo?.type || "output",
|
||||
// comfyui-tosvg の Save SVG String など、絶対パスが既知の場合に渡す
|
||||
localPath: fileInfo?.localPath || "",
|
||||
}),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.status === "success") {
|
||||
console.log("[Eagle] Saved:", name);
|
||||
} else {
|
||||
console.warn("[Eagle] Save failed:", data.message);
|
||||
}
|
||||
} catch (err) {
|
||||
console.warn("[Eagle] Save error:", err.message);
|
||||
}
|
||||
}
|
||||
import { initLabTab, refreshLabLiveDefaults } from "./lab-tab.js";
|
||||
import { getSettings, readJsonStorage, escapeHtml, getEagleSettings, saveToEagle } from "./util.js";
|
||||
import { isVideoFilename } from "./video-utils.js";
|
||||
|
||||
// ============================================
|
||||
// Gallery Metadata - ワークフロー保存
|
||||
@@ -206,6 +165,17 @@ async function saveCurrentWorkflow() {
|
||||
nameEl.dataset.filename = filename;
|
||||
}
|
||||
}
|
||||
|
||||
// このファイル名が「デフォルトワークフロー」(workflow-tab.jsの「デフォルトに設定」で
|
||||
// localStorageにスナップショットされたAPI形式JSON、ページ起動時に自動読込される)に
|
||||
// 一致する場合、保存内容と食い違ったまま残らないようスナップショットも更新する。
|
||||
// 更新しないと、ここで上書き保存しても次回リロード時に古いモデル等が復活する。
|
||||
const settings = getSettings();
|
||||
if (settings.defaultWorkflow === filename) {
|
||||
settings.defaultWorkflowData = workflow;
|
||||
localStorage.setItem("wfm_settings", JSON.stringify(settings));
|
||||
}
|
||||
|
||||
showToast(t("savedAs", filename), "success");
|
||||
} catch (err) {
|
||||
showToast(t("saveFailed", err.message), "error");
|
||||
@@ -273,7 +243,9 @@ export async function loadWorkflowIntoEditor(workflow, filename) {
|
||||
comfyUI.currentAnalysis = comfyWorkflow.analyzeWorkflow(apiWorkflow);
|
||||
|
||||
// Render editor tabs
|
||||
comfyEditor.renderAll(comfyUI.currentAnalysis, apiWorkflow);
|
||||
// resetStackMode: 新規ワークフロー読み込み時のみ、LoraManagerノードのloras/text状態を
|
||||
// スナップショットし、Stackグループ使用チェックボックスをOFFへリセットする
|
||||
comfyEditor.renderAll(comfyUI.currentAnalysis, apiWorkflow, { resetStackMode: true });
|
||||
|
||||
// Update raw JSON with highlight
|
||||
const rawTextarea = document.getElementById("wfm-gen-raw-json");
|
||||
@@ -1305,6 +1277,12 @@ async function _loadStyles() {
|
||||
_rebuildStyleList();
|
||||
}
|
||||
|
||||
// Promptタブ「Style」サブタブでスタイルを作成・編集・削除した後に呼ばれる。
|
||||
// GenerateUIのStyleドロップダウン・Batchタブのチェックリストを最新の状態へ再同期する。
|
||||
export async function refreshStylesList() {
|
||||
await _loadStyles();
|
||||
}
|
||||
|
||||
function _renderStyleDropdown() {
|
||||
const sel = document.getElementById("wfm-style-select");
|
||||
if (!sel) return;
|
||||
@@ -1333,6 +1311,21 @@ function _rebuildStyleList() {
|
||||
);
|
||||
}
|
||||
|
||||
// Style Catalogギャラリーの「Select as Style」から呼ばれる。名前が一致する登録済み
|
||||
// Styleを探し、GenerateUIのStyleドロップダウンをそれに切り替えてタブへ移動する。
|
||||
// 画像に埋め込まれたプロンプトを直接適用するのではなく、既存Styleシステムへの
|
||||
// 視覚的なショートカットとして機能する（元のStyle JSONが変更・削除されても影響を受けない）。
|
||||
export function selectStyleByName(name) {
|
||||
const style = _stylesData.find((s) => s.name === name);
|
||||
if (!style) return false;
|
||||
const sel = document.getElementById("wfm-style-select");
|
||||
if (sel) sel.value = name;
|
||||
const enabled = document.getElementById("wfm-style-enabled");
|
||||
if (enabled) enabled.checked = true;
|
||||
document.querySelector('.wfm-tab[data-tab="generate"]')?.click();
|
||||
return true;
|
||||
}
|
||||
|
||||
function _applyNamedStyle(workflow, style) {
|
||||
if (!style) return workflow;
|
||||
const analysis = comfyUI.currentAnalysis;
|
||||
@@ -1433,7 +1426,7 @@ async function _expandWildcardText(text) {
|
||||
|
||||
const _IMPACT_WC_TYPES = new Set(["ImpactWildcardEncode", "ImpactWildcardProcessor"]);
|
||||
|
||||
async function _expandWildcardsInWorkflow(workflow) {
|
||||
export async function _expandWildcardsInWorkflow(workflow) {
|
||||
let hasWildcard = false;
|
||||
outer: for (const node of Object.values(workflow)) {
|
||||
if (_IMPACT_WC_TYPES.has(node.class_type)) continue;
|
||||
@@ -1462,26 +1455,38 @@ async function _expandWildcardsInWorkflow(workflow) {
|
||||
// Generation (core — throws on error)
|
||||
// ============================================
|
||||
|
||||
async function _coreGenerate(silent = false, workflowOverride = null) {
|
||||
async function _coreGenerate(silent = false, workflowOverride = null, genOptions = {}) {
|
||||
const progressBar = document.getElementById("wfm-gen-progress-bar");
|
||||
const progressText = document.getElementById("wfm-gen-progress-text");
|
||||
const resultImg = document.getElementById("wfm-gen-result-img");
|
||||
const resultVideo = document.getElementById("wfm-gen-result-video");
|
||||
const resultThumbs = document.getElementById("wfm-gen-result-thumbs");
|
||||
|
||||
const seedMode = document.getElementById("wfm-gen-seed-mode")?.value || "random";
|
||||
const seedValue = parseInt(document.getElementById("wfm-gen-seed-value")?.value) || -1;
|
||||
// genOptions.seedValue lets a caller (e.g. AI TOOL Chat's generate_image tool)
|
||||
// force a specific seed on an override workflow without touching the GenerateUI
|
||||
// seed fields — those still drive normal (non-override) generation as before.
|
||||
const seedMode = genOptions.seedValue != null ? "fixed" : (document.getElementById("wfm-gen-seed-mode")?.value || "random");
|
||||
const seedValue = genOptions.seedValue ?? (parseInt(document.getElementById("wfm-gen-seed-value")?.value) || -1);
|
||||
|
||||
if (progressBar) progressBar.style.width = "0%";
|
||||
if (progressText) progressText.textContent = "Starting...";
|
||||
|
||||
// Style must be applied *before* wildcard expansion so a __name__ reference written
|
||||
// inside a Style's prompt/negative_prompt also gets expanded, not just wildcards
|
||||
// already present in the base workflow's prompt nodes.
|
||||
const baseWorkflow = workflowOverride || comfyUI.currentWorkflow;
|
||||
const workflowExpanded = await _expandWildcardsInWorkflow({ ...baseWorkflow });
|
||||
const workflowForGenerate = _applyStyleToWorkflow(workflowExpanded);
|
||||
const workflowStyled = _applyStyleToWorkflow({ ...baseWorkflow });
|
||||
const workflowForGenerate = await _expandWildcardsInWorkflow(workflowStyled);
|
||||
// Video workflows (e.g. MiniMax H3) run far longer than a still image — use a longer
|
||||
// timeout so a multi-minute generation doesn't get force-rejected mid-run (see
|
||||
// comfyWorkflow.isVideoWorkflow and video-tab.js's own 30-minute timeout).
|
||||
const isVideo = comfyWorkflow.isVideoWorkflow(comfyUI.currentAnalysis);
|
||||
const { images, seed, svgOutputs } = await comfyUI.generate(
|
||||
workflowForGenerate,
|
||||
{
|
||||
seedMode,
|
||||
seedValue,
|
||||
timeoutMs: isVideo ? 30 * 60 * 1000 : undefined,
|
||||
onProgress: (pct) => {
|
||||
if (progressBar) progressBar.style.width = `${(pct * 100).toFixed(1)}%`;
|
||||
if (progressText) progressText.textContent = `${(pct * 100).toFixed(0)}%`;
|
||||
@@ -1527,23 +1532,36 @@ async function _coreGenerate(silent = false, workflowOverride = null) {
|
||||
});
|
||||
}
|
||||
} else if (outputImages.length > 0) {
|
||||
// Video outputs (SaveVideo/PreviewVideo, e.g. MiniMax H3) come back through the
|
||||
// same {"images":[...]} shape as still images — dispatch to <video> vs <img>
|
||||
// per-item by extension rather than assuming the whole batch is one media type.
|
||||
const showMedia = (blob, filename) => {
|
||||
const url = URL.createObjectURL(blob);
|
||||
if (isVideoFilename(filename)) {
|
||||
if (resultVideo) { resultVideo.src = url; resultVideo.style.display = "block"; }
|
||||
if (resultImg) resultImg.style.display = "none";
|
||||
} else {
|
||||
if (resultImg) { resultImg.src = url; resultImg.style.display = "block"; }
|
||||
if (resultVideo) { resultVideo.pause(); resultVideo.style.display = "none"; }
|
||||
}
|
||||
};
|
||||
|
||||
const blob = await comfyUI.getImageBlob(outputImages[0]);
|
||||
const url = URL.createObjectURL(blob);
|
||||
if (resultImg) {
|
||||
resultImg.src = url;
|
||||
resultImg.style.display = "block";
|
||||
}
|
||||
showMedia(blob, outputImages[0].filename);
|
||||
|
||||
if (resultThumbs && outputImages.length > 1) {
|
||||
resultThumbs.innerHTML = "";
|
||||
for (let i = 0; i < outputImages.length; i++) {
|
||||
const b = i === 0 ? blob : await comfyUI.getImageBlob(outputImages[i]);
|
||||
const u = i === 0 ? url : URL.createObjectURL(b);
|
||||
const thumb = document.createElement("img");
|
||||
const filename = outputImages[i].filename;
|
||||
const u = URL.createObjectURL(b);
|
||||
const isVid = isVideoFilename(filename);
|
||||
const thumb = document.createElement(isVid ? "video" : "img");
|
||||
thumb.src = u;
|
||||
if (isVid) thumb.muted = true;
|
||||
thumb.className = `wfm-gen-thumb ${i === 0 ? "active" : ""}`;
|
||||
thumb.addEventListener("click", () => {
|
||||
resultImg.src = u;
|
||||
showMedia(b, filename);
|
||||
resultThumbs.querySelectorAll(".wfm-gen-thumb").forEach((t) => t.classList.remove("active"));
|
||||
thumb.classList.add("active");
|
||||
});
|
||||
@@ -1581,7 +1599,9 @@ async function _coreGenerate(silent = false, workflowOverride = null) {
|
||||
// ============================================
|
||||
|
||||
// 汎用バッチループ: items を順に applyFn(item) → _coreGenerate() で処理する
|
||||
async function _runBatchLoop(items, applyFn, labelFn = (x) => String(x)) {
|
||||
// onResultFn(item, {images, seed}) が渡された場合、各アイテムの生成結果ごとに呼び出す
|
||||
// （Style Catalogのカタログ作成が、生成画像をスタイル名で保存するために使用）
|
||||
async function _runBatchLoop(items, applyFn, labelFn = (x) => String(x), onResultFn = null) {
|
||||
const batchProgress = document.getElementById("wfm-ckpt-batch-progress");
|
||||
const batchCurrentName = document.getElementById("wfm-ckpt-batch-current-name");
|
||||
const batchCount = document.getElementById("wfm-ckpt-batch-count");
|
||||
@@ -1616,7 +1636,8 @@ async function _runBatchLoop(items, applyFn, labelFn = (x) => String(x)) {
|
||||
|
||||
try {
|
||||
await applyFn(item);
|
||||
await _coreGenerate(true);
|
||||
const result = await _coreGenerate(true);
|
||||
if (onResultFn) await onResultFn(item, result);
|
||||
completed++;
|
||||
} catch (err) {
|
||||
if (_ckptBatch.aborted) break;
|
||||
@@ -1645,6 +1666,157 @@ async function _runBatchLoop(items, applyFn, labelFn = (x) => String(x)) {
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Style Catalog 作成
|
||||
// ============================================
|
||||
// Batchタブ「Style」サブタブで選択済みのスタイル(_batchStyleSelected)を対象に、
|
||||
// 現在ロード中のワークフローでそれぞれ生成し、結果をスタイル名でws_style_catalog
|
||||
// 配下の指定フォルダへ保存する（Style Catalogギャラリーからの視覚的スタイル選択に使う）。
|
||||
|
||||
function _blobToDataUrl(blob) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const reader = new FileReader();
|
||||
reader.onload = () => resolve(reader.result);
|
||||
reader.onerror = reject;
|
||||
reader.readAsDataURL(blob);
|
||||
});
|
||||
}
|
||||
|
||||
function _flattenFolderTree(node, acc = [], isRoot = true) {
|
||||
acc.push({ path: isRoot ? "[root]" : node.path, abs_path: node.abs_path });
|
||||
(node.children || []).forEach((c) => _flattenFolderTree(c, acc, false));
|
||||
return acc;
|
||||
}
|
||||
|
||||
async function openCatalogCreateModal() {
|
||||
const list = _stylesData.filter((s) => _batchStyleSelected.has(s.name));
|
||||
if (list.length === 0) {
|
||||
showToast(t("catalogCreateNoStyles"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
let catalogRoot = null;
|
||||
let folderTree = null;
|
||||
try {
|
||||
const rootRes = await fetch("/wfm/gallery/style-catalog/root");
|
||||
const rootData = await rootRes.json();
|
||||
catalogRoot = rootData.root;
|
||||
if (!catalogRoot) throw new Error(rootData.error || "Style Catalog root not resolved");
|
||||
const treeRes = await fetch(`/wfm/gallery/folders?root=${encodeURIComponent(catalogRoot)}`);
|
||||
folderTree = await treeRes.json();
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
const folderOptions = folderTree && !folderTree.error ? _flattenFolderTree(folderTree) : [];
|
||||
|
||||
const html = `
|
||||
<p style="margin:0 0 10px;font-size:13px;color:var(--wfm-text-secondary);">${t("catalogCreateSelectedCount", list.length)}</p>
|
||||
<div style="display:flex;flex-direction:column;gap:8px;">
|
||||
<label style="display:flex;align-items:center;gap:6px;">
|
||||
<input type="radio" name="wfm-catalog-dest" value="new" checked>
|
||||
${t("catalogCreateNewFolder")}
|
||||
</label>
|
||||
<input type="text" id="wfm-catalog-new-folder-name" class="wfm-input" placeholder="folder name" style="margin-left:22px;">
|
||||
<label style="display:flex;align-items:center;gap:6px;">
|
||||
<input type="radio" name="wfm-catalog-dest" value="existing" ${folderOptions.length === 0 ? "disabled" : ""}>
|
||||
${t("catalogCreateExistingFolder")}
|
||||
</label>
|
||||
<select id="wfm-catalog-existing-folder" class="wfm-input" style="margin-left:22px;" ${folderOptions.length === 0 ? "disabled" : ""}>
|
||||
${folderOptions.map((f) => `<option value="${escapeHtml(f.abs_path)}">${escapeHtml(f.path)}</option>`).join("")}
|
||||
</select>
|
||||
<hr class="wfm-group-divider" style="margin:4px 0;">
|
||||
<label style="display:flex;align-items:center;gap:6px;">
|
||||
<input type="checkbox" id="wfm-catalog-skip-output">
|
||||
${t("catalogCreateSkipOutput")}
|
||||
</label>
|
||||
</div>
|
||||
<button id="wfm-catalog-run-btn" class="wfm-btn wfm-btn-primary" style="width:100%;margin-top:14px;">${t("catalogCreateRun")}</button>
|
||||
`;
|
||||
openModal(t("catalogCreateTitle"), html);
|
||||
|
||||
document.getElementById("wfm-catalog-run-btn")?.addEventListener("click", async () => {
|
||||
const destType = document.querySelector('input[name="wfm-catalog-dest"]:checked')?.value;
|
||||
let destFolder = null;
|
||||
|
||||
if (destType === "new") {
|
||||
const name = document.getElementById("wfm-catalog-new-folder-name")?.value.trim();
|
||||
if (!name) {
|
||||
showToast(t("catalogCreateFolderNameRequired"), "error");
|
||||
return;
|
||||
}
|
||||
try {
|
||||
const res = await fetch("/wfm/gallery/folder", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ parent: catalogRoot, name }),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (!data.ok) throw new Error(data.error || "Failed to create folder");
|
||||
destFolder = data.path;
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
return;
|
||||
}
|
||||
} else {
|
||||
destFolder = document.getElementById("wfm-catalog-existing-folder")?.value || null;
|
||||
if (!destFolder) {
|
||||
showToast(t("catalogCreateFolderRequired"), "error");
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
const skipOutput = document.getElementById("wfm-catalog-skip-output")?.checked || false;
|
||||
|
||||
closeModal();
|
||||
await _runCatalogCreate(list, destFolder, skipOutput);
|
||||
});
|
||||
}
|
||||
|
||||
async function _runCatalogCreate(list, destFolder, skipOutput) {
|
||||
try {
|
||||
await _runBatchLoop(
|
||||
list,
|
||||
(style) => { _batchStyleOverride = style; },
|
||||
(s) => s.name,
|
||||
async (style, result) => {
|
||||
const outputImages = (result?.images || []).filter((img) => img.type !== "temp");
|
||||
if (outputImages.length === 0) return;
|
||||
const srcImage = outputImages[0];
|
||||
const blob = await comfyUI.getImageBlob(srcImage);
|
||||
const dataUrl = await _blobToDataUrl(blob);
|
||||
const res = await fetch("/wfm/gallery/image/save-to-folder", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ folder: destFolder, filename: style.name, imageData: dataUrl }),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (!data.ok) throw new Error(data.error || "Failed to save catalog image");
|
||||
|
||||
// Outputフォルダに元画像を残さないオプション: カタログへのコピー保存が
|
||||
// 成功した後にのみ、ComfyUIのOutputフォルダ側の元ファイルを削除する
|
||||
// （コピー失敗時は削除せず、元画像を保持したままにする）
|
||||
if (skipOutput) {
|
||||
try {
|
||||
await fetch("/wfm/gallery/output-image/delete", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
filename: srcImage.filename,
|
||||
subfolder: srcImage.subfolder || "",
|
||||
type: srcImage.type || "output",
|
||||
}),
|
||||
});
|
||||
} catch { /* 削除失敗はカタログ作成自体を止めない */ }
|
||||
}
|
||||
}
|
||||
);
|
||||
} finally {
|
||||
_batchStyleOverride = null;
|
||||
}
|
||||
}
|
||||
|
||||
// アクティブなバッチタイプに応じてバッチを実行するディスパッチャ
|
||||
async function _runBatchGenerate() {
|
||||
switch (_activeBatchType) {
|
||||
@@ -1837,9 +2009,11 @@ async function _runBatchGenerate() {
|
||||
// ============================================
|
||||
|
||||
// workflowOverride: 明示的に渡された場合、comfyUI.currentWorkflow の代わりにこのワークフローで
|
||||
// 生成する（GenerateUIの表示状態には触れない。Image Edit「Inpaint」の専用ワークフロー実行から利用）。
|
||||
// 生成する（GenerateUIの表示状態には触れない。Image Edit「Inpaint」の専用ワークフロー実行や
|
||||
// AI TOOL Chatのgenerate_imageツールから利用）。
|
||||
// genOptions: 現状 seedValue のみ（指定時は_coreGenerateがseedMode "fixed"扱いにする）。
|
||||
// ボタンのclickハンドラから呼ばれる場合は第一引数がMouseEventになるため、その場合は無視する。
|
||||
async function handleGenerate(workflowOverride = null) {
|
||||
async function handleGenerate(workflowOverride = null, genOptions = {}) {
|
||||
if (workflowOverride instanceof Event) workflowOverride = null;
|
||||
|
||||
if (!workflowOverride && !comfyUI.currentWorkflow) {
|
||||
@@ -1868,7 +2042,7 @@ async function handleGenerate(workflowOverride = null) {
|
||||
await _runBatchGenerate();
|
||||
} else {
|
||||
try {
|
||||
return await _coreGenerate(false, workflowOverride);
|
||||
return await _coreGenerate(false, workflowOverride, genOptions);
|
||||
} catch (err) {
|
||||
const progressText = document.getElementById("wfm-gen-progress-text");
|
||||
if (progressText) progressText.textContent = "Error";
|
||||
@@ -1933,6 +2107,25 @@ export async function initGenerateTab() {
|
||||
}
|
||||
});
|
||||
|
||||
// Style Catalog 作成ボタン
|
||||
const catalogCreateBtn = document.getElementById("wfm-gen-catalog-create-btn");
|
||||
if (catalogCreateBtn) {
|
||||
catalogCreateBtn.textContent = t("catalogCreateBtn");
|
||||
catalogCreateBtn.addEventListener("click", () => {
|
||||
openCatalogCreateModal().catch((e) => showToast(t("errorWithMsg", e.message), "error"));
|
||||
});
|
||||
}
|
||||
|
||||
// Style Catalog を開くボタン（Gallery タブ → Style_Catalog サブタブへ遷移）
|
||||
const catalogOpenBtn = document.getElementById("wfm-gen-catalog-open-btn");
|
||||
if (catalogOpenBtn) {
|
||||
catalogOpenBtn.textContent = t("catalogOpenBtn");
|
||||
catalogOpenBtn.addEventListener("click", () => {
|
||||
document.querySelector('.wfm-tab[data-tab="gallery"]')?.click();
|
||||
document.querySelector('.wfm-gallery-subtab-btn[data-gallery-subtab="styleCatalog"]')?.click();
|
||||
});
|
||||
}
|
||||
|
||||
// Generate button
|
||||
document.getElementById("wfm-gen-generate-btn")?.addEventListener("click", () => {
|
||||
handleGenerate().catch(() => {});
|
||||
@@ -1961,7 +2154,7 @@ export async function initGenerateTab() {
|
||||
function moveRawJsonToTab(tabKey) {
|
||||
const widget = document.getElementById("wfm-gen-rawjson-widget");
|
||||
if (!widget) return;
|
||||
if (tabKey === "feeder" || tabKey === "batch") {
|
||||
if (tabKey === "feeder" || tabKey === "batch" || tabKey === "lab") {
|
||||
widget.style.display = "none";
|
||||
return;
|
||||
}
|
||||
@@ -1985,6 +2178,17 @@ export async function initGenerateTab() {
|
||||
if (target === "model" && comfyUI.currentAnalysis) {
|
||||
comfyEditor.renderLoraPane(comfyUI.currentAnalysis, "wfm-gen-lora-fields");
|
||||
}
|
||||
// Re-render Lab's column grid so Setting 1's live-reflected cells (checkpoint/
|
||||
// VAE/prompt/KSampler) pick up whatever is currently loaded in GenerateUI, not
|
||||
// whatever was loaded the last time Lab was rendered.
|
||||
if (target === "lab") refreshLabLiveDefaults();
|
||||
// Lab has its own Run/Results panel — hide the shared Generate/Seed/Batch column
|
||||
const rightCol = document.querySelector(".wfm-gen-right");
|
||||
if (rightCol) rightCol.style.display = (target === "lab") ? "none" : "";
|
||||
// Save button writes the currently loaded workflow as-is — not meaningful while
|
||||
// browsing Batch/Lab (which operate on workflow copies / their own Plan files)
|
||||
const saveBtn = document.getElementById("wfm-gen-save-btn");
|
||||
if (saveBtn) saveBtn.style.display = (target === "batch" || target === "lab") ? "none" : "";
|
||||
});
|
||||
});
|
||||
|
||||
@@ -1995,6 +2199,14 @@ export async function initGenerateTab() {
|
||||
}
|
||||
});
|
||||
|
||||
// Bypass button in LoRA column header — forces the target LoRA node's strength to
|
||||
// 0 (or restores it), independent of the Single/Stack tabs' own Apply buttons.
|
||||
document.getElementById("wfm-lora-bypass-toggle")?.addEventListener("click", () => {
|
||||
if (!comfyUI.currentAnalysis) return;
|
||||
comfyEditor.toggleLoraNodeBypass(comfyUI.currentAnalysis);
|
||||
comfyEditor.renderLoraPane(comfyUI.currentAnalysis, "wfm-gen-lora-fields");
|
||||
});
|
||||
|
||||
// Input inner tab (Prompt / Image)
|
||||
document.querySelectorAll(".wfm-input-inner-tab").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
@@ -2161,6 +2373,9 @@ export async function initGenerateTab() {
|
||||
// Feeder tab
|
||||
await initFeederTab();
|
||||
|
||||
// Lab tab (experimental I2I batch generation)
|
||||
initLabTab();
|
||||
|
||||
// Style dropdown
|
||||
await _loadStyles();
|
||||
|
||||
|
||||
+887
-51
File diff suppressed because it is too large
Load Diff
+101
-1173
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,208 @@
|
||||
/**
|
||||
* Image Edit Tab - Background Removal
|
||||
* Two backends: a lightweight in-browser model (@imgly/background-removal)
|
||||
* and BiRefNet via the Mask Editor One server endpoints.
|
||||
*/
|
||||
|
||||
import { showToast } from "../app.js";
|
||||
|
||||
export class BgRemove {
|
||||
/**
|
||||
* @param {object} callbacks
|
||||
* @param {() => object|null} callbacks.getLayerManager
|
||||
* @param {() => void} callbacks.saveUndo
|
||||
* @param {() => void} callbacks.updateCompositeView
|
||||
* @param {() => void} callbacks.refreshLayerList
|
||||
* @param {(newLayer: object) => void} callbacks.onLayerAdded
|
||||
*/
|
||||
constructor(callbacks) {
|
||||
this._cb = callbacks;
|
||||
this.birefnetAvailable = false;
|
||||
}
|
||||
|
||||
async checkAvailability() {
|
||||
try {
|
||||
const resp = await fetch("/mask_editor/birefnet/status");
|
||||
if (!resp.ok) return;
|
||||
const json = await resp.json();
|
||||
this.birefnetAvailable = json.loaded === true || json.model_found === true;
|
||||
} catch {
|
||||
this.birefnetAvailable = false;
|
||||
}
|
||||
}
|
||||
|
||||
renderPanel(el) {
|
||||
const birefnetDisabled = this.birefnetAvailable ? "" : "disabled";
|
||||
const birefnetLabel = this.birefnetAvailable
|
||||
? "BiRefNet (Mask Editor One)"
|
||||
: "BiRefNet (Mask Editor One required)";
|
||||
el.innerHTML = `
|
||||
<div class="ie-opt-group">
|
||||
<label>Model</label>
|
||||
<select id="ie-bgremove-model" class="ie-opt-select">
|
||||
<option value="imgly">Lightweight (@imgly)</option>
|
||||
<option value="birefnet" ${birefnetDisabled}>${birefnetLabel}</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="ie-opt-group">
|
||||
<label style="font-size:11px;cursor:pointer;">
|
||||
<input type="checkbox" id="ie-bgremove-new-layer" checked> New Layer
|
||||
</label>
|
||||
</div>
|
||||
<div class="ie-opt-group">
|
||||
<button class="wfm-btn wfm-btn-sm" id="ie-bgremove-btn">Remove BG</button>
|
||||
</div>
|
||||
<span id="ie-bgremove-status" style="font-size:11px;color:var(--wfm-text-secondary);margin-left:4px;"></span>
|
||||
`;
|
||||
document.getElementById("ie-bgremove-btn")?.addEventListener("click", () => this.apply());
|
||||
}
|
||||
|
||||
async apply() {
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
if (!layerMgr) { showToast("No image loaded", "error"); return; }
|
||||
const layer = layerMgr.activeLayer;
|
||||
if (!layer) { showToast("No active layer", "error"); return; }
|
||||
|
||||
const model = document.getElementById("ie-bgremove-model")?.value ?? "imgly";
|
||||
const asNew = document.getElementById("ie-bgremove-new-layer")?.checked ?? true;
|
||||
const statusEl = document.getElementById("ie-bgremove-status");
|
||||
const btn = document.getElementById("ie-bgremove-btn");
|
||||
const setStatus = msg => { if (statusEl) statusEl.textContent = msg; };
|
||||
|
||||
if (btn) btn.disabled = true;
|
||||
setStatus("Starting...");
|
||||
|
||||
try {
|
||||
const dataUrl = layer.canvas.toDataURL("image/png");
|
||||
|
||||
let resultDataUrl;
|
||||
if (model === "imgly") {
|
||||
resultDataUrl = await this._removeImgly(dataUrl, setStatus);
|
||||
} else {
|
||||
resultDataUrl = await this._removeBiRefNet(dataUrl, setStatus);
|
||||
}
|
||||
|
||||
const img = await new Promise((resolve, reject) => {
|
||||
const i = new Image();
|
||||
i.onload = () => resolve(i);
|
||||
i.onerror = () => reject(new Error("Result image load failed"));
|
||||
i.src = resultDataUrl;
|
||||
});
|
||||
|
||||
this._cb.saveUndo();
|
||||
|
||||
if (asNew) {
|
||||
const newL = layerMgr.addLayer("image", layer.name + " (no bg)", {
|
||||
contentW: img.width, contentH: img.height,
|
||||
displayW: layer.displayW, displayH: layer.displayH,
|
||||
x: layer.x, y: layer.y,
|
||||
});
|
||||
newL.ctx.drawImage(img, 0, 0);
|
||||
layerMgr.setActive(newL.id);
|
||||
this._cb.onLayerAdded(newL);
|
||||
} else {
|
||||
layer.canvas.width = img.width;
|
||||
layer.canvas.height = img.height;
|
||||
layer.ctx = layer.canvas.getContext("2d");
|
||||
layer.ctx.drawImage(img, 0, 0);
|
||||
}
|
||||
|
||||
this._cb.updateCompositeView();
|
||||
this._cb.refreshLayerList();
|
||||
setStatus("Done!");
|
||||
setTimeout(() => setStatus(""), 3000);
|
||||
showToast("Background removed", "success");
|
||||
|
||||
} catch (err) {
|
||||
setStatus("Error: " + err.message);
|
||||
showToast("BG remove failed: " + err.message, "error");
|
||||
} finally {
|
||||
if (btn) btn.disabled = false;
|
||||
}
|
||||
}
|
||||
|
||||
async _removeImgly(dataUrl, onStatus) {
|
||||
if (!window._wfmImglyRemoveBg) {
|
||||
onStatus("Loading model...");
|
||||
const mod = await import("https://esm.sh/@imgly/background-removal@1.5.7?bundle&target=es2022");
|
||||
window._wfmImglyRemoveBg = mod.removeBackground;
|
||||
}
|
||||
onStatus("Processing...");
|
||||
const res = await fetch(dataUrl);
|
||||
const blob = await res.blob();
|
||||
const resultBlob = await window._wfmImglyRemoveBg(blob, {
|
||||
publicPath: "https://staticimgly.com/@imgly/background-removal-data/1.5.7/dist/",
|
||||
});
|
||||
return await new Promise((resolve, reject) => {
|
||||
const reader = new FileReader();
|
||||
reader.onload = () => resolve(reader.result);
|
||||
reader.onerror = reject;
|
||||
reader.readAsDataURL(resultBlob);
|
||||
});
|
||||
}
|
||||
|
||||
async _removeBiRefNet(dataUrl, onStatus) {
|
||||
const NODE_ID = "wfs_bgremove";
|
||||
|
||||
// 1. アクティブレイヤー画像を Mask Editor One のキャッシュに登録
|
||||
onStatus("Sending image...");
|
||||
const storeResp = await fetch("/mask_editor/store_image", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ node_id: NODE_ID, image_b64: dataUrl }),
|
||||
});
|
||||
if (!storeResp.ok) throw new Error("Failed to cache image for BiRefNet");
|
||||
|
||||
// 2. BiRefNet 推論実行
|
||||
onStatus("Running BiRefNet...");
|
||||
const resp = await fetch("/mask_editor/birefnet/remove_bg", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ node_id: NODE_ID }),
|
||||
});
|
||||
const json = await resp.json();
|
||||
if (json.error) throw new Error(json.error);
|
||||
|
||||
// 3. グレースケールマスク (白=前景) → RGBA PNG に変換して返す
|
||||
onStatus("Compositing...");
|
||||
return await this._applyMaskToImage(dataUrl, json.mask_b64);
|
||||
}
|
||||
|
||||
async _applyMaskToImage(imageB64, maskB64) {
|
||||
const loadImage = src => new Promise((resolve, reject) => {
|
||||
const img = new Image();
|
||||
img.onload = () => resolve(img);
|
||||
img.onerror = () => reject(new Error("Image load failed: " + src.slice(0, 40)));
|
||||
img.src = src;
|
||||
});
|
||||
const [origImg, maskImg] = await Promise.all([loadImage(imageB64), loadImage(maskB64)]);
|
||||
|
||||
const w = origImg.naturalWidth;
|
||||
const h = origImg.naturalHeight;
|
||||
|
||||
// マスク: グレースケール輝度値 → アルファチャンネル (RGBA 白マスク)
|
||||
const maskCanvas = document.createElement("canvas");
|
||||
maskCanvas.width = w;
|
||||
maskCanvas.height = h;
|
||||
const mc = maskCanvas.getContext("2d");
|
||||
mc.drawImage(maskImg, 0, 0, w, h);
|
||||
const maskData = mc.getImageData(0, 0, w, h);
|
||||
const md = maskData.data;
|
||||
for (let i = 0; i < md.length; i += 4) {
|
||||
md[i + 3] = md[i]; // 輝度 → アルファ
|
||||
md[i] = md[i + 1] = md[i + 2] = 255;
|
||||
}
|
||||
mc.putImageData(maskData, 0, 0);
|
||||
|
||||
// 元画像にマスクを destination-in で適用
|
||||
const out = document.createElement("canvas");
|
||||
out.width = w;
|
||||
out.height = h;
|
||||
const ctx = out.getContext("2d");
|
||||
ctx.drawImage(origImg, 0, 0, w, h);
|
||||
ctx.globalCompositeOperation = "destination-in";
|
||||
ctx.drawImage(maskCanvas, 0, 0, w, h);
|
||||
|
||||
return out.toDataURL("image/png");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,259 @@
|
||||
/**
|
||||
* Image Edit Tab - Blur / Mosaic Tool
|
||||
* Whole-layer blur/mosaic buttons plus a drag-to-select rectangle mode
|
||||
* that applies blur or mosaic to just the dragged region.
|
||||
*/
|
||||
|
||||
import { showToast } from "../app.js";
|
||||
|
||||
function _applyMosaicToRegion(ctx, x, y, w, h, size) {
|
||||
if (w <= 0 || h <= 0 || size < 1) return;
|
||||
const imgData = ctx.getImageData(x, y, w, h);
|
||||
const d = imgData.data;
|
||||
for (let py = 0; py < h; py += size) {
|
||||
for (let px = 0; px < w; px += size) {
|
||||
const i = (py * w + px) * 4;
|
||||
const r = d[i], g = d[i + 1], b = d[i + 2], a = d[i + 3];
|
||||
for (let by = py; by < Math.min(py + size, h); by++) {
|
||||
for (let bx = px; bx < Math.min(px + size, w); bx++) {
|
||||
const j = (by * w + bx) * 4;
|
||||
d[j] = r; d[j + 1] = g; d[j + 2] = b; d[j + 3] = a;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
ctx.putImageData(imgData, x, y);
|
||||
}
|
||||
|
||||
export class BlurTool {
|
||||
/**
|
||||
* @param {object} callbacks
|
||||
* @param {() => object|null} callbacks.getLayerManager
|
||||
* @param {() => number} callbacks.getZoom
|
||||
* @param {() => void} callbacks.saveUndo
|
||||
* @param {() => void} callbacks.updateCompositeView
|
||||
* @param {() => void} callbacks.refreshLayerList
|
||||
* @param {(toolId: string) => void} callbacks.renderToolOptions
|
||||
*/
|
||||
constructor(callbacks) {
|
||||
this._cb = callbacks;
|
||||
this.rectMode = null; // null | 'blur' | 'mosaic'
|
||||
this._dragging = false;
|
||||
this._dragStart = null;
|
||||
this._dragCur = null;
|
||||
}
|
||||
|
||||
activate() {
|
||||
const overlay = document.getElementById("ie-canvas-overlay");
|
||||
if (overlay) overlay.style.cursor = this.rectMode ? "crosshair" : "default";
|
||||
}
|
||||
|
||||
deactivate() {
|
||||
this.rectMode = null;
|
||||
this._dragging = false;
|
||||
const overlay = document.getElementById("ie-canvas-overlay");
|
||||
if (overlay) {
|
||||
overlay.style.cursor = "";
|
||||
overlay.getContext("2d").clearRect(0, 0, overlay.width, overlay.height);
|
||||
}
|
||||
}
|
||||
|
||||
renderPanel(el) {
|
||||
const blurOn = this.rectMode === "blur";
|
||||
const mosaicOn = this.rectMode === "mosaic";
|
||||
el.innerHTML = `
|
||||
<div class="ie-opt-group">
|
||||
<span style="font-size:11px;color:var(--wfm-text-secondary);">Whole:</span>
|
||||
</div>
|
||||
<div class="ie-opt-group">
|
||||
<label style="font-size:11px;">Blur</label>
|
||||
<input type="range" id="ie-whole-blur" min="1" max="50" value="10" style="width:70px;">
|
||||
<span id="ie-whole-blur-val" style="font-size:11px;min-width:22px;">10</span>px
|
||||
<button class="wfm-btn wfm-btn-sm" id="ie-whole-blur-apply">Apply</button>
|
||||
</div>
|
||||
<div class="ie-opt-group">
|
||||
<label style="font-size:11px;">Mosaic</label>
|
||||
<input type="range" id="ie-whole-mosaic" min="5" max="100" value="20" style="width:70px;">
|
||||
<span id="ie-whole-mosaic-val" style="font-size:11px;min-width:22px;">20</span>px
|
||||
<button class="wfm-btn wfm-btn-sm" id="ie-whole-mosaic-apply">Apply</button>
|
||||
</div>
|
||||
<div style="width:1px;height:22px;background:var(--wfm-border);margin:0 6px;flex-shrink:0;"></div>
|
||||
<div class="ie-opt-group">
|
||||
<span style="font-size:11px;color:var(--wfm-text-secondary);">Rect:</span>
|
||||
</div>
|
||||
<div class="ie-opt-group">
|
||||
<button class="wfm-btn wfm-btn-sm${blurOn ? " ie-opt-active" : ""}" id="ie-rect-blur-toggle"
|
||||
style="${blurOn ? "background:var(--wfm-accent,#4682e6);color:#fff;" : ""}">
|
||||
Rect Blur: ${blurOn ? "ON" : "OFF"}
|
||||
</button>
|
||||
<input type="range" id="ie-rect-blur" min="1" max="50" value="10" style="width:70px;">
|
||||
<span id="ie-rect-blur-val" style="font-size:11px;min-width:22px;">10</span>px
|
||||
</div>
|
||||
<div class="ie-opt-group">
|
||||
<button class="wfm-btn wfm-btn-sm${mosaicOn ? " ie-opt-active" : ""}" id="ie-rect-mosaic-toggle"
|
||||
style="${mosaicOn ? "background:var(--wfm-accent,#4682e6);color:#fff;" : ""}">
|
||||
Rect Mosaic: ${mosaicOn ? "ON" : "OFF"}
|
||||
</button>
|
||||
<input type="range" id="ie-rect-mosaic" min="5" max="50" value="15" style="width:70px;">
|
||||
<span id="ie-rect-mosaic-val" style="font-size:11px;min-width:22px;">15</span>px
|
||||
</div>
|
||||
`;
|
||||
document.getElementById("ie-whole-blur")?.addEventListener("input", e => {
|
||||
document.getElementById("ie-whole-blur-val").textContent = e.target.value;
|
||||
});
|
||||
document.getElementById("ie-whole-mosaic")?.addEventListener("input", e => {
|
||||
document.getElementById("ie-whole-mosaic-val").textContent = e.target.value;
|
||||
});
|
||||
document.getElementById("ie-whole-blur-apply")?.addEventListener("click", () => {
|
||||
this.applyWholeBlur(parseInt(document.getElementById("ie-whole-blur").value));
|
||||
});
|
||||
document.getElementById("ie-whole-mosaic-apply")?.addEventListener("click", () => {
|
||||
this.applyWholeMosaic(parseInt(document.getElementById("ie-whole-mosaic").value));
|
||||
});
|
||||
document.getElementById("ie-rect-blur-toggle")?.addEventListener("click", () => {
|
||||
this.rectMode = this.rectMode === "blur" ? null : "blur";
|
||||
this._cb.renderToolOptions("blur");
|
||||
const ov = document.getElementById("ie-canvas-overlay");
|
||||
if (ov) ov.style.cursor = this.rectMode ? "crosshair" : "default";
|
||||
});
|
||||
document.getElementById("ie-rect-mosaic-toggle")?.addEventListener("click", () => {
|
||||
this.rectMode = this.rectMode === "mosaic" ? null : "mosaic";
|
||||
this._cb.renderToolOptions("blur");
|
||||
const ov = document.getElementById("ie-canvas-overlay");
|
||||
if (ov) ov.style.cursor = this.rectMode ? "crosshair" : "default";
|
||||
});
|
||||
document.getElementById("ie-rect-blur")?.addEventListener("input", e => {
|
||||
document.getElementById("ie-rect-blur-val").textContent = e.target.value;
|
||||
});
|
||||
document.getElementById("ie-rect-mosaic")?.addEventListener("input", e => {
|
||||
document.getElementById("ie-rect-mosaic-val").textContent = e.target.value;
|
||||
});
|
||||
}
|
||||
|
||||
onMouseDown(pos) {
|
||||
if (!this.rectMode) return;
|
||||
this._dragging = true;
|
||||
this._dragStart = { x: pos.x, y: pos.y };
|
||||
this._dragCur = { x: pos.x, y: pos.y };
|
||||
}
|
||||
|
||||
onMouseMove(pos) {
|
||||
if (!this._dragging) return;
|
||||
this._dragCur = pos;
|
||||
this._drawPreview();
|
||||
}
|
||||
|
||||
onMouseUp() {
|
||||
if (!this._dragging) return;
|
||||
this._dragging = false;
|
||||
this._applyRectEffect();
|
||||
const overlay = document.getElementById("ie-canvas-overlay");
|
||||
if (overlay) overlay.getContext("2d").clearRect(0, 0, overlay.width, overlay.height);
|
||||
}
|
||||
|
||||
onMouseLeave() {
|
||||
if (!this._dragging) return;
|
||||
this._dragging = false;
|
||||
const overlay = document.getElementById("ie-canvas-overlay");
|
||||
if (overlay) overlay.getContext("2d").clearRect(0, 0, overlay.width, overlay.height);
|
||||
}
|
||||
|
||||
applyWholeBlur(amount) {
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
if (!layerMgr) return;
|
||||
const layer = layerMgr.activeLayer;
|
||||
if (!layer) { showToast("No active layer", "error"); return; }
|
||||
this._cb.saveUndo();
|
||||
const w = layer.canvas.width, h = layer.canvas.height;
|
||||
const tmp = document.createElement("canvas");
|
||||
tmp.width = w; tmp.height = h;
|
||||
const tc = tmp.getContext("2d");
|
||||
tc.filter = `blur(${amount}px)`;
|
||||
tc.drawImage(layer.canvas, 0, 0);
|
||||
layer.ctx.clearRect(0, 0, w, h);
|
||||
layer.ctx.drawImage(tmp, 0, 0);
|
||||
this._cb.updateCompositeView();
|
||||
this._cb.refreshLayerList();
|
||||
}
|
||||
|
||||
applyWholeMosaic(size) {
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
if (!layerMgr) return;
|
||||
const layer = layerMgr.activeLayer;
|
||||
if (!layer) { showToast("No active layer", "error"); return; }
|
||||
this._cb.saveUndo();
|
||||
_applyMosaicToRegion(layer.ctx, 0, 0, layer.canvas.width, layer.canvas.height, size);
|
||||
this._cb.updateCompositeView();
|
||||
this._cb.refreshLayerList();
|
||||
}
|
||||
|
||||
// canvas座標 (cx,cy) → layer.canvas 座標への逆変換
|
||||
_canvasToLayerCoords(layer, cx, cy) {
|
||||
const centerX = layer.x + layer.displayW / 2;
|
||||
const centerY = layer.y + layer.displayH / 2;
|
||||
const dx = cx - centerX, dy = cy - centerY;
|
||||
const angle = -(layer.rotation || 0) * Math.PI / 180;
|
||||
const rdx = dx * Math.cos(angle) - dy * Math.sin(angle);
|
||||
const rdy = dx * Math.sin(angle) + dy * Math.cos(angle);
|
||||
const scaleX = layer.displayW / layer.canvas.width;
|
||||
const scaleY = layer.displayH / layer.canvas.height;
|
||||
let lx = rdx / scaleX + layer.canvas.width / 2;
|
||||
let ly = rdy / scaleY + layer.canvas.height / 2;
|
||||
if (layer.flipX) lx = layer.canvas.width - lx;
|
||||
if (layer.flipY) ly = layer.canvas.height - ly;
|
||||
return { x: lx, y: ly };
|
||||
}
|
||||
|
||||
_drawPreview() {
|
||||
const overlay = document.getElementById("ie-canvas-overlay");
|
||||
if (!overlay) return;
|
||||
const ctx = overlay.getContext("2d");
|
||||
ctx.clearRect(0, 0, overlay.width, overlay.height);
|
||||
const s = this._dragStart, c = this._dragCur;
|
||||
if (!s || !c) return;
|
||||
const x = Math.min(s.x, c.x), y = Math.min(s.y, c.y);
|
||||
const w = Math.abs(c.x - s.x), h = Math.abs(c.y - s.y);
|
||||
const zoom = this._cb.getZoom();
|
||||
ctx.strokeStyle = this.rectMode === "blur" ? "#4af" : "#fa4";
|
||||
ctx.lineWidth = 1 / zoom;
|
||||
ctx.setLineDash([4 / zoom, 2 / zoom]);
|
||||
ctx.strokeRect(x, y, w, h);
|
||||
ctx.setLineDash([]);
|
||||
}
|
||||
|
||||
_applyRectEffect() {
|
||||
const layer = this._cb.getLayerManager()?.activeLayer;
|
||||
if (!layer || !this._dragStart || !this._dragCur) return;
|
||||
const s = this._dragStart, c = this._dragCur;
|
||||
if (Math.abs(c.x - s.x) < 3 || Math.abs(c.y - s.y) < 3) return;
|
||||
|
||||
const minX = Math.min(s.x, c.x), minY = Math.min(s.y, c.y);
|
||||
const maxX = Math.max(s.x, c.x), maxY = Math.max(s.y, c.y);
|
||||
const p1 = this._canvasToLayerCoords(layer, minX, minY);
|
||||
const p2 = this._canvasToLayerCoords(layer, maxX, maxY);
|
||||
|
||||
const lx = Math.round(Math.max(0, Math.min(p1.x, p2.x)));
|
||||
const ly = Math.round(Math.max(0, Math.min(p1.y, p2.y)));
|
||||
const lw = Math.round(Math.min(layer.canvas.width - lx, Math.abs(p2.x - p1.x)));
|
||||
const lh = Math.round(Math.min(layer.canvas.height - ly, Math.abs(p2.y - p1.y)));
|
||||
if (lw <= 0 || lh <= 0) return;
|
||||
|
||||
this._cb.saveUndo();
|
||||
|
||||
if (this.rectMode === "blur") {
|
||||
const amount = parseInt(document.getElementById("ie-rect-blur")?.value ?? "10");
|
||||
const tmp = document.createElement("canvas");
|
||||
tmp.width = layer.canvas.width; tmp.height = layer.canvas.height;
|
||||
const tc = tmp.getContext("2d");
|
||||
tc.filter = `blur(${amount}px)`;
|
||||
tc.drawImage(layer.canvas, 0, 0);
|
||||
layer.ctx.drawImage(tmp, lx, ly, lw, lh, lx, ly, lw, lh);
|
||||
} else {
|
||||
const size = parseInt(document.getElementById("ie-rect-mosaic")?.value ?? "15");
|
||||
_applyMosaicToRegion(layer.ctx, lx, ly, lw, lh, size);
|
||||
}
|
||||
|
||||
this._cb.updateCompositeView();
|
||||
this._cb.refreshLayerList();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,136 @@
|
||||
/**
|
||||
* Image Edit Tab - File Export
|
||||
* Composites the visible layers (respecting mask-apply clipping) into a
|
||||
* flat canvas and offers three destinations: local PNG download, WFS
|
||||
* Gallery, and the selected node's image widget on the ComfyUI canvas
|
||||
* (Send to LI node).
|
||||
*/
|
||||
|
||||
import { showToast } from "../app.js";
|
||||
import { Layer } from "./LayerManager.js";
|
||||
|
||||
export class FileExport {
|
||||
/**
|
||||
* @param {object} callbacks
|
||||
* @param {() => object|null} callbacks.getLayerManager
|
||||
* @param {() => {w: number, h: number}} callbacks.getCanvasSize
|
||||
* @param {() => string} callbacks.getBaseName
|
||||
* @param {(ctx: CanvasRenderingContext2D, drawCanvas: HTMLCanvasElement, maskLayer: object, targetLayer: object, showOverlay: boolean) => void} callbacks.renderMaskedLayer
|
||||
*/
|
||||
constructor(callbacks) {
|
||||
this._cb = callbacks;
|
||||
}
|
||||
|
||||
_buildCompositeCanvas() {
|
||||
const { w, h } = this._cb.getCanvasSize();
|
||||
const canvas = document.createElement("canvas");
|
||||
canvas.width = w;
|
||||
canvas.height = h;
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
if (!layerMgr) return canvas;
|
||||
this._compositeForExport(canvas, layerMgr);
|
||||
return canvas;
|
||||
}
|
||||
|
||||
// 保存用合成: maskApply=true のクリッピングを適用、マスクオーバーレイは除外
|
||||
_compositeForExport(target, layerMgr) {
|
||||
const ctx = target.getContext("2d");
|
||||
ctx.clearRect(0, 0, target.width, target.height);
|
||||
const layers = layerMgr.layers;
|
||||
const maskedIndices = new Set();
|
||||
for (let i = 0; i < layers.length; i++) {
|
||||
if (layers[i].type === "mask" && layers[i].maskApply && layers[i].visible && i + 1 < layers.length) {
|
||||
maskedIndices.add(i + 1);
|
||||
}
|
||||
}
|
||||
for (let i = layers.length - 1; i >= 0; i--) {
|
||||
const layer = layers[i];
|
||||
if (!layer.visible) continue;
|
||||
if (maskedIndices.has(i)) continue;
|
||||
if (layer.type === "mask") {
|
||||
if (layer.maskApply && i + 1 < layers.length) {
|
||||
this._cb.renderMaskedLayer(ctx, target, layer, layers[i + 1], false);
|
||||
}
|
||||
// maskApply=false のマスクはエクスポートに含めない
|
||||
} else {
|
||||
ctx.save();
|
||||
ctx.globalAlpha = layer.opacity;
|
||||
ctx.globalCompositeOperation = layer.blendMode;
|
||||
Layer.applyTransform(ctx, layer);
|
||||
ctx.drawImage(layer.canvas, -layer.canvas.width / 2, -layer.canvas.height / 2);
|
||||
ctx.restore();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
savePng() {
|
||||
if (!this._cb.getLayerManager()) { showToast("No image loaded", "error"); return; }
|
||||
const canvas = this._buildCompositeCanvas();
|
||||
const a = document.createElement("a");
|
||||
a.href = canvas.toDataURL("image/png");
|
||||
a.download = (this._cb.getBaseName() || "wfs-edit") + "-output.png";
|
||||
a.click();
|
||||
showToast("PNG saved", "success");
|
||||
}
|
||||
|
||||
async saveToGallery() {
|
||||
if (!this._cb.getLayerManager()) { showToast("No image loaded", "error"); return; }
|
||||
|
||||
// デフォルトファイル名: wfs-image-YYYYMMDDHHmmss
|
||||
const now = new Date();
|
||||
const pad = n => String(n).padStart(2, "0");
|
||||
const ts = `${now.getFullYear()}${pad(now.getMonth()+1)}${pad(now.getDate())}${pad(now.getHours())}${pad(now.getMinutes())}${pad(now.getSeconds())}`;
|
||||
const defaultName = `wfs-image-${ts}`;
|
||||
|
||||
const filename = window.prompt("Save to Gallery — file name (without extension):", defaultName);
|
||||
if (filename === null) return; // キャンセル
|
||||
const safeName = filename.trim() || defaultName;
|
||||
|
||||
const canvas = this._buildCompositeCanvas();
|
||||
const imageData = canvas.toDataURL("image/png");
|
||||
|
||||
try {
|
||||
const r = await fetch("/wfm/gallery/image/save", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ filename: safeName, imageData }),
|
||||
});
|
||||
if (!r.ok) {
|
||||
const e = await r.json().catch(() => ({}));
|
||||
throw new Error(e.error || r.statusText);
|
||||
}
|
||||
showToast(`Saved to Gallery: ${safeName}.png`, "success");
|
||||
} catch (err) {
|
||||
showToast(`Gallery save failed: ${err.message}`, "error");
|
||||
}
|
||||
}
|
||||
|
||||
// Uploads the composite via /upload/image, then (via window.opener,
|
||||
// since WFS runs in its own window — same cross-window pattern as
|
||||
// wfmReceiveWorkflow/wfmOpenMaskEditorForNode) writes the resulting
|
||||
// filename into the "image" widget of the currently selected ComfyUI
|
||||
// node (falling back to the first node in the graph with one), mirroring
|
||||
// the chat_TE custom node's "Send to workflow" button.
|
||||
async sendToWorkflow() {
|
||||
if (!this._cb.getLayerManager()) { showToast("No image loaded", "error"); return; }
|
||||
if (!window.opener || typeof window.opener.wfmSendImageToSelectedNode !== "function") {
|
||||
showToast("Open Workflow Studio from ComfyUI's top menu to use this feature", "error");
|
||||
return;
|
||||
}
|
||||
const canvas = this._buildCompositeCanvas();
|
||||
const blob = await new Promise(resolve => canvas.toBlob(resolve, "image/png"));
|
||||
const file = new File([blob], (this._cb.getBaseName() || "wfs-edit") + "-output.png", { type: "image/png" });
|
||||
const form = new FormData();
|
||||
form.append("image", file);
|
||||
form.append("overwrite", "true");
|
||||
try {
|
||||
const r = await fetch("/upload/image", { method: "POST", body: form });
|
||||
const data = await r.json();
|
||||
const filename = data.subfolder ? `${data.subfolder}/${data.name}` : data.name;
|
||||
window.opener.wfmSendImageToSelectedNode(filename);
|
||||
showToast(`Sent "${filename}" to LI node`, "success");
|
||||
} catch (err) {
|
||||
showToast(`Send to LI node failed: ${err.message}`, "error");
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,197 @@
|
||||
/**
|
||||
* Image Edit Tab - G'MIC Integration
|
||||
* Launches the G'MIC GUI on the server for the active layer, polls job
|
||||
* status, and applies the resulting image back onto the layer.
|
||||
*/
|
||||
|
||||
import { showToast } from "../app.js";
|
||||
|
||||
export class GmicIntegration {
|
||||
/**
|
||||
* @param {object} callbacks
|
||||
* @param {() => object|null} callbacks.getLayerManager
|
||||
* @param {() => void} callbacks.syncActiveLayerFromCanvas
|
||||
* @param {() => void} callbacks.saveUndo
|
||||
* @param {() => void} callbacks.updateCompositeView
|
||||
* @param {() => void} callbacks.refreshLayerList
|
||||
* @param {(toolId: string) => void} callbacks.renderToolOptions
|
||||
*/
|
||||
constructor(callbacks) {
|
||||
this._cb = callbacks;
|
||||
this.state = {
|
||||
lastResultJobId: null,
|
||||
processing: false,
|
||||
aborted: false
|
||||
};
|
||||
}
|
||||
|
||||
renderPanel(el) {
|
||||
const openBtnDisabled = this.state.processing ? "disabled" : "";
|
||||
const applyBtnDisabled = (!this.state.lastResultJobId || this.state.processing) ? "disabled" : "";
|
||||
const progressStyle = this.state.processing ? "display:flex" : "display:none";
|
||||
|
||||
el.innerHTML = `
|
||||
<div class="ie-opt-group">
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="ie-gmic-open-btn" ${openBtnDisabled}>G'MIC GUIで編集</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="ie-gmic-apply-btn" ${applyBtnDisabled}>結果を反映</button>
|
||||
</div>
|
||||
<div class="ie-opt-group" id="ie-gmic-progress-area" style="${progressStyle}; align-items:center; gap:6px;">
|
||||
<span id="ie-gmic-progress-lbl" style="font-size:11px; color:var(--wfm-text-secondary);">G'MIC GUIを起動中...</span>
|
||||
<button class="wfm-btn wfm-btn-sm" id="ie-gmic-abort-btn" style="background:#ea4335;color:#fff;">中断</button>
|
||||
</div>
|
||||
`;
|
||||
document.getElementById("ie-gmic-open-btn")?.addEventListener("click", () => this.openGui());
|
||||
document.getElementById("ie-gmic-apply-btn")?.addEventListener("click", () => this.applyResult());
|
||||
document.getElementById("ie-gmic-abort-btn")?.addEventListener("click", () => this.abort());
|
||||
}
|
||||
|
||||
async openGui() {
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
if (!layerMgr) { showToast("No image loaded", "error"); return; }
|
||||
const layer = layerMgr.activeLayer;
|
||||
if (!layer) { showToast("No active layer", "error"); return; }
|
||||
this._cb.syncActiveLayerFromCanvas();
|
||||
|
||||
if (this.state.processing) return;
|
||||
|
||||
const dataUrl = layer.canvas.toDataURL("image/png");
|
||||
|
||||
this.state.processing = true;
|
||||
const openBtn = document.getElementById("ie-gmic-open-btn");
|
||||
const progressArea = document.getElementById("ie-gmic-progress-area");
|
||||
const progressLbl = document.getElementById("ie-gmic-progress-lbl");
|
||||
const applyBtn = document.getElementById("ie-gmic-apply-btn");
|
||||
|
||||
if (openBtn) openBtn.disabled = true;
|
||||
if (applyBtn) applyBtn.disabled = true;
|
||||
if (progressArea) progressArea.style.display = "flex";
|
||||
if (progressLbl) progressLbl.textContent = "画像をサーバーへ送信中...";
|
||||
|
||||
try {
|
||||
const res = await fetch("/api/wfm/gmic/open", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ image_b64: dataUrl })
|
||||
});
|
||||
if (!res.ok) {
|
||||
const err = await res.json().catch(() => ({}));
|
||||
throw new Error(err.error || `HTTP ${res.status}`);
|
||||
}
|
||||
const data = await res.json();
|
||||
this.state.lastResultJobId = data.job_id;
|
||||
if (progressLbl) progressLbl.textContent = "G'MIC GUIで編集中... フィルターを選択して「OK」を押すと適用可能になります";
|
||||
await this._waitForJob(data.job_id);
|
||||
} catch (e) {
|
||||
if (e.message !== "__aborted__") {
|
||||
showToast("G'MIC Error: " + e.message, "error");
|
||||
}
|
||||
this.state.processing = false;
|
||||
this.state.aborted = false;
|
||||
if (openBtn) openBtn.disabled = false;
|
||||
if (progressArea) progressArea.style.display = "none";
|
||||
}
|
||||
}
|
||||
|
||||
abort() {
|
||||
this.state.aborted = true;
|
||||
this.state.processing = false;
|
||||
const openBtn = document.getElementById("ie-gmic-open-btn");
|
||||
const progressArea = document.getElementById("ie-gmic-progress-area");
|
||||
if (progressArea) progressArea.style.display = "none";
|
||||
if (openBtn) openBtn.disabled = false;
|
||||
}
|
||||
|
||||
async _waitForJob(jobId) {
|
||||
const progressLbl = document.getElementById("ie-gmic-progress-lbl");
|
||||
const applyBtn = document.getElementById("ie-gmic-apply-btn");
|
||||
const maxWait = 600, interval = 2000;
|
||||
const start = Date.now();
|
||||
this.state.aborted = false;
|
||||
|
||||
while (true) {
|
||||
if (this.state.aborted) throw new Error("__aborted__");
|
||||
if ((Date.now() - start) / 1000 > maxWait) throw new Error("Timeout");
|
||||
await new Promise(r => setTimeout(r, interval));
|
||||
if (this.state.aborted) throw new Error("__aborted__");
|
||||
try {
|
||||
const res = await fetch(`/api/wfm/gmic/status/${jobId}`);
|
||||
if (res.status === 404) throw new Error("__aborted__");
|
||||
if (!res.ok) continue;
|
||||
const status = await res.json();
|
||||
if (status.status === "completed") {
|
||||
this.state.lastResultJobId = jobId;
|
||||
if (applyBtn) applyBtn.disabled = false;
|
||||
if (progressLbl) progressLbl.textContent = "処理完了 → 「結果を反映」で画像に適用";
|
||||
this.state.processing = false;
|
||||
showToast("G'MIC filtering complete. Click Apply to insert result.", "success");
|
||||
return;
|
||||
}
|
||||
if (status.status === "failed") {
|
||||
if (progressLbl) progressLbl.textContent = status.error || "G'MIC GUIがキャンセルされました";
|
||||
throw new Error("__aborted__");
|
||||
}
|
||||
if (progressLbl) progressLbl.textContent = status.message || "G'MIC GUIで編集中...";
|
||||
} catch (e) {
|
||||
if (e.message === "__aborted__" || e.message.includes("Timeout")) throw e;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async applyResult() {
|
||||
if (!this.state.lastResultJobId) {
|
||||
showToast("No G'MIC result to apply", "error");
|
||||
return;
|
||||
}
|
||||
const applyBtn = document.getElementById("ie-gmic-apply-btn");
|
||||
const openBtn = document.getElementById("ie-gmic-open-btn");
|
||||
const progressArea = document.getElementById("ie-gmic-progress-area");
|
||||
if (applyBtn) applyBtn.disabled = true;
|
||||
|
||||
try {
|
||||
const statusRes = await fetch(`/api/wfm/gmic/status/${this.state.lastResultJobId}`);
|
||||
if (!statusRes.ok) throw new Error(`HTTP ${statusRes.status}`);
|
||||
const statusData = await statusRes.json();
|
||||
if (!statusData.result_path) throw new Error("No result path found");
|
||||
|
||||
const b64res = await fetch("/api/wfm/gmic/result", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ result_path: statusData.result_path })
|
||||
});
|
||||
if (!b64res.ok) throw new Error(`HTTP ${b64res.status}`);
|
||||
const { image_b64: dataUrl } = await b64res.json();
|
||||
|
||||
const img = await new Promise((resolve, reject) => {
|
||||
const i = new Image();
|
||||
i.onload = () => resolve(i);
|
||||
i.onerror = () => reject(new Error("Failed to load result image"));
|
||||
i.src = dataUrl;
|
||||
});
|
||||
|
||||
this._cb.saveUndo();
|
||||
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
const layer = layerMgr?.activeLayer;
|
||||
if (!layer) throw new Error("No active layer");
|
||||
|
||||
layer.canvas.width = img.width;
|
||||
layer.canvas.height = img.height;
|
||||
layer.ctx = layer.canvas.getContext("2d");
|
||||
layer.ctx.drawImage(img, 0, 0);
|
||||
|
||||
this._cb.updateCompositeView();
|
||||
this._cb.refreshLayerList();
|
||||
showToast("G'MIC filter applied successfully", "success");
|
||||
|
||||
// Reset G'MIC status
|
||||
this.state.lastResultJobId = null;
|
||||
this.state.processing = false;
|
||||
if (openBtn) openBtn.disabled = false;
|
||||
if (progressArea) progressArea.style.display = "none";
|
||||
this._cb.renderToolOptions("filter");
|
||||
} catch (err) {
|
||||
showToast("Failed to apply G'MIC result: " + err.message, "error");
|
||||
if (applyBtn) applyBtn.disabled = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,303 @@
|
||||
/**
|
||||
* Image Edit Tab - Inpaint / I2I Actions
|
||||
* Composites the active mask layer against the background and runs it
|
||||
* through GenerateUI's currently loaded workflow (or a dedicated one) for
|
||||
* inpainting, plus a mask-less I2I variant. Also exposes the external
|
||||
* entry points used by other same-origin panels (e.g. Comic Creator).
|
||||
*/
|
||||
|
||||
import { showToast } from "../app.js";
|
||||
import { comfyUI } from "../comfyui-client.js";
|
||||
import { comfyEditor } from "../comfyui-editor.js";
|
||||
import { comfyWorkflow } from "../comfyui-workflow.js";
|
||||
|
||||
export class InpaintI2IActions {
|
||||
/**
|
||||
* @param {object} callbacks
|
||||
* @param {() => object|null} callbacks.getLayerManager
|
||||
* @param {() => HTMLCanvasElement} callbacks.buildBgCanvas
|
||||
*/
|
||||
constructor(callbacks) {
|
||||
this._cb = callbacks;
|
||||
|
||||
this.positive = "";
|
||||
this.negative = "";
|
||||
this.growMaskBy = null; // null = 未初期化（ワークフローの現在値から初期化）
|
||||
this.denoise = null; // null = 未初期化（ワークフローの現在値から初期化）
|
||||
this.running = false;
|
||||
this.resultUrl = null;
|
||||
this.i2iExternalRunning = false;
|
||||
|
||||
// Inpaint 専用ワークフロー（OFF時は GenerateUI で読み込み中のワークフローを使用）
|
||||
this.useDedicated = false;
|
||||
this.dedicatedFilename = null;
|
||||
this.workflowList = [];
|
||||
}
|
||||
|
||||
async fetchWorkflowList(onLoaded) {
|
||||
try {
|
||||
const resp = await fetch(`${comfyUI.baseUrl}/api/wfm/workflows`);
|
||||
if (!resp.ok) return;
|
||||
const list = await resp.json();
|
||||
this.workflowList = (list || []).map((w) => w.filename).filter(Boolean);
|
||||
onLoaded?.();
|
||||
} catch {
|
||||
this.workflowList = [];
|
||||
}
|
||||
}
|
||||
|
||||
renderPanel() {
|
||||
const pane = document.getElementById("ie-props-pane");
|
||||
const body = document.getElementById("ie-props-body");
|
||||
const title = document.getElementById("ie-props-title");
|
||||
if (!pane || !body) return;
|
||||
pane.style.display = "flex";
|
||||
if (title) title.textContent = "Inpaint";
|
||||
|
||||
// 未初期化ならワークフローの現在値から初期化（VAEEncodeForInpaint.grow_mask_by / KSampler.denoise）
|
||||
if (this.growMaskBy == null) {
|
||||
this.growMaskBy = comfyUI.currentAnalysis?.inpaint_encode_nodes?.[0]?.grow_mask_by ?? 6;
|
||||
}
|
||||
if (this.denoise == null) {
|
||||
const samplerNode = (comfyUI.currentAnalysis?.sampler_nodes || []).find((n) => n.denoise !== undefined);
|
||||
this.denoise = samplerNode?.denoise ?? 1.0;
|
||||
}
|
||||
|
||||
body.innerHTML = `
|
||||
<div class="ie-props-row">
|
||||
<label style="display:flex;align-items:center;gap:6px;cursor:pointer;">
|
||||
<input type="checkbox" id="ie-inpaint-use-dedicated" ${this.useDedicated ? "checked" : ""}>
|
||||
Use dedicated workflow
|
||||
</label>
|
||||
</div>
|
||||
<div class="ie-props-row" id="ie-inpaint-dedicated-row" style="${this.useDedicated ? "" : "display:none;"}">
|
||||
<select id="ie-inpaint-dedicated-select" style="flex:1;">
|
||||
<option value="">-- select workflow --</option>
|
||||
${this.workflowList.map((f) => `<option value="${f}" ${f === this.dedicatedFilename ? "selected" : ""}>${f}</option>`).join("")}
|
||||
</select>
|
||||
</div>
|
||||
<div class="ie-props-row" style="flex-direction:column;align-items:stretch;">
|
||||
<label>Positive Prompt</label>
|
||||
<textarea id="ie-inpaint-positive" rows="3" style="width:100%;resize:vertical;font-size:12px;">${this.positive}</textarea>
|
||||
</div>
|
||||
<div class="ie-props-row" style="flex-direction:column;align-items:stretch;">
|
||||
<label>Negative Prompt</label>
|
||||
<textarea id="ie-inpaint-negative" rows="3" style="width:100%;resize:vertical;font-size:12px;">${this.negative}</textarea>
|
||||
</div>
|
||||
<div class="ie-props-row">
|
||||
<label>Grow Mask By</label>
|
||||
<input type="number" id="ie-inpaint-grow-mask" min="0" max="200" step="1" value="${this.growMaskBy}" style="width:60px;">
|
||||
</div>
|
||||
<div class="ie-props-row">
|
||||
<label>Denoise</label>
|
||||
<input type="number" id="ie-inpaint-denoise" min="0" max="1" step="0.01" value="${this.denoise}" style="width:60px;">
|
||||
</div>
|
||||
<div class="ie-props-row">
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="ie-inpaint-run-btn" style="flex:1;" ${this.running ? "disabled" : ""}>
|
||||
${this.running ? "Running..." : "Run"}
|
||||
</button>
|
||||
</div>
|
||||
<span id="ie-inpaint-status" style="font-size:11px;color:var(--wfm-text-secondary);"></span>
|
||||
${this.resultUrl ? `
|
||||
<div class="ie-props-row" style="flex-direction:column;align-items:stretch;">
|
||||
<label>Result</label>
|
||||
<img src="${this.resultUrl}" style="max-width:100%;border-radius:var(--wfm-radius-sm);border:1px solid var(--wfm-border);">
|
||||
</div>
|
||||
` : ""}
|
||||
`;
|
||||
|
||||
document.getElementById("ie-inpaint-use-dedicated")?.addEventListener("change", e => {
|
||||
this.useDedicated = e.target.checked;
|
||||
this.renderPanel();
|
||||
});
|
||||
document.getElementById("ie-inpaint-dedicated-select")?.addEventListener("change", e => {
|
||||
this.dedicatedFilename = e.target.value || null;
|
||||
});
|
||||
document.getElementById("ie-inpaint-positive")?.addEventListener("input", e => { this.positive = e.target.value; });
|
||||
document.getElementById("ie-inpaint-negative")?.addEventListener("input", e => { this.negative = e.target.value; });
|
||||
document.getElementById("ie-inpaint-grow-mask")?.addEventListener("input", e => {
|
||||
this.growMaskBy = parseInt(e.target.value) || 0;
|
||||
});
|
||||
document.getElementById("ie-inpaint-denoise")?.addEventListener("input", e => {
|
||||
this.denoise = Math.max(0, Math.min(1, parseFloat(e.target.value)));
|
||||
if (Number.isNaN(this.denoise)) this.denoise = 1.0;
|
||||
});
|
||||
document.getElementById("ie-inpaint-run-btn")?.addEventListener("click", () => this.run());
|
||||
}
|
||||
|
||||
_exportMaskCanvas(maskLayer) {
|
||||
const w = maskLayer.canvas.width, h = maskLayer.canvas.height;
|
||||
const canvas = document.createElement("canvas");
|
||||
canvas.width = w; canvas.height = h;
|
||||
const ctx = canvas.getContext("2d");
|
||||
ctx.fillStyle = "#000";
|
||||
ctx.fillRect(0, 0, w, h);
|
||||
ctx.drawImage(maskLayer.canvas, 0, 0);
|
||||
return canvas;
|
||||
}
|
||||
|
||||
async run() {
|
||||
if (this.running) return;
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
if (!layerMgr) { showToast("No image loaded", "error"); return; }
|
||||
|
||||
// 非表示のマスクレイヤーはInpaint対象から除外する（目玉アイコンOFF=対象外）
|
||||
const maskLayer = (layerMgr.activeLayer?.type === "mask" && layerMgr.activeLayer.visible)
|
||||
? layerMgr.activeLayer
|
||||
: layerMgr.layers.find(l => l.type === "mask" && l.visible);
|
||||
if (!maskLayer) { showToast("Create or show a mask layer first (Mask tool)", "info"); return; }
|
||||
|
||||
if (this.useDedicated) {
|
||||
if (!this.dedicatedFilename) { showToast("Select a dedicated workflow first", "info"); return; }
|
||||
} else if (!comfyUI.currentAnalysis) {
|
||||
showToast("Load a workflow in GenerateUI first", "info");
|
||||
return;
|
||||
}
|
||||
|
||||
const runBtn = document.getElementById("ie-inpaint-run-btn");
|
||||
const statusEl = document.getElementById("ie-inpaint-status");
|
||||
const setStatus = (msg) => { if (statusEl) statusEl.textContent = msg; };
|
||||
|
||||
this.running = true;
|
||||
if (runBtn) { runBtn.disabled = true; runBtn.textContent = "Running..."; }
|
||||
setStatus(this.useDedicated ? "Loading dedicated workflow..." : "Compositing...");
|
||||
|
||||
try {
|
||||
// 専用ワークフロー使用時は GenerateUI の comfyUI.currentWorkflow/currentAnalysis には
|
||||
// 一切触れず、ここでロード・解析したローカルなワークフロー/解析結果だけを使う。
|
||||
let overrideOpts = {};
|
||||
|
||||
if (this.useDedicated) {
|
||||
const resp = await fetch(`${comfyUI.baseUrl}/api/wfm/workflows/raw?filename=${encodeURIComponent(this.dedicatedFilename)}`);
|
||||
if (!resp.ok) throw new Error(`Failed to load workflow (HTTP ${resp.status})`);
|
||||
let dedicatedWorkflow = await resp.json();
|
||||
const format = comfyWorkflow.detectFormat(dedicatedWorkflow, this.dedicatedFilename);
|
||||
if (format === "ui") {
|
||||
dedicatedWorkflow = await comfyWorkflow.convertUiToApi(dedicatedWorkflow);
|
||||
} else if (format !== "api") {
|
||||
throw new Error("Unsupported or unrecognized workflow format");
|
||||
}
|
||||
overrideOpts = { workflow: dedicatedWorkflow, analysis: comfyWorkflow.analyzeWorkflow(dedicatedWorkflow) };
|
||||
}
|
||||
|
||||
setStatus("Compositing...");
|
||||
const bgCanvas = this._cb.buildBgCanvas();
|
||||
const imageBlob = await new Promise((resolve) => bgCanvas.toBlob(resolve, "image/png"));
|
||||
const maskCanvas = this._exportMaskCanvas(maskLayer);
|
||||
const maskBlob = await new Promise((resolve) => maskCanvas.toBlob(resolve, "image/png"));
|
||||
|
||||
const resultUrl = await this._runWithImages(imageBlob, maskBlob, {
|
||||
positive: this.positive,
|
||||
negative: this.negative,
|
||||
growMaskBy: this.growMaskBy,
|
||||
denoise: this.denoise,
|
||||
overrideOpts,
|
||||
onStatus: setStatus,
|
||||
});
|
||||
if (resultUrl) this.resultUrl = resultUrl;
|
||||
|
||||
setStatus("Done");
|
||||
showToast("Inpaint generation complete", "success");
|
||||
} catch (err) {
|
||||
setStatus("Error");
|
||||
showToast(`Inpaint failed: ${err.message}`, "error");
|
||||
} finally {
|
||||
this.running = false;
|
||||
this.renderPanel();
|
||||
}
|
||||
}
|
||||
|
||||
// 共通処理: 画像+マスクBlobを対象ノード（LoadImage/MaskEditorOne）へ反映し、
|
||||
// プロンプト/grow_mask_by/denoiseを設定してGenerate UIで実行、結果URLを返す。
|
||||
// UI駆動の run() と外部（Comic Creator等）向けの runExternal() の両方から使う。
|
||||
async _runWithImages(imageBlob, maskBlob, { positive, negative, growMaskBy, denoise, overrideOpts = {}, onStatus } = {}) {
|
||||
const setStatus = onStatus || (() => {});
|
||||
|
||||
// LoadImage(の MASK 出力が使われている)スロットを優先し、無ければ Mask Editor One
|
||||
// ノード（LoadImage を介さず画像/マスクを内包する custom node）にフォールバックする
|
||||
const analysisForLookup = overrideOpts.analysis || comfyUI.currentAnalysis;
|
||||
if (!analysisForLookup) throw new Error("No workflow loaded in GenerateUI");
|
||||
const slotIndex = (analysisForLookup.load_image_nodes || []).findIndex(n => n.mask_used);
|
||||
const meoNode = (analysisForLookup.mask_editor_one_nodes || [])[0];
|
||||
if (slotIndex === -1 && !meoNode) {
|
||||
throw new Error("No inpaint-capable LoadImage or Mask Editor One node found in the workflow");
|
||||
}
|
||||
|
||||
setStatus("Uploading...");
|
||||
if (slotIndex !== -1) {
|
||||
await comfyEditor.applyImageAndMaskToSlot(imageBlob, maskBlob, slotIndex, overrideOpts);
|
||||
} else {
|
||||
await comfyEditor.applyImageAndMaskToMaskEditorOneNode(imageBlob, maskBlob, meoNode.id, overrideOpts);
|
||||
}
|
||||
|
||||
comfyEditor.setPromptText("positive", positive, overrideOpts);
|
||||
comfyEditor.setPromptText("negative", negative, overrideOpts);
|
||||
comfyEditor.setInpaintParams({ growMaskBy, denoise, ...overrideOpts });
|
||||
|
||||
if (!window._wfmGenerateTab?.generate) throw new Error("GenerateUI is not ready yet");
|
||||
|
||||
setStatus("Generating...");
|
||||
await window._wfmGenerateTab.generate(overrideOpts.workflow || undefined);
|
||||
|
||||
const resultImg = document.getElementById("wfm-gen-result-img");
|
||||
return resultImg?.src || null;
|
||||
}
|
||||
|
||||
// Comic Creator等、同一オリジンiframe越しの外部呼び出し専用エントリポイント。
|
||||
// 専用ワークフロー選択(useDedicated)は扱わず、常にGenerate UIに現在
|
||||
// ロード中のワークフロー（comfyUI.currentWorkflow/currentAnalysis）を対象にする。
|
||||
async runExternal(imageBlob, maskBlob, { positive = "", negative = "", growMaskBy = 6, denoise = 1.0 } = {}) {
|
||||
if (this.running) throw new Error("Inpaint is already running");
|
||||
this.running = true;
|
||||
try {
|
||||
const resultUrl = await this._runWithImages(imageBlob, maskBlob, { positive, negative, growMaskBy, denoise });
|
||||
if (!resultUrl) throw new Error("No result image produced");
|
||||
return { ok: true, url: resultUrl };
|
||||
} finally {
|
||||
this.running = false;
|
||||
}
|
||||
}
|
||||
|
||||
// 共通処理: 画像Blob（マスクなし）を対象LoadImageノード(slot 0)へ反映し、
|
||||
// プロンプト/denoiseを設定してGenerate UIで実行、結果URLを返す。
|
||||
// _runWithImages のマスクなし版（Comic CreatorのSelect I2I連携専用）。
|
||||
async _runI2IWithImage(imageBlob, { positive, negative, denoise, overrideOpts = {}, onStatus } = {}) {
|
||||
const setStatus = onStatus || (() => {});
|
||||
|
||||
const analysisForLookup = overrideOpts.analysis || comfyUI.currentAnalysis;
|
||||
if (!analysisForLookup) throw new Error("No workflow loaded in GenerateUI");
|
||||
if (!(analysisForLookup.load_image_nodes?.length > 0)) {
|
||||
throw new Error("No LoadImage node found in the workflow");
|
||||
}
|
||||
|
||||
setStatus("Uploading...");
|
||||
const file = new File([imageBlob], `i2i_${Math.random().toString(36).slice(2, 10)}.png`, { type: "image/png" });
|
||||
await comfyEditor.applyImageToSlot(file, 0, overrideOpts);
|
||||
|
||||
comfyEditor.setPromptText("positive", positive, overrideOpts);
|
||||
comfyEditor.setPromptText("negative", negative, overrideOpts);
|
||||
comfyEditor.setInpaintParams({ denoise, ...overrideOpts });
|
||||
|
||||
if (!window._wfmGenerateTab?.generate) throw new Error("GenerateUI is not ready yet");
|
||||
|
||||
setStatus("Generating...");
|
||||
await window._wfmGenerateTab.generate(overrideOpts.workflow || undefined);
|
||||
|
||||
const resultImg = document.getElementById("wfm-gen-result-img");
|
||||
return resultImg?.src || null;
|
||||
}
|
||||
|
||||
// Comic Creator等、同一オリジンiframe越しの外部呼び出し専用エントリポイント（I2I版）。
|
||||
// runExternal と対になる、マスク不要のシンプルなI2I実行。
|
||||
async runI2IExternal(imageBlob, { positive = "", negative = "", denoise = 1.0 } = {}) {
|
||||
if (this.i2iExternalRunning) throw new Error("I2I is already running");
|
||||
this.i2iExternalRunning = true;
|
||||
try {
|
||||
const resultUrl = await this._runI2IWithImage(imageBlob, { positive, negative, denoise });
|
||||
if (!resultUrl) throw new Error("No result image produced");
|
||||
return { ok: true, url: resultUrl };
|
||||
} finally {
|
||||
this.i2iExternalRunning = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,102 @@
|
||||
/**
|
||||
* Image Edit Tab - Mask Editor One 連携ブリッジ
|
||||
* WFS Image Edit タブの合成画像を、ComfyUIキャンバス上のMaskEditorOneノード（選択中のノードを
|
||||
* 優先、無ければグラフ内で最初に見つかったもの）へ送り、window.opener（ComfyUIタブ）側で実際の
|
||||
* モーダルを開いて編集させ、Apply後の結果マスクを新規マスクレイヤーとしてWFS側に取り込む。
|
||||
* 「Send to Workflow」の image ウィジェット探索と同じ「選択中優先・フォールバック」方式のため、
|
||||
* WFS側のワークフロー読み込み状況とは無関係に動作する。
|
||||
* モーダル自体は ComfyUI ページの LiteGraph ノードに強く依存するため、WFS 側からは
|
||||
* window.opener.wfmOpenMaskEditorForNode()（web/comfyui/node_sets_menu.js 側で定義）を介して操作する。
|
||||
*/
|
||||
|
||||
import { showToast } from "../app.js";
|
||||
|
||||
export class MaskEditorOneBridge {
|
||||
/**
|
||||
* @param {object} callbacks
|
||||
* @param {() => object|null} callbacks.getLayerManager
|
||||
* @param {() => void} callbacks.saveUndo
|
||||
* @param {() => void} callbacks.updateCompositeView
|
||||
* @param {() => void} callbacks.refreshLayerList
|
||||
* @param {() => HTMLCanvasElement} callbacks.buildBgCanvas
|
||||
*/
|
||||
constructor(callbacks) {
|
||||
this._cb = callbacks;
|
||||
this.running = false;
|
||||
}
|
||||
|
||||
get bridgeReady() {
|
||||
return !!(window.opener && typeof window.opener.wfmOpenMaskEditorForNode === "function");
|
||||
}
|
||||
|
||||
async openEditor() {
|
||||
if (this.running) return;
|
||||
if (!this.bridgeReady) {
|
||||
showToast("Open Workflow Studio from ComfyUI's top menu to use this feature", "error");
|
||||
return;
|
||||
}
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
if (!layerMgr) { showToast("No image loaded", "error"); return; }
|
||||
|
||||
this.running = true;
|
||||
try {
|
||||
const dataUrl = this._cb.buildBgCanvas().toDataURL("image/png");
|
||||
// アクティブレイヤーが既存のマスクレイヤーなら、その内容もそのまま Mask Editor One の
|
||||
// 初期マスクとして渡す（画像だけでなく、今描いているマスクも引き継ぐ）
|
||||
const activeLayer = layerMgr.activeLayer;
|
||||
const existingMaskDataUrl = activeLayer?.type === "mask" ? activeLayer.canvas.toDataURL("image/png") : null;
|
||||
window.opener.focus();
|
||||
showToast("Switch to the ComfyUI tab, edit the mask, then click Apply", "info");
|
||||
|
||||
const maskDataUrl = await window.opener.wfmOpenMaskEditorForNode(dataUrl, existingMaskDataUrl);
|
||||
if (!maskDataUrl) {
|
||||
showToast("Mask Editor One: cancelled (no changes)", "info");
|
||||
return;
|
||||
}
|
||||
|
||||
this._cb.saveUndo();
|
||||
const maskLayer = layerMgr.addLayer("mask", "Mask Editor One", {
|
||||
contentW: layerMgr.width, contentH: layerMgr.height,
|
||||
displayW: layerMgr.width, displayH: layerMgr.height,
|
||||
x: 0, y: 0,
|
||||
});
|
||||
layerMgr.setActive(maskLayer.id);
|
||||
await this._applyMask(maskLayer, maskDataUrl);
|
||||
|
||||
this._cb.updateCompositeView();
|
||||
this._cb.refreshLayerList();
|
||||
showToast("Mask Editor One: mask imported", "success");
|
||||
} catch (err) {
|
||||
showToast("Mask Editor One error: " + err.message, "error");
|
||||
} finally {
|
||||
this.running = false;
|
||||
}
|
||||
}
|
||||
|
||||
// グレースケール輝度 → アルファ白マスクに変換して書き込む（Sam3Segmentation._applyMask と同じ変換）
|
||||
_applyMask(maskLayer, maskDataUrl) {
|
||||
return new Promise(resolve => {
|
||||
const img = new Image();
|
||||
img.onload = () => {
|
||||
const W = maskLayer.canvas.width;
|
||||
const H = maskLayer.canvas.height;
|
||||
const off = document.createElement("canvas");
|
||||
off.width = W; off.height = H;
|
||||
const mc = off.getContext("2d");
|
||||
mc.drawImage(img, 0, 0, W, H);
|
||||
const imgData = mc.getImageData(0, 0, W, H);
|
||||
const d = imgData.data;
|
||||
for (let i = 0; i < d.length; i += 4) {
|
||||
const lum = d[i];
|
||||
d[i] = d[i + 1] = d[i + 2] = 255;
|
||||
d[i + 3] = lum;
|
||||
}
|
||||
mc.putImageData(imgData, 0, 0);
|
||||
maskLayer.ctx.drawImage(off, 0, 0);
|
||||
resolve();
|
||||
};
|
||||
img.onerror = resolve;
|
||||
img.src = maskDataUrl;
|
||||
});
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,248 @@
|
||||
/**
|
||||
* Image Edit Tab - SAM3 Segmentation (Mask Editor One integration)
|
||||
* Text-prompted segmentation via the Mask Editor One SAM3 server endpoints.
|
||||
* Rendered as a subtool of the Mask tool; the toolbar shell itself stays in
|
||||
* ImageEditTab (shared with the other mask subtools), but the prompt/run
|
||||
* controls and the result thumbnail grid are owned here.
|
||||
*/
|
||||
|
||||
import { showToast } from "../app.js";
|
||||
|
||||
export class Sam3Segmentation {
|
||||
/**
|
||||
* @param {object} callbacks
|
||||
* @param {() => object|null} callbacks.getLayerManager
|
||||
* @param {() => void} callbacks.saveUndo
|
||||
* @param {() => void} callbacks.updateCompositeView
|
||||
* @param {() => void} callbacks.refreshLayerList
|
||||
* @param {(toolId: string) => void} callbacks.renderToolOptions
|
||||
* @param {(sub: string) => void} callbacks.renderMaskProps
|
||||
*/
|
||||
constructor(callbacks) {
|
||||
this._cb = callbacks;
|
||||
this.available = false;
|
||||
this.results = []; // [{mask_b64, score, area}, ...]
|
||||
this.prompt = "";
|
||||
this.maxMasks = 9;
|
||||
this.loading = false;
|
||||
this.mode = "add"; // "add" | "erase"
|
||||
this.selected = new Set(); // 選択中の結果インデックス
|
||||
}
|
||||
|
||||
async checkAvailability() {
|
||||
try {
|
||||
const resp = await fetch("/mask_editor/sam3/status");
|
||||
if (!resp.ok) return;
|
||||
const json = await resp.json();
|
||||
this.available = json.loaded === true || json.ckpt_found === true;
|
||||
} catch {
|
||||
this.available = false;
|
||||
}
|
||||
}
|
||||
|
||||
// マスクツールバー内、SAM3選択時にのみ表示するprompt/max/runコントロール
|
||||
renderToolbarExtra(activeSub) {
|
||||
if (!this.available || activeSub !== "sam3") return "";
|
||||
return `
|
||||
<div class="ie-opt-group">
|
||||
<input type="text" id="ie-sam3-prompt" class="ie-opt-input"
|
||||
placeholder="e.g. cat, person..."
|
||||
value="${this.prompt}"
|
||||
style="width:160px;font-size:11px;padding:2px 6px;border:1px solid var(--wfm-border);border-radius:3px;background:var(--wfm-surface);color:var(--wfm-text);">
|
||||
</div>
|
||||
<div class="ie-opt-group">
|
||||
<label style="font-size:11px;color:var(--wfm-text-secondary);">Max</label>
|
||||
<select id="ie-sam3-max" class="ie-opt-select" style="width:44px;">
|
||||
${[3,6,9,12].map(n => `<option value="${n}"${n === this.maxMasks ? " selected" : ""}>${n}</option>`).join("")}
|
||||
</select>
|
||||
</div>
|
||||
<div class="ie-opt-group">
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="ie-sam3-run-btn" ${this.loading ? "disabled" : ""}>
|
||||
${this.loading ? "Running..." : "Segment"}
|
||||
</button>
|
||||
</div>
|
||||
<span id="ie-sam3-status" style="font-size:11px;color:var(--wfm-text-secondary);margin-left:4px;">
|
||||
${this.results.length > 0 ? `${this.results.length} masks found` : ""}
|
||||
</span>
|
||||
`;
|
||||
}
|
||||
|
||||
bindToolbarEvents() {
|
||||
document.getElementById("ie-sam3-prompt")?.addEventListener("input", e => {
|
||||
this.prompt = e.target.value;
|
||||
});
|
||||
document.getElementById("ie-sam3-max")?.addEventListener("change", e => {
|
||||
this.maxMasks = parseInt(e.target.value);
|
||||
});
|
||||
document.getElementById("ie-sam3-run-btn")?.addEventListener("click", () => this.runSegment());
|
||||
}
|
||||
|
||||
// props ペイン（"sam3" サブツール選択時）: モード切替 + 結果サムネイルグリッド
|
||||
renderResultsPanel(body) {
|
||||
const modeAdd = this.mode === "add";
|
||||
const selCount = this.selected.size;
|
||||
const hasResult = this.results.length > 0;
|
||||
body.innerHTML = `
|
||||
<div class="ie-props-row">
|
||||
<label>Mode</label>
|
||||
<div style="display:flex;gap:4px;">
|
||||
<button class="wfm-btn wfm-btn-sm${modeAdd ? " ie-opt-active" : ""}" id="ie-sam3-mode-add" style="flex:1;">Add</button>
|
||||
<button class="wfm-btn wfm-btn-sm${!modeAdd ? " ie-opt-active" : ""}" id="ie-sam3-mode-erase" style="flex:1;">Erase</button>
|
||||
</div>
|
||||
</div>
|
||||
${hasResult ? `
|
||||
<div class="ie-props-row" style="flex-direction:column;align-items:stretch;gap:4px;">
|
||||
<div style="font-size:11px;color:var(--wfm-text-secondary);">Click to select / deselect:</div>
|
||||
<div id="ie-sam3-results" style="display:grid;grid-template-columns:repeat(3,1fr);gap:4px;">
|
||||
${this.results.map((r, i) => {
|
||||
const sel = this.selected.has(i);
|
||||
const borderColor = sel ? "var(--wfm-primary,#4682e6)" : "var(--wfm-border)";
|
||||
const bg = sel ? "color-mix(in srgb,var(--wfm-primary,#4682e6) 15%,transparent)" : "transparent";
|
||||
return `<div class="ie-sam3-thumb" data-idx="${i}"
|
||||
style="cursor:pointer;border:2px solid ${borderColor};border-radius:4px;overflow:hidden;text-align:center;background:${bg};position:relative;">
|
||||
<img src="${r.mask_b64}" style="width:100%;display:block;background:#000;">
|
||||
<div style="font-size:10px;padding:2px 0;color:var(--wfm-text-secondary);">
|
||||
${Math.round((r.score ?? 0) * 100)}%
|
||||
</div>
|
||||
${sel ? '<div style="position:absolute;top:2px;right:3px;font-size:12px;line-height:1;color:var(--wfm-primary,#4682e6);">✓</div>' : ""}
|
||||
</div>`;
|
||||
}).join("")}
|
||||
</div>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="ie-sam3-apply-btn"
|
||||
${selCount === 0 ? "disabled" : ""} style="margin-top:4px;">
|
||||
Apply Selected${selCount > 0 ? ` (${selCount})` : ""}
|
||||
</button>
|
||||
</div>` : `
|
||||
<div style="font-size:11px;color:var(--wfm-text-secondary);padding:4px 0;">
|
||||
${this.loading ? "Segmenting..." : "Enter a prompt and press Segment"}
|
||||
</div>`}
|
||||
`;
|
||||
document.getElementById("ie-sam3-mode-add")?.addEventListener("click", () => {
|
||||
this.mode = "add";
|
||||
this._cb.renderMaskProps("sam3");
|
||||
});
|
||||
document.getElementById("ie-sam3-mode-erase")?.addEventListener("click", () => {
|
||||
this.mode = "erase";
|
||||
this._cb.renderMaskProps("sam3");
|
||||
});
|
||||
body.querySelectorAll(".ie-sam3-thumb").forEach(el => {
|
||||
el.addEventListener("click", () => {
|
||||
const idx = parseInt(el.dataset.idx);
|
||||
if (this.selected.has(idx)) this.selected.delete(idx);
|
||||
else this.selected.add(idx);
|
||||
this._cb.renderMaskProps("sam3");
|
||||
});
|
||||
});
|
||||
document.getElementById("ie-sam3-apply-btn")?.addEventListener("click", () => {
|
||||
this.applySelectedMasks();
|
||||
});
|
||||
}
|
||||
|
||||
async runSegment() {
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
if (!layerMgr || this.loading) return;
|
||||
const prompt = this.prompt.trim();
|
||||
if (!prompt) { showToast("Please enter a prompt", "warning"); return; }
|
||||
|
||||
// 推論対象: アクティブレイヤー（マスクなら下のイメージレイヤーを探す）
|
||||
let imageLayer = layerMgr.activeLayer;
|
||||
if (!imageLayer || imageLayer.type === "mask") {
|
||||
imageLayer = layerMgr.layers.find(l => l.type !== "mask" && l.visible);
|
||||
}
|
||||
if (!imageLayer) { showToast("No image layer found", "error"); return; }
|
||||
|
||||
const NODE_ID = "wfs_sam3";
|
||||
this.loading = true;
|
||||
this.results = [];
|
||||
this.selected = new Set();
|
||||
this._cb.renderToolOptions("mask");
|
||||
this._cb.renderMaskProps("sam3");
|
||||
|
||||
try {
|
||||
const dataUrl = imageLayer.canvas.toDataURL("image/png");
|
||||
await fetch("/mask_editor/store_image", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ node_id: NODE_ID, image_b64: dataUrl }),
|
||||
});
|
||||
|
||||
const resp = await fetch("/mask_editor/sam3/segment", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ node_id: NODE_ID, prompt, max_masks: this.maxMasks }),
|
||||
});
|
||||
const json = await resp.json();
|
||||
if (json.error) throw new Error(json.error);
|
||||
this.results = json.masks || [];
|
||||
if (this.results.length === 0) showToast("No masks found", "warning");
|
||||
else showToast(`${this.results.length} mask(s) found`, "success");
|
||||
} catch (err) {
|
||||
showToast("SAM3 error: " + err.message, "error");
|
||||
} finally {
|
||||
this.loading = false;
|
||||
this._cb.renderToolOptions("mask");
|
||||
this._cb.renderMaskProps("sam3");
|
||||
}
|
||||
}
|
||||
|
||||
async applySelectedMasks() {
|
||||
const layerMgr = this._cb.getLayerManager();
|
||||
if (!layerMgr || this.selected.size === 0) return;
|
||||
const indices = [...this.selected].sort((a, b) => a - b);
|
||||
const masks = indices.map(i => this.results[i]).filter(Boolean);
|
||||
if (masks.length === 0) return;
|
||||
|
||||
let maskLayer = layerMgr.activeLayer;
|
||||
if (!maskLayer || maskLayer.type !== "mask") {
|
||||
const ref = layerMgr.activeLayer;
|
||||
maskLayer = layerMgr.addLayer("mask", "SAM3 Mask", {
|
||||
contentW: ref?.canvas.width ?? layerMgr.width,
|
||||
contentH: ref?.canvas.height ?? layerMgr.height,
|
||||
displayW: ref?.displayW ?? layerMgr.width,
|
||||
displayH: ref?.displayH ?? layerMgr.height,
|
||||
x: ref?.x ?? 0, y: ref?.y ?? 0,
|
||||
});
|
||||
layerMgr.setActive(maskLayer.id);
|
||||
}
|
||||
|
||||
this._cb.saveUndo();
|
||||
for (const r of masks) {
|
||||
await this._applyMask(maskLayer, r.mask_b64, this.mode);
|
||||
}
|
||||
|
||||
this._cb.updateCompositeView();
|
||||
this._cb.refreshLayerList();
|
||||
showToast(`SAM3: ${masks.length} mask(s) applied (${this.mode})`, "success");
|
||||
}
|
||||
|
||||
_applyMask(maskLayer, maskB64, mode = "add") {
|
||||
return new Promise(resolve => {
|
||||
const img = new Image();
|
||||
img.onload = () => {
|
||||
const W = maskLayer.canvas.width;
|
||||
const H = maskLayer.canvas.height;
|
||||
// グレースケール輝度 → アルファ白マスクに変換
|
||||
const off = document.createElement("canvas");
|
||||
off.width = W; off.height = H;
|
||||
const mc = off.getContext("2d");
|
||||
mc.drawImage(img, 0, 0, W, H);
|
||||
const imgData = mc.getImageData(0, 0, W, H);
|
||||
const d = imgData.data;
|
||||
for (let i = 0; i < d.length; i += 4) {
|
||||
const lum = d[i];
|
||||
d[i] = d[i+1] = d[i+2] = 255;
|
||||
d[i+3] = lum;
|
||||
}
|
||||
mc.putImageData(imgData, 0, 0);
|
||||
// モードに合わせてマスクキャンバスに合成
|
||||
maskLayer.ctx.save();
|
||||
maskLayer.ctx.globalCompositeOperation = mode === "erase" ? "destination-out" : "source-over";
|
||||
maskLayer.ctx.drawImage(off, 0, 0);
|
||||
maskLayer.ctx.restore();
|
||||
resolve();
|
||||
};
|
||||
img.onerror = resolve;
|
||||
img.src = maskB64;
|
||||
});
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,363 @@
|
||||
/**
|
||||
* ImagePrompt Gallery — Gallery タブのサブタブ。
|
||||
* ws_image_prompt フォルダ配下の画像+.txtサイドカーを閲覧し、
|
||||
* クリックで選択→「追加」でプロンプトを組み立てて最終的にコピーする。
|
||||
*/
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { escapeHtml, setupSearchClearBtn } from "./util.js";
|
||||
|
||||
const API = {
|
||||
root: () => `/wfm/gallery/image-prompt/root`,
|
||||
folders: (root) => `/wfm/gallery/folders?root=${encodeURIComponent(root)}`,
|
||||
images: (params) => `/wfm/gallery/images?${new URLSearchParams(params)}`,
|
||||
imageMeta: (path) => `/wfm/gallery/image/meta?path=${encodeURIComponent(path)}`,
|
||||
thumb: (path, w = 200) => `/wfm/gallery/image/thumb?path=${encodeURIComponent(path)}&w=${w}`,
|
||||
serveImage: (path) => `/wfm/gallery/image/serve?path=${encodeURIComponent(path)}`,
|
||||
savePrompt: `/wfm/gallery/image/image-prompt`,
|
||||
};
|
||||
|
||||
const state = {
|
||||
root: null,
|
||||
currentFolder: null,
|
||||
search: "",
|
||||
images: [],
|
||||
selectedImage: null,
|
||||
chips: [], // [{ path, filename, prompt }]
|
||||
};
|
||||
|
||||
let _initialized = false;
|
||||
|
||||
async function apiFetch(url, opts = {}) {
|
||||
const res = await fetch(url, opts);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return res.json();
|
||||
}
|
||||
|
||||
function debounce(fn, wait) {
|
||||
let timer;
|
||||
return (...args) => {
|
||||
clearTimeout(timer);
|
||||
timer = setTimeout(() => fn(...args), wait);
|
||||
};
|
||||
}
|
||||
|
||||
// ── 初期化 ────────────────────────────────────────────────────
|
||||
|
||||
export function initImagePromptTab() {
|
||||
document.getElementById("wfm-imageprompt-refresh-btn")?.addEventListener("click", () => {
|
||||
loadFolderTree();
|
||||
loadImages();
|
||||
});
|
||||
|
||||
setupSearchClearBtn("wfm-imageprompt-search", "wfm-imageprompt-search-clear-btn", () => {
|
||||
state.search = "";
|
||||
loadImages();
|
||||
});
|
||||
|
||||
const searchInput = document.getElementById("wfm-imageprompt-search");
|
||||
searchInput?.addEventListener("input", debounce(() => {
|
||||
state.search = searchInput.value.trim();
|
||||
loadImages();
|
||||
}, 300));
|
||||
|
||||
document.getElementById("wfm-imageprompt-add-btn")?.addEventListener("click", addSelectedToChips);
|
||||
document.getElementById("wfm-imageprompt-save-prompt-btn")?.addEventListener("click", saveSelectedPrompt);
|
||||
document.getElementById("wfm-imageprompt-clear-btn")?.addEventListener("click", clearAllChips);
|
||||
document.getElementById("wfm-imageprompt-copy-btn")?.addEventListener("click", copyFinalPrompt);
|
||||
}
|
||||
|
||||
export async function activateImagePromptTab() {
|
||||
if (_initialized) return;
|
||||
_initialized = true;
|
||||
try {
|
||||
const data = await apiFetch(API.root());
|
||||
state.root = data.root;
|
||||
await loadFolderTree();
|
||||
} catch (e) {
|
||||
const tree = document.getElementById("wfm-imageprompt-tree");
|
||||
if (tree) tree.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
// ── フォルダツリー ────────────────────────────────────────────
|
||||
|
||||
async function loadFolderTree() {
|
||||
if (!state.root) return;
|
||||
const tree = document.getElementById("wfm-imageprompt-tree");
|
||||
tree.innerHTML = `<p class="wfm-placeholder">${t("loading")}</p>`;
|
||||
try {
|
||||
const data = await apiFetch(API.folders(state.root));
|
||||
if (data.error) {
|
||||
tree.innerHTML = `<p class="wfm-placeholder">${escapeHtml(data.error)}</p>`;
|
||||
return;
|
||||
}
|
||||
tree.innerHTML = "";
|
||||
renderTreeNode(data, tree, 0, true);
|
||||
const firstLabel = tree.querySelector(".wfm-gallery-tree-label");
|
||||
if (firstLabel) firstLabel.click();
|
||||
} catch (e) {
|
||||
tree.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
function renderTreeNode(node, container, depth, isRoot) {
|
||||
const item = document.createElement("div");
|
||||
item.className = "wfm-gallery-tree-item";
|
||||
item.style.paddingLeft = `${depth * 12}px`;
|
||||
item.dataset.path = node.abs_path;
|
||||
|
||||
const hasChildren = node.children && node.children.length > 0;
|
||||
const absPath = node.abs_path;
|
||||
|
||||
const arrow = document.createElement("span");
|
||||
arrow.className = "wfm-gallery-tree-arrow";
|
||||
if (hasChildren) {
|
||||
arrow.textContent = "▶";
|
||||
arrow.dataset.expanded = "false";
|
||||
} else {
|
||||
arrow.textContent = " ";
|
||||
arrow.style.visibility = "hidden";
|
||||
}
|
||||
item.appendChild(arrow);
|
||||
|
||||
const label = document.createElement("span");
|
||||
label.className = "wfm-gallery-tree-label";
|
||||
label.textContent = isRoot ? "[root]" : node.name;
|
||||
// サブフォルダ内も含めた合計件数（クリックで再帰的に全画像を表示するため）
|
||||
const totalCount = node.image_count_total ?? node.image_count;
|
||||
if (totalCount > 0) {
|
||||
const badge = document.createElement("span");
|
||||
badge.className = "wfm-gallery-tree-count";
|
||||
badge.textContent = totalCount;
|
||||
label.appendChild(badge);
|
||||
}
|
||||
item.appendChild(label);
|
||||
|
||||
label.addEventListener("click", () => {
|
||||
document.querySelectorAll("#wfm-imageprompt-tree .wfm-gallery-tree-item.selected").forEach(el => el.classList.remove("selected"));
|
||||
item.classList.add("selected");
|
||||
state.currentFolder = absPath;
|
||||
loadImages();
|
||||
});
|
||||
|
||||
if (hasChildren) {
|
||||
const childContainer = document.createElement("div");
|
||||
childContainer.className = "wfm-gallery-tree-children";
|
||||
childContainer.style.display = "none";
|
||||
|
||||
arrow.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
const expanded = arrow.dataset.expanded === "true";
|
||||
if (!expanded) {
|
||||
arrow.dataset.expanded = "true";
|
||||
arrow.textContent = "▼";
|
||||
childContainer.style.display = "";
|
||||
if (childContainer.children.length === 0) {
|
||||
node.children.forEach(child => renderTreeNode(child, childContainer, depth + 1, false));
|
||||
}
|
||||
} else {
|
||||
arrow.dataset.expanded = "false";
|
||||
arrow.textContent = "▶";
|
||||
childContainer.style.display = "none";
|
||||
}
|
||||
});
|
||||
|
||||
container.appendChild(item);
|
||||
container.appendChild(childContainer);
|
||||
} else {
|
||||
container.appendChild(item);
|
||||
}
|
||||
}
|
||||
|
||||
// ── 画像一覧 ─────────────────────────────────────────────────
|
||||
|
||||
async function loadImages() {
|
||||
if (!state.currentFolder) return;
|
||||
const grid = document.getElementById("wfm-imageprompt-grid");
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("loading")}</p>`;
|
||||
|
||||
// 上位フォルダを選択した場合もサブフォルダの画像をまとめて表示する
|
||||
const params = { folder: state.currentFolder, sort: "name_asc", recursive: "true" };
|
||||
if (state.search) params.search = state.search;
|
||||
|
||||
try {
|
||||
const data = await apiFetch(API.images(params));
|
||||
state.images = data.images || [];
|
||||
document.getElementById("wfm-imageprompt-count").textContent = `${state.images.length} images`;
|
||||
renderImages();
|
||||
} catch (e) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
function renderImages() {
|
||||
const grid = document.getElementById("wfm-imageprompt-grid");
|
||||
if (state.images.length === 0) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("galleryNoImages")}</p>`;
|
||||
return;
|
||||
}
|
||||
grid.innerHTML = "";
|
||||
const fragment = document.createDocumentFragment();
|
||||
state.images.forEach(img => fragment.appendChild(createThumbCard(img)));
|
||||
grid.appendChild(fragment);
|
||||
}
|
||||
|
||||
function createThumbCard(img) {
|
||||
const card = document.createElement("div");
|
||||
card.className = "wfm-gallery-thumb-card";
|
||||
card.title = img.filename;
|
||||
card.dataset.path = img.path;
|
||||
if (state.selectedImage && state.selectedImage.path === img.path) {
|
||||
card.classList.add("selected");
|
||||
}
|
||||
|
||||
const imgEl = document.createElement("img");
|
||||
imgEl.className = "wfm-gallery-thumb-img";
|
||||
imgEl.loading = "lazy";
|
||||
imgEl.src = API.thumb(img.path);
|
||||
imgEl.alt = img.filename;
|
||||
imgEl.onerror = () => {
|
||||
imgEl.style.display = "none";
|
||||
const placeholder = document.createElement("div");
|
||||
placeholder.className = "wfm-gallery-thumb-placeholder";
|
||||
placeholder.textContent = "?";
|
||||
card.insertBefore(placeholder, imgEl.nextSibling);
|
||||
};
|
||||
card.appendChild(imgEl);
|
||||
|
||||
card.addEventListener("click", () => selectImage(img));
|
||||
|
||||
return card;
|
||||
}
|
||||
|
||||
// ── 選択画像 / プロンプト表示 ─────────────────────────────────
|
||||
|
||||
async function selectImage(img) {
|
||||
state.selectedImage = img;
|
||||
document.querySelectorAll("#wfm-imageprompt-grid .wfm-gallery-thumb-card").forEach(el => {
|
||||
el.classList.toggle("selected", el.dataset.path === img.path);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-imageprompt-selected-name").textContent = img.filename;
|
||||
const preview = document.getElementById("wfm-imageprompt-selected-preview");
|
||||
preview.innerHTML = "";
|
||||
const previewImg = document.createElement("img");
|
||||
previewImg.src = API.serveImage(img.path);
|
||||
previewImg.alt = img.filename;
|
||||
previewImg.style.cssText = "max-width:100%;max-height:100%;object-fit:contain;";
|
||||
preview.appendChild(previewImg);
|
||||
|
||||
const promptBox = document.getElementById("wfm-imageprompt-selected-prompt");
|
||||
promptBox.value = "";
|
||||
document.getElementById("wfm-imageprompt-save-prompt-btn").disabled = true;
|
||||
document.getElementById("wfm-imageprompt-add-btn").disabled = true;
|
||||
|
||||
try {
|
||||
const meta = await apiFetch(API.imageMeta(img.path));
|
||||
promptBox.value = meta.image_prompt || "";
|
||||
document.getElementById("wfm-imageprompt-save-prompt-btn").disabled = false;
|
||||
document.getElementById("wfm-imageprompt-add-btn").disabled = !meta.image_prompt;
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
}
|
||||
}
|
||||
|
||||
async function saveSelectedPrompt() {
|
||||
if (!state.selectedImage) return;
|
||||
const promptBox = document.getElementById("wfm-imageprompt-selected-prompt");
|
||||
try {
|
||||
const res = await apiFetch(API.savePrompt, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ path: state.selectedImage.path, prompt: promptBox.value }),
|
||||
});
|
||||
if (res.ok) {
|
||||
showToast(t("imagePromptSaved"), "success");
|
||||
document.getElementById("wfm-imageprompt-add-btn").disabled = !promptBox.value.trim();
|
||||
} else {
|
||||
showToast(t("saveFailed"), "error");
|
||||
}
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
}
|
||||
}
|
||||
|
||||
// ── プロンプトビルダー (チップ / 最終プロンプト) ─────────────────
|
||||
|
||||
function cleanPromptText(text) {
|
||||
return String(text || "")
|
||||
.replace(/^[,\s]+|[,\s]+$/g, "")
|
||||
.replace(/\s*,\s*/g, ", ")
|
||||
.trim();
|
||||
}
|
||||
|
||||
function addSelectedToChips() {
|
||||
const img = state.selectedImage;
|
||||
if (!img) return;
|
||||
const promptBox = document.getElementById("wfm-imageprompt-selected-prompt");
|
||||
const prompt = cleanPromptText(promptBox.value);
|
||||
if (!prompt) return;
|
||||
if (state.chips.some(c => c.path === img.path)) {
|
||||
showToast(t("imagePromptAlreadyAdded"), "info");
|
||||
return;
|
||||
}
|
||||
state.chips.push({ path: img.path, filename: img.filename, prompt });
|
||||
renderChips();
|
||||
rebuildFinalPrompt();
|
||||
}
|
||||
|
||||
function removeChip(path) {
|
||||
state.chips = state.chips.filter(c => c.path !== path);
|
||||
renderChips();
|
||||
rebuildFinalPrompt();
|
||||
}
|
||||
|
||||
function clearAllChips() {
|
||||
state.chips = [];
|
||||
renderChips();
|
||||
document.getElementById("wfm-imageprompt-final-prompt").value = "";
|
||||
}
|
||||
|
||||
function renderChips() {
|
||||
const container = document.getElementById("wfm-imageprompt-chips");
|
||||
container.innerHTML = "";
|
||||
state.chips.forEach(chip => {
|
||||
const el = document.createElement("span");
|
||||
el.className = "wfm-imageprompt-chip";
|
||||
el.title = chip.prompt;
|
||||
|
||||
const text = document.createElement("span");
|
||||
text.className = "wfm-imageprompt-chip-text";
|
||||
text.textContent = chip.filename.replace(/\.[^.]+$/, "");
|
||||
el.appendChild(text);
|
||||
|
||||
const removeBtn = document.createElement("button");
|
||||
removeBtn.className = "wfm-imageprompt-chip-remove";
|
||||
removeBtn.textContent = "×";
|
||||
removeBtn.addEventListener("click", () => removeChip(chip.path));
|
||||
el.appendChild(removeBtn);
|
||||
|
||||
container.appendChild(el);
|
||||
});
|
||||
}
|
||||
|
||||
function rebuildFinalPrompt() {
|
||||
const combined = state.chips.map(c => c.prompt).join(", ");
|
||||
document.getElementById("wfm-imageprompt-final-prompt").value = cleanPromptText(combined);
|
||||
}
|
||||
|
||||
async function copyFinalPrompt() {
|
||||
const text = document.getElementById("wfm-imageprompt-final-prompt").value;
|
||||
if (!text.trim()) {
|
||||
showToast(t("imagePromptNothingToCopy"), "info");
|
||||
return;
|
||||
}
|
||||
try {
|
||||
await navigator.clipboard.writeText(text);
|
||||
showToast(t("imagePromptCopiedToast"), "success");
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
+268
-37
@@ -75,7 +75,7 @@ function parseITXtChunk(data, latin1, utf8) {
|
||||
pos = findNull(data, pos); if (pos === -1) return null; pos++;
|
||||
return { keyword, text: utf8.decode(data.slice(pos)) };
|
||||
}
|
||||
async function readAllPNGTextChunks(file) {
|
||||
export async function readAllPNGTextChunks(file) {
|
||||
const buffer = await file.arrayBuffer();
|
||||
const bytes = new Uint8Array(buffer);
|
||||
const PNG_SIG = [0x89, 0x50, 0x4e, 0x47, 0x0d, 0x0a, 0x1a, 0x0a];
|
||||
@@ -97,6 +97,81 @@ async function readAllPNGTextChunks(file) {
|
||||
return chunks;
|
||||
}
|
||||
|
||||
// ── MP4 container metadata (moov/udta/meta の mdta keys/ilst) ──
|
||||
// ComfyUIのSaveVideoはPNGのtEXtチャンクと同じキー名(workflow=UI形式, prompt=API形式)で
|
||||
// JSON文字列をisobmff(mp4)のudta/meta(mdta)にコンテナレベルタグとして書き込む(FFmpegの
|
||||
// movflags=+use_metadata_tags)。ここではPyAV等に頼らずブラウザ側でボックスを直接辿って読む。
|
||||
function findMP4Box(view, bytes, start, end, targetType) {
|
||||
let offset = start;
|
||||
while (offset + 8 <= end) {
|
||||
const size = view.getUint32(offset);
|
||||
const type = String.fromCharCode(bytes[offset + 4], bytes[offset + 5], bytes[offset + 6], bytes[offset + 7]);
|
||||
let boxSize = size;
|
||||
let bodyStart = offset + 8;
|
||||
if (size === 1) {
|
||||
if (offset + 16 > end) break;
|
||||
const hi = view.getUint32(offset + 8);
|
||||
const lo = view.getUint32(offset + 12);
|
||||
boxSize = hi * 4294967296 + lo;
|
||||
bodyStart = offset + 16;
|
||||
} else if (size === 0) {
|
||||
boxSize = end - offset;
|
||||
}
|
||||
if (boxSize < 8) break;
|
||||
if (type === targetType) return { bodyStart, end: offset + boxSize };
|
||||
offset += boxSize;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
export async function readMP4MetaTags(file) {
|
||||
const buffer = await file.arrayBuffer();
|
||||
const bytes = new Uint8Array(buffer);
|
||||
const view = new DataView(buffer);
|
||||
if (bytes.length < 12) return null;
|
||||
const ftyp = String.fromCharCode(bytes[4], bytes[5], bytes[6], bytes[7]);
|
||||
if (ftyp !== "ftyp") return null;
|
||||
|
||||
const moov = findMP4Box(view, bytes, 0, bytes.length, "moov");
|
||||
if (!moov) return null;
|
||||
const udta = findMP4Box(view, bytes, moov.bodyStart, moov.end, "udta");
|
||||
let meta = udta ? findMP4Box(view, bytes, udta.bodyStart, udta.end, "meta") : null;
|
||||
if (!meta) meta = findMP4Box(view, bytes, moov.bodyStart, moov.end, "meta");
|
||||
if (!meta) return null;
|
||||
const metaBody = meta.bodyStart + 4; // meta は FullBox (version+flags 4バイト)
|
||||
const keysBox = findMP4Box(view, bytes, metaBody, meta.end, "keys");
|
||||
const ilstBox = findMP4Box(view, bytes, metaBody, meta.end, "ilst");
|
||||
if (!keysBox || !ilstBox) return null;
|
||||
|
||||
const utf8 = new TextDecoder("utf-8", { fatal: false });
|
||||
let off = keysBox.bodyStart + 4; // version+flags をスキップ
|
||||
const count = view.getUint32(off); off += 4;
|
||||
const keyNames = [];
|
||||
for (let i = 0; i < count && off + 8 <= keysBox.end; i++) {
|
||||
const ksize = view.getUint32(off);
|
||||
if (ksize < 8) break;
|
||||
keyNames.push(utf8.decode(bytes.slice(off + 8, off + ksize)));
|
||||
off += ksize;
|
||||
}
|
||||
|
||||
const result = {};
|
||||
let ioff = ilstBox.bodyStart;
|
||||
while (ioff + 8 <= ilstBox.end) {
|
||||
const itemSize = view.getUint32(ioff);
|
||||
const keyIndex = view.getUint32(ioff + 4);
|
||||
if (itemSize < 8) break;
|
||||
const itemEnd = Math.min(ioff + itemSize, ilstBox.end);
|
||||
const data = findMP4Box(view, bytes, ioff + 8, itemEnd, "data");
|
||||
if (data) {
|
||||
const payload = bytes.slice(data.bodyStart + 8, data.end); // type(4)+locale(4)をスキップ
|
||||
const name = keyNames[keyIndex - 1];
|
||||
if (name) { try { result[name] = utf8.decode(payload); } catch { /* skip */ } }
|
||||
}
|
||||
ioff = itemEnd;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// ── Workflow extraction helpers ───────────────────────────────
|
||||
function collectUnique(arr) {
|
||||
const seen = new Set(), out = [];
|
||||
@@ -188,11 +263,67 @@ function isTextEncoderNode(ct) { return ct === "CLIPTextEncode" || ct.includes("
|
||||
function isSamplerNode(ct) { return ct === "KSampler" || ct === "KSamplerAdvanced" || ct.includes("KSampler") || ct.includes("Sampler"); }
|
||||
function isPromptStylerNode(ct) { return ct.includes("PromptStyler"); }
|
||||
|
||||
function extractPrompts(wf) {
|
||||
// CLIPTextEncodeEditPlus (model-and-prompt-from-metadata) の encode() と同じ結合ルール。
|
||||
// RAW: text1のみ。EDIT: text_editのみ。front/back: text2(未接続ならtext_edit)をtext1の前/後に結合。
|
||||
function resolveEditPlusText(mode, textEdit, text1, text2) {
|
||||
const t1 = typeof text1 === "string" ? text1 : "";
|
||||
const edit = typeof textEdit === "string" ? textEdit : "";
|
||||
const insert = typeof text2 === "string" && text2 !== "" ? text2 : edit;
|
||||
switch (mode) {
|
||||
case "RAW": return t1;
|
||||
case "EDIT": return edit;
|
||||
case "front": return t1 ? `${insert}, ${t1}` : insert;
|
||||
case "back": return t1 ? `${t1}, ${insert}` : insert;
|
||||
default: return t1 || edit;
|
||||
}
|
||||
}
|
||||
|
||||
export function extractPrompts(wf) {
|
||||
if (!wf || typeof wf !== "object") return { positives: [], negatives: [] };
|
||||
return Array.isArray(wf.nodes) ? extractPromptsLiteGraph(wf) : extractPromptsAPI(wf);
|
||||
}
|
||||
|
||||
// LiteGraph形式: リンクを辿ってテキストを解決（ComfySwitchNode等の中継ノードにも対応）
|
||||
function resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, srcId, slot, depth = 0) {
|
||||
if (depth > 6) return null;
|
||||
const srcNode = nodeMap.get(srcId);
|
||||
if (!srcNode) return null;
|
||||
const srcType = srcNode.type ?? "";
|
||||
if (isPromptStylerNode(srcType)) {
|
||||
const v = srcNode.widgets_values?.[slot];
|
||||
return (v && typeof v === "string") ? v : null;
|
||||
}
|
||||
// ComfySwitchNode ("If/Else Switch") — 片方(TextGenerateなどLLMノード)は静的解決不能なため
|
||||
// on_false/on_true の両方を試し、リテラルへ解決できた方を採用する。
|
||||
if (srcType === "ComfySwitchNode" && Array.isArray(srcNode.inputs)) {
|
||||
for (const name of ["on_false", "on_true"]) {
|
||||
const inp = srcNode.inputs.find(i => i.name === name);
|
||||
if (inp?.link == null) continue;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
const originSlot = linkSlot.get(inp.link) ?? 0;
|
||||
if (originId == null) continue;
|
||||
const text = resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot, depth + 1);
|
||||
if (text) return text;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
// WFS_PromptText, PrimitiveStringMultiline 等、任意の STRING 出力ノード
|
||||
const v = srcNode.widgets_values?.[slot] ?? srcNode.widgets_values?.[0];
|
||||
if (v && typeof v === "string") return v;
|
||||
// さらにリンクされている場合（StringReplace等の中継ノード）は追跡する
|
||||
// "source" = PreviewAny（値タップ中継）, "string_a" = StringConcatenate（LoRAトリガーワード
|
||||
// 連結等）— Krea-2のプロンプト強化配線で使われる中継ノード
|
||||
if (Array.isArray(srcNode.inputs)) {
|
||||
const nextInput = srcNode.inputs.find(i => ["text", "value", "prompt", "string", "source", "string_a"].includes(i.name));
|
||||
if (nextInput?.link != null) {
|
||||
const originId = linkOrigin.get(nextInput.link);
|
||||
const originSlot = linkSlot.get(nextInput.link) ?? 0;
|
||||
if (originId != null) return resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot, depth + 1);
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
// CLIPTextEncode + KSampler でプロンプト抽出（トップレベル・サブグラフ共用）
|
||||
// サンプラーが見つかりテキストが取れた場合のみ非null を返す
|
||||
function extractPromptsFromNodeSet(nodes, links) {
|
||||
@@ -212,7 +343,22 @@ function extractPromptsFromNodeSet(nodes, links) {
|
||||
}
|
||||
const textMap = new Map();
|
||||
for (const n of nodes) {
|
||||
if (!isTextEncoderNode(n.type ?? "")) continue;
|
||||
const type = n.type ?? "";
|
||||
if (type === "CLIPTextEncodeEditPlus") {
|
||||
const resolveNamedInput = (name) => {
|
||||
if (!Array.isArray(n.inputs)) return null;
|
||||
const inp = n.inputs.find(i => i.name === name);
|
||||
if (inp?.link == null) return null;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
const originSlot = linkSlot.get(inp.link) ?? 0;
|
||||
if (originId == null) return null;
|
||||
return resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot);
|
||||
};
|
||||
const combined = resolveEditPlusText(n.widgets_values?.[1], n.widgets_values?.[0], resolveNamedInput("text1"), resolveNamedInput("text2"));
|
||||
if (combined) textMap.set(n.id, combined);
|
||||
continue;
|
||||
}
|
||||
if (!isTextEncoderNode(type)) continue;
|
||||
const text = n.widgets_values?.[0];
|
||||
if (text && typeof text === "string") {
|
||||
textMap.set(n.id, text);
|
||||
@@ -221,18 +367,8 @@ function extractPromptsFromNodeSet(nodes, links) {
|
||||
if (textInput?.link != null) {
|
||||
const originId = linkOrigin.get(textInput.link);
|
||||
const originSlot = linkSlot.get(textInput.link) ?? 0;
|
||||
const srcNode = originId != null ? nodeMap.get(originId) : null;
|
||||
if (srcNode) {
|
||||
const srcType = srcNode.type ?? "";
|
||||
if (isPromptStylerNode(srcType)) {
|
||||
const v = srcNode.widgets_values?.[originSlot];
|
||||
if (v && typeof v === "string") textMap.set(n.id, v);
|
||||
} else {
|
||||
// WFS_PromptText, PrimitiveStringMultiline, ComfySwitchNode 等、任意の STRING 出力ノード
|
||||
const v = srcNode.widgets_values?.[originSlot] ?? srcNode.widgets_values?.[0];
|
||||
if (v && typeof v === "string") textMap.set(n.id, v);
|
||||
}
|
||||
}
|
||||
const text2 = resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot);
|
||||
if (text2) textMap.set(n.id, text2);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -241,24 +377,46 @@ function extractPromptsFromNodeSet(nodes, links) {
|
||||
for (const n of nodes) {
|
||||
if (!isSamplerNode(n.type ?? "") || !Array.isArray(n.inputs)) continue;
|
||||
foundSampler = true;
|
||||
for (const inp of n.inputs) {
|
||||
// SamplerCustomAdvanced doesn't hold positive/negative directly — it drives a separate
|
||||
// Guider node (CFGGuider/DualCFGGuider/BasicGuider) via its "guider" input. Scan that
|
||||
// node's inputs instead so the loop below can find the positive/negative-role slots.
|
||||
let inputsToScan = n.inputs;
|
||||
const guiderInput = n.inputs.find(inp => inp.name === "guider");
|
||||
if (guiderInput?.link != null) {
|
||||
const guiderId = linkOrigin.get(guiderInput.link);
|
||||
const guiderNode = guiderId != null ? nodeMap.get(guiderId) : null;
|
||||
if (Array.isArray(guiderNode?.inputs)) inputsToScan = guiderNode.inputs;
|
||||
}
|
||||
for (const inp of inputsToScan) {
|
||||
if (!inp || inp.link == null) continue;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
if (originId == null) continue;
|
||||
const name = inp.name ?? "";
|
||||
const isPos = name === "positive" || name.startsWith("positive");
|
||||
// DualCFGGuider (HiDream E1): cond1 carries the positive-derived conditioning.
|
||||
const isPos = name === "positive" || name === "cond1" || name.startsWith("positive");
|
||||
const isNeg = name === "negative" || name.startsWith("negative");
|
||||
if (!isPos && !isNeg) continue;
|
||||
// TextEncodeMageFlowEdit — one node carries both prompt & negative_prompt
|
||||
// directly, so textMap (single text per node) can't represent both roles.
|
||||
// TextEncodeMageFlowEdit / TextEncodeBooguEdit — one node carries both prompt &
|
||||
// negative_prompt directly, so textMap (single text per node) can't represent both
|
||||
// roles.
|
||||
const srcNode = nodeMap.get(originId);
|
||||
if (srcNode?.type === "TextEncodeMageFlowEdit") {
|
||||
if (srcNode?.type === "TextEncodeMageFlowEdit" || srcNode?.type === "TextEncodeBooguEdit") {
|
||||
const widgetIdx = isPos ? 0 : 1; // widgets_values: [prompt, negative_prompt, ...]
|
||||
const text = srcNode.widgets_values?.[widgetIdx];
|
||||
if (text && typeof text === "string") { if (isPos) pos.add(text); else neg.add(text); }
|
||||
continue;
|
||||
}
|
||||
const text = textMap.get(originId);
|
||||
// InstructPixToPixConditioning (HiDream E1): forwards the actual text-encoder
|
||||
// conditioning through its own positive/negative inputs — follow one more hop.
|
||||
let resolvedOriginId = originId;
|
||||
if (srcNode?.type === "InstructPixToPixConditioning" && Array.isArray(srcNode.inputs)) {
|
||||
const innerInput = srcNode.inputs.find(i => i.name === (isPos ? "positive" : "negative"));
|
||||
if (innerInput?.link != null) {
|
||||
const innerId = linkOrigin.get(innerInput.link);
|
||||
if (innerId != null) resolvedOriginId = innerId;
|
||||
}
|
||||
}
|
||||
const text = textMap.get(resolvedOriginId);
|
||||
if (!text) continue;
|
||||
if (isPos) pos.add(text); else neg.add(text);
|
||||
}
|
||||
@@ -312,7 +470,8 @@ function extractMarkdownNoteModels(wf) {
|
||||
}
|
||||
|
||||
// API形式: リンク参照 [srcNodeId, slot] からテキストを解決
|
||||
function resolveLinkedText(wf, srcId, slot) {
|
||||
function resolveLinkedText(wf, srcId, slot, depth = 0) {
|
||||
if (depth > 6) return null;
|
||||
const src = wf[String(srcId)];
|
||||
if (!src || typeof src !== "object") return null;
|
||||
const ct = src.class_type ?? "";
|
||||
@@ -321,13 +480,34 @@ function resolveLinkedText(wf, srcId, slot) {
|
||||
const v = slot === 0 ? src.inputs?.text_positive : src.inputs?.text_negative;
|
||||
return (v && typeof v === "string") ? v : null;
|
||||
}
|
||||
// 汎用テキストキー
|
||||
// ComfySwitchNode ("If/Else Switch", on_false/on_true/switch) — Ernie Imageのプロンプト強化
|
||||
// トグルなどで使われる。片方(TextGenerateなどLLMノード)は静的解決不能なため両方試し、
|
||||
// リテラルへ解決できた方を採用する。
|
||||
if (ct === "ComfySwitchNode") {
|
||||
for (const key of ["on_false", "on_true"]) {
|
||||
const v = src.inputs?.[key];
|
||||
if (Array.isArray(v)) {
|
||||
const text = resolveLinkedText(wf, v[0], v[1] ?? 0, depth + 1);
|
||||
if (text) return text;
|
||||
} else if (typeof v === "string" && v) {
|
||||
return v;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
// 汎用テキストキー（"value" は PrimitiveString/PrimitiveStringMultiline、"source" は
|
||||
// PreviewAny の値タップ中継、"string_a" は StringConcatenate の主オペランド — いずれも
|
||||
// Krea-2のプロンプト強化配線で使われる中継ノード）
|
||||
const keys = slot === 0
|
||||
? ["text_positive", "text", "text_g", "prompt"]
|
||||
: ["text_negative", "text_l"];
|
||||
? ["text_positive", "text", "text_g", "prompt", "value", "source", "string_a"]
|
||||
: ["text_negative", "text_l", "source", "string_a"];
|
||||
for (const k of keys) {
|
||||
const v = src.inputs?.[k];
|
||||
if (v && typeof v === "string") return v;
|
||||
if (typeof v === "string" && v) return v;
|
||||
if (Array.isArray(v)) {
|
||||
const text = resolveLinkedText(wf, v[0], v[1] ?? 0, depth + 1);
|
||||
if (text) return text;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
@@ -341,8 +521,30 @@ function extractPromptsAPI(wf) {
|
||||
}
|
||||
const textMap = new Map();
|
||||
for (const [id, n] of Object.entries(wf)) {
|
||||
if (!n || !isTextEncoderNode(n.class_type ?? "")) continue;
|
||||
const raw = n.inputs?.text ?? n.inputs?.text_g ?? null;
|
||||
if (!n) continue;
|
||||
const ct = n.class_type ?? "";
|
||||
if (ct === "CLIPTextEncodeEditPlus") {
|
||||
const resolveField = (key) => {
|
||||
const v = n.inputs?.[key];
|
||||
if (typeof v === "string") return v;
|
||||
if (Array.isArray(v)) return resolveLinkedText(wf, v[0], v[1] ?? 0);
|
||||
return null;
|
||||
};
|
||||
const combined = resolveEditPlusText(n.inputs?.mode, n.inputs?.text_edit, resolveField("text1"), resolveField("text2"));
|
||||
if (combined) textMap.set(id, combined);
|
||||
continue;
|
||||
}
|
||||
// MiniMaxH3ImageToVideo等の動画生成オールインワンノード。CLIPTextEncode等を経由せず
|
||||
// 自ノードの"prompt"入力に直接テキストを持つ(positive/negativeの区別も無い)ため、
|
||||
// isTextEncoderNode判定を通さずtextMapへ直接投入する(fallbackのtextsに載る)。
|
||||
if (typeof n.inputs?.prompt === "string" && /ImageToVideo|TextToVideo/i.test(ct)) {
|
||||
textMap.set(id, n.inputs.prompt);
|
||||
continue;
|
||||
}
|
||||
if (!isTextEncoderNode(ct)) continue;
|
||||
// "prompt" — TextEncodeQwenImageEdit(Plus)/TextEncodeBooguEdit and similar Image Edit
|
||||
// model text encoders use this key instead of "text"/"text_g".
|
||||
const raw = n.inputs?.text ?? n.inputs?.text_g ?? n.inputs?.prompt ?? null;
|
||||
if (raw && typeof raw === "string") {
|
||||
textMap.set(id, raw);
|
||||
} else if (Array.isArray(raw)) {
|
||||
@@ -356,20 +558,35 @@ function extractPromptsAPI(wf) {
|
||||
for (const n of Object.values(wf)) {
|
||||
if (!n || !isSamplerNode(n.class_type ?? "")) continue;
|
||||
foundSampler = true;
|
||||
for (const [key, val] of Object.entries(n.inputs ?? {})) {
|
||||
// SamplerCustomAdvanced doesn't hold positive/negative directly — it drives a separate
|
||||
// Guider node (CFGGuider/DualCFGGuider/BasicGuider) via its "guider" input. Scan that
|
||||
// node's inputs instead so the loop below can find the positive/negative-role keys.
|
||||
let inputsToScan = n.inputs ?? {};
|
||||
if (Array.isArray(n.inputs?.guider)) {
|
||||
const guiderNode = wf[String(n.inputs.guider[0])];
|
||||
if (guiderNode?.inputs) inputsToScan = guiderNode.inputs;
|
||||
}
|
||||
for (const [key, val] of Object.entries(inputsToScan)) {
|
||||
if (!Array.isArray(val)) continue;
|
||||
const isPos = key === "positive" || key.startsWith("positive");
|
||||
// DualCFGGuider (HiDream E1): cond1 carries the positive-derived conditioning.
|
||||
const isPos = key === "positive" || key === "cond1" || key.startsWith("positive");
|
||||
const isNeg = key === "negative" || key.startsWith("negative");
|
||||
if (!isPos && !isNeg) continue;
|
||||
// TextEncodeMageFlowEdit — one node carries both prompt & negative_prompt
|
||||
// directly, so textMap (single text per node) can't represent both roles.
|
||||
// TextEncodeMageFlowEdit / TextEncodeBooguEdit — one node carries both prompt &
|
||||
// negative_prompt directly, so textMap (single text per node) can't represent both
|
||||
// roles.
|
||||
const srcNode = wf[String(val[0])];
|
||||
if (srcNode?.class_type === "TextEncodeMageFlowEdit") {
|
||||
if (srcNode?.class_type === "TextEncodeMageFlowEdit" || srcNode?.class_type === "TextEncodeBooguEdit") {
|
||||
const text = isPos ? srcNode.inputs?.prompt : srcNode.inputs?.negative_prompt;
|
||||
if (text && typeof text === "string") { if (isPos) pos.add(text); else neg.add(text); }
|
||||
continue;
|
||||
}
|
||||
const text = textMap.get(String(val[0]));
|
||||
// InstructPixToPixConditioning (HiDream E1): forwards the actual text-encoder
|
||||
// conditioning through its own positive/negative inputs — follow one more hop.
|
||||
const resolvedId = srcNode?.class_type === "InstructPixToPixConditioning" && Array.isArray(srcNode.inputs?.[isPos ? "positive" : "negative"])
|
||||
? String(srcNode.inputs[isPos ? "positive" : "negative"][0])
|
||||
: String(val[0]);
|
||||
const text = textMap.get(resolvedId);
|
||||
if (!text) continue;
|
||||
if (isPos) pos.add(text); else neg.add(text);
|
||||
}
|
||||
@@ -468,10 +685,11 @@ function parseFooocusMetadata(raw) {
|
||||
}
|
||||
|
||||
// ── Master extractAllMetadata ─────────────────────────────────
|
||||
async function extractAllMetadata(file) {
|
||||
export async function extractAllMetadata(file) {
|
||||
const name = file.name.toLowerCase();
|
||||
const isJSON = file.type === "application/json" || name.endsWith(".json");
|
||||
const isWebP = file.type === "image/webp" || name.endsWith(".webp");
|
||||
const isMP4 = file.type === "video/mp4" || name.endsWith(".mp4");
|
||||
|
||||
async function fromWorkflow(originalWf, source) {
|
||||
let wf = originalWf;
|
||||
@@ -509,6 +727,17 @@ async function extractAllMetadata(file) {
|
||||
const wf = extractWorkflowFromEXIF(exif);
|
||||
return wf ? await fromWorkflow(wf, "comfyui") : null;
|
||||
}
|
||||
if (isMP4) {
|
||||
let tags; try { tags = await readMP4MetaTags(file); } catch { return null; }
|
||||
if (!tags) return null;
|
||||
for (const key of ["prompt", "workflow"]) {
|
||||
const raw = tags[key];
|
||||
if (!raw) continue;
|
||||
let wf; try { wf = JSON.parse(sanitizeJSON(raw)); } catch { continue; }
|
||||
if (wf) return await fromWorkflow(wf, "comfyui");
|
||||
}
|
||||
return null;
|
||||
}
|
||||
// PNG
|
||||
const chunks = await readAllPNGTextChunks(file);
|
||||
if (!chunks) return null;
|
||||
@@ -558,7 +787,7 @@ function buildLoRAItem(lora) {
|
||||
return el;
|
||||
}
|
||||
|
||||
function buildPromptItem(label, type, full, fullArea, fullLabel, listEl) {
|
||||
export function buildPromptItem(label, type, full, fullArea, fullLabel, listEl) {
|
||||
const el = document.createElement("div");
|
||||
el.className = "wfm-meta-item wfm-meta-item-clickable";
|
||||
const snippet = label.length > 60 ? label.slice(0, 60) + "…" : label;
|
||||
@@ -588,7 +817,8 @@ function renderSection(sectionEl, listEl, items, buildFn) {
|
||||
let _externalHandleFile = null;
|
||||
|
||||
export async function loadFileIntoMetadataTab(file) {
|
||||
document.querySelector('.wfm-tab[data-tab="metadata"]')?.click();
|
||||
document.querySelector('.wfm-tab[data-tab="gallery"]')?.click();
|
||||
document.querySelector('.wfm-gallery-subtab-btn[data-gallery-subtab="metadata"]')?.click();
|
||||
await new Promise(r => setTimeout(r, 0));
|
||||
if (_externalHandleFile) await _externalHandleFile(file);
|
||||
}
|
||||
@@ -655,6 +885,7 @@ export function initMetadataTab() {
|
||||
"wfm-help-metadata-3": "helpMetadata3",
|
||||
"wfm-help-metadata-4": "helpMetadata4",
|
||||
"wfm-help-metadata-5": "helpMetadata5",
|
||||
"wfm-help-metadata-6": "helpMetadata6",
|
||||
};
|
||||
for (const [id, key] of Object.entries(helpIds)) {
|
||||
const el = document.getElementById(id);
|
||||
|
||||
+17
-1947
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,159 @@
|
||||
/**
|
||||
* Models Tab - Badge palette, badge rendering, badge edit modal, badge filter bar
|
||||
*/
|
||||
|
||||
import { openModal } from "../app.js";
|
||||
import { t } from "../i18n.js";
|
||||
import { escapeHtml, readJsonStorage } from "../util.js";
|
||||
import { state } from "./state.js";
|
||||
|
||||
// Grid re-render is owned by models-tab.js (grid rendering hasn't been split
|
||||
// out yet); registered once from initModelsTab() to avoid a circular import.
|
||||
let _onGridChange = () => {};
|
||||
export function setGridChangeCallback(fn) { _onGridChange = fn; }
|
||||
|
||||
export function getBadgePalette() {
|
||||
return readJsonStorage("wfm_models_badge_palette");
|
||||
}
|
||||
|
||||
export function saveBadgePalette(palette) {
|
||||
localStorage.setItem("wfm_models_badge_palette", JSON.stringify(palette));
|
||||
}
|
||||
|
||||
export function badgeHtml(label) {
|
||||
const palette = getBadgePalette();
|
||||
const color = palette[label] || "";
|
||||
const style = color ? ` style="background:${color};color:#fff;"` : "";
|
||||
return `<span class="wfm-badge wfm-badge-model"${style}>${escapeHtml(label)}</span>`;
|
||||
}
|
||||
|
||||
export function modelBadgesHtml(modelName, clickable = false) {
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
const badges = meta.badges || [];
|
||||
if (badges.length === 0) return "";
|
||||
return badges.map((label) => {
|
||||
const palette = getBadgePalette();
|
||||
const color = palette[label] || "";
|
||||
const style = color ? ` style="background:${color};color:#fff;"` : "";
|
||||
const dataAttr = clickable ? ` data-badge-filter="${escapeHtml(label)}"` : "";
|
||||
return `<span class="wfm-badge wfm-badge-model${clickable ? " wfm-badge-clickable" : ""}"${style}${dataAttr}>${escapeHtml(label)}</span>`;
|
||||
}).join("");
|
||||
}
|
||||
|
||||
export function openBadgeEditModal(onPaletteChange = null) {
|
||||
const palette = getBadgePalette();
|
||||
const labels = Object.keys(palette).sort();
|
||||
|
||||
const rowsHtml = labels.map((label) => {
|
||||
const color = palette[label] || "#6366f1";
|
||||
return `<div class="wfm-badge-color-row" data-badge-label="${escapeHtml(label)}">
|
||||
${badgeHtml(label)}
|
||||
<input type="color" value="${color}" data-badge-label="${escapeHtml(label)}" class="wfm-badge-color-input" title="${t("badgeColorHint")}">
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger wfm-badge-delete-btn" data-badge-label="${escapeHtml(label)}" title="${t("badgeDelete")}">×</button>
|
||||
</div>`;
|
||||
}).join("");
|
||||
|
||||
const html = `
|
||||
<div style="min-width:320px;">
|
||||
<div id="wfm-badge-list">${rowsHtml || `<p style="color:var(--wfm-text-secondary);font-size:12px;">${t("badgeNone")}</p>`}</div>
|
||||
<div style="border-top:1px solid var(--wfm-border);margin-top:12px;padding-top:12px;display:flex;gap:6px;align-items:center;">
|
||||
<input type="text" id="wfm-badge-new-label" class="wfm-input" style="flex:1;" placeholder="${t("badgeNewLabel")}">
|
||||
<input type="color" id="wfm-badge-new-color" value="#6366f1" style="width:36px;height:28px;padding:1px;border-radius:4px;cursor:pointer;">
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="wfm-badge-add-btn">${t("badgeAdd")}</button>
|
||||
</div>
|
||||
</div>`;
|
||||
|
||||
openModal(t("badgeManage"), html);
|
||||
bindBadgeModalEvents(onPaletteChange);
|
||||
}
|
||||
|
||||
function bindBadgeModalEvents(onPaletteChange = null) {
|
||||
const afterChange = () => {
|
||||
renderBadgeFilter();
|
||||
_onGridChange();
|
||||
if (onPaletteChange) onPaletteChange();
|
||||
};
|
||||
|
||||
const refreshList = () => {
|
||||
const palette = getBadgePalette();
|
||||
const labels = Object.keys(palette).sort();
|
||||
const listEl = document.getElementById("wfm-badge-list");
|
||||
if (!listEl) return;
|
||||
listEl.innerHTML = labels.map((label) => {
|
||||
const color = palette[label] || "#6366f1";
|
||||
return `<div class="wfm-badge-color-row" data-badge-label="${escapeHtml(label)}">
|
||||
${badgeHtml(label)}
|
||||
<input type="color" value="${color}" data-badge-label="${escapeHtml(label)}" class="wfm-badge-color-input" title="${t("badgeColorHint")}">
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger wfm-badge-delete-btn" data-badge-label="${escapeHtml(label)}" title="${t("badgeDelete")}">×</button>
|
||||
</div>`;
|
||||
}).join("") || `<p style="color:var(--wfm-text-secondary);font-size:12px;">${t("badgeNone")}</p>`;
|
||||
bindBadgeRowEvents(refreshList, afterChange);
|
||||
afterChange();
|
||||
};
|
||||
|
||||
bindBadgeRowEvents(refreshList, afterChange);
|
||||
|
||||
document.getElementById("wfm-badge-add-btn")?.addEventListener("click", () => {
|
||||
const labelInput = document.getElementById("wfm-badge-new-label");
|
||||
const colorInput = document.getElementById("wfm-badge-new-color");
|
||||
const label = labelInput?.value.trim();
|
||||
if (!label) return;
|
||||
const palette = getBadgePalette();
|
||||
palette[label] = colorInput?.value || "#6366f1";
|
||||
saveBadgePalette(palette);
|
||||
if (labelInput) labelInput.value = "";
|
||||
refreshList();
|
||||
});
|
||||
}
|
||||
|
||||
function bindBadgeRowEvents(refreshList, afterChange = null) {
|
||||
document.querySelectorAll(".wfm-badge-color-input").forEach((input) => {
|
||||
input.addEventListener("input", (e) => {
|
||||
const label = e.target.dataset.badgeLabel;
|
||||
const palette = getBadgePalette();
|
||||
palette[label] = e.target.value;
|
||||
saveBadgePalette(palette);
|
||||
refreshList();
|
||||
});
|
||||
});
|
||||
document.querySelectorAll(".wfm-badge-delete-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const label = btn.dataset.badgeLabel;
|
||||
const palette = getBadgePalette();
|
||||
delete palette[label];
|
||||
saveBadgePalette(palette);
|
||||
refreshList();
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
// ── Badge Filter Bar ──────────────────────────────────────
|
||||
|
||||
export function renderBadgeFilter() {
|
||||
const container = document.getElementById("wfm-models-badge-filter-bar");
|
||||
if (!container) return;
|
||||
const palette = getBadgePalette();
|
||||
const labels = Object.keys(palette).sort();
|
||||
|
||||
if (labels.length === 0) {
|
||||
container.innerHTML = "";
|
||||
return;
|
||||
}
|
||||
|
||||
container.innerHTML = labels.map((label) => {
|
||||
const color = palette[label] || "";
|
||||
const isActive = state.badgeFilter === label;
|
||||
const style = color ? `background:${color};color:#fff;` : "";
|
||||
return `<button class="wfm-badge-filter-btn${isActive ? " active" : ""}" data-badge="${escapeHtml(label)}" style="${style}">${escapeHtml(label)}</button>`;
|
||||
}).join("");
|
||||
|
||||
container.querySelectorAll(".wfm-badge-filter-btn").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const label = btn.dataset.badge;
|
||||
state.badgeFilter = state.badgeFilter === label ? "" : label;
|
||||
state.currentPage = 0;
|
||||
renderBadgeFilter();
|
||||
_onGridChange();
|
||||
});
|
||||
});
|
||||
}
|
||||
@@ -0,0 +1,898 @@
|
||||
/**
|
||||
* Models Tab - Detail modal (double-click) and the side panel's
|
||||
* Info / Group / CivitAI tabs.
|
||||
*
|
||||
* Part of the grid-view.js / selection-bulk.js / detail-panel.js circular
|
||||
* import triangle — see the note at the top of grid-view.js.
|
||||
*/
|
||||
|
||||
import { showToast, openModal, closeModal } from "../app.js";
|
||||
import { t } from "../i18n.js";
|
||||
import { escapeHtml } from "../util.js";
|
||||
import { state, RESERVED_GROUPS, GENUI_TYPE_MAP } from "./state.js";
|
||||
import { getCurrentModels, renderTagFilter, renderDirFilter } from "./filters.js";
|
||||
import { getBadgePalette, modelBadgesHtml, openBadgeEditModal } from "./badges.js";
|
||||
import { parseModelPath, getExtension, getStem, loadPreviewImage, previewUrl } from "./helpers.js";
|
||||
import { renderModelGrid } from "./grid-view.js";
|
||||
import {
|
||||
applyToGenUI, applyEmbeddingToPrompt, saveModelMetadata, toggleGroupEnable,
|
||||
fetchModelMetadata, saveModelGroups,
|
||||
} from "../models-tab.js";
|
||||
|
||||
// ── Detail Modal (double-click, with badges) ──────────────
|
||||
|
||||
export function openDetailModal(modelName) {
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
const { dir, name } = parseModelPath(modelName);
|
||||
const ext = getExtension(name);
|
||||
const isFav = !!meta.favorite;
|
||||
const tagsStr = (meta.tags || []).join(", ");
|
||||
const selectedBadges = meta.badges || [];
|
||||
const palette = getBadgePalette();
|
||||
const allBadgeLabels = Object.keys(palette).sort();
|
||||
|
||||
const badgeCheckboxes = allBadgeLabels.map((label) => {
|
||||
const checked = selectedBadges.includes(label) ? " checked" : "";
|
||||
const color = palette[label] || "";
|
||||
const style = color ? `background:${color};color:#fff;` : "";
|
||||
return `<label class="wfm-badge-check-label">
|
||||
<input type="checkbox" class="wfm-badge-checkbox" value="${escapeHtml(label)}"${checked}>
|
||||
<span class="wfm-badge wfm-badge-model" style="${style}">${escapeHtml(label)}</span>
|
||||
</label>`;
|
||||
}).join("");
|
||||
|
||||
const html = `
|
||||
<div class="wfm-modal-thumb-section">
|
||||
<img class="wfm-modal-thumb-img" style="display:none" />
|
||||
<div class="wfm-modal-thumb-placeholder">${t("modelsNoPreview")}</div>
|
||||
</div>
|
||||
<div style="text-align:center;margin-bottom:8px;">
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-modal-change-thumb">${t("changeThumbnail")}</button>
|
||||
<input type="file" id="wfm-modal-thumb-file" accept="image/*" style="display:none">
|
||||
</div>
|
||||
<div class="wfm-modal-two-col">
|
||||
<div class="wfm-modal-left">
|
||||
<section>
|
||||
<h4>${t("modelsInfo")}</h4>
|
||||
<div><span class="wfm-badge wfm-badge-sm">${escapeHtml(ext)}</span>${dir ? ` <span class="wfm-badge wfm-badge-sm wfm-badge-dir">${escapeHtml(dir)}</span>` : ""}</div>
|
||||
</section>
|
||||
<section>
|
||||
<h4>${t("modelsBadges")} <button class="wfm-btn wfm-btn-sm" id="wfm-modal-badge-manage" style="margin-left:6px;font-size:10px;">⚙ ${t("badgeManage")}</button></h4>
|
||||
<div id="wfm-modal-badge-checkboxes" class="wfm-badge-checkboxes">
|
||||
${allBadgeLabels.length === 0
|
||||
? `<span style="color:var(--wfm-text-secondary);font-size:12px;">${t("badgeNoneHint")}</span>`
|
||||
: badgeCheckboxes}
|
||||
</div>
|
||||
</section>
|
||||
<section>
|
||||
<h4>${t("modelsTags")} <span style="font-weight:normal;font-size:11px;">${t("modelsTagsHint")}</span></h4>
|
||||
<input type="text" class="wfm-input" id="wfm-modal-model-tags" value="${escapeHtml(tagsStr)}" placeholder="${t("modelsTagsPlaceholder")}">
|
||||
</section>
|
||||
<section>
|
||||
<h4>${t("modelsMemo")}</h4>
|
||||
<textarea class="wfm-textarea" id="wfm-modal-model-memo" rows="4" placeholder="${t("modelsMemoPlaceholder")}">${escapeHtml(meta.memo || "")}</textarea>
|
||||
</section>
|
||||
<div class="wfm-modal-actions">
|
||||
<button class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-modal-model-save">${t("modelsSave")}</button>
|
||||
${state.activeModelType === "embedding"
|
||||
? `<button class="wfm-btn wfm-btn-sm" id="wfm-modal-genui-pp" title="Positive Promptにエンベッディング追加">GenUI PP</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-modal-genui-np" title="Negative Promptにエンベッディング追加">GenUI NP</button>`
|
||||
: (GENUI_TYPE_MAP[state.activeModelType]
|
||||
? `<button class="wfm-btn wfm-btn-sm" id="wfm-modal-genui-model" title="${t("modelsGenUITitle")}">${t("modelsGenUIBtn")}</button>`
|
||||
: "")}
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger" id="wfm-modal-model-delete" style="margin-left:auto;">${t("modelsDelete")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-modal-model-close">${t("close")}</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>`;
|
||||
|
||||
openModal(getStem(name), html);
|
||||
|
||||
// Load preview image
|
||||
const modalImg = document.querySelector(".wfm-modal-thumb-img");
|
||||
const modalPlaceholder = document.querySelector(".wfm-modal-thumb-placeholder");
|
||||
if (modalImg) loadPreviewImage(modalImg, modalPlaceholder, modelName);
|
||||
|
||||
// Add favorite button in header
|
||||
const titleEl = document.getElementById("wfm-modal-title");
|
||||
if (titleEl) {
|
||||
titleEl.parentNode.querySelectorAll(".wfm-fav-btn").forEach((el) => el.remove());
|
||||
const favBtn = document.createElement("button");
|
||||
favBtn.className = isFav ? "wfm-fav-btn active" : "wfm-fav-btn";
|
||||
favBtn.style.cssText = "position:static;font-size:18px;margin-right:8px;";
|
||||
favBtn.textContent = isFav ? "★" : "☆";
|
||||
favBtn.addEventListener("click", async () => {
|
||||
const newVal = !meta.favorite;
|
||||
await saveModelMetadata(modelName, { favorite: newVal });
|
||||
meta.favorite = newVal;
|
||||
favBtn.textContent = newVal ? "★" : "☆";
|
||||
favBtn.classList.toggle("active", newVal);
|
||||
renderModelGrid();
|
||||
});
|
||||
titleEl.parentNode.insertBefore(favBtn, titleEl);
|
||||
}
|
||||
|
||||
// Badge manage button → open badge edit modal
|
||||
document.getElementById("wfm-modal-badge-manage")?.addEventListener("click", () => {
|
||||
openBadgeEditModal();
|
||||
});
|
||||
|
||||
// Save
|
||||
document.getElementById("wfm-modal-model-save")?.addEventListener("click", async () => {
|
||||
const tagsInput = document.getElementById("wfm-modal-model-tags");
|
||||
const memoInput = document.getElementById("wfm-modal-model-memo");
|
||||
const tags = tagsInput ? tagsInput.value.split(",").map((s) => s.trim()).filter(Boolean) : [];
|
||||
const memo = memoInput ? memoInput.value : "";
|
||||
const badges = [...document.querySelectorAll(".wfm-badge-checkbox:checked")].map((cb) => cb.value);
|
||||
await saveModelMetadata(modelName, { tags, memo, badges });
|
||||
showToast(t("modelsSaved"), "success");
|
||||
renderTagFilter();
|
||||
renderDirFilter();
|
||||
renderModelGrid();
|
||||
});
|
||||
|
||||
// GenUI Model / Embedding buttons
|
||||
document.getElementById("wfm-modal-genui-model")?.addEventListener("click", () => {
|
||||
applyToGenUI(modelName, state.activeModelType);
|
||||
});
|
||||
document.getElementById("wfm-modal-genui-pp")?.addEventListener("click", () => {
|
||||
applyEmbeddingToPrompt(modelName, "positive");
|
||||
});
|
||||
document.getElementById("wfm-modal-genui-np")?.addEventListener("click", () => {
|
||||
applyEmbeddingToPrompt(modelName, "negative");
|
||||
});
|
||||
|
||||
// Close button (連続作業時にXボタンまでマウス移動しなくて済むように)
|
||||
document.getElementById("wfm-modal-model-close")?.addEventListener("click", () => {
|
||||
closeModal();
|
||||
});
|
||||
|
||||
// Delete model button
|
||||
document.getElementById("wfm-modal-model-delete")?.addEventListener("click", async () => {
|
||||
const confirmMsg = t("modelBulkDeleteConfirm").replace("{count}", "1");
|
||||
if (!confirm(confirmMsg)) return;
|
||||
try {
|
||||
const res = await fetch("/api/wfm/models/delete", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model_type: state.activeModelType, model_names: [modelName] }),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.errors?.length > 0) {
|
||||
showToast(`${t("modelBulkDeleteError")}: ${data.errors[0].error}`, "error");
|
||||
return;
|
||||
}
|
||||
showToast(`${getStem(name)} ${t("modelBulkDeleteDone")}`, "success");
|
||||
closeModal();
|
||||
const list = state.modelsByType[state.activeModelType];
|
||||
const idx = list.indexOf(modelName);
|
||||
if (idx !== -1) list.splice(idx, 1);
|
||||
delete state.modelMetadata[modelName];
|
||||
const ds = state.disabledModels[state.activeModelType];
|
||||
if (ds) ds.delete(modelName);
|
||||
state.selectedModels.delete(modelName);
|
||||
if (state.selectedModel === modelName) {
|
||||
state.selectedModel = null;
|
||||
const titleEl2 = document.getElementById("wfm-models-panel-title");
|
||||
if (titleEl2) titleEl2.textContent = "";
|
||||
}
|
||||
renderModelGrid();
|
||||
} catch (err) {
|
||||
showToast(`${t("modelBulkDeleteError")}: ${err.message}`, "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Change thumbnail
|
||||
const changeBtn = document.getElementById("wfm-modal-change-thumb");
|
||||
const thumbFile = document.getElementById("wfm-modal-thumb-file");
|
||||
if (changeBtn && thumbFile) {
|
||||
changeBtn.addEventListener("click", () => { thumbFile.value = ""; thumbFile.click(); });
|
||||
thumbFile.addEventListener("change", async () => {
|
||||
const file = thumbFile.files?.[0];
|
||||
if (!file) return;
|
||||
changeBtn.disabled = true;
|
||||
changeBtn.textContent = t("uploading");
|
||||
try {
|
||||
const fd = new FormData();
|
||||
fd.append("type", state.activeModelType);
|
||||
fd.append("name", modelName);
|
||||
fd.append("file", file);
|
||||
const res = await fetch("/api/wfm/models/change-preview", { method: "POST", body: fd });
|
||||
const data = await res.json();
|
||||
if (data.error) throw new Error(data.error);
|
||||
// Reload preview in modal
|
||||
const newUrl = previewUrl(modelName) + "&t=" + Date.now();
|
||||
const mImg = document.querySelector(".wfm-modal-thumb-img");
|
||||
const mPlaceholder = document.querySelector(".wfm-modal-thumb-placeholder");
|
||||
if (mImg) { mImg.src = newUrl; mImg.style.display = ""; }
|
||||
if (mPlaceholder) mPlaceholder.style.display = "none";
|
||||
renderModelGrid();
|
||||
if (state.selectedModel === modelName) renderSideInfo(modelName);
|
||||
showToast(t("thumbnailChanged"), "success");
|
||||
} catch (err) {
|
||||
showToast(t("thumbnailError") + ": " + err.message, "error");
|
||||
} finally {
|
||||
changeBtn.disabled = false;
|
||||
changeBtn.textContent = t("changeThumbnail");
|
||||
thumbFile.value = "";
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// ── Side Panel ────────────────────────────────────────────
|
||||
|
||||
export function showSidePanel(modelName) {
|
||||
state.selectedModel = modelName;
|
||||
|
||||
// Highlight selected
|
||||
document.querySelectorAll("#wfm-models-grid .wfm-card, #wfm-models-grid .wfm-models-table-row").forEach((el) => {
|
||||
el.classList.toggle("wfm-card-selected", el.dataset.modelName === modelName);
|
||||
});
|
||||
|
||||
const { name } = parseModelPath(modelName);
|
||||
document.getElementById("wfm-models-panel-title").textContent = name;
|
||||
|
||||
// Reset to CivitAI tab
|
||||
document.querySelectorAll(".wfm-models-side-tab-btn").forEach((b) => b.classList.remove("active"));
|
||||
document.querySelector('.wfm-models-side-tab-btn[data-side-tab="civitai"]')?.classList.add("active");
|
||||
document.querySelectorAll(".wfm-models-side-content").forEach((c) => (c.style.display = "none"));
|
||||
const civitaiEl = document.getElementById("wfm-models-side-civitai");
|
||||
if (civitaiEl) civitaiEl.style.display = "block";
|
||||
|
||||
const genuiNavBtn = document.getElementById("wfm-side-genui-nav-btn");
|
||||
const genuiNpBtn = document.getElementById("wfm-side-genui-np-btn");
|
||||
if (genuiNavBtn) {
|
||||
if (state.activeModelType === "embedding") {
|
||||
genuiNavBtn.textContent = "GenUI PP";
|
||||
genuiNavBtn.title = "Positive Promptにエンベッディング追加";
|
||||
genuiNavBtn.style.display = "";
|
||||
if (genuiNpBtn) {
|
||||
genuiNpBtn.textContent = "GenUI NP";
|
||||
genuiNpBtn.title = "Negative Promptにエンベッディング追加";
|
||||
genuiNpBtn.style.display = "";
|
||||
}
|
||||
} else if (GENUI_TYPE_MAP[state.activeModelType]) {
|
||||
genuiNavBtn.textContent = t("modelsGenUIBtn");
|
||||
genuiNavBtn.title = t("modelsGenUITitle");
|
||||
genuiNavBtn.style.display = "";
|
||||
if (genuiNpBtn) genuiNpBtn.style.display = "none";
|
||||
} else {
|
||||
genuiNavBtn.style.display = "none";
|
||||
if (genuiNpBtn) genuiNpBtn.style.display = "none";
|
||||
}
|
||||
}
|
||||
|
||||
renderSideInfo(modelName);
|
||||
renderSideGroup(modelName);
|
||||
renderSideCivitai(modelName);
|
||||
}
|
||||
|
||||
export function closeSidePanel() {
|
||||
state.selectedModel = null;
|
||||
const titleEl = document.getElementById("wfm-models-panel-title");
|
||||
if (titleEl) titleEl.textContent = "";
|
||||
document.querySelectorAll("#wfm-models-grid .wfm-card, #wfm-models-grid .wfm-models-table-row").forEach((el) => {
|
||||
el.classList.remove("wfm-card-selected");
|
||||
});
|
||||
const npBtn = document.getElementById("wfm-side-genui-np-btn");
|
||||
if (npBtn) npBtn.style.display = "none";
|
||||
const ppBtn = document.getElementById("wfm-side-genui-nav-btn");
|
||||
if (ppBtn) ppBtn.style.display = "none";
|
||||
}
|
||||
|
||||
// ── Side Panel: Info Tab ──────────────────────────────────
|
||||
|
||||
export function renderSideInfo(modelName) {
|
||||
const el = document.getElementById("wfm-models-side-info");
|
||||
if (!el) return;
|
||||
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
const { dir, name } = parseModelPath(modelName);
|
||||
const ext = getExtension(name);
|
||||
const tagsStr = (meta.tags || []).join(", ");
|
||||
const userBadgesHtml = modelBadgesHtml(modelName, false);
|
||||
|
||||
el.innerHTML = `
|
||||
<div class="wfm-side-thumb-container">
|
||||
<div class="wfm-side-thumb-img-wrap">
|
||||
<img style="display:none" />
|
||||
<span class="wfm-side-thumb-placeholder">${t("modelsNoPreview")}</span>
|
||||
</div>
|
||||
<div class="wfm-side-thumb-info">
|
||||
<div class="wfm-side-thumb-name wfm-model-name-copy" title="${t("modelsCopyName")}">${escapeHtml(name)}</div>
|
||||
<div class="wfm-side-thumb-meta">
|
||||
<span class="wfm-badge wfm-badge-sm">${escapeHtml(ext)}</span>
|
||||
${dir ? `<span class="wfm-badge wfm-badge-sm wfm-badge-dir">${escapeHtml(dir)}</span>` : ""}
|
||||
${userBadgesHtml}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="wfm-node-detail-section">
|
||||
<div class="wfm-node-detail-label">${t("modelsFilePath")}</div>
|
||||
<div class="wfm-node-detail-value">
|
||||
<span id="wfm-models-side-filepath" class="wfm-model-filepath" title="${t("modelsCopyPath")}" style="cursor:pointer;word-break:break-all;font-size:0.85em;color:#aaa;">${t("modelsLoading")}...</span>
|
||||
</div>
|
||||
</div>
|
||||
<div class="wfm-node-detail-section">
|
||||
<div class="wfm-node-detail-label">${t("modelsTags")}</div>
|
||||
<div class="wfm-node-detail-value">
|
||||
<input type="text" id="wfm-models-side-tags" class="wfm-search-input" value="${escapeHtml(tagsStr)}" placeholder="${t("modelsTagsPlaceholder")}">
|
||||
</div>
|
||||
</div>
|
||||
<div class="wfm-node-detail-section">
|
||||
<div class="wfm-node-detail-label">${t("modelsMemo")}</div>
|
||||
<div class="wfm-node-detail-value">
|
||||
<textarea id="wfm-models-side-memo" class="wfm-textarea" rows="4" placeholder="${t("modelsMemoPlaceholder")}">${escapeHtml(meta.memo || "")}</textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="wfm-node-detail-section" style="display:flex;gap:6px;">
|
||||
<button id="wfm-models-side-save-btn" class="wfm-btn wfm-btn-sm wfm-btn-primary">${t("modelsSave")}</button>
|
||||
${state.activeModelType === "embedding"
|
||||
? `<button id="wfm-models-side-genui-pp-btn" class="wfm-btn wfm-btn-sm" title="Positive Promptにエンベッディング追加">GenUI PP</button>
|
||||
<button id="wfm-models-side-genui-np-btn" class="wfm-btn wfm-btn-sm" title="Negative Promptにエンベッディング追加">GenUI NP</button>`
|
||||
: (GENUI_TYPE_MAP[state.activeModelType]
|
||||
? `<button id="wfm-models-side-genui-btn" class="wfm-btn wfm-btn-sm" title="${t("modelsGenUITitle")}">${t("modelsGenUIBtn")}</button>`
|
||||
: "")}
|
||||
</div>`;
|
||||
|
||||
// Load preview image
|
||||
const sideImg = el.querySelector(".wfm-side-thumb-img-wrap img");
|
||||
const sidePlaceholder = el.querySelector(".wfm-side-thumb-placeholder");
|
||||
if (sideImg) loadPreviewImage(sideImg, sidePlaceholder, modelName);
|
||||
|
||||
// Copy model name on click
|
||||
el.querySelector(".wfm-model-name-copy")?.addEventListener("click", () => {
|
||||
navigator.clipboard.writeText(modelName).then(() => {
|
||||
showToast(t("modelsCopiedName"), "success");
|
||||
});
|
||||
});
|
||||
|
||||
// Fetch and display file path
|
||||
fetch(`/api/wfm/models/filepath?type=${encodeURIComponent(state.activeModelType)}&name=${encodeURIComponent(modelName)}`)
|
||||
.then((r) => r.json())
|
||||
.then((data) => {
|
||||
const fpEl = document.getElementById("wfm-models-side-filepath");
|
||||
if (fpEl && data.path) {
|
||||
fpEl.textContent = data.path;
|
||||
fpEl.title = t("modelsCopyPath");
|
||||
} else if (fpEl) {
|
||||
fpEl.textContent = modelName;
|
||||
}
|
||||
})
|
||||
.catch(() => {
|
||||
const fpEl = document.getElementById("wfm-models-side-filepath");
|
||||
if (fpEl) fpEl.textContent = modelName;
|
||||
});
|
||||
|
||||
// Copy file path on click
|
||||
el.querySelector("#wfm-models-side-filepath")?.addEventListener("click", () => {
|
||||
const fpEl = document.getElementById("wfm-models-side-filepath");
|
||||
if (fpEl) {
|
||||
navigator.clipboard.writeText(fpEl.textContent).then(() => {
|
||||
showToast(t("modelsCopiedPath"), "success");
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
// Save button
|
||||
el.querySelector("#wfm-models-side-save-btn")?.addEventListener("click", () => {
|
||||
const tagsInput = document.getElementById("wfm-models-side-tags");
|
||||
const memoInput = document.getElementById("wfm-models-side-memo");
|
||||
const tags = tagsInput ? tagsInput.value.split(",").map((s) => s.trim()).filter(Boolean) : [];
|
||||
const memo = memoInput ? memoInput.value : "";
|
||||
saveModelMetadata(modelName, { tags, memo }).then(() => {
|
||||
showToast(t("modelsSaved"), "success");
|
||||
renderTagFilter();
|
||||
renderModelGrid();
|
||||
});
|
||||
});
|
||||
|
||||
// GenUI Model / Embedding buttons
|
||||
el.querySelector("#wfm-models-side-genui-btn")?.addEventListener("click", () => {
|
||||
applyToGenUI(modelName, state.activeModelType);
|
||||
});
|
||||
el.querySelector("#wfm-models-side-genui-pp-btn")?.addEventListener("click", () => {
|
||||
applyEmbeddingToPrompt(modelName, "positive");
|
||||
});
|
||||
el.querySelector("#wfm-models-side-genui-np-btn")?.addEventListener("click", () => {
|
||||
applyEmbeddingToPrompt(modelName, "negative");
|
||||
});
|
||||
}
|
||||
|
||||
// ── Side Panel: Group Tab ─────────────────────────────────
|
||||
|
||||
export function renderSideGroup(modelName) {
|
||||
const el = document.getElementById("wfm-models-side-group");
|
||||
if (!el) return;
|
||||
|
||||
// Find groups this model belongs to
|
||||
const memberOf = [];
|
||||
for (const [gName, members] of Object.entries(state.modelGroups)) {
|
||||
if (members.includes(modelName)) memberOf.push(gName);
|
||||
}
|
||||
|
||||
// All group names for the assign dropdown
|
||||
const allGroups = Object.keys(state.modelGroups).sort();
|
||||
const availableGroups = allGroups.filter((g) => !memberOf.includes(g));
|
||||
|
||||
el.innerHTML = `
|
||||
<div style="padding:0 4px;">
|
||||
<div style="margin-bottom:12px;">
|
||||
<div style="font-weight:600;font-size:13px;margin-bottom:6px;">${t("modelsCurrentGroups")}</div>
|
||||
${memberOf.length === 0
|
||||
? `<p style="color:var(--wfm-text-secondary);font-size:12px;">${t("modelsNoGroup")}</p>`
|
||||
: memberOf.map((g) => `<div style="display:flex;align-items:center;justify-content:space-between;padding:3px 0;">
|
||||
<span style="font-size:13px;">${escapeHtml(g)}</span>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger wfm-group-remove" data-group="${escapeHtml(g)}" title="${t("modelsRemoveFromGroup")}">×</button>
|
||||
</div>`).join("")}
|
||||
</div>
|
||||
<div style="margin-bottom:12px;">
|
||||
<div style="font-weight:600;font-size:13px;margin-bottom:6px;">${t("modelsAssignGroup")}</div>
|
||||
<div style="display:flex;gap:4px;">
|
||||
<select id="wfm-models-group-assign" class="wfm-select" style="flex:1;font-size:12px;">
|
||||
${availableGroups.length === 0
|
||||
? `<option value="">${t("modelsNoGroupAvailable")}</option>`
|
||||
: availableGroups.map((g) => `<option value="${escapeHtml(g)}">${escapeHtml(g)}</option>`).join("")}
|
||||
</select>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="wfm-models-group-assign-btn" ${availableGroups.length === 0 ? "disabled" : ""}>${t("modelsAdd")}</button>
|
||||
</div>
|
||||
</div>
|
||||
<div>
|
||||
<div style="font-weight:600;font-size:13px;margin-bottom:6px;">${t("modelsCreateGroup")}</div>
|
||||
<div style="display:flex;gap:4px;">
|
||||
<input type="text" id="wfm-models-group-new" class="wfm-search-input" style="flex:1;font-size:12px;" placeholder="${t("modelsGroupName")}">
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="wfm-models-group-create-btn">${t("modelsCreate")}</button>
|
||||
</div>
|
||||
</div>
|
||||
<div style="margin-top:16px;border-top:1px solid var(--wfm-border);padding-top:12px;">
|
||||
<div style="font-weight:600;font-size:13px;margin-bottom:6px;">${t("modelsManageGroups")}</div>
|
||||
<div style="display:flex;gap:4px;margin-bottom:6px;">
|
||||
<select id="wfm-models-group-manage-select" class="wfm-select" style="flex:1;font-size:12px;">
|
||||
${allGroups.length === 0
|
||||
? `<option value="">${t("modelsNoGroupAvailable")}</option>`
|
||||
: allGroups.map((g) => `<option value="${escapeHtml(g)}">${escapeHtml(g)}</option>`).join("")}
|
||||
</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-models-group-rename-btn" ${allGroups.length === 0 ? "disabled" : ""}>${t("modelsRename")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger" id="wfm-models-group-delete-btn" ${allGroups.length === 0 ? "disabled" : ""}>${t("modelsDelete")}</button>
|
||||
</div>
|
||||
<div style="display:flex;gap:4px;">
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="wfm-models-group-enable-btn" ${allGroups.length === 0 ? "disabled" : ""}>${t("modelGroupEnableAll")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-models-group-disable-btn" ${allGroups.length === 0 ? "disabled" : ""}>${t("modelGroupDisableAll")}</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
|
||||
// Remove from group
|
||||
el.querySelectorAll(".wfm-group-remove").forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const g = btn.dataset.group;
|
||||
const members = state.modelGroups[g] || [];
|
||||
state.modelGroups[g] = members.filter((m) => m !== modelName);
|
||||
if (state.modelGroups[g].length === 0) delete state.modelGroups[g];
|
||||
saveModelGroups(state.modelGroups).then(() => {
|
||||
renderSideGroup(modelName);
|
||||
renderModelGrid();
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
// Assign to group
|
||||
document.getElementById("wfm-models-group-assign-btn")?.addEventListener("click", () => {
|
||||
const sel = document.getElementById("wfm-models-group-assign");
|
||||
const g = sel?.value;
|
||||
if (!g) return;
|
||||
if (!state.modelGroups[g]) state.modelGroups[g] = [];
|
||||
if (!state.modelGroups[g].includes(modelName)) state.modelGroups[g].push(modelName);
|
||||
saveModelGroups(state.modelGroups).then(() => {
|
||||
renderSideGroup(modelName);
|
||||
});
|
||||
});
|
||||
|
||||
// Create new group
|
||||
document.getElementById("wfm-models-group-create-btn")?.addEventListener("click", () => {
|
||||
const input = document.getElementById("wfm-models-group-new");
|
||||
const name = input?.value.trim();
|
||||
if (!name) return;
|
||||
if (state.modelGroups[name]) {
|
||||
showToast(t("modelsGroupExists"), "warning");
|
||||
return;
|
||||
}
|
||||
state.modelGroups[name] = [modelName];
|
||||
saveModelGroups(state.modelGroups).then(() => {
|
||||
renderSideGroup(modelName);
|
||||
});
|
||||
});
|
||||
|
||||
// Rename group
|
||||
document.getElementById("wfm-models-group-rename-btn")?.addEventListener("click", () => {
|
||||
const sel = document.getElementById("wfm-models-group-manage-select");
|
||||
const oldName = sel?.value;
|
||||
if (!oldName) return;
|
||||
if (RESERVED_GROUPS.includes(oldName)) {
|
||||
showToast(t("modelsGroupReserved"), "warning");
|
||||
return;
|
||||
}
|
||||
const newName = prompt(t("modelsRenamePrompt"), oldName);
|
||||
if (!newName || newName === oldName) return;
|
||||
if (state.modelGroups[newName]) {
|
||||
showToast(t("modelsGroupExists"), "warning");
|
||||
return;
|
||||
}
|
||||
state.modelGroups[newName] = state.modelGroups[oldName];
|
||||
delete state.modelGroups[oldName];
|
||||
saveModelGroups(state.modelGroups).then(() => {
|
||||
renderSideGroup(modelName);
|
||||
renderModelGrid();
|
||||
});
|
||||
});
|
||||
|
||||
// Delete group
|
||||
document.getElementById("wfm-models-group-delete-btn")?.addEventListener("click", () => {
|
||||
const sel = document.getElementById("wfm-models-group-manage-select");
|
||||
const g = sel?.value;
|
||||
if (!g) return;
|
||||
if (RESERVED_GROUPS.includes(g)) {
|
||||
showToast(t("modelsGroupReserved"), "warning");
|
||||
return;
|
||||
}
|
||||
if (!confirm(t("modelsDeleteGroupConfirm").replace("{name}", g))) return;
|
||||
delete state.modelGroups[g];
|
||||
saveModelGroups(state.modelGroups).then(() => {
|
||||
renderSideGroup(modelName);
|
||||
renderModelGrid();
|
||||
});
|
||||
});
|
||||
|
||||
// Enable all models in selected group
|
||||
document.getElementById("wfm-models-group-enable-btn")?.addEventListener("click", () => {
|
||||
const sel = document.getElementById("wfm-models-group-manage-select");
|
||||
const g = sel?.value;
|
||||
if (!g) return;
|
||||
toggleGroupEnable(g, true).then(() => renderSideGroup(modelName));
|
||||
});
|
||||
|
||||
// Disable all models in selected group
|
||||
document.getElementById("wfm-models-group-disable-btn")?.addEventListener("click", () => {
|
||||
const sel = document.getElementById("wfm-models-group-manage-select");
|
||||
const g = sel?.value;
|
||||
if (!g) return;
|
||||
toggleGroupEnable(g, false).then(() => renderSideGroup(modelName));
|
||||
});
|
||||
}
|
||||
|
||||
// ── Side Panel: CivitAI Tab ───────────────────────────────
|
||||
|
||||
export function renderSideCivitai(modelName) {
|
||||
const el = document.getElementById("wfm-models-side-civitai");
|
||||
if (!el) return;
|
||||
|
||||
// Check if we have cached civitai data via sha256
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
const sha256 = meta.sha256;
|
||||
const cached = sha256 && state.civitaiCache[sha256];
|
||||
|
||||
if (cached) {
|
||||
renderCivitaiInfo(el, cached, modelName);
|
||||
} else if (sha256) {
|
||||
// SHA256 is known but model was not found on CivitAI
|
||||
el.innerHTML = `
|
||||
<div style="padding:0 4px;text-align:center;">
|
||||
<p style="color:var(--wfm-text-secondary);font-size:13px;margin-bottom:12px;">
|
||||
${t("civitaiNotFoundDesc")}
|
||||
</p>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-civitai-fetch-btn">
|
||||
${t("civitaiRefetchBtn")}
|
||||
</button>
|
||||
<div id="wfm-civitai-status" style="margin-top:8px;font-size:12px;color:var(--wfm-text-secondary);"></div>
|
||||
</div>`;
|
||||
document.getElementById("wfm-civitai-fetch-btn")?.addEventListener("click", () => {
|
||||
fetchCivitaiForModel(modelName, el);
|
||||
});
|
||||
} else {
|
||||
el.innerHTML = `
|
||||
<div style="padding:0 4px;text-align:center;">
|
||||
<p style="color:var(--wfm-text-secondary);font-size:13px;margin-bottom:12px;">
|
||||
${t("civitaiFetchDesc")}
|
||||
</p>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="wfm-civitai-fetch-btn">
|
||||
${t("civitaiFetch")}
|
||||
</button>
|
||||
<div id="wfm-civitai-status" style="margin-top:8px;font-size:12px;color:var(--wfm-text-secondary);"></div>
|
||||
</div>`;
|
||||
document.getElementById("wfm-civitai-fetch-btn")?.addEventListener("click", () => {
|
||||
fetchCivitaiForModel(modelName, el);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
export async function fetchCivitaiForModel(modelName, el) {
|
||||
const statusEl = document.getElementById("wfm-civitai-status");
|
||||
const fetchBtn = document.getElementById("wfm-civitai-fetch-btn");
|
||||
if (fetchBtn) fetchBtn.disabled = true;
|
||||
if (statusEl) statusEl.textContent = t("civitaiHashing");
|
||||
|
||||
try {
|
||||
const res = await fetch("/api/wfm/models/civitai/fetch", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ type: state.activeModelType, name: modelName }),
|
||||
});
|
||||
const data = await res.json();
|
||||
|
||||
if (data.status === "ok" && data.civitai) {
|
||||
// Update caches
|
||||
if (data.sha256) {
|
||||
state.civitaiCache[data.sha256] = data.civitai;
|
||||
// Update metadata with sha256
|
||||
if (!state.modelMetadata[modelName]) state.modelMetadata[modelName] = {};
|
||||
state.modelMetadata[modelName].sha256 = data.sha256;
|
||||
}
|
||||
renderCivitaiInfo(el, data.civitai, modelName);
|
||||
showToast(t("civitaiFound"), "success");
|
||||
// Always refresh preview: local file if saved, otherwise civitai image as fallback
|
||||
const sidePanel = document.getElementById("wfm-models-side-panel");
|
||||
if (sidePanel) {
|
||||
const sideImg = sidePanel.querySelector(".wfm-side-thumb-img-wrap img");
|
||||
const sidePh = sidePanel.querySelector(".wfm-side-thumb-placeholder");
|
||||
if (sideImg) {
|
||||
if (data.preview_saved) {
|
||||
sideImg.src = previewUrl(modelName) + "&t=" + Date.now();
|
||||
} else {
|
||||
const civitaiImg = data.civitai.images && data.civitai.images[0];
|
||||
if (civitaiImg) sideImg.src = civitaiImg;
|
||||
}
|
||||
sideImg.style.display = "";
|
||||
if (sidePh) sidePh.style.display = "none";
|
||||
}
|
||||
}
|
||||
renderModelGrid();
|
||||
} else if (data.status === "not_found") {
|
||||
if (statusEl) statusEl.textContent = t("civitaiNotFound");
|
||||
if (fetchBtn) fetchBtn.disabled = false;
|
||||
} else {
|
||||
if (statusEl) statusEl.textContent = data.error || t("civitaiError");
|
||||
if (fetchBtn) fetchBtn.disabled = false;
|
||||
}
|
||||
} catch (err) {
|
||||
console.error("CivitAI fetch error:", err);
|
||||
if (statusEl) statusEl.textContent = t("civitaiError");
|
||||
if (fetchBtn) fetchBtn.disabled = false;
|
||||
}
|
||||
}
|
||||
|
||||
export function renderCivitaiInfo(el, info, modelName) {
|
||||
// URL: ユーザー設定ホスト（localStorage）を使用、modelId なし時は /model-versions/ にフォールバック
|
||||
const civitaiHost = localStorage.getItem("wfm_civitai_host") || "civitai.com";
|
||||
const modelUrl = info.modelId && info.versionId
|
||||
? `https://${civitaiHost}/models/${info.modelId}?modelVersionId=${info.versionId}`
|
||||
: info.versionId
|
||||
? `https://${civitaiHost}/model-versions/${info.versionId}`
|
||||
: (info.modelUrl || "#");
|
||||
|
||||
// Hash: BLAKE3 優先、なければ SHA256
|
||||
const fileHashes = info.fileHashes || {};
|
||||
const blake3 = fileHashes.BLAKE3 || fileHashes.Blake3 || "";
|
||||
const sha256 = fileHashes.SHA256 || (state.modelMetadata[modelName] || {}).sha256 || "";
|
||||
const hashType = blake3 ? "BLAKE3" : (sha256 ? "SHA256" : "");
|
||||
const hashFull = blake3 || sha256;
|
||||
|
||||
// Detail rows
|
||||
const ROW = "display:flex;align-items:center;font-size:12px;margin-bottom:5px;";
|
||||
const LABEL = "color:var(--wfm-text-secondary);min-width:80px;flex-shrink:0;";
|
||||
|
||||
const typeRow = info.type ? `
|
||||
<div style="${ROW}">
|
||||
<span style="${LABEL}">${t("civitaiType")}</span>
|
||||
<span style="font-size:10px;font-weight:700;background:var(--wfm-bg-tertiary,rgba(255,255,255,0.1));padding:2px 7px;border-radius:3px;letter-spacing:0.6px;">${escapeHtml(info.type.toUpperCase())}</span>
|
||||
</div>` : "";
|
||||
|
||||
const baseModelRow = info.baseModel ? `
|
||||
<div style="${ROW}">
|
||||
<span style="${LABEL}">${t("civitaiBaseModel")}</span>
|
||||
<span>${escapeHtml(info.baseModel)}</span>
|
||||
</div>` : "";
|
||||
|
||||
const hashRow = hashFull ? `
|
||||
<div style="${ROW}">
|
||||
<span style="${LABEL}">${t("civitaiHashLabel")}</span>
|
||||
<code class="wfm-hash-value" data-hash="${escapeHtml(hashFull)}"
|
||||
style="background:var(--wfm-bg-secondary);padding:2px 6px;border-radius:3px;cursor:pointer;font-size:11px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;max-width:calc(100% - 84px);"
|
||||
title="${t("civitaiCopyHash")}">${escapeHtml(hashType)}: ${escapeHtml(hashFull.substring(0, 16).toUpperCase())}…</code>
|
||||
</div>` : "";
|
||||
|
||||
const detailSection = (typeRow || baseModelRow || hashRow)
|
||||
? `<div style="margin-bottom:10px;">${typeRow}${baseModelRow}${hashRow}</div>` : "";
|
||||
|
||||
const tagsHtml = (info.tags || []).map((tag) =>
|
||||
`<span class="wfm-badge wfm-badge-sm">${escapeHtml(tag)}</span>`
|
||||
).join(" ");
|
||||
|
||||
const trainedWordsHtml = (info.trainedWords || []).map((w) =>
|
||||
`<code style="font-size:11px;background:var(--wfm-bg-secondary);padding:1px 4px;border-radius:3px;cursor:pointer;" class="wfm-trained-word" title="${t("civitaiCopyWord")}">${escapeHtml(w)}</code>`
|
||||
).join(" ");
|
||||
|
||||
// Sample images — all go to Sample pane
|
||||
const images = info.images || [];
|
||||
const sampleImagesHtml = images.map((url) =>
|
||||
`<a href="${escapeHtml(url)}" target="_blank" rel="noopener" title="${t("civitaiOpenImage")}"><img src="${escapeHtml(url)}" style="width:100%;border-radius:4px;margin-bottom:6px;cursor:pointer;display:block;" loading="lazy" /></a>`
|
||||
).join("");
|
||||
|
||||
el.innerHTML = `
|
||||
<div style="padding:0 4px;">
|
||||
<div style="display:flex;border-bottom:1px solid var(--wfm-border);margin-bottom:10px;">
|
||||
<button class="wfm-civitai-subtab-btn" data-pane="info"
|
||||
style="padding:4px 10px;font-size:12px;border:none;background:none;cursor:pointer;border-bottom:2px solid var(--wfm-primary);color:var(--wfm-primary);font-weight:600;margin-bottom:-1px;">
|
||||
${t("civitaiTabInfo")}
|
||||
</button>
|
||||
<button class="wfm-civitai-subtab-btn" data-pane="sample"
|
||||
style="padding:4px 10px;font-size:12px;border:none;background:none;cursor:pointer;border-bottom:2px solid transparent;color:var(--wfm-text-secondary);margin-bottom:-1px;">
|
||||
${t("civitaiTabSample")}${images.length ? ` (${images.length})` : ""}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div id="wfm-civitai-pane-info">
|
||||
<div style="margin-bottom:10px;">
|
||||
<div style="font-weight:700;font-size:14px;margin-bottom:2px;">
|
||||
<a href="${escapeHtml(modelUrl)}" target="_blank" style="color:var(--wfm-primary);text-decoration:none;">${escapeHtml(info.modelName)}</a>
|
||||
</div>
|
||||
<div style="font-size:12px;color:var(--wfm-text-secondary);">
|
||||
${escapeHtml(info.versionName)}${info.creator ? ` · by ${escapeHtml(info.creator)}` : ""}
|
||||
</div>
|
||||
</div>
|
||||
${detailSection}
|
||||
${tagsHtml ? `<div style="margin-bottom:8px;">${tagsHtml}</div>` : ""}
|
||||
${trainedWordsHtml ? `<div style="margin-bottom:10px;"><div style="font-weight:600;font-size:12px;margin-bottom:4px;">${t("civitaiTriggerWords")}</div>${trainedWordsHtml}</div>` : ""}
|
||||
${info.description ? `<div style="font-size:12px;color:var(--wfm-text-secondary);line-height:1.5;max-height:120px;overflow-y:auto;">${info.description}</div>` : ""}
|
||||
<div style="margin-top:10px;">
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-civitai-refresh-btn">${t("civitaiRefresh")}</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="wfm-civitai-pane-sample" style="display:none;">
|
||||
${sampleImagesHtml || `<p style="color:var(--wfm-text-secondary);font-size:13px;text-align:center;margin-top:20px;">${t("civitaiNoImages")}</p>`}
|
||||
</div>
|
||||
</div>`;
|
||||
|
||||
// Sub-tab switching
|
||||
const subtabBtns = el.querySelectorAll(".wfm-civitai-subtab-btn");
|
||||
subtabBtns.forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
const pane = btn.dataset.pane;
|
||||
subtabBtns.forEach((b) => {
|
||||
const isActive = b === btn;
|
||||
b.style.borderBottom = isActive ? "2px solid var(--wfm-primary)" : "2px solid transparent";
|
||||
b.style.color = isActive ? "var(--wfm-primary)" : "var(--wfm-text-secondary)";
|
||||
b.style.fontWeight = isActive ? "600" : "400";
|
||||
});
|
||||
el.querySelector("#wfm-civitai-pane-info").style.display = pane === "info" ? "" : "none";
|
||||
el.querySelector("#wfm-civitai-pane-sample").style.display = pane === "sample" ? "" : "none";
|
||||
});
|
||||
});
|
||||
|
||||
// Copy trigger word on click
|
||||
el.querySelectorAll(".wfm-trained-word").forEach((wordEl) => {
|
||||
wordEl.addEventListener("click", () => {
|
||||
navigator.clipboard.writeText(wordEl.textContent).then(() => {
|
||||
showToast(t("civitaiWordCopied"), "success");
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
// Copy hash on click
|
||||
el.querySelectorAll(".wfm-hash-value").forEach((hashEl) => {
|
||||
hashEl.addEventListener("click", () => {
|
||||
navigator.clipboard.writeText(hashEl.dataset.hash).then(() => {
|
||||
showToast(t("civitaiHashCopied"), "success");
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
// Refresh button
|
||||
document.getElementById("wfm-civitai-refresh-btn")?.addEventListener("click", () => {
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
if (meta.sha256) delete state.civitaiCache[meta.sha256];
|
||||
delete meta.sha256;
|
||||
fetchCivitaiForModel(modelName, el);
|
||||
});
|
||||
}
|
||||
|
||||
// ── CivitAI cache / batch fetch ───────────────────────────
|
||||
|
||||
export async function fetchCivitaiCache() {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/models/civitai/cache");
|
||||
return res.ok ? await res.json() : {};
|
||||
} catch { return {}; }
|
||||
}
|
||||
|
||||
export async function batchFetchCivitai() {
|
||||
const models = getCurrentModels();
|
||||
if (models.length === 0) {
|
||||
showToast(t("modelsNoModels"), "warning");
|
||||
return;
|
||||
}
|
||||
|
||||
// Filter out models that already have CivitAI data
|
||||
const meta = state.modelMetadata;
|
||||
const uncached = models.filter((m) => {
|
||||
const sha = meta[m]?.sha256;
|
||||
return !sha || !state.civitaiCache[sha];
|
||||
});
|
||||
|
||||
if (uncached.length === 0) {
|
||||
showToast(t("civitaiBatchAllCached"), "info");
|
||||
return;
|
||||
}
|
||||
|
||||
const btn = document.getElementById("wfm-models-civitai-batch-btn");
|
||||
const progressEl = document.getElementById("wfm-models-civitai-progress");
|
||||
if (btn) btn.disabled = true;
|
||||
|
||||
try {
|
||||
const res = await fetch("/api/wfm/models/civitai/batch", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ type: state.activeModelType, models: uncached }),
|
||||
});
|
||||
|
||||
const reader = res.body.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
let buffer = "";
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
buffer += decoder.decode(value, { stream: true });
|
||||
|
||||
// Parse SSE events
|
||||
const lines = buffer.split("\n");
|
||||
buffer = lines.pop() || "";
|
||||
|
||||
let eventType = "";
|
||||
for (const line of lines) {
|
||||
if (line.startsWith("event: ")) {
|
||||
eventType = line.slice(7).trim();
|
||||
} else if (line.startsWith("data: ")) {
|
||||
const data = JSON.parse(line.slice(6));
|
||||
if (eventType === "progress") {
|
||||
const pct = data.total > 0 ? Math.round((data.current / data.total) * 100) : 0;
|
||||
const statusText = data.status === "hashing" ? t("civitaiHashing2")
|
||||
: data.status === "fetching" ? t("civitaiFetching")
|
||||
: data.status === "cached" ? "✓"
|
||||
: data.status === "found" ? "✓"
|
||||
: data.status === "not_found" ? "—"
|
||||
: "";
|
||||
if (progressEl) progressEl.textContent = `${pct}% (${data.current}/${data.total}) ${statusText}`;
|
||||
} else if (eventType === "done") {
|
||||
if (progressEl) progressEl.textContent = "";
|
||||
const previewNote = data.preview_saved > 0 ? ` (+${data.preview_saved} preview)` : "";
|
||||
showToast(t("civitaiBatchDone", data.found, data.not_found) + previewNote, "success");
|
||||
// Reload caches
|
||||
const [newMeta, newCache] = await Promise.all([fetchModelMetadata(), fetchCivitaiCache()]);
|
||||
// Apply sha256 hashes from batch result directly in case fetchModelMetadata
|
||||
// returns before the server has flushed updated metadata to disk
|
||||
if (data.hashes) {
|
||||
for (const [modelName, sha256] of Object.entries(data.hashes)) {
|
||||
if (!newMeta[modelName]) newMeta[modelName] = {};
|
||||
if (!newMeta[modelName].sha256) newMeta[modelName].sha256 = sha256;
|
||||
}
|
||||
}
|
||||
state.modelMetadata = newMeta;
|
||||
state.civitaiCache = newCache;
|
||||
renderModelGrid();
|
||||
// Refresh side panel if open
|
||||
if (state.selectedModel) renderSideCivitai(state.selectedModel);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (err) {
|
||||
console.error("Batch CivitAI error:", err);
|
||||
showToast(t("civitaiError"), "error");
|
||||
} finally {
|
||||
if (btn) btn.disabled = false;
|
||||
if (progressEl) progressEl.textContent = "";
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,179 @@
|
||||
/**
|
||||
* Models Tab - Filtering, sorting, and the tag/group/directory filter dropdowns
|
||||
*/
|
||||
|
||||
import { t } from "../i18n.js";
|
||||
import { escapeHtml } from "../util.js";
|
||||
import { state } from "./state.js";
|
||||
import { parseModelPath, getExtension } from "./helpers.js";
|
||||
|
||||
export function getCurrentModels() {
|
||||
return state.modelsByType[state.activeModelType] || [];
|
||||
}
|
||||
|
||||
export function isModelDisabled(modelName) {
|
||||
const s = state.disabledModels[state.activeModelType];
|
||||
return s ? s.has(modelName) : false;
|
||||
}
|
||||
|
||||
export function filterModels() {
|
||||
let models = getCurrentModels();
|
||||
|
||||
if (state.statusFilter === "enabled") {
|
||||
models = models.filter((m) => !isModelDisabled(m));
|
||||
} else if (state.statusFilter === "disabled") {
|
||||
models = models.filter((m) => isModelDisabled(m));
|
||||
}
|
||||
|
||||
if (state.showFavoritesOnly) {
|
||||
models = models.filter((m) => {
|
||||
const meta = state.modelMetadata[m];
|
||||
return meta && meta.favorite;
|
||||
});
|
||||
}
|
||||
|
||||
if (state.showBatchOnly) {
|
||||
const batchMembers = state.modelGroups["Batch"] || [];
|
||||
models = models.filter((m) => batchMembers.includes(m));
|
||||
}
|
||||
|
||||
if (state.tagFilter) {
|
||||
models = models.filter((m) => {
|
||||
const meta = state.modelMetadata[m];
|
||||
return meta && meta.tags && meta.tags.includes(state.tagFilter);
|
||||
});
|
||||
}
|
||||
|
||||
if (state.badgeFilter) {
|
||||
models = models.filter((m) => {
|
||||
const meta = state.modelMetadata[m];
|
||||
return meta && meta.badges && meta.badges.includes(state.badgeFilter);
|
||||
});
|
||||
}
|
||||
|
||||
if (state.dirFilter) {
|
||||
models = models.filter((m) => {
|
||||
const { dir } = parseModelPath(m);
|
||||
return dir === state.dirFilter;
|
||||
});
|
||||
}
|
||||
|
||||
if (state.groupFilter) {
|
||||
const members = state.modelGroups[state.groupFilter] || [];
|
||||
models = models.filter((m) => members.includes(m));
|
||||
}
|
||||
|
||||
if (state.searchText) {
|
||||
const q = state.searchText.toLowerCase();
|
||||
models = models.filter((m) => {
|
||||
const meta = state.modelMetadata[m];
|
||||
const searchable = [m, ...(meta?.tags || []), meta?.memo || ""]
|
||||
.join(" ")
|
||||
.toLowerCase();
|
||||
return searchable.includes(q);
|
||||
});
|
||||
}
|
||||
|
||||
return sortModels(models);
|
||||
}
|
||||
|
||||
export function sortKeyOf(modelName) {
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
switch (state.sortColumn) {
|
||||
case "fav":
|
||||
return meta.favorite ? 1 : 0;
|
||||
case "filename":
|
||||
return parseModelPath(modelName).name.toLowerCase();
|
||||
case "subdir":
|
||||
return parseModelPath(modelName).dir.toLowerCase();
|
||||
case "civtype": {
|
||||
const civ = meta.sha256 && state.civitaiCache[meta.sha256];
|
||||
return (civ?.type || "").toLowerCase();
|
||||
}
|
||||
case "basemodel": {
|
||||
const civ = meta.sha256 && state.civitaiCache[meta.sha256];
|
||||
return (civ?.baseModel || "").toLowerCase();
|
||||
}
|
||||
case "ext":
|
||||
return getExtension(parseModelPath(modelName).name).toLowerCase();
|
||||
case "tags":
|
||||
return (meta.tags || []).join(", ").toLowerCase();
|
||||
case "memo":
|
||||
return (meta.memo || "").toLowerCase();
|
||||
case "enabled":
|
||||
return isModelDisabled(modelName) ? 1 : 0;
|
||||
default:
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
export function sortModels(models) {
|
||||
if (!state.sortColumn) return models;
|
||||
// ソートキーを1モデル1回だけ計算（比較ごとのparseModelPath等の再計算を回避）
|
||||
const dir = state.sortDir === "asc" ? 1 : -1;
|
||||
return models
|
||||
.map((m) => [sortKeyOf(m), m])
|
||||
.sort((a, b) => (a[0] < b[0] ? -dir : a[0] > b[0] ? dir : 0))
|
||||
.map((pair) => pair[1]);
|
||||
}
|
||||
|
||||
export function getAllTags() {
|
||||
const set = new Set();
|
||||
const models = getCurrentModels();
|
||||
models.forEach((m) => {
|
||||
const meta = state.modelMetadata[m];
|
||||
if (meta?.tags) meta.tags.forEach((tag) => set.add(tag));
|
||||
});
|
||||
return [...set].sort();
|
||||
}
|
||||
|
||||
// ── Render: Tag Filter ────────────────────────────────────
|
||||
|
||||
export function renderTagFilter() {
|
||||
const select = document.getElementById("wfm-models-tag-filter");
|
||||
if (!select) return;
|
||||
const tags = getAllTags();
|
||||
select.innerHTML =
|
||||
`<option value="">${t("modelsAllTags")}</option>` +
|
||||
tags.map((tag) => `<option value="${escapeHtml(tag)}">${escapeHtml(tag)}</option>`).join("");
|
||||
select.value = state.tagFilter;
|
||||
}
|
||||
|
||||
export function renderGroupFilter() {
|
||||
const select = document.getElementById("wfm-models-group-filter");
|
||||
if (!select) return;
|
||||
|
||||
const groups = state.allModelGroups[state.activeModelType] || {};
|
||||
const names = Object.keys(groups).sort();
|
||||
|
||||
// 選択中のグループが削除された場合はフィルターをリセット
|
||||
if (state.groupFilter && !groups[state.groupFilter]) {
|
||||
state.groupFilter = "";
|
||||
}
|
||||
|
||||
const currentValue = state.groupFilter
|
||||
? `${state.activeModelType}::${state.groupFilter}`
|
||||
: "";
|
||||
|
||||
select.innerHTML =
|
||||
`<option value="">${t("modelsAllGroups")}</option>` +
|
||||
names.map((name) => {
|
||||
const value = `${state.activeModelType}::${name}`;
|
||||
const isActive = value === currentValue;
|
||||
return `<option value="${escapeHtml(value)}"${isActive ? " selected" : ""}>${escapeHtml(name)}</option>`;
|
||||
}).join("");
|
||||
}
|
||||
|
||||
export function renderDirFilter() {
|
||||
const select = document.getElementById("wfm-models-dir-filter");
|
||||
if (!select) return;
|
||||
const dirs = [...new Set(
|
||||
getCurrentModels()
|
||||
.map((m) => parseModelPath(m).dir)
|
||||
.filter((d) => d !== "")
|
||||
)].sort();
|
||||
select.innerHTML =
|
||||
`<option value="">${t("modelsAllDirs")}</option>` +
|
||||
dirs.map((d) => `<option value="${escapeHtml(d)}">${escapeHtml(d)}</option>`).join("");
|
||||
select.value = state.dirFilter;
|
||||
}
|
||||
@@ -0,0 +1,329 @@
|
||||
/**
|
||||
* Models Tab - Grid rendering (Thumbnail / Table views) and per-card
|
||||
* Batch/Stack/Favorite toggles.
|
||||
*
|
||||
* This module and selection-bulk.js / detail-panel.js form a circular
|
||||
* import triangle with each other and with models-tab.js: rendering a card
|
||||
* needs to wire up selection, side-panel, and detail-modal click handlers,
|
||||
* and those in turn re-render the grid after they change state. All the
|
||||
* cross-module calls happen inside event handlers (never at module
|
||||
* evaluation time), and every export here is a `function` declaration
|
||||
* (hoisted), so the cycle resolves safely under ES Modules.
|
||||
*/
|
||||
|
||||
import { showToast } from "../app.js";
|
||||
import { t } from "../i18n.js";
|
||||
import { escapeHtml } from "../util.js";
|
||||
import { state } from "./state.js";
|
||||
import { filterModels, getCurrentModels, isModelDisabled } from "./filters.js";
|
||||
import { modelBadgesHtml } from "./badges.js";
|
||||
import { parseModelPath, getExtension, getStem, loadPreviewImage } from "./helpers.js";
|
||||
import { toggleModelSelection } from "./selection-bulk.js";
|
||||
import { showSidePanel, openDetailModal, renderSideInfo } from "./detail-panel.js";
|
||||
import { saveModelGroups, saveModelMetadata, toggleModelEnable } from "../models-tab.js";
|
||||
|
||||
export function renderModelGrid() {
|
||||
const grid = document.getElementById("wfm-models-grid");
|
||||
if (!grid) return;
|
||||
|
||||
grid.className = `wfm-grid wfm-view-${state.viewMode}`;
|
||||
const filtered = filterModels();
|
||||
|
||||
// Update count
|
||||
const countEl = document.getElementById("wfm-models-count");
|
||||
if (countEl) {
|
||||
const total = getCurrentModels().length;
|
||||
countEl.textContent = `${filtered.length} / ${total}`;
|
||||
}
|
||||
|
||||
if (filtered.length === 0) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("modelsNoModels")}</p>`;
|
||||
return;
|
||||
}
|
||||
|
||||
if (state.viewMode === "table") {
|
||||
renderTableView(grid, filtered);
|
||||
} else {
|
||||
renderThumbView(grid, filtered);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Thumbnail View (same as Workflow tab) ─────────────────
|
||||
|
||||
export function renderThumbView(grid, models) {
|
||||
grid.innerHTML = "";
|
||||
models.forEach((modelName) => {
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
const { name } = parseModelPath(modelName);
|
||||
const disabled = isModelDisabled(modelName);
|
||||
const userBadges = modelBadgesHtml(modelName);
|
||||
const tagsHtml = (meta.tags || []).map((tag) => `<span class="wfm-badge wfm-badge-sm">${escapeHtml(tag)}</span>`).join("");
|
||||
const favStar = meta.favorite ? "★" : "☆";
|
||||
const favClass = meta.favorite ? "wfm-fav-btn active" : "wfm-fav-btn";
|
||||
const showBatchBtn = ["checkpoint", "lora"].includes(state.activeModelType);
|
||||
const showStackBtn = state.activeModelType === "lora";
|
||||
const inBatch = showBatchBtn && isInBatch(modelName);
|
||||
const inStack = showStackBtn && isInStack(modelName);
|
||||
const batchClass = inBatch ? "wfm-batch-btn active" : "wfm-batch-btn";
|
||||
const stackClass = inStack ? "wfm-stack-btn active" : "wfm-stack-btn";
|
||||
|
||||
const card = document.createElement("div");
|
||||
card.className = "wfm-card";
|
||||
if (disabled) card.classList.add("wfm-model-disabled");
|
||||
if (state.selectedModel === modelName) card.classList.add("wfm-card-selected");
|
||||
card.dataset.modelName = modelName;
|
||||
|
||||
card.innerHTML = `
|
||||
<div class="wfm-card-thumb">
|
||||
<img style="display:none" />
|
||||
<span class="wfm-card-thumb-placeholder">${t("modelsNoPreview")}</span>
|
||||
${disabled ? `<span class="wfm-disabled-overlay">${t("modelDisabled")}</span>` : ""}
|
||||
</div>
|
||||
<div class="wfm-card-body">
|
||||
<div class="wfm-card-title" title="${escapeHtml(modelName)}">${escapeHtml(getStem(name))}</div>
|
||||
<div class="wfm-card-meta">${userBadges} ${tagsHtml}</div>
|
||||
</div>
|
||||
${showBatchBtn ? `<button class="${batchClass}" title="${t("modelsBatch")}">B</button>` : ""}
|
||||
${showStackBtn ? `<button class="${stackClass}" title="Stack (Lora multi-apply)">S</button>` : ""}
|
||||
<button class="${favClass}" title="${t("modelsFavorite")}">${favStar}</button>
|
||||
<button class="wfm-toggle-btn${disabled ? " wfm-toggle-disabled" : ""}" title="${disabled ? t("modelEnable") : t("modelDisable")}">${disabled ? "▶" : "⏸"}</button>`;
|
||||
|
||||
// Load preview without 404 console spam
|
||||
const img = card.querySelector(".wfm-card-thumb img");
|
||||
const placeholder = card.querySelector(".wfm-card-thumb-placeholder");
|
||||
loadPreviewImage(img, placeholder, modelName);
|
||||
|
||||
card.querySelector(".wfm-batch-btn")?.addEventListener("click", async (e) => {
|
||||
e.stopPropagation();
|
||||
const btn = e.currentTarget;
|
||||
await toggleBatch(modelName);
|
||||
if (state.showBatchOnly) {
|
||||
renderModelGrid();
|
||||
} else {
|
||||
btn.classList.toggle("active", isInBatch(modelName));
|
||||
}
|
||||
});
|
||||
card.querySelector(".wfm-stack-btn")?.addEventListener("click", async (e) => {
|
||||
e.stopPropagation();
|
||||
const btn = e.currentTarget;
|
||||
await toggleStack(modelName);
|
||||
btn.classList.toggle("active", isInStack(modelName));
|
||||
});
|
||||
card.querySelector(".wfm-fav-btn").addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
toggleFavorite(modelName);
|
||||
});
|
||||
card.querySelector(".wfm-toggle-btn").addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
toggleModelEnable(modelName);
|
||||
});
|
||||
if (state.selectMode) {
|
||||
const isChecked = state.selectedModels.has(modelName);
|
||||
card.classList.toggle("wfm-card-checked", isChecked);
|
||||
const checkEl = document.createElement("div");
|
||||
checkEl.className = "wfm-select-check" + (isChecked ? " checked" : "");
|
||||
card.appendChild(checkEl);
|
||||
card.addEventListener("click", (e) => {
|
||||
if (e.target.closest(".wfm-batch-btn, .wfm-stack-btn, .wfm-fav-btn, .wfm-toggle-btn")) return;
|
||||
toggleModelSelection(modelName);
|
||||
});
|
||||
} else {
|
||||
card.addEventListener("click", () => showSidePanel(modelName));
|
||||
card.addEventListener("dblclick", (e) => { e.stopPropagation(); openDetailModal(modelName); });
|
||||
}
|
||||
grid.appendChild(card);
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
// ── Table View ────────────────────────────────────────────
|
||||
|
||||
export function thSortHtml(label, col, extraClass = "", extraStyle = "") {
|
||||
const isActive = state.sortColumn === col;
|
||||
const arrow = isActive ? (state.sortDir === "asc" ? " ▲" : " ▼") : "";
|
||||
const activeStyle = isActive ? "color:var(--wfm-accent,#6366f1);" : "";
|
||||
const cls = ["wfm-table-th-sortable", extraClass].filter(Boolean).join(" ");
|
||||
return `<th class="${cls}" data-sort-col="${col}" style="${activeStyle}${extraStyle}">${label}${arrow}</th>`;
|
||||
}
|
||||
|
||||
export function renderTableView(grid, models) {
|
||||
const showBatchBtn = ["checkpoint", "lora"].includes(state.activeModelType);
|
||||
const showStackBtn = state.activeModelType === "lora";
|
||||
const rows = models
|
||||
.map((modelName) => {
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
const { dir, name } = parseModelPath(modelName);
|
||||
const ext = getExtension(name);
|
||||
const sha256 = meta.sha256;
|
||||
const civitai = sha256 && state.civitaiCache[sha256];
|
||||
const civitaiType = civitai ? (civitai.type || "") : "";
|
||||
const civitaiBaseModel = civitai ? (civitai.baseModel || "") : "";
|
||||
const disabled = isModelDisabled(modelName);
|
||||
const isChecked = state.selectMode && state.selectedModels.has(modelName);
|
||||
const favIcon = meta.favorite ? "★" : "☆";
|
||||
const batchIcon = !showBatchBtn ? "" : isInBatch(modelName) ? `<button class="wfm-batch-btn active" title="${t("modelsBatch")}">B</button>` : `<button class="wfm-batch-btn" title="${t("modelsBatch")}">B</button>`;
|
||||
const stackIcon = isInStack(modelName) ? `<button class="wfm-stack-btn active" title="Stack (Lora multi-apply)">S</button>` : `<button class="wfm-stack-btn" title="Stack (Lora multi-apply)">S</button>`;
|
||||
const tagsStr = (meta.tags || []).join(", ");
|
||||
const memo = meta.memo || "";
|
||||
const toggleLabel = disabled ? t("modelEnable") : t("modelDisable");
|
||||
const toggleIcon = disabled ? "▶" : "⏸";
|
||||
const checkCell = state.selectMode
|
||||
? `<td class="wfm-table-td-check"><div class="wfm-select-check${isChecked ? " checked" : ""}"></div></td>`
|
||||
: "";
|
||||
return `<tr class="wfm-models-table-row${state.selectedModel === modelName ? " wfm-card-selected" : ""}${disabled ? " wfm-model-disabled" : ""}${isChecked ? " wfm-card-checked" : ""}" data-model-name="${escapeHtml(modelName)}">
|
||||
${checkCell}
|
||||
<td class="wfm-models-table-fav" title="Favorite">${favIcon}</td>
|
||||
<td class="wfm-table-td-thumb"><img class="wfm-table-thumb" style="display:none" /></td>
|
||||
<td class="wfm-table-td-filename" title="${escapeHtml(modelName)}">${escapeHtml(name)}</td>
|
||||
<td class="wfm-table-td-subdir">${escapeHtml(dir)}</td>
|
||||
<td class="wfm-table-td-civtype" title="${escapeHtml(civitaiType)}">${escapeHtml(civitaiType)}</td>
|
||||
<td class="wfm-table-td-basemodel" title="${escapeHtml(civitaiBaseModel)}">${escapeHtml(civitaiBaseModel)}</td>
|
||||
<td class="wfm-table-td-ext">${escapeHtml(ext)}</td>
|
||||
<td>${escapeHtml(tagsStr)}</td>
|
||||
<td class="wfm-table-td-memo" title="${escapeHtml(memo)}">${escapeHtml(memo)}</td>
|
||||
<td class="wfm-table-td-toggle"><button class="wfm-toggle-btn${disabled ? " wfm-toggle-disabled" : ""}" title="${toggleLabel}">${toggleIcon}</button></td>
|
||||
${showBatchBtn ? `<td class="wfm-table-td-batch">${batchIcon}</td>` : ""}
|
||||
${showStackBtn ? `<td class="wfm-table-td-stack">${stackIcon}</td>` : ""}
|
||||
</tr>`;
|
||||
})
|
||||
.join("");
|
||||
|
||||
const checkTh = state.selectMode ? `<th style="width:24px;"></th>` : "";
|
||||
grid.innerHTML = `<table class="wfm-models-table"><thead><tr>
|
||||
${checkTh}
|
||||
${thSortHtml("★", "fav", "", "width:30px;text-align:center;")}
|
||||
<th style="width:40px;"></th>
|
||||
${thSortHtml(t("modelsFileName"), "filename", "wfm-table-th-filename")}
|
||||
${thSortHtml(t("modelsSubdir"), "subdir", "wfm-table-th-subdir")}
|
||||
${thSortHtml(t("civitaiType"), "civtype", "wfm-table-th-civtype")}
|
||||
${thSortHtml(t("civitaiBaseModel"), "basemodel", "wfm-table-th-basemodel")}
|
||||
${thSortHtml(t("modelsExt"), "ext", "wfm-table-th-ext")}
|
||||
${thSortHtml(t("modelsTags"), "tags")}
|
||||
${thSortHtml(t("modelsMemo"), "memo")}
|
||||
${thSortHtml("E/D", "enabled", "", "width:50px;text-align:center;")}
|
||||
${showBatchBtn ? `<th style="width:30px;">B</th>` : ""}
|
||||
${showStackBtn ? `<th style="width:30px;">S</th>` : ""}
|
||||
</tr></thead><tbody>${rows}</tbody></table>`;
|
||||
|
||||
grid.querySelectorAll(".wfm-models-table-row").forEach((row) => {
|
||||
const mn = row.dataset.modelName;
|
||||
const img = row.querySelector(".wfm-table-thumb");
|
||||
if (img) loadPreviewImage(img, null, mn);
|
||||
|
||||
if (state.selectMode) {
|
||||
row.addEventListener("click", (e) => {
|
||||
if (e.target.closest(".wfm-models-table-fav, .wfm-toggle-btn, .wfm-batch-btn, .wfm-stack-btn")) return;
|
||||
toggleModelSelection(mn);
|
||||
});
|
||||
} else {
|
||||
row.addEventListener("click", () => showSidePanel(mn));
|
||||
row.addEventListener("dblclick", (e) => { e.stopPropagation(); openDetailModal(mn); });
|
||||
}
|
||||
row.querySelector(".wfm-models-table-fav").addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
toggleFavorite(mn);
|
||||
});
|
||||
row.querySelector(".wfm-toggle-btn").addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
toggleModelEnable(mn);
|
||||
});
|
||||
row.querySelector(".wfm-batch-btn")?.addEventListener("click", async (e) => {
|
||||
e.stopPropagation();
|
||||
await toggleBatch(mn);
|
||||
if (state.showBatchOnly) {
|
||||
renderModelGrid();
|
||||
} else {
|
||||
e.currentTarget.classList.toggle("active", isInBatch(mn));
|
||||
}
|
||||
});
|
||||
row.querySelector(".wfm-stack-btn")?.addEventListener("click", async (e) => {
|
||||
e.stopPropagation();
|
||||
await toggleStack(mn);
|
||||
e.currentTarget.classList.toggle("active", isInStack(mn));
|
||||
});
|
||||
});
|
||||
|
||||
grid.querySelectorAll(".wfm-table-th-sortable").forEach((th) => {
|
||||
th.addEventListener("click", () => {
|
||||
const col = th.dataset.sortCol;
|
||||
if (state.sortColumn === col) {
|
||||
if (state.sortDir === "asc") {
|
||||
state.sortDir = "desc";
|
||||
} else {
|
||||
state.sortColumn = null;
|
||||
state.sortDir = "asc";
|
||||
}
|
||||
} else {
|
||||
state.sortColumn = col;
|
||||
state.sortDir = "asc";
|
||||
}
|
||||
renderModelGrid();
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
// ── Batch group helpers ───────────────────────────────────
|
||||
|
||||
export function isInBatch(modelName) {
|
||||
return (state.modelGroups["Batch"] || []).includes(modelName);
|
||||
}
|
||||
|
||||
export async function toggleBatch(modelName) {
|
||||
const batch = state.modelGroups["Batch"] || [];
|
||||
const idx = batch.indexOf(modelName);
|
||||
if (idx >= 0) {
|
||||
batch.splice(idx, 1);
|
||||
} else {
|
||||
batch.push(modelName);
|
||||
}
|
||||
state.modelGroups["Batch"] = batch;
|
||||
state.allModelGroups[state.activeModelType] = state.modelGroups;
|
||||
await saveModelGroups(state.modelGroups);
|
||||
}
|
||||
|
||||
export async function clearBatchGroup() {
|
||||
state.modelGroups["Batch"] = [];
|
||||
state.allModelGroups[state.activeModelType] = state.modelGroups;
|
||||
await saveModelGroups(state.modelGroups);
|
||||
if (state.showBatchOnly) {
|
||||
state.showBatchOnly = false;
|
||||
document.getElementById("wfm-models-batch-filter-btn")?.classList.remove("active");
|
||||
}
|
||||
renderModelGrid();
|
||||
showToast(t("modelsBatchClear"), "success");
|
||||
}
|
||||
|
||||
export function isInStack(modelName) {
|
||||
return (state.modelGroups["Stack"] || []).includes(modelName);
|
||||
}
|
||||
|
||||
export async function toggleStack(modelName) {
|
||||
const stack = state.modelGroups["Stack"] || [];
|
||||
const idx = stack.indexOf(modelName);
|
||||
if (idx >= 0) {
|
||||
stack.splice(idx, 1);
|
||||
} else {
|
||||
stack.push(modelName);
|
||||
}
|
||||
state.modelGroups["Stack"] = stack;
|
||||
state.allModelGroups[state.activeModelType] = state.modelGroups;
|
||||
await saveModelGroups(state.modelGroups);
|
||||
}
|
||||
|
||||
export async function clearStackGroup() {
|
||||
state.modelGroups["Stack"] = [];
|
||||
state.allModelGroups[state.activeModelType] = state.modelGroups;
|
||||
await saveModelGroups(state.modelGroups);
|
||||
renderModelGrid();
|
||||
showToast(t("stackCleared"), "success");
|
||||
}
|
||||
|
||||
// ── Favorite toggle ───────────────────────────────────────
|
||||
|
||||
export async function toggleFavorite(modelName) {
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
const newFav = !meta.favorite;
|
||||
await saveModelMetadata(modelName, { favorite: newFav });
|
||||
renderModelGrid();
|
||||
if (state.selectedModel === modelName) renderSideInfo(modelName);
|
||||
}
|
||||
@@ -0,0 +1,59 @@
|
||||
/**
|
||||
* Models Tab - Small path/preview helpers shared across the models/* modules
|
||||
*/
|
||||
|
||||
import { state } from "./state.js";
|
||||
|
||||
export function parseModelPath(fullName) {
|
||||
const lastSlash = Math.max(fullName.lastIndexOf("/"), fullName.lastIndexOf("\\"));
|
||||
if (lastSlash === -1) return { dir: "", name: fullName };
|
||||
return { dir: fullName.substring(0, lastSlash), name: fullName.substring(lastSlash + 1) };
|
||||
}
|
||||
|
||||
export function getExtension(name) {
|
||||
const dot = name.lastIndexOf(".");
|
||||
return dot >= 0 ? name.substring(dot) : "";
|
||||
}
|
||||
|
||||
export function getStem(name) {
|
||||
const dot = name.lastIndexOf(".");
|
||||
return dot >= 0 ? name.substring(0, dot) : name;
|
||||
}
|
||||
|
||||
export function previewUrl(modelName, modelType) {
|
||||
const type = modelType || state.activeModelType;
|
||||
return `/api/wfm/models/preview?type=${encodeURIComponent(type)}&name=${encodeURIComponent(modelName)}`;
|
||||
}
|
||||
|
||||
/**
|
||||
* Load preview image. Uses img onload/onerror instead of HEAD request
|
||||
* (aiohttp add_get does not auto-handle HEAD method).
|
||||
* Falls back to CivitAI cached image if no local preview exists.
|
||||
*/
|
||||
export function loadPreviewImage(imgEl, placeholderEl, modelName, modelType) {
|
||||
const url = previewUrl(modelName, modelType);
|
||||
imgEl.onload = () => {
|
||||
imgEl.style.display = "";
|
||||
if (placeholderEl) placeholderEl.style.display = "none";
|
||||
};
|
||||
imgEl.onerror = () => {
|
||||
// Fallback: use CivitAI cached image if available
|
||||
const meta = state.modelMetadata[modelName] || {};
|
||||
const sha256 = meta.sha256;
|
||||
const civitai = sha256 && state.civitaiCache[sha256];
|
||||
const civitaiImg = civitai && civitai.images && civitai.images[0];
|
||||
if (civitaiImg) {
|
||||
imgEl.onerror = () => {
|
||||
imgEl.style.display = "none";
|
||||
if (placeholderEl) placeholderEl.style.display = "";
|
||||
};
|
||||
imgEl.src = civitaiImg;
|
||||
imgEl.style.display = "";
|
||||
if (placeholderEl) placeholderEl.style.display = "none";
|
||||
} else {
|
||||
imgEl.style.display = "none";
|
||||
if (placeholderEl) placeholderEl.style.display = "";
|
||||
}
|
||||
};
|
||||
imgEl.src = url;
|
||||
}
|
||||
@@ -0,0 +1,318 @@
|
||||
/**
|
||||
* Models Tab - Multi-select mode and bulk group/badge/favorite/move/delete
|
||||
* operations on the current selection.
|
||||
*
|
||||
* Part of the grid-view.js / selection-bulk.js / detail-panel.js circular
|
||||
* import triangle — see the note at the top of grid-view.js.
|
||||
*/
|
||||
|
||||
import { showToast } from "../app.js";
|
||||
import { t } from "../i18n.js";
|
||||
import { escapeHtml } from "../util.js";
|
||||
import { state, RESERVED_GROUPS } from "./state.js";
|
||||
import { filterModels, renderDirFilter } from "./filters.js";
|
||||
import { getBadgePalette } from "./badges.js";
|
||||
import { renderModelGrid } from "./grid-view.js";
|
||||
import { saveModelGroups, saveModelMetadata } from "../models-tab.js";
|
||||
|
||||
export function toggleSelectMode() {
|
||||
state.selectMode = !state.selectMode;
|
||||
if (!state.selectMode) state.selectedModels.clear();
|
||||
const btn = document.getElementById("wfm-models-select-btn");
|
||||
if (btn) {
|
||||
btn.classList.toggle("active", state.selectMode);
|
||||
btn.textContent = state.selectMode ? t("modelSelectExit") : t("modelSelectMode");
|
||||
}
|
||||
renderModelGrid();
|
||||
renderBulkActionBar();
|
||||
}
|
||||
|
||||
export function toggleModelSelection(modelName) {
|
||||
if (state.selectedModels.has(modelName)) {
|
||||
state.selectedModels.delete(modelName);
|
||||
} else {
|
||||
state.selectedModels.add(modelName);
|
||||
}
|
||||
// Update DOM directly (avoid full re-render)
|
||||
document.querySelectorAll("[data-model-name]").forEach((el) => {
|
||||
if (el.dataset.modelName !== modelName) return;
|
||||
const checked = state.selectedModels.has(modelName);
|
||||
el.classList.toggle("wfm-card-checked", checked);
|
||||
const c = el.querySelector(".wfm-select-check");
|
||||
if (c) c.classList.toggle("checked", checked);
|
||||
});
|
||||
renderBulkActionBar();
|
||||
}
|
||||
|
||||
export function clearSelection() {
|
||||
state.selectedModels.clear();
|
||||
renderModelGrid();
|
||||
renderBulkActionBar();
|
||||
}
|
||||
|
||||
export function selectAll() {
|
||||
filterModels().forEach(m => state.selectedModels.add(m));
|
||||
renderModelGrid();
|
||||
renderBulkActionBar();
|
||||
}
|
||||
|
||||
export function renderBulkActionBar() {
|
||||
const bar = document.getElementById("wfm-models-bulk-bar");
|
||||
if (!bar) return;
|
||||
if (!state.selectMode || state.selectedModels.size === 0) {
|
||||
bar.style.display = "none";
|
||||
return;
|
||||
}
|
||||
const count = state.selectedModels.size;
|
||||
const groupNames = Object.keys(state.modelGroups).sort();
|
||||
const noGroups = groupNames.length === 0;
|
||||
const groupOptions = noGroups
|
||||
? `<option value="">${t("modelsNoGroupAvailable")}</option>`
|
||||
: groupNames.map((g) => `<option value="${escapeHtml(g)}">${escapeHtml(g)}</option>`).join("");
|
||||
|
||||
const palette = getBadgePalette();
|
||||
const badgeLabels = Object.keys(palette).sort();
|
||||
const noBadges = badgeLabels.length === 0;
|
||||
const badgeOptions = noBadges
|
||||
? `<option value="">${t("modelBulkNoBadge")}</option>`
|
||||
: badgeLabels.map((b) => `<option value="${escapeHtml(b)}">${escapeHtml(b)}</option>`).join("");
|
||||
|
||||
bar.style.display = "flex";
|
||||
bar.innerHTML = `
|
||||
<div class="wfm-bulk-header">
|
||||
<span class="wfm-bulk-count">${count} ${t("modelSelected")}</span>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-bulk-deselect-all-btn">${t("modelBulkDeselectAll")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-bulk-select-all-btn">${t("modelBulkSelectAll")}</button>
|
||||
<span class="wfm-bulk-sep"></span>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-bulk-fav-add-btn">${t("modelBulkFavAdd")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-bulk-fav-remove-btn">${t("modelBulkFavRemove")}</button>
|
||||
</div>
|
||||
<div class="wfm-bulk-rows">
|
||||
<div class="wfm-bulk-row">
|
||||
<span class="wfm-bulk-row-label">Group:</span>
|
||||
<select id="wfm-bulk-group-select" class="wfm-select wfm-bulk-select">${groupOptions}</select>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="wfm-bulk-add-btn"${noGroups ? " disabled" : ""}>${t("modelBulkAddGroup")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger" id="wfm-bulk-remove-btn"${noGroups ? " disabled" : ""}>${t("modelBulkRemoveGroup")}</button>
|
||||
<span class="wfm-bulk-sep"></span>
|
||||
<input type="text" id="wfm-bulk-new-group-input" class="wfm-search-input wfm-bulk-input" placeholder="${t("modelsGroupName")}">
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-bulk-create-add-btn">${t("modelBulkCreateAdd")}</button>
|
||||
</div>
|
||||
<div class="wfm-bulk-row">
|
||||
<span class="wfm-bulk-row-label">Badge:</span>
|
||||
<select id="wfm-bulk-badge-select" class="wfm-select wfm-bulk-select">${badgeOptions}</select>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-primary" id="wfm-bulk-badge-apply-btn"${noBadges ? " disabled" : ""}>${t("modelBulkBadgeApply")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger" id="wfm-bulk-badge-remove-btn"${noBadges ? " disabled" : ""}>${t("modelBulkBadgeRemove")}</button>
|
||||
</div>
|
||||
<div class="wfm-bulk-row">
|
||||
<span class="wfm-bulk-row-label">File:</span>
|
||||
<select id="wfm-bulk-move-select" class="wfm-select wfm-bulk-select">
|
||||
<option value="">${t("modelBulkMoveRoot")}</option>
|
||||
${state.subdirs.map((d) => `<option value="${escapeHtml(d)}">${escapeHtml(d)}</option>`).join("")}
|
||||
</select>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-bulk-move-btn">${t("modelBulkMoveBtn")}</button>
|
||||
<span class="wfm-bulk-sep"></span>
|
||||
<input type="text" id="wfm-bulk-new-dir-input" class="wfm-search-input wfm-bulk-input" placeholder="${t("modelBulkMoveNewFolder")}">
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-bulk-mkdir-move-btn">${t("modelBulkMoveCreateMove")}</button>
|
||||
<span style="flex:1"></span>
|
||||
<button class="wfm-btn wfm-btn-sm wfm-btn-danger" id="wfm-bulk-delete-btn">${t("modelBulkDelete")}</button>
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
|
||||
document.getElementById("wfm-bulk-deselect-all-btn")?.addEventListener("click", clearSelection);
|
||||
document.getElementById("wfm-bulk-select-all-btn")?.addEventListener("click", selectAll);
|
||||
document.getElementById("wfm-bulk-fav-add-btn")?.addEventListener("click", () => bulkSetFavorite(true));
|
||||
document.getElementById("wfm-bulk-fav-remove-btn")?.addEventListener("click", () => bulkSetFavorite(false));
|
||||
document.getElementById("wfm-bulk-add-btn")?.addEventListener("click", () => {
|
||||
const g = document.getElementById("wfm-bulk-group-select")?.value;
|
||||
if (g) bulkAddToGroup(g);
|
||||
});
|
||||
document.getElementById("wfm-bulk-remove-btn")?.addEventListener("click", () => {
|
||||
const g = document.getElementById("wfm-bulk-group-select")?.value;
|
||||
if (g) bulkRemoveFromGroup(g);
|
||||
});
|
||||
document.getElementById("wfm-bulk-create-add-btn")?.addEventListener("click", () => {
|
||||
const input = document.getElementById("wfm-bulk-new-group-input");
|
||||
const name = input?.value.trim();
|
||||
if (!name) return;
|
||||
if (state.modelGroups[name]) { showToast(t("modelsGroupExists"), "warning"); return; }
|
||||
bulkAddToGroup(name);
|
||||
});
|
||||
document.getElementById("wfm-bulk-badge-apply-btn")?.addEventListener("click", () => {
|
||||
const label = document.getElementById("wfm-bulk-badge-select")?.value;
|
||||
if (label) bulkApplyBadge(label, true);
|
||||
});
|
||||
document.getElementById("wfm-bulk-badge-remove-btn")?.addEventListener("click", () => {
|
||||
const label = document.getElementById("wfm-bulk-badge-select")?.value;
|
||||
if (label) bulkApplyBadge(label, false);
|
||||
});
|
||||
document.getElementById("wfm-bulk-move-btn")?.addEventListener("click", () => {
|
||||
const dest = document.getElementById("wfm-bulk-move-select")?.value || "";
|
||||
bulkMoveModels(dest);
|
||||
});
|
||||
document.getElementById("wfm-bulk-mkdir-move-btn")?.addEventListener("click", () => {
|
||||
const input = document.getElementById("wfm-bulk-new-dir-input");
|
||||
const name = input?.value.trim();
|
||||
if (!name) return;
|
||||
bulkMoveModels(name);
|
||||
});
|
||||
document.getElementById("wfm-bulk-delete-btn")?.addEventListener("click", bulkDeleteModels);
|
||||
}
|
||||
|
||||
export async function bulkAddToGroup(groupName) {
|
||||
const groups = { ...state.modelGroups };
|
||||
if (!groups[groupName]) groups[groupName] = [];
|
||||
const toAdd = [...state.selectedModels].filter((m) => !groups[groupName].includes(m));
|
||||
groups[groupName] = [...groups[groupName], ...toAdd];
|
||||
await saveModelGroups(groups);
|
||||
showToast(`${toAdd.length} ${t("modelBulkAddDone")}`, "success");
|
||||
renderBulkActionBar();
|
||||
}
|
||||
|
||||
export async function bulkRemoveFromGroup(groupName) {
|
||||
const groups = { ...state.modelGroups };
|
||||
if (!groups[groupName]) return;
|
||||
const before = groups[groupName].length;
|
||||
groups[groupName] = groups[groupName].filter((m) => !state.selectedModels.has(m));
|
||||
const removed = before - groups[groupName].length;
|
||||
// RESERVED_GROUPS (Batch / Stack) は空になってもキーを保持する
|
||||
if (groups[groupName].length === 0 && !RESERVED_GROUPS.includes(groupName)) delete groups[groupName];
|
||||
await saveModelGroups(groups);
|
||||
showToast(`${removed} ${t("modelBulkRemoveDone")}`, "success");
|
||||
renderModelGrid();
|
||||
renderBulkActionBar();
|
||||
}
|
||||
|
||||
export async function bulkSetFavorite(isFav) {
|
||||
const models = [...state.selectedModels];
|
||||
let count = 0;
|
||||
for (const mn of models) {
|
||||
const meta = state.modelMetadata[mn] || {};
|
||||
if (meta.favorite === isFav) continue;
|
||||
await saveModelMetadata(mn, { favorite: isFav });
|
||||
count++;
|
||||
}
|
||||
if (count > 0) {
|
||||
showToast(`${count} ${isFav ? t("modelBulkFavDone") : t("modelBulkUnfavDone")}`, "success");
|
||||
renderModelGrid();
|
||||
}
|
||||
}
|
||||
|
||||
export async function bulkApplyBadge(badgeLabel, add) {
|
||||
const models = [...state.selectedModels];
|
||||
let count = 0;
|
||||
for (const mn of models) {
|
||||
const meta = state.modelMetadata[mn] || {};
|
||||
const badges = [...(meta.badges || [])];
|
||||
if (add) {
|
||||
if (badges.includes(badgeLabel)) continue;
|
||||
badges.push(badgeLabel);
|
||||
} else {
|
||||
const idx = badges.indexOf(badgeLabel);
|
||||
if (idx === -1) continue;
|
||||
badges.splice(idx, 1);
|
||||
}
|
||||
await saveModelMetadata(mn, { badges });
|
||||
count++;
|
||||
}
|
||||
if (count > 0) {
|
||||
showToast(`${count} ${add ? t("modelBulkBadgeApplyDone") : t("modelBulkBadgeRemoveDone")}`, "success");
|
||||
renderModelGrid();
|
||||
}
|
||||
}
|
||||
|
||||
export async function bulkDeleteModels() {
|
||||
const count = state.selectedModels.size;
|
||||
if (count === 0) return;
|
||||
const msg = t("modelBulkDeleteConfirm").replace("{count}", count);
|
||||
if (!window.confirm(msg)) return;
|
||||
|
||||
const model_names = [...state.selectedModels];
|
||||
try {
|
||||
const res = await fetch("/api/wfm/models/delete", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model_type: state.activeModelType, model_names }),
|
||||
});
|
||||
const data = await res.json();
|
||||
const okCount = (data.ok || []).length;
|
||||
const errCount = (data.errors || []).length;
|
||||
if (errCount > 0) {
|
||||
showToast(`${t("modelBulkDeleteError")}: ${errCount} errors`, "error");
|
||||
}
|
||||
if (okCount > 0) {
|
||||
showToast(`${okCount} ${t("modelBulkDeleteDone")}`, "success");
|
||||
// Remove deleted models from local state
|
||||
model_names.forEach((mn) => {
|
||||
const list = state.modelsByType[state.activeModelType];
|
||||
const idx = list.indexOf(mn);
|
||||
if (idx !== -1) list.splice(idx, 1);
|
||||
delete state.modelMetadata[mn];
|
||||
const ds = state.disabledModels[state.activeModelType];
|
||||
if (ds) ds.delete(mn);
|
||||
state.selectedModels.delete(mn);
|
||||
});
|
||||
state.loaded[state.activeModelType] = false;
|
||||
renderModelGrid();
|
||||
renderBulkActionBar();
|
||||
}
|
||||
} catch (err) {
|
||||
showToast(`${t("modelBulkDeleteError")}: ${err.message}`, "error");
|
||||
}
|
||||
}
|
||||
|
||||
export async function fetchSubdirs() {
|
||||
try {
|
||||
const res = await fetch(`/api/wfm/models/subdirs?type=${encodeURIComponent(state.activeModelType)}`);
|
||||
state.subdirs = res.ok ? await res.json() : [];
|
||||
} catch { state.subdirs = []; }
|
||||
}
|
||||
|
||||
export async function bulkMoveModels(destSubdir) {
|
||||
const model_names = [...state.selectedModels];
|
||||
if (model_names.length === 0) return;
|
||||
try {
|
||||
const res = await fetch("/api/wfm/models/move", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model_type: state.activeModelType, model_names, dest: destSubdir }),
|
||||
});
|
||||
const data = await res.json();
|
||||
const okCount = data.moved?.length || 0;
|
||||
const errCount = data.errors?.length || 0;
|
||||
|
||||
if (okCount > 0) {
|
||||
data.moved.forEach(({ from, to }) => {
|
||||
const list = state.modelsByType[state.activeModelType];
|
||||
const idx = list.indexOf(from);
|
||||
if (idx !== -1) list[idx] = to;
|
||||
if (state.modelMetadata[from]) {
|
||||
state.modelMetadata[to] = state.modelMetadata[from];
|
||||
delete state.modelMetadata[from];
|
||||
}
|
||||
const ds = state.disabledModels[state.activeModelType];
|
||||
if (ds && ds.has(from)) { ds.delete(from); ds.add(to); }
|
||||
state.selectedModels.delete(from);
|
||||
for (const members of Object.values(state.modelGroups)) {
|
||||
const gi = members.indexOf(from);
|
||||
if (gi !== -1) members[gi] = to;
|
||||
}
|
||||
});
|
||||
if (state.selectedModel && data.moved.some((m) => m.from === state.selectedModel)) {
|
||||
state.selectedModel = null;
|
||||
const titleEl = document.getElementById("wfm-models-panel-title");
|
||||
if (titleEl) titleEl.textContent = "";
|
||||
}
|
||||
showToast(`${okCount} ${t("modelBulkMoveDone")}`, "success");
|
||||
await fetchSubdirs();
|
||||
renderDirFilter();
|
||||
renderModelGrid();
|
||||
renderBulkActionBar();
|
||||
}
|
||||
if (errCount > 0) {
|
||||
showToast(`${t("modelBulkMoveError")}: ${data.errors[0].error}`, "error");
|
||||
}
|
||||
} catch (err) {
|
||||
showToast(`${t("modelBulkMoveError")}: ${err.message}`, "error");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,82 @@
|
||||
/**
|
||||
* Models Tab - Shared state and static lookup tables
|
||||
* `state` is a `const` object never reassigned — modules read/write its
|
||||
* properties directly, which stays within ESM's read-only binding rule
|
||||
* (only the binding itself, not the object it points to, is immutable).
|
||||
*/
|
||||
|
||||
import { comfyUI } from "../comfyui-client.js";
|
||||
|
||||
export const RESERVED_GROUPS = ["Batch", "Stack"];
|
||||
export const BATCH_MODEL_TYPES = ["checkpoint", "lora"];
|
||||
export const STACK_MODEL_TYPES = ["lora"];
|
||||
|
||||
export const state = {
|
||||
modelsByType: {
|
||||
checkpoint: [],
|
||||
lora: [],
|
||||
vae: [],
|
||||
controlnet: [],
|
||||
unet: [],
|
||||
textencoder: [],
|
||||
hypernetwork: [],
|
||||
embedding: [],
|
||||
},
|
||||
modelMetadata: {},
|
||||
modelGroups: {},
|
||||
allModelGroups: {}, // { type: { groupName: [models] } } — all types
|
||||
civitaiCache: {},
|
||||
disabledModels: {}, // { type: Set<modelName> }
|
||||
subdirs: [],
|
||||
selectMode: false,
|
||||
selectedModels: new Set(),
|
||||
searchText: "",
|
||||
tagFilter: "",
|
||||
badgeFilter: "",
|
||||
dirFilter: "",
|
||||
groupFilter: "",
|
||||
statusFilter: "all", // "all" | "enabled" | "disabled"
|
||||
showFavoritesOnly: false,
|
||||
showBatchOnly: false,
|
||||
viewMode: localStorage.getItem("wfm_models_view") === "table" ? "table" : "thumb",
|
||||
activeModelType: "checkpoint",
|
||||
selectedModel: null,
|
||||
loaded: {},
|
||||
currentPage: 0,
|
||||
sortColumn: null, // "fav" | "filename" | "subdir" | "civtype" | "basemodel" | "ext" | "tags" | "memo" | "enabled"
|
||||
sortDir: "asc", // "asc" | "desc"
|
||||
};
|
||||
|
||||
|
||||
export const FETCH_MAP = {
|
||||
checkpoint: () => comfyUI.fetchCheckpoints(),
|
||||
lora: () => comfyUI.fetchLoras(),
|
||||
vae: () => comfyUI.fetchVaes(),
|
||||
controlnet: () => comfyUI.fetchControlNets(),
|
||||
unet: () => comfyUI.fetchDiffusionModels(),
|
||||
textencoder: () => comfyUI.fetchTextEncoders(),
|
||||
hypernetwork: () => comfyUI.fetchHypernetworks(),
|
||||
embedding: () => comfyUI.fetchEmbeddings(),
|
||||
};
|
||||
|
||||
export const TYPE_LABELS = {
|
||||
checkpoint: "Checkpoint",
|
||||
lora: "LoRA",
|
||||
vae: "VAE",
|
||||
controlnet: "ControlNet",
|
||||
unet: "UNET",
|
||||
textencoder: "TextEncoder",
|
||||
hypernetwork: "Hypernetwork",
|
||||
embedding: "Embedding",
|
||||
};
|
||||
|
||||
// Mapping from models-tab type → comfyui-editor key + inputKey
|
||||
export const GENUI_TYPE_MAP = {
|
||||
checkpoint: { key: "checkpoints", inputKey: "ckpt_name" },
|
||||
lora: { key: "loras", inputKey: "lora_name" },
|
||||
vae: { key: "vaes", inputKey: "vae_name" },
|
||||
controlnet: { key: "controlNets", inputKey: "control_net_name" },
|
||||
unet: { key: "diffusionModels",inputKey: "unet_name" },
|
||||
textencoder: { key: "textEncoders", inputKey: "clip_name1" },
|
||||
hypernetwork: { key: "hypernetworks", inputKey: "hypernetwork_name" },
|
||||
};
|
||||
@@ -0,0 +1,394 @@
|
||||
/**
|
||||
* Prompt Tab - AI Assistant (Ollama / LM Studio chat, translate, apply to GenerateUI)
|
||||
*/
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { getAiBackendDefaultUrl } from "./util.js";
|
||||
|
||||
// ============================================
|
||||
// State
|
||||
// ============================================
|
||||
|
||||
const ollamaState = {
|
||||
chatHistory: [],
|
||||
attachedImage: null,
|
||||
};
|
||||
|
||||
const PROMPT_AI_KEY = "wfm_prompt_ai_settings";
|
||||
|
||||
function loadPromptAiSettings() {
|
||||
try { return JSON.parse(localStorage.getItem(PROMPT_AI_KEY) || "{}"); } catch { return {}; }
|
||||
}
|
||||
|
||||
// バックエンド変更検知用: モデル一覧を取得した時点のバックエンド名を記録
|
||||
let _promptAiBackendForModels = null;
|
||||
|
||||
export function getPromptAiConfig() {
|
||||
const s = loadPromptAiSettings();
|
||||
const backend = s.backend || "ollama";
|
||||
const url = (s.backendUrl || getAiBackendDefaultUrl(backend)).replace(/\/$/, "");
|
||||
return { backend, url };
|
||||
}
|
||||
|
||||
export async function fetchAiModels() {
|
||||
const { backend, url } = getPromptAiConfig();
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/tags`);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).models?.map(m => m.name) || [];
|
||||
} else {
|
||||
const res = await fetch(`${url}/v1/models`);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return ((await res.json()).data || []).map(m => m.id);
|
||||
}
|
||||
}
|
||||
|
||||
async function chatWithAi(model, messages) {
|
||||
const { backend, url } = getPromptAiConfig();
|
||||
if (backend === "ollama") {
|
||||
const res = await fetch(`${url}/api/chat`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages, stream: false }),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).message?.content || "";
|
||||
} else {
|
||||
// LM Studio / Lemonade (OpenAI-compatible): convert Ollama-style messages (with .images) to OpenAI content arrays
|
||||
const openAiMessages = messages.map(msg => {
|
||||
if (msg.images?.length) {
|
||||
return {
|
||||
role: msg.role,
|
||||
content: [
|
||||
{ type: "text", text: msg.content },
|
||||
...msg.images.map(b64 => ({ type: "image_url", image_url: { url: `data:image/jpeg;base64,${b64}` } })),
|
||||
],
|
||||
};
|
||||
}
|
||||
return { role: msg.role, content: msg.content };
|
||||
});
|
||||
const res = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages: openAiMessages, stream: false }),
|
||||
});
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return (await res.json()).choices?.[0]?.message?.content || "";
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Chat UI
|
||||
// ============================================
|
||||
|
||||
function addChatMessage(role, content) {
|
||||
const chat = document.getElementById("wfm-ollama-chat");
|
||||
if (!chat) return;
|
||||
|
||||
const welcome = chat.querySelector(".wfm-ollama-welcome");
|
||||
if (welcome) welcome.remove();
|
||||
|
||||
const msg = document.createElement("div");
|
||||
msg.className = `wfm-ollama-msg ${role}`;
|
||||
|
||||
const roleLabel = document.createElement("div");
|
||||
roleLabel.className = "wfm-ollama-msg-role";
|
||||
roleLabel.textContent = role === "user" ? t("you") : "AI";
|
||||
msg.appendChild(roleLabel);
|
||||
|
||||
const contentDiv = document.createElement("div");
|
||||
contentDiv.className = "wfm-ollama-msg-content";
|
||||
contentDiv.textContent = content;
|
||||
msg.appendChild(contentDiv);
|
||||
|
||||
// Apply button for assistant messages
|
||||
if (role === "assistant") {
|
||||
const applyRow = document.createElement("div");
|
||||
applyRow.className = "wfm-ollama-apply-row";
|
||||
|
||||
const applyBtn = document.createElement("button");
|
||||
applyBtn.className = "wfm-btn wfm-btn-sm wfm-btn-primary";
|
||||
applyBtn.textContent = t("applyToGenerateUI");
|
||||
applyBtn.addEventListener("click", () => applyToGenerateUI(content));
|
||||
applyRow.appendChild(applyBtn);
|
||||
|
||||
const copyBtn = document.createElement("button");
|
||||
copyBtn.className = "wfm-btn wfm-btn-sm";
|
||||
copyBtn.textContent = t("copy");
|
||||
copyBtn.addEventListener("click", () => {
|
||||
navigator.clipboard.writeText(content).then(() => {
|
||||
copyBtn.textContent = t("copied");
|
||||
setTimeout(() => { copyBtn.textContent = t("copy"); }, 1500);
|
||||
});
|
||||
});
|
||||
applyRow.appendChild(copyBtn);
|
||||
|
||||
msg.appendChild(applyRow);
|
||||
}
|
||||
|
||||
chat.appendChild(msg);
|
||||
chat.scrollTop = chat.scrollHeight;
|
||||
}
|
||||
|
||||
function clearChat() {
|
||||
ollamaState.chatHistory = [];
|
||||
ollamaState.attachedImage = null;
|
||||
updateAttachmentDisplay();
|
||||
|
||||
const chat = document.getElementById("wfm-ollama-chat");
|
||||
if (chat) {
|
||||
chat.innerHTML = `
|
||||
<div class="wfm-ollama-welcome">
|
||||
<p>AI Assistant</p>
|
||||
<p>${t("assistantWelcome")}</p>
|
||||
</div>
|
||||
`;
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Image Attachment
|
||||
// ============================================
|
||||
|
||||
function attachFile(file) {
|
||||
if (!file) return;
|
||||
const reader = new FileReader();
|
||||
reader.onload = (e) => {
|
||||
ollamaState.attachedImage = { name: file.name, dataUrl: e.target.result };
|
||||
updateAttachmentDisplay();
|
||||
};
|
||||
reader.readAsDataURL(file);
|
||||
}
|
||||
|
||||
function updateAttachmentDisplay() {
|
||||
const container = document.getElementById("wfm-ollama-attachments");
|
||||
if (!container) return;
|
||||
container.innerHTML = "";
|
||||
|
||||
if (!ollamaState.attachedImage) return;
|
||||
|
||||
const item = document.createElement("div");
|
||||
item.className = "wfm-ollama-attachment";
|
||||
|
||||
const img = document.createElement("img");
|
||||
img.src = ollamaState.attachedImage.dataUrl;
|
||||
item.appendChild(img);
|
||||
|
||||
const name = document.createElement("span");
|
||||
name.className = "wfm-ollama-attachment-name";
|
||||
name.textContent = ollamaState.attachedImage.name;
|
||||
item.appendChild(name);
|
||||
|
||||
const removeBtn = document.createElement("button");
|
||||
removeBtn.className = "wfm-ollama-attachment-remove";
|
||||
removeBtn.textContent = "×";
|
||||
removeBtn.addEventListener("click", () => {
|
||||
ollamaState.attachedImage = null;
|
||||
updateAttachmentDisplay();
|
||||
});
|
||||
item.appendChild(removeBtn);
|
||||
|
||||
container.appendChild(item);
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Send Message
|
||||
// ============================================
|
||||
|
||||
async function sendMessage() {
|
||||
const input = document.getElementById("wfm-ollama-input");
|
||||
const modelSelect = document.getElementById("wfm-ollama-model");
|
||||
if (!input || !modelSelect) return;
|
||||
|
||||
const message = input.value.trim();
|
||||
if (!message) return;
|
||||
|
||||
// バックエンドが変更されていたらモデル一覧を自動更新
|
||||
const { backend } = getPromptAiConfig();
|
||||
if (backend !== _promptAiBackendForModels) {
|
||||
const status = document.getElementById("wfm-ollama-status");
|
||||
if (status) { status.textContent = `Switching to ${backend}...`; status.className = "wfm-ollama-status"; }
|
||||
try {
|
||||
const models = await fetchAiModels();
|
||||
const savedModel = loadPromptAiSettings().model || "";
|
||||
modelSelect.innerHTML = models.length
|
||||
? models.map(name => `<option value="${name}" ${name === savedModel ? "selected" : ""}>${name}</option>`).join("")
|
||||
: `<option value="">${t("noModelsFound")}</option>`;
|
||||
_promptAiBackendForModels = backend;
|
||||
if (status) { status.textContent = `${models.length} models [${backend}]`; status.className = "wfm-ollama-status connected"; }
|
||||
} catch {
|
||||
if (status) { status.textContent = t("failedConnect"); status.className = "wfm-ollama-status error"; }
|
||||
}
|
||||
}
|
||||
|
||||
const model = modelSelect.value;
|
||||
if (!model) {
|
||||
showToast(t("selectModelFirst"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
addChatMessage("user", message);
|
||||
input.value = "";
|
||||
|
||||
const userMsg = { role: "user", content: message };
|
||||
if (ollamaState.attachedImage) {
|
||||
userMsg.images = [ollamaState.attachedImage.dataUrl.split(",")[1]];
|
||||
}
|
||||
ollamaState.chatHistory.push(userMsg);
|
||||
|
||||
const sendBtn = document.getElementById("wfm-ollama-send-btn");
|
||||
if (sendBtn) sendBtn.disabled = true;
|
||||
|
||||
try {
|
||||
const reply = await chatWithAi(model, ollamaState.chatHistory);
|
||||
addChatMessage("assistant", reply);
|
||||
ollamaState.chatHistory.push({ role: "assistant", content: reply });
|
||||
ollamaState.attachedImage = null;
|
||||
updateAttachmentDisplay();
|
||||
} catch (err) {
|
||||
showToast(t("error") + ": " + err.message, "error");
|
||||
} finally {
|
||||
if (sendBtn) sendBtn.disabled = false;
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Translation
|
||||
// ============================================
|
||||
|
||||
async function sendTranslate(direction) {
|
||||
const input = document.getElementById("wfm-ollama-input");
|
||||
const modelSelect = document.getElementById("wfm-ollama-model");
|
||||
if (!input || !modelSelect) return;
|
||||
|
||||
const text = input.value.trim();
|
||||
if (!text) return;
|
||||
|
||||
const model = modelSelect.value;
|
||||
if (!model) {
|
||||
showToast(t("selectModelFirst"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
const labels = { ja2en: "JA→EN", en2ja: "EN→JA", zh2en: "ZH→EN", en2zh: "EN→ZH" };
|
||||
addChatMessage("user", `[${labels[direction] || direction}]\n${text}`);
|
||||
input.value = "";
|
||||
|
||||
const btns = ["wfm-ollama-send-btn", "wfm-ollama-ja2en-btn", "wfm-ollama-en2ja-btn", "wfm-ollama-zh2en-btn", "wfm-ollama-en2zh-btn"]
|
||||
.map((id) => document.getElementById(id));
|
||||
btns.forEach((b) => { if (b) b.disabled = true; });
|
||||
|
||||
try {
|
||||
const prompts = {
|
||||
ja2en: `Translate the following Japanese text into English. Output only the translated text, nothing else.\n\n${text}`,
|
||||
en2ja: `以下の英語のテキストを日本語に翻訳してください。翻訳結果のみを出力し、他の説明は不要です。\n\n${text}`,
|
||||
zh2en: `Translate the following Chinese text into English. Output only the translated text, nothing else.\n\n${text}`,
|
||||
en2zh: `请将以下英文翻译成中文。只输出翻译结果，不需要其他说明。\n\n${text}`,
|
||||
};
|
||||
const prompt = prompts[direction];
|
||||
|
||||
const reply = await chatWithAi(model, [{ role: "user", content: prompt }]);
|
||||
addChatMessage("assistant", reply);
|
||||
} catch (err) {
|
||||
showToast(t("error") + ": " + err.message, "error");
|
||||
} finally {
|
||||
btns.forEach((b) => { if (b) b.disabled = false; });
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Apply to GenerateUI
|
||||
// ============================================
|
||||
|
||||
function applyToGenerateUI(text) {
|
||||
const textareas = document.querySelectorAll("#wfm-gen-prompt-fields textarea");
|
||||
if (textareas.length === 0) {
|
||||
showToast(t("noPromptFields"), "error");
|
||||
return;
|
||||
}
|
||||
textareas[0].value = text;
|
||||
textareas[0].dispatchEvent(new Event("input", { bubbles: true }));
|
||||
textareas[0].dispatchEvent(new Event("change", { bubbles: true }));
|
||||
showToast(t("appliedToGenerateUI"), "success");
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// UI wiring (called from prompt-tab.js's initPromptTab)
|
||||
// ============================================
|
||||
|
||||
export function initAiChatUI() {
|
||||
// AI model refresh
|
||||
async function refreshModels() {
|
||||
const select = document.getElementById("wfm-ollama-model");
|
||||
const status = document.getElementById("wfm-ollama-status");
|
||||
const { backend } = getPromptAiConfig();
|
||||
try {
|
||||
const models = await fetchAiModels();
|
||||
const savedModel = loadPromptAiSettings().model || "";
|
||||
if (select) {
|
||||
select.innerHTML = models.length
|
||||
? models.map(name => `<option value="${name}" ${name === savedModel ? "selected" : ""}>${name}</option>`).join("")
|
||||
: `<option value="">${t("noModelsFound")}</option>`;
|
||||
}
|
||||
_promptAiBackendForModels = backend;
|
||||
if (status) {
|
||||
status.textContent = `${models.length} models [${backend}]`;
|
||||
status.className = "wfm-ollama-status connected";
|
||||
}
|
||||
} catch {
|
||||
if (status) {
|
||||
status.textContent = t("failedConnect");
|
||||
status.className = "wfm-ollama-status error";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Auto-load models on init
|
||||
refreshModels();
|
||||
|
||||
document.getElementById("wfm-ollama-refresh-btn")?.addEventListener("click", refreshModels);
|
||||
|
||||
// Test connection
|
||||
document.getElementById("wfm-ollama-test-btn")?.addEventListener("click", async () => {
|
||||
const status = document.getElementById("wfm-ollama-status");
|
||||
if (status) { status.textContent = "Testing..."; status.className = "wfm-ollama-status"; }
|
||||
try {
|
||||
await fetchAiModels();
|
||||
if (status) { status.textContent = t("connectedCheck"); status.className = "wfm-ollama-status connected"; }
|
||||
await refreshModels();
|
||||
} catch {
|
||||
if (status) { status.textContent = t("failedConnect"); status.className = "wfm-ollama-status error"; }
|
||||
}
|
||||
});
|
||||
|
||||
// Send message
|
||||
document.getElementById("wfm-ollama-send-btn")?.addEventListener("click", sendMessage);
|
||||
|
||||
// Enter to send
|
||||
document.getElementById("wfm-ollama-input")?.addEventListener("keydown", (e) => {
|
||||
if (e.key === "Enter" && !e.shiftKey) {
|
||||
e.preventDefault();
|
||||
sendMessage();
|
||||
}
|
||||
});
|
||||
|
||||
// Clear chat
|
||||
document.getElementById("wfm-ollama-clear-btn")?.addEventListener("click", clearChat);
|
||||
|
||||
// File attachment
|
||||
const fileInput = document.getElementById("wfm-ollama-file-input");
|
||||
document.getElementById("wfm-ollama-attach-btn")?.addEventListener("click", () => fileInput?.click());
|
||||
fileInput?.addEventListener("change", () => {
|
||||
if (fileInput.files.length > 0) {
|
||||
attachFile(fileInput.files[0]);
|
||||
fileInput.value = "";
|
||||
}
|
||||
});
|
||||
|
||||
// Translation
|
||||
document.getElementById("wfm-ollama-ja2en-btn")?.addEventListener("click", () => sendTranslate("ja2en"));
|
||||
document.getElementById("wfm-ollama-en2ja-btn")?.addEventListener("click", () => sendTranslate("en2ja"));
|
||||
document.getElementById("wfm-ollama-zh2en-btn")?.addEventListener("click", () => sendTranslate("zh2en"));
|
||||
document.getElementById("wfm-ollama-en2zh-btn")?.addEventListener("click", () => sendTranslate("en2zh"));
|
||||
}
|
||||
@@ -0,0 +1,567 @@
|
||||
/**
|
||||
* Prompt Tab - Presets (API CRUD, groups, Preset Manager panel)
|
||||
*/
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { readJsonStorage } from "./util.js";
|
||||
|
||||
// ============================================
|
||||
// State
|
||||
// ============================================
|
||||
|
||||
// Preset data (loaded from API, with localStorage migration)
|
||||
let promptPresets = [];
|
||||
let pmActiveTab = "all"; // "all" | "favorites" | "groups"
|
||||
let pmSearchText = "";
|
||||
let pmSelectedId = null; // currently selected preset id
|
||||
export let pmGroups = {}; // { groupName: [presetId, ...] }
|
||||
const PM_GROUPS_KEY = "wfm_prompt_preset_groups";
|
||||
export const PROMPT_RESERVED_GROUPS = ["Batch"];
|
||||
|
||||
// ============================================
|
||||
// Preset API helpers
|
||||
// ============================================
|
||||
|
||||
export async function fetchPresets() {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/prompts");
|
||||
return res.ok ? await res.json() : [];
|
||||
} catch { return []; }
|
||||
}
|
||||
|
||||
export async function apiCreatePreset(data) {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/prompts", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify(data),
|
||||
});
|
||||
const result = await res.json();
|
||||
return result.status === "ok" ? result.prompt : null;
|
||||
} catch { return null; }
|
||||
}
|
||||
|
||||
export async function apiUpdatePreset(id, updates) {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/prompts/update", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ id, ...updates }),
|
||||
});
|
||||
const result = await res.json();
|
||||
return result.status === "ok" ? result.prompt : null;
|
||||
} catch { return null; }
|
||||
}
|
||||
|
||||
export async function apiDeletePreset(id) {
|
||||
try {
|
||||
await fetch("/api/wfm/prompts/delete", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ id }),
|
||||
});
|
||||
} catch { /* ignore */ }
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Migrate localStorage presets to API
|
||||
// ============================================
|
||||
|
||||
const PRESETS_KEY = "wfm_prompt_presets";
|
||||
|
||||
async function migrateLocalStoragePresets() {
|
||||
try {
|
||||
const raw = localStorage.getItem(PRESETS_KEY);
|
||||
if (!raw) return;
|
||||
const local = JSON.parse(raw);
|
||||
if (!Array.isArray(local) || local.length === 0) return;
|
||||
|
||||
// Only migrate if API has no presets yet
|
||||
const existing = await fetchPresets();
|
||||
if (existing.length > 0) {
|
||||
localStorage.removeItem(PRESETS_KEY);
|
||||
return;
|
||||
}
|
||||
|
||||
for (const p of local) {
|
||||
await apiCreatePreset({
|
||||
name: p.name || "Untitled",
|
||||
text: p.posText || p.text || "",
|
||||
negText: p.negText || "",
|
||||
category: "",
|
||||
tags: [],
|
||||
favorite: false,
|
||||
});
|
||||
}
|
||||
localStorage.removeItem(PRESETS_KEY);
|
||||
} catch { /* ignore migration errors */ }
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Preset data management
|
||||
// ============================================
|
||||
|
||||
export async function loadAllPresets() {
|
||||
await migrateLocalStoragePresets();
|
||||
promptPresets = await fetchPresets();
|
||||
|
||||
// Load groups from localStorage
|
||||
pmGroups = readJsonStorage(PM_GROUPS_KEY);
|
||||
|
||||
// Clean stale entries from groups (preserve reserved groups even if empty)
|
||||
const validIds = new Set(promptPresets.map(p => p.id));
|
||||
for (const g of Object.keys(pmGroups)) {
|
||||
pmGroups[g] = (pmGroups[g] || []).filter(id => validIds.has(id));
|
||||
if (pmGroups[g].length === 0 && !PROMPT_RESERVED_GROUPS.includes(g)) delete pmGroups[g];
|
||||
}
|
||||
// Ensure reserved groups always exist
|
||||
for (const g of PROMPT_RESERVED_GROUPS) {
|
||||
if (!pmGroups[g]) pmGroups[g] = [];
|
||||
}
|
||||
saveGroups();
|
||||
|
||||
renderPresetSelect();
|
||||
renderGroupSelect();
|
||||
renderPresetManager();
|
||||
}
|
||||
|
||||
export function saveGroups() {
|
||||
localStorage.setItem(PM_GROUPS_KEY, JSON.stringify(pmGroups));
|
||||
renderGroupSelect();
|
||||
}
|
||||
|
||||
function renderGroupSelect() {
|
||||
const select = document.getElementById("wfm-preset-group-select");
|
||||
if (!select) return;
|
||||
const prevVal = select.value;
|
||||
select.innerHTML = `<option value="">Select group...</option>`;
|
||||
for (const g of Object.keys(pmGroups).sort()) {
|
||||
const opt = document.createElement("option");
|
||||
opt.value = g;
|
||||
opt.textContent = g;
|
||||
select.appendChild(opt);
|
||||
}
|
||||
if (prevVal && pmGroups[prevVal]) select.value = prevVal;
|
||||
}
|
||||
|
||||
function renderPresetSelect() {
|
||||
const select = document.getElementById("wfm-preset-select");
|
||||
if (!select) return;
|
||||
const prevVal = select.value;
|
||||
select.innerHTML = `<option value="">${t("newPreset")}</option>`;
|
||||
promptPresets.forEach((p) => {
|
||||
const opt = document.createElement("option");
|
||||
opt.value = p.id;
|
||||
opt.textContent = p.name;
|
||||
select.appendChild(opt);
|
||||
});
|
||||
// Restore selection if still valid
|
||||
if (prevVal && promptPresets.find(p => p.id === prevVal)) {
|
||||
select.value = prevVal;
|
||||
}
|
||||
}
|
||||
|
||||
function selectPresetInEditor(preset) {
|
||||
const presetSelect = document.getElementById("wfm-preset-select");
|
||||
const presetName = document.getElementById("wfm-preset-name");
|
||||
const presetCategory = document.getElementById("wfm-preset-category");
|
||||
const presetPos = document.getElementById("wfm-preset-pos");
|
||||
const presetNeg = document.getElementById("wfm-preset-neg");
|
||||
|
||||
if (presetSelect) presetSelect.value = preset ? preset.id : "";
|
||||
if (presetName) presetName.value = preset ? preset.name : "";
|
||||
if (presetCategory) presetCategory.value = preset ? (preset.category || "") : "";
|
||||
if (presetPos) presetPos.value = preset ? (preset.text || preset.posText || "") : "";
|
||||
if (presetNeg) presetNeg.value = preset ? (preset.negText || "") : "";
|
||||
|
||||
pmSelectedId = preset ? preset.id : null;
|
||||
renderPresetManager();
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Preset Manager rendering
|
||||
// ============================================
|
||||
|
||||
export function esc(s) {
|
||||
return s ? String(s).replace(/&/g, "&").replace(/</g, "<").replace(/>/g, ">") : "";
|
||||
}
|
||||
|
||||
export function renderPresetManager() {
|
||||
const container = document.getElementById("wfm-pm-list");
|
||||
if (!container) return;
|
||||
container.innerHTML = "";
|
||||
|
||||
switch (pmActiveTab) {
|
||||
case "all": renderPmAll(container); break;
|
||||
case "favorites": renderPmFavorites(container); break;
|
||||
case "groups": renderPmGroups(container); break;
|
||||
}
|
||||
}
|
||||
|
||||
function matchesSearch(p) {
|
||||
if (!pmSearchText) return true;
|
||||
const s = pmSearchText.toLowerCase();
|
||||
return (p.name || "").toLowerCase().includes(s) ||
|
||||
(p.text || "").toLowerCase().includes(s) ||
|
||||
(p.category || "").toLowerCase().includes(s) ||
|
||||
(p.tags || []).some(t => t.toLowerCase().includes(s));
|
||||
}
|
||||
|
||||
// Ordered list of actual preset objects for a group (stale ids already filtered out by
|
||||
// loadAllPresets, but this is defensive for callers that read pmGroups directly too).
|
||||
export function getPresetsInGroup(groupName) {
|
||||
const ids = pmGroups[groupName] || [];
|
||||
return ids.map(id => promptPresets.find(p => p.id === id)).filter(Boolean);
|
||||
}
|
||||
|
||||
export function isInBatchPreset(id) {
|
||||
return (pmGroups["Batch"] || []).includes(id);
|
||||
}
|
||||
|
||||
export function toggleBatchPreset(id) {
|
||||
const batch = pmGroups["Batch"] || [];
|
||||
const idx = batch.indexOf(id);
|
||||
if (idx >= 0) { batch.splice(idx, 1); } else { batch.push(id); }
|
||||
pmGroups["Batch"] = batch;
|
||||
saveGroups();
|
||||
}
|
||||
|
||||
export function clearBatchPresets() {
|
||||
pmGroups["Batch"] = [];
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
showToast(t("promptBatchClear"), "success");
|
||||
}
|
||||
|
||||
function createPmItem(preset) {
|
||||
const el = document.createElement("div");
|
||||
el.className = "wfm-pm-item" + (pmSelectedId === preset.id ? " active" : "");
|
||||
|
||||
const previewText = (preset.text || "").length > 50
|
||||
? preset.text.substring(0, 50) + "..."
|
||||
: (preset.text || "");
|
||||
|
||||
const catBadge = preset.category
|
||||
? `<span style="font-size:9px;color:var(--wfm-primary);margin-left:4px;">[${esc(preset.category)}]</span>`
|
||||
: "";
|
||||
|
||||
const inBatch = isInBatchPreset(preset.id);
|
||||
|
||||
el.innerHTML = `
|
||||
<div class="wfm-pm-item-body">
|
||||
<div class="wfm-pm-item-name">${preset.favorite ? '<span style="color:#ffd700;">★</span> ' : ""}${esc(preset.name)}${catBadge}</div>
|
||||
<div class="wfm-pm-item-sub">${esc(previewText)}</div>
|
||||
</div>
|
||||
<div class="wfm-pm-item-actions">
|
||||
<button class="wfm-pm-action-btn pm-batch-btn${inBatch ? " batch-active" : ""}" title="Batch">B</button>
|
||||
<button class="wfm-pm-action-btn pm-fav-btn${preset.favorite ? " fav-active" : ""}" title="Favorite">★</button>
|
||||
<button class="wfm-pm-action-btn pm-del-btn" title="Delete" style="color:var(--wfm-danger);">✕</button>
|
||||
</div>
|
||||
`;
|
||||
|
||||
// Click to select in editor
|
||||
el.addEventListener("click", (e) => {
|
||||
if (e.target.closest(".wfm-pm-item-actions")) return;
|
||||
selectPresetInEditor(preset);
|
||||
});
|
||||
|
||||
// Batch toggle
|
||||
el.querySelector(".pm-batch-btn").addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
toggleBatchPreset(preset.id);
|
||||
renderPresetManager();
|
||||
});
|
||||
|
||||
// Favorite toggle
|
||||
el.querySelector(".pm-fav-btn").addEventListener("click", async (e) => {
|
||||
e.stopPropagation();
|
||||
const updated = await apiUpdatePreset(preset.id, { favorite: !preset.favorite });
|
||||
if (updated) {
|
||||
const idx = promptPresets.findIndex(p => p.id === preset.id);
|
||||
if (idx >= 0) promptPresets[idx] = updated;
|
||||
renderPresetManager();
|
||||
}
|
||||
});
|
||||
|
||||
// Delete
|
||||
el.querySelector(".pm-del-btn").addEventListener("click", async (e) => {
|
||||
e.stopPropagation();
|
||||
if (!confirm(`Delete "${preset.name}"?`)) return;
|
||||
await apiDeletePreset(preset.id);
|
||||
promptPresets = promptPresets.filter(p => p.id !== preset.id);
|
||||
if (pmSelectedId === preset.id) {
|
||||
pmSelectedId = null;
|
||||
selectPresetInEditor(null);
|
||||
}
|
||||
renderPresetSelect();
|
||||
renderPresetManager();
|
||||
showToast(t("deleted"), "success");
|
||||
});
|
||||
|
||||
return el;
|
||||
}
|
||||
|
||||
function renderPmAll(container) {
|
||||
const items = promptPresets.filter(matchesSearch);
|
||||
if (items.length === 0) {
|
||||
container.innerHTML = `<div class="wfm-pm-empty">${promptPresets.length === 0 ? "No presets yet.<br><small>Create one in the Presets panel</small>" : "No matches"}</div>`;
|
||||
return;
|
||||
}
|
||||
for (const p of items) {
|
||||
container.appendChild(createPmItem(p));
|
||||
}
|
||||
}
|
||||
|
||||
function renderPmFavorites(container) {
|
||||
const favs = promptPresets.filter(p => p.favorite);
|
||||
const items = favs.filter(matchesSearch);
|
||||
if (items.length === 0) {
|
||||
container.innerHTML = `<div class="wfm-pm-empty">${favs.length === 0 ? "No favorites yet.<br><small>Star presets to add them here</small>" : "No matches"}</div>`;
|
||||
return;
|
||||
}
|
||||
for (const p of items) {
|
||||
container.appendChild(createPmItem(p));
|
||||
}
|
||||
}
|
||||
|
||||
function renderPmGroups(container) {
|
||||
const groupNames = Object.keys(pmGroups).sort();
|
||||
|
||||
if (groupNames.length === 0) {
|
||||
container.innerHTML = `<div class="wfm-pm-empty">No groups yet.<br><small>Create groups from the Presets panel below</small></div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
for (const groupName of groupNames) {
|
||||
const ids = pmGroups[groupName] || [];
|
||||
const presets = ids.map(id => promptPresets.find(p => p.id === id)).filter(Boolean);
|
||||
|
||||
const section = document.createElement("div");
|
||||
|
||||
const header = document.createElement("div");
|
||||
header.className = "wfm-pm-group-header collapsed";
|
||||
header.innerHTML = `<span>${esc(groupName)}</span> <span class="wfm-pm-badge">${presets.length}</span>`;
|
||||
|
||||
const list = document.createElement("div");
|
||||
list.style.display = "none";
|
||||
|
||||
header.addEventListener("click", () => {
|
||||
list.style.display = list.style.display === "none" ? "block" : "none";
|
||||
header.classList.toggle("collapsed");
|
||||
});
|
||||
|
||||
for (const p of presets) {
|
||||
const item = createPmItem(p);
|
||||
// Add remove-from-group button
|
||||
const removeBtn = document.createElement("button");
|
||||
removeBtn.className = "wfm-pm-action-btn";
|
||||
removeBtn.title = "Remove from group";
|
||||
removeBtn.textContent = "➖";
|
||||
removeBtn.style.fontSize = "10px";
|
||||
removeBtn.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
pmGroups[groupName] = pmGroups[groupName].filter(id => id !== p.id);
|
||||
// 予約グループ (Batch) は空になってもキーを保持する
|
||||
if (pmGroups[groupName].length === 0 && !PROMPT_RESERVED_GROUPS.includes(groupName)) delete pmGroups[groupName];
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
});
|
||||
item.querySelector(".wfm-pm-item-actions").prepend(removeBtn);
|
||||
list.appendChild(item);
|
||||
}
|
||||
section.appendChild(header);
|
||||
section.appendChild(list);
|
||||
container.appendChild(section);
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// UI wiring (called from prompt-tab.js's initPromptTab)
|
||||
// ============================================
|
||||
|
||||
export function initPresetsUI() {
|
||||
loadAllPresets();
|
||||
|
||||
const presetSelect = document.getElementById("wfm-preset-select");
|
||||
const presetName = document.getElementById("wfm-preset-name");
|
||||
const presetCategory = document.getElementById("wfm-preset-category");
|
||||
const presetPos = document.getElementById("wfm-preset-pos");
|
||||
const presetNeg = document.getElementById("wfm-preset-neg");
|
||||
|
||||
// Load preset on selection
|
||||
presetSelect?.addEventListener("change", () => {
|
||||
const id = presetSelect.value;
|
||||
const p = promptPresets.find(pp => pp.id === id);
|
||||
selectPresetInEditor(p || null);
|
||||
});
|
||||
|
||||
// Copy positive prompt to clipboard
|
||||
document.getElementById("wfm-preset-copy-pos-btn")?.addEventListener("click", () => {
|
||||
const text = presetPos?.value || "";
|
||||
if (!text.trim()) {
|
||||
showToast(t("noTextToCopy"), "error");
|
||||
return;
|
||||
}
|
||||
navigator.clipboard.writeText(text).then(() => {
|
||||
showToast(t("copiedToClipboard"), "success");
|
||||
});
|
||||
});
|
||||
|
||||
// Copy negative prompt to clipboard
|
||||
document.getElementById("wfm-preset-copy-neg-btn")?.addEventListener("click", () => {
|
||||
const text = presetNeg?.value || "";
|
||||
if (!text.trim()) {
|
||||
showToast(t("noTextToCopy"), "error");
|
||||
return;
|
||||
}
|
||||
navigator.clipboard.writeText(text).then(() => {
|
||||
showToast(t("copiedToClipboard"), "success");
|
||||
});
|
||||
});
|
||||
|
||||
// Save preset (create or update via API)
|
||||
document.getElementById("wfm-preset-save-btn")?.addEventListener("click", async () => {
|
||||
const name = presetName?.value.trim();
|
||||
const category = presetCategory?.value.trim() || "";
|
||||
const pos = presetPos?.value || "";
|
||||
const neg = presetNeg?.value || "";
|
||||
|
||||
if (!name) {
|
||||
showToast(t("enterPresetName"), "error");
|
||||
return;
|
||||
}
|
||||
if (!pos.trim() && !neg.trim()) {
|
||||
showToast(t("noPromptToSave"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
const selectedId = presetSelect?.value;
|
||||
const existing = selectedId ? promptPresets.find(p => p.id === selectedId) : null;
|
||||
|
||||
if (existing) {
|
||||
// Update
|
||||
const updated = await apiUpdatePreset(existing.id, {
|
||||
name, text: pos, negText: neg, category,
|
||||
});
|
||||
if (updated) {
|
||||
const idx = promptPresets.findIndex(p => p.id === existing.id);
|
||||
if (idx >= 0) promptPresets[idx] = updated;
|
||||
showToast(t("presetSaved"), "success");
|
||||
}
|
||||
} else {
|
||||
// Create
|
||||
const created = await apiCreatePreset({
|
||||
name, text: pos, negText: neg, category,
|
||||
tags: [], favorite: false,
|
||||
});
|
||||
if (created) {
|
||||
promptPresets.push(created);
|
||||
showToast(t("presetSaved"), "success");
|
||||
}
|
||||
}
|
||||
|
||||
pmSelectedId = null;
|
||||
renderPresetSelect();
|
||||
renderPresetManager();
|
||||
if (presetSelect) presetSelect.value = "";
|
||||
});
|
||||
|
||||
// Apply preset to GenerateUI
|
||||
document.getElementById("wfm-preset-apply-btn")?.addEventListener("click", () => {
|
||||
const pos = presetPos?.value || "";
|
||||
const neg = presetNeg?.value || "";
|
||||
const textareas = document.querySelectorAll("#wfm-gen-prompt-fields textarea");
|
||||
if (textareas.length === 0) {
|
||||
showToast(t("noPromptFields"), "error");
|
||||
return;
|
||||
}
|
||||
let applied = false;
|
||||
if (textareas.length > 0 && pos) {
|
||||
textareas[0].value = pos;
|
||||
textareas[0].dispatchEvent(new Event("input", { bubbles: true }));
|
||||
applied = true;
|
||||
}
|
||||
if (textareas.length > 1 && neg) {
|
||||
textareas[1].value = neg;
|
||||
textareas[1].dispatchEvent(new Event("input", { bubbles: true }));
|
||||
applied = true;
|
||||
}
|
||||
if (applied) showToast(t("appliedToGenerateUI"), "success");
|
||||
});
|
||||
|
||||
// --- Group management (Presets side) ---
|
||||
document.getElementById("wfm-preset-new-group-btn")?.addEventListener("click", () => {
|
||||
const name = prompt("Group name:");
|
||||
if (!name || !name.trim()) return;
|
||||
const key = name.trim();
|
||||
if (pmGroups[key]) {
|
||||
showToast(t("groupExists"), "error");
|
||||
return;
|
||||
}
|
||||
pmGroups[key] = [];
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
showToast(t("groupCreated", key), "success");
|
||||
});
|
||||
|
||||
document.getElementById("wfm-preset-add-to-group-btn")?.addEventListener("click", () => {
|
||||
const groupSelect = document.getElementById("wfm-preset-group-select");
|
||||
const groupName = groupSelect?.value;
|
||||
if (!groupName) {
|
||||
showToast(t("selectGroupFirst"), "error");
|
||||
return;
|
||||
}
|
||||
const id = presetSelect?.value;
|
||||
if (!id) {
|
||||
showToast(t("selectPresetFirst"), "error");
|
||||
return;
|
||||
}
|
||||
if (!pmGroups[groupName]) pmGroups[groupName] = [];
|
||||
if (pmGroups[groupName].includes(id)) {
|
||||
showToast(t("alreadyInGroup"), "info");
|
||||
return;
|
||||
}
|
||||
pmGroups[groupName].push(id);
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
showToast(t("addedToGroup"), "success");
|
||||
});
|
||||
|
||||
document.getElementById("wfm-preset-del-group-btn")?.addEventListener("click", () => {
|
||||
const groupSelect = document.getElementById("wfm-preset-group-select");
|
||||
const groupName = groupSelect?.value;
|
||||
if (!groupName) {
|
||||
showToast(t("selectGroupFirst"), "error");
|
||||
return;
|
||||
}
|
||||
if (PROMPT_RESERVED_GROUPS.includes(groupName)) {
|
||||
showToast(t("modelsGroupReserved"), "warning");
|
||||
return;
|
||||
}
|
||||
if (!confirm(`Delete group "${groupName}"?`)) return;
|
||||
delete pmGroups[groupName];
|
||||
saveGroups();
|
||||
renderPresetManager();
|
||||
showToast(t("groupDeleted", groupName), "success");
|
||||
});
|
||||
|
||||
// --- Preset Manager ---
|
||||
document.querySelectorAll(".wfm-pm-tab").forEach(btn => {
|
||||
btn.addEventListener("click", () => {
|
||||
pmActiveTab = btn.dataset.pmtab;
|
||||
document.querySelectorAll(".wfm-pm-tab").forEach(b => b.classList.remove("active"));
|
||||
btn.classList.add("active");
|
||||
renderPresetManager();
|
||||
});
|
||||
});
|
||||
|
||||
document.getElementById("wfm-pm-search-input")?.addEventListener("input", (e) => {
|
||||
pmSearchText = e.target.value.trim();
|
||||
renderPresetManager();
|
||||
});
|
||||
|
||||
document.getElementById("wfm-pm-batch-clear-btn")?.addEventListener("click", () => {
|
||||
clearBatchPresets();
|
||||
});
|
||||
}
|
||||
@@ -0,0 +1,220 @@
|
||||
/**
|
||||
* Prompt Tab - Style manager (Col3 "Style" タブ)
|
||||
*/
|
||||
// Wildcardファイルマネージャーと同じCRUDパターン（一覧→エディタ→保存/削除/キャンセル）。
|
||||
// 保存・削除後は generate-tab.js の refreshStylesList() を呼び、GenerateUIのStyleドロップダウンや
|
||||
// Batchタブのチェックリストを再読み込み無しで最新状態へ同期する。
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { setupSearchClearBtn } from "./util.js";
|
||||
import { refreshStylesList } from "./generate-tab.js";
|
||||
import { esc } from "./prompt-presets.js";
|
||||
|
||||
let styleListData = [];
|
||||
let styleSearchText = "";
|
||||
let styleEditingOriginalName = null; // null = 新規作成
|
||||
let styleEditingFile = null; // 編集中/追加先スタイルの定義元ファイル名（style/配下、null = デフォルトのcustom.json）
|
||||
|
||||
export async function styleFetchList() {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/styles");
|
||||
return res.ok ? await res.json() : [];
|
||||
} catch { return []; }
|
||||
}
|
||||
|
||||
export async function styleApiCreate(name, prompt, negativePrompt, targetFile) {
|
||||
try {
|
||||
const body = { name, prompt, negative_prompt: negativePrompt };
|
||||
if (targetFile) body.file = targetFile;
|
||||
const res = await fetch("/api/wfm/styles", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
const data = await res.json().catch(() => ({}));
|
||||
return res.ok ? { ok: true } : { ok: false, error: data.error || `HTTP ${res.status}` };
|
||||
} catch (e) {
|
||||
return { ok: false, error: e.message };
|
||||
}
|
||||
}
|
||||
|
||||
export async function styleApiUpdate(originalName, name, prompt, negativePrompt) {
|
||||
try {
|
||||
const res = await fetch(`/api/wfm/styles/${encodeURIComponent(originalName)}`, {
|
||||
method: "PUT",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ name, prompt, negative_prompt: negativePrompt }),
|
||||
});
|
||||
const data = await res.json().catch(() => ({}));
|
||||
return res.ok ? { ok: true } : { ok: false, error: data.error || `HTTP ${res.status}` };
|
||||
} catch (e) {
|
||||
return { ok: false, error: e.message };
|
||||
}
|
||||
}
|
||||
|
||||
export async function styleApiDelete(name) {
|
||||
try {
|
||||
const res = await fetch(`/api/wfm/styles/${encodeURIComponent(name)}`, { method: "DELETE" });
|
||||
const data = await res.json().catch(() => ({}));
|
||||
return res.ok ? { ok: true } : { ok: false, error: data.error || `HTTP ${res.status}` };
|
||||
} catch (e) {
|
||||
return { ok: false, error: e.message };
|
||||
}
|
||||
}
|
||||
|
||||
function styleRenderList() {
|
||||
const list = document.getElementById("wfm-style-list");
|
||||
if (!list) return;
|
||||
list.innerHTML = "";
|
||||
|
||||
const q = styleSearchText.trim().toLowerCase();
|
||||
const filtered = q ? styleListData.filter(s => s.name.toLowerCase().includes(q)) : styleListData;
|
||||
|
||||
if (filtered.length === 0) {
|
||||
list.innerHTML = styleListData.length === 0
|
||||
? `<div class="wfm-pm-empty" style="font-size:12px;">No styles yet.<br><small>Click "+ New" to create one.</small></div>`
|
||||
: `<div class="wfm-pm-empty" style="font-size:12px;">No matching styles.</div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
for (const s of filtered) {
|
||||
const item = document.createElement("div");
|
||||
item.className = "wfm-wc-file-item";
|
||||
item.innerHTML = `
|
||||
<div class="wfm-wc-file-item-info">
|
||||
<span style="flex:1;min-width:0;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;">${esc(s.name)}</span>
|
||||
${s.file ? `<span class="wfm-wc-file-ext" title="${esc(s.file)}">${esc(s.file)}</span>` : ""}
|
||||
</div>
|
||||
<div class="wfm-wc-file-item-actions">
|
||||
<button class="wfm-pm-action-btn wfm-style-edit-btn" title="Edit style">✎</button>
|
||||
</div>
|
||||
`;
|
||||
item.querySelector(".wfm-style-edit-btn").addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
styleOpenEditor(s);
|
||||
});
|
||||
item.addEventListener("click", () => styleOpenEditor(s));
|
||||
list.appendChild(item);
|
||||
}
|
||||
}
|
||||
|
||||
// forcedFile: 「+ Add to this file」から呼ばれた場合に、既存スタイルではなく
|
||||
// 特定ファイルを追加先として指定するために使う（styleは渡さずnullにする）
|
||||
function styleOpenEditor(style, forcedFile) {
|
||||
styleEditingOriginalName = style ? style.name : null;
|
||||
styleEditingFile = style ? (style.file || null) : (forcedFile || null);
|
||||
|
||||
const editor = document.getElementById("wfm-style-editor");
|
||||
const nameInput = document.getElementById("wfm-style-editor-name");
|
||||
const promptTA = document.getElementById("wfm-style-editor-prompt");
|
||||
const negativeTA = document.getElementById("wfm-style-editor-negative");
|
||||
const deleteBtn = document.getElementById("wfm-style-editor-delete-btn");
|
||||
const addFileBtn = document.getElementById("wfm-style-editor-addfile-btn");
|
||||
const fileLabel = document.getElementById("wfm-style-editor-file-label");
|
||||
|
||||
if (!editor) return;
|
||||
nameInput.value = style?.name || "";
|
||||
promptTA.value = style?.prompt || "";
|
||||
negativeTA.value = style?.negative_prompt || "";
|
||||
deleteBtn.style.display = style ? "" : "none";
|
||||
// 「このファイルへ追加」は既存スタイルを編集中（=対象ファイルが分かっている）時だけ表示
|
||||
addFileBtn.style.display = style ? "" : "none";
|
||||
if (styleEditingFile) {
|
||||
fileLabel.textContent = `File: ${styleEditingFile}`;
|
||||
fileLabel.style.display = "";
|
||||
} else {
|
||||
fileLabel.style.display = "none";
|
||||
}
|
||||
editor.style.display = "";
|
||||
nameInput.focus();
|
||||
}
|
||||
|
||||
function styleCloseEditor() {
|
||||
styleEditingOriginalName = null;
|
||||
styleEditingFile = null;
|
||||
const editor = document.getElementById("wfm-style-editor");
|
||||
if (editor) editor.style.display = "none";
|
||||
}
|
||||
|
||||
export async function styleRefreshList() {
|
||||
styleListData = await styleFetchList();
|
||||
styleRenderList();
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// UI wiring (called from prompt-tab.js's initPromptTab)
|
||||
// ============================================
|
||||
|
||||
export function initStylesUI() {
|
||||
document.getElementById("wfm-style-new-btn")?.addEventListener("click", () => {
|
||||
styleOpenEditor(null);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-style-refresh-btn")?.addEventListener("click", () => {
|
||||
styleRefreshList();
|
||||
});
|
||||
|
||||
// 編集中の既存スタイルと同じファイルへ、新しい空のスタイルを追加する
|
||||
document.getElementById("wfm-style-editor-addfile-btn")?.addEventListener("click", () => {
|
||||
if (!styleEditingFile) return;
|
||||
styleOpenEditor(null, styleEditingFile);
|
||||
});
|
||||
|
||||
setupSearchClearBtn("wfm-style-search", "wfm-style-search-clear-btn", () => {
|
||||
styleSearchText = "";
|
||||
styleRenderList();
|
||||
});
|
||||
document.getElementById("wfm-style-search")?.addEventListener("input", (e) => {
|
||||
styleSearchText = e.target.value;
|
||||
styleRenderList();
|
||||
});
|
||||
|
||||
document.getElementById("wfm-style-editor-save-btn")?.addEventListener("click", async () => {
|
||||
const nameInput = document.getElementById("wfm-style-editor-name");
|
||||
const promptTA = document.getElementById("wfm-style-editor-prompt");
|
||||
const negativeTA = document.getElementById("wfm-style-editor-negative");
|
||||
|
||||
const name = (nameInput?.value || "").trim();
|
||||
const prompt = promptTA?.value || "";
|
||||
const negativePrompt = negativeTA?.value || "";
|
||||
|
||||
if (!name) { showToast(t("pleaseEnterStyleName"), "error"); return; }
|
||||
|
||||
const result = styleEditingOriginalName
|
||||
? await styleApiUpdate(styleEditingOriginalName, name, prompt, negativePrompt)
|
||||
: await styleApiCreate(name, prompt, negativePrompt, styleEditingFile);
|
||||
|
||||
if (result.ok) {
|
||||
showToast(t("savedAs", name), "success");
|
||||
styleCloseEditor();
|
||||
await styleRefreshList();
|
||||
await refreshStylesList();
|
||||
} else {
|
||||
showToast(t("errorWithMsg", result.error), "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Editor: delete
|
||||
document.getElementById("wfm-style-editor-delete-btn")?.addEventListener("click", async () => {
|
||||
if (!styleEditingOriginalName) return;
|
||||
if (!confirm(`Delete "${styleEditingOriginalName}"?`)) return;
|
||||
const result = await styleApiDelete(styleEditingOriginalName);
|
||||
if (result.ok) {
|
||||
showToast(t("deletedName", styleEditingOriginalName), "success");
|
||||
styleCloseEditor();
|
||||
await styleRefreshList();
|
||||
await refreshStylesList();
|
||||
} else {
|
||||
showToast(t("errorWithMsg", result.error), "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Editor: cancel
|
||||
document.getElementById("wfm-style-editor-cancel-btn")?.addEventListener("click", () => {
|
||||
styleCloseEditor();
|
||||
});
|
||||
|
||||
// Initial style list load
|
||||
styleRefreshList();
|
||||
}
|
||||
+39
-1295
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,475 @@
|
||||
/**
|
||||
* Prompt Tab - Wildcard file manager (list, editor, insert-at-cursor helpers)
|
||||
*/
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { esc } from "./prompt-presets.js";
|
||||
|
||||
// ============================================
|
||||
// Wildcard API helpers
|
||||
// ============================================
|
||||
|
||||
export async function wcFetchFiles() {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/wildcards");
|
||||
return res.ok ? await res.json() : [];
|
||||
} catch { return []; }
|
||||
}
|
||||
|
||||
export async function wcFetchContent(filename) {
|
||||
try {
|
||||
const res = await fetch(`/api/wfm/wildcards/content?filename=${encodeURIComponent(filename)}`);
|
||||
const data = await res.json();
|
||||
return data.content ?? null;
|
||||
} catch { return null; }
|
||||
}
|
||||
|
||||
export async function wcSaveFile(filename, content) {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/wildcards/save", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ filename, content }),
|
||||
});
|
||||
const data = await res.json();
|
||||
return data.status === "ok" ? data.file : null;
|
||||
} catch { return null; }
|
||||
}
|
||||
|
||||
export async function wcDeleteFile(filename) {
|
||||
try {
|
||||
await fetch("/api/wfm/wildcards/delete", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ filename }),
|
||||
});
|
||||
} catch { /* ignore */ }
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Wildcard state & helpers
|
||||
// ============================================
|
||||
|
||||
let wcFiles = [];
|
||||
let wcEditingFilename = null; // null = new file
|
||||
|
||||
// Insert text at cursor; if open+close provided wraps selection
|
||||
function wcInsertAtCursor(textarea, open, close = "") {
|
||||
const start = textarea.selectionStart;
|
||||
const end = textarea.selectionEnd;
|
||||
const selected = textarea.value.substring(start, end);
|
||||
let inserted, cursorStart, cursorEnd;
|
||||
|
||||
if (selected && close) {
|
||||
inserted = open + selected + close;
|
||||
cursorStart = start + open.length;
|
||||
cursorEnd = cursorStart + selected.length;
|
||||
} else if (close) {
|
||||
inserted = open + close;
|
||||
cursorStart = start + open.length;
|
||||
cursorEnd = cursorStart;
|
||||
} else {
|
||||
inserted = open;
|
||||
cursorStart = start + open.length;
|
||||
cursorEnd = cursorStart;
|
||||
}
|
||||
|
||||
textarea.value =
|
||||
textarea.value.substring(0, start) +
|
||||
inserted +
|
||||
textarea.value.substring(end);
|
||||
textarea.setSelectionRange(cursorStart, cursorEnd);
|
||||
textarea.focus();
|
||||
textarea.dispatchEvent(new Event("input"));
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Wildcard file list rendering
|
||||
// ============================================
|
||||
|
||||
function wcRenderFileList() {
|
||||
const list = document.getElementById("wfm-wc-file-list");
|
||||
if (!list) return;
|
||||
list.innerHTML = "";
|
||||
|
||||
if (wcFiles.length === 0) {
|
||||
list.innerHTML = `<div class="wfm-pm-empty" style="font-size:12px;">No wildcard files yet.<br><small>Click "+ New" to create one.</small></div>`;
|
||||
return;
|
||||
}
|
||||
|
||||
// Group by directory; root ("") first, then alphabetical
|
||||
const grouped = {};
|
||||
for (const f of wcFiles) {
|
||||
const dir = f.dir || "";
|
||||
if (!grouped[dir]) grouped[dir] = [];
|
||||
grouped[dir].push(f);
|
||||
}
|
||||
const dirs = Object.keys(grouped).sort((a, b) => {
|
||||
if (a === "") return -1;
|
||||
if (b === "") return 1;
|
||||
return a.localeCompare(b);
|
||||
});
|
||||
|
||||
function addFileItem(f, indented) {
|
||||
const item = document.createElement("div");
|
||||
item.className = "wfm-wc-file-item" + (indented ? " wfm-wc-file-item--sub" : "");
|
||||
item.innerHTML = `
|
||||
<div class="wfm-wc-file-item-info">
|
||||
<span class="wfm-wc-file-name">${esc(f.name)}</span>
|
||||
<span class="wfm-wc-file-ext">.${esc(f.ext)}</span>
|
||||
</div>
|
||||
<div class="wfm-wc-file-item-actions">
|
||||
<button class="wfm-pm-action-btn wfm-wc-use-btn" title="Insert __${esc(f.wc_name)}__ at cursor">Use</button>
|
||||
<button class="wfm-pm-action-btn wfm-wc-edit-btn" title="Edit file">✎</button>
|
||||
</div>
|
||||
`;
|
||||
item.querySelector(".wfm-wc-use-btn").addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (ta) wcInsertAtCursor(ta, `__${f.wc_name}__`);
|
||||
});
|
||||
item.querySelector(".wfm-wc-edit-btn").addEventListener("click", async (e) => {
|
||||
e.stopPropagation();
|
||||
const content = await wcFetchContent(f.filename);
|
||||
wcOpenEditor(f.wc_name, f.ext, content ?? "", f.filename);
|
||||
});
|
||||
item.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (ta) wcInsertAtCursor(ta, `__${f.wc_name}__`);
|
||||
});
|
||||
list.appendChild(item);
|
||||
}
|
||||
|
||||
for (const dir of dirs) {
|
||||
if (dir) {
|
||||
const header = document.createElement("div");
|
||||
header.className = "wfm-wc-dir-header";
|
||||
header.textContent = dir + "/";
|
||||
list.appendChild(header);
|
||||
}
|
||||
for (const f of grouped[dir]) {
|
||||
addFileItem(f, dir !== "");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Wildcard file editor
|
||||
// ============================================
|
||||
|
||||
function wcOpenEditor(name, ext, content, existingFilename) {
|
||||
wcEditingFilename = existingFilename || null;
|
||||
|
||||
const editor = document.getElementById("wfm-wc-editor");
|
||||
const nameInput = document.getElementById("wfm-wc-editor-name");
|
||||
const extSelect = document.getElementById("wfm-wc-editor-ext");
|
||||
const contentTA = document.getElementById("wfm-wc-editor-content");
|
||||
const deleteBtn = document.getElementById("wfm-wc-editor-delete-btn");
|
||||
|
||||
if (!editor) return;
|
||||
nameInput.value = name || "";
|
||||
extSelect.value = ext || "txt";
|
||||
contentTA.value = content || "";
|
||||
deleteBtn.style.display = existingFilename ? "" : "none";
|
||||
editor.style.display = "";
|
||||
nameInput.focus();
|
||||
}
|
||||
|
||||
function wcCloseEditor() {
|
||||
wcEditingFilename = null;
|
||||
const editor = document.getElementById("wfm-wc-editor");
|
||||
if (editor) editor.style.display = "none";
|
||||
}
|
||||
|
||||
export async function wcRefreshFiles() {
|
||||
wcFiles = await wcFetchFiles();
|
||||
wcRenderFileList();
|
||||
wcUpdateFilePicker();
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Wildcard file picker popup
|
||||
// ============================================
|
||||
|
||||
// Renders the wildcard-file-reference list into `picker`, inserting into `targetTa` on click.
|
||||
// Shared by the Form tab's fixed file-picker popup and any dynamically built toolbar
|
||||
// (see createWildcardToolbar) so both stay in sync with a single implementation.
|
||||
function renderFilePicker(picker, targetTa) {
|
||||
picker.innerHTML = "";
|
||||
if (wcFiles.length === 0) {
|
||||
picker.innerHTML = `<div style="padding:8px;font-size:11px;color:var(--wfm-text-secondary);">No wildcard files</div>`;
|
||||
return;
|
||||
}
|
||||
// Group by directory same as file list
|
||||
const grouped = {};
|
||||
for (const f of wcFiles) {
|
||||
const dir = f.dir || "";
|
||||
if (!grouped[dir]) grouped[dir] = [];
|
||||
grouped[dir].push(f);
|
||||
}
|
||||
const dirs = Object.keys(grouped).sort((a, b) => {
|
||||
if (a === "") return -1;
|
||||
if (b === "") return 1;
|
||||
return a.localeCompare(b);
|
||||
});
|
||||
for (const dir of dirs) {
|
||||
if (dir) {
|
||||
const sep = document.createElement("div");
|
||||
sep.className = "wfm-wc-picker-dir";
|
||||
sep.textContent = dir + "/";
|
||||
picker.appendChild(sep);
|
||||
}
|
||||
for (const f of grouped[dir]) {
|
||||
const btn = document.createElement("button");
|
||||
btn.className = "wfm-wc-picker-item" + (dir ? " wfm-wc-picker-item--sub" : "");
|
||||
btn.textContent = `__${f.wc_name}__`;
|
||||
btn.addEventListener("click", () => {
|
||||
wcInsertAtCursor(targetTa, `__${f.wc_name}__`);
|
||||
picker.style.display = "none";
|
||||
});
|
||||
picker.appendChild(btn);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function wcUpdateFilePicker() {
|
||||
const picker = document.getElementById("wfm-wc-file-picker");
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (!picker || !ta) return;
|
||||
renderFilePicker(picker, ta);
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Reusable wildcard syntax toolbar (buttons + file-reference picker)
|
||||
// ============================================
|
||||
|
||||
const WC_SYNTAX_BUTTONS = [
|
||||
{ open: "{", close: "}", label: "{ }", title: "Random pick set e.g. {cat|dog}" },
|
||||
{ open: "|", close: "", label: "|", title: "Separator" },
|
||||
{ open: "__", close: "__", label: "__", title: "Wildcard file reference e.g. __animals__" },
|
||||
{ open: ":", close: "", label: ":", title: "Colon" },
|
||||
{ open: ";", close: "", label: ";", title: "Semicolon" },
|
||||
{ open: "$$", close: "", label: "$$", title: "Multi-pick e.g. {2$$cat|dog|bird}" },
|
||||
{ open: "[", close: "]", label: "[ ]", title: "Bracket set e.g. [cat|dog]" },
|
||||
];
|
||||
|
||||
// Builds a standalone wildcard-syntax toolbar (syntax buttons, LoRA/n$${} helpers, and the
|
||||
// file-reference picker) wired to insert into the given textarea. The Form tab's Col3 panel
|
||||
// uses the static toolbar markup in index.html instead (wired in initWildcardsUI below); this
|
||||
// is for callers that need the same toolbar attached to a textarea built at runtime, such as
|
||||
// the Table tab's wildcard edit modal (see prompt-table.js).
|
||||
export function createWildcardToolbar(textareaEl) {
|
||||
const toolbar = document.createElement("div");
|
||||
toolbar.className = "wfm-wc-toolbar";
|
||||
|
||||
for (const b of WC_SYNTAX_BUTTONS) {
|
||||
const btn = document.createElement("button");
|
||||
btn.className = "wfm-wc-btn";
|
||||
btn.title = b.title;
|
||||
btn.textContent = b.label;
|
||||
btn.addEventListener("click", () => wcInsertAtCursor(textareaEl, b.open, b.close));
|
||||
toolbar.appendChild(btn);
|
||||
}
|
||||
|
||||
const loraBtn = document.createElement("button");
|
||||
loraBtn.className = "wfm-wc-btn";
|
||||
loraBtn.title = "LoRA with LBW e.g. <lora:name:1:LBW=...;...>";
|
||||
loraBtn.textContent = "<lora>";
|
||||
loraBtn.addEventListener("click", () => {
|
||||
wcInsertAtCursor(textareaEl, "<lora::1:LBW=;>");
|
||||
// Move cursor to after "lora:" for the name
|
||||
const pos = textareaEl.selectionStart - "<lora::1:LBW=;>".length + 6;
|
||||
textareaEl.setSelectionRange(pos, pos);
|
||||
textareaEl.focus();
|
||||
});
|
||||
toolbar.appendChild(loraBtn);
|
||||
|
||||
const nsetBtn = document.createElement("button");
|
||||
nsetBtn.className = "wfm-wc-btn";
|
||||
nsetBtn.title = "Multi-pick set e.g. {2$$cat|dog|bird}";
|
||||
nsetBtn.textContent = "n$${ }";
|
||||
nsetBtn.addEventListener("click", () => {
|
||||
const n = prompt("Number of items to pick (n):", "2");
|
||||
if (n === null) return;
|
||||
const num = parseInt(n, 10);
|
||||
if (isNaN(num) || num < 1) { showToast(t("enterPositiveInt"), "error"); return; }
|
||||
const template = "{" + num + "$$|}";
|
||||
const start = textareaEl.selectionStart;
|
||||
wcInsertAtCursor(textareaEl, template);
|
||||
// Move cursor between $$ and |
|
||||
const inner = start + ("{" + num + "$$").length;
|
||||
textareaEl.setSelectionRange(inner, inner);
|
||||
textareaEl.focus();
|
||||
});
|
||||
toolbar.appendChild(nsetBtn);
|
||||
|
||||
const pickerWrap = document.createElement("div");
|
||||
pickerWrap.className = "wfm-wc-file-picker-wrap";
|
||||
const pickBtn = document.createElement("button");
|
||||
pickBtn.className = "wfm-wc-btn";
|
||||
pickBtn.title = "Insert wildcard file reference";
|
||||
pickBtn.innerHTML = "📂 File";
|
||||
const picker = document.createElement("div");
|
||||
picker.className = "wfm-wc-file-picker";
|
||||
picker.style.display = "none";
|
||||
pickBtn.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
const isOpen = picker.style.display !== "none";
|
||||
picker.style.display = isOpen ? "none" : "";
|
||||
if (!isOpen) renderFilePicker(picker, textareaEl);
|
||||
});
|
||||
document.addEventListener("click", () => { picker.style.display = "none"; });
|
||||
pickerWrap.appendChild(pickBtn);
|
||||
pickerWrap.appendChild(picker);
|
||||
toolbar.appendChild(pickerWrap);
|
||||
|
||||
return toolbar;
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// UI wiring (called from prompt-tab.js's initPromptTab)
|
||||
// ============================================
|
||||
|
||||
export function initWildcardsUI() {
|
||||
// ── Wildcard syntax buttons ──────────────────────────────
|
||||
document.querySelectorAll(".wfm-wc-btn[data-wc-open]").forEach(btn => {
|
||||
btn.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (!ta) return;
|
||||
const open = btn.dataset.wcOpen || "";
|
||||
const close = btn.dataset.wcClose || "";
|
||||
wcInsertAtCursor(ta, open, close);
|
||||
});
|
||||
});
|
||||
|
||||
// LoRA LBW template button
|
||||
document.getElementById("wfm-wc-lora-btn")?.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (!ta) return;
|
||||
wcInsertAtCursor(ta, "<lora::1:LBW=;>");
|
||||
// Move cursor to after "lora:" for the name
|
||||
const pos = ta.selectionStart - "<lora::1:LBW=;>".length + 6;
|
||||
ta.setSelectionRange(pos, pos);
|
||||
ta.focus();
|
||||
});
|
||||
|
||||
// n$${ } button — asks for n then inserts multi-pick template
|
||||
document.getElementById("wfm-wc-nset-btn")?.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (!ta) return;
|
||||
const n = prompt("Number of items to pick (n):", "2");
|
||||
if (n === null) return;
|
||||
const num = parseInt(n, 10);
|
||||
if (isNaN(num) || num < 1) { showToast(t("enterPositiveInt"), "error"); return; }
|
||||
const template = "{" + num + "$$|}";
|
||||
const start = ta.selectionStart;
|
||||
wcInsertAtCursor(ta, template);
|
||||
// Move cursor between $$ and |
|
||||
const inner = start + ("{" + num + "$$").length;
|
||||
ta.setSelectionRange(inner, inner);
|
||||
ta.focus();
|
||||
});
|
||||
|
||||
// File picker toggle
|
||||
const filePickBtn = document.getElementById("wfm-wc-filepick-btn");
|
||||
const filePicker = document.getElementById("wfm-wc-file-picker");
|
||||
filePickBtn?.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
if (!filePicker) return;
|
||||
const isOpen = filePicker.style.display !== "none";
|
||||
filePicker.style.display = isOpen ? "none" : "";
|
||||
if (!isOpen) wcUpdateFilePicker();
|
||||
});
|
||||
document.addEventListener("click", () => {
|
||||
if (filePicker) filePicker.style.display = "none";
|
||||
});
|
||||
|
||||
// ── Wildcard prompt actions ──────────────────────────────
|
||||
document.getElementById("wfm-wc-copy-btn")?.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
const text = ta?.value || "";
|
||||
if (!text.trim()) { showToast(t("noTextToCopy"), "error"); return; }
|
||||
navigator.clipboard.writeText(text).then(() => showToast(t("copiedToClipboard"), "success"));
|
||||
});
|
||||
|
||||
document.getElementById("wfm-wc-to-pos-btn")?.addEventListener("click", () => {
|
||||
const src = document.getElementById("wfm-wc-prompt");
|
||||
const dst = document.getElementById("wfm-preset-pos");
|
||||
if (!src || !dst) return;
|
||||
dst.value = src.value;
|
||||
dst.dispatchEvent(new Event("input", { bubbles: true }));
|
||||
showToast(t("sentToPositive"), "success");
|
||||
});
|
||||
|
||||
document.getElementById("wfm-wc-to-neg-btn")?.addEventListener("click", () => {
|
||||
const src = document.getElementById("wfm-wc-prompt");
|
||||
const dst = document.getElementById("wfm-preset-neg");
|
||||
if (!src || !dst) return;
|
||||
dst.value = src.value;
|
||||
dst.dispatchEvent(new Event("input", { bubbles: true }));
|
||||
showToast(t("sentToNegative"), "success");
|
||||
});
|
||||
|
||||
document.getElementById("wfm-wc-clear-btn")?.addEventListener("click", () => {
|
||||
const ta = document.getElementById("wfm-wc-prompt");
|
||||
if (ta) { ta.value = ""; ta.focus(); }
|
||||
});
|
||||
|
||||
// ── Wildcard file manager ────────────────────────────────
|
||||
document.getElementById("wfm-wc-new-file-btn")?.addEventListener("click", () => {
|
||||
wcOpenEditor("", "txt", "", null);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-wc-files-refresh-btn")?.addEventListener("click", () => {
|
||||
wcRefreshFiles();
|
||||
});
|
||||
|
||||
// Editor: save
|
||||
document.getElementById("wfm-wc-editor-save-btn")?.addEventListener("click", async () => {
|
||||
const nameInput = document.getElementById("wfm-wc-editor-name");
|
||||
const extSelect = document.getElementById("wfm-wc-editor-ext");
|
||||
const contentTA = document.getElementById("wfm-wc-editor-content");
|
||||
|
||||
// Normalize separators and strip leading/trailing slashes
|
||||
const rawName = (nameInput?.value || "").trim().replace(/\\/g, "/").replace(/\/+/g, "/").replace(/^\/|\/$/g, "");
|
||||
const ext = extSelect?.value || "txt";
|
||||
const content = contentTA?.value || "";
|
||||
|
||||
if (!rawName) { showToast(t("pleaseEnterFilename"), "error"); return; }
|
||||
// Validate each path component (allow letters, numbers, -, _, ., space)
|
||||
const nameParts = rawName.split("/");
|
||||
if (nameParts.some(p => !p || !/^[\w\-. ]+$/.test(p))) {
|
||||
showToast(t("invalidPathFormat"), "error");
|
||||
return;
|
||||
}
|
||||
const name = rawName;
|
||||
const filename = `${name}.${ext}`;
|
||||
const saved = await wcSaveFile(filename, content);
|
||||
if (saved) {
|
||||
showToast(t("savedAs", filename), "success");
|
||||
wcCloseEditor();
|
||||
wcRefreshFiles();
|
||||
} else {
|
||||
showToast(t("saveFailed"), "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Editor: delete
|
||||
document.getElementById("wfm-wc-editor-delete-btn")?.addEventListener("click", async () => {
|
||||
if (!wcEditingFilename) return;
|
||||
if (!confirm(`Delete "${wcEditingFilename}"?`)) return;
|
||||
await wcDeleteFile(wcEditingFilename);
|
||||
showToast(t("deletedName", wcEditingFilename), "success");
|
||||
wcCloseEditor();
|
||||
wcRefreshFiles();
|
||||
});
|
||||
|
||||
// Editor: cancel
|
||||
document.getElementById("wfm-wc-editor-cancel-btn")?.addEventListener("click", () => {
|
||||
wcCloseEditor();
|
||||
});
|
||||
|
||||
// Initial wildcard file load
|
||||
wcRefreshFiles();
|
||||
}
|
||||
+112
-2
@@ -366,6 +366,21 @@ export function applyJsonColors(colors) {
|
||||
].join("\n");
|
||||
}
|
||||
|
||||
// GenerateUI Model tab: highlight color for section labels that match the loaded workflow
|
||||
const MODEL_ACTIVE_COLOR_STYLE_ID = "wfm-model-active-color-style";
|
||||
const MODEL_ACTIVE_COLOR_DEFAULT = "#4ade80";
|
||||
|
||||
export function applyModelTabActiveColor(color) {
|
||||
const val = color || MODEL_ACTIVE_COLOR_DEFAULT;
|
||||
let el = document.getElementById(MODEL_ACTIVE_COLOR_STYLE_ID);
|
||||
if (!el) {
|
||||
el = document.createElement("style");
|
||||
el.id = MODEL_ACTIVE_COLOR_STYLE_ID;
|
||||
document.head.appendChild(el);
|
||||
}
|
||||
el.textContent = `.wfm-form-group label.wfm-model-label-active,\n.wfm-settings-summary.wfm-model-label-active,\n#wfm-gen-lora-col-title.wfm-model-label-active,\n.wfm-lab-model-view-btn.wfm-model-label-active { color: ${val}; }`;
|
||||
}
|
||||
|
||||
export function applyTheme(themeId) {
|
||||
if (themeId) {
|
||||
document.documentElement.setAttribute("data-theme", themeId);
|
||||
@@ -577,6 +592,21 @@ export async function initSettingsTab() {
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- GenerateUI Model Tab Highlight Color -->
|
||||
<details class="wfm-settings-section" open>
|
||||
<summary class="wfm-settings-summary">${t("modelTabColorSection") || "GenerateUI Modelタブ ハイライト色"}</summary>
|
||||
<div class="wfm-form-group">
|
||||
<label>${t("modelTabColorLabel") || "ワークフローに含まれる項目の色"}</label>
|
||||
<div style="display:flex;align-items:center;gap:8px;">
|
||||
<input type="color" id="wfm-model-active-color" value="${settings.modelTabActiveColor || MODEL_ACTIVE_COLOR_DEFAULT}">
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-model-active-color-reset">${t("jsonColorReset") || "デフォルトに戻す"}</button>
|
||||
</div>
|
||||
<small style="color:var(--wfm-text-secondary);font-size:11px;display:block;margin-top:4px;">
|
||||
${t("modelTabColorHint") || "GenerateUIのModelタブで、読み込んだワークフローに実際に含まれる項目（Checkpoint / VAE など）のラベルをこの色で表示します"}
|
||||
</small>
|
||||
</div>
|
||||
</details>
|
||||
|
||||
<!-- Workflow Data Folder -->
|
||||
<details class="wfm-settings-section">
|
||||
<summary class="wfm-settings-summary">${t("workflowsDir")}</summary>
|
||||
@@ -794,6 +824,25 @@ export async function initSettingsTab() {
|
||||
<input type="file" id="wfm-settings-import-file" accept=".json" style="display:none;">
|
||||
</label>
|
||||
</div>
|
||||
<p style="font-size:12px;color:var(--wfm-text-secondary);margin:12px 0 8px 0;">${t("fullBackupHint")}</p>
|
||||
<div style="display:flex;flex-direction:column;gap:4px;margin-bottom:8px;">
|
||||
<label class="wfm-lab-checkbox-label" style="font-size:12px;">
|
||||
<input type="checkbox" id="wfm-settings-backup-include-workflows">
|
||||
${t("fullBackupIncludeWorkflows")}
|
||||
</label>
|
||||
<label class="wfm-lab-checkbox-label" style="font-size:12px;">
|
||||
<input type="checkbox" id="wfm-settings-backup-include-wildcard">
|
||||
${t("fullBackupIncludeWildcard")}
|
||||
</label>
|
||||
<span style="font-size:11px;color:var(--wfm-text-secondary);">${t("fullBackupExternalHint")}</span>
|
||||
</div>
|
||||
<div style="display:flex;gap:8px;flex-wrap:wrap;">
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-settings-export-full">${t("exportFullBackup")}</button>
|
||||
<label class="wfm-btn wfm-btn-sm" style="cursor:pointer;margin:0;">
|
||||
${t("importFullBackup")}
|
||||
<input type="file" id="wfm-settings-import-full-file" accept=".zip" style="display:none;">
|
||||
</label>
|
||||
</div>
|
||||
<div id="wfm-settings-data-status" style="font-size:12px;margin-top:8px;"></div>
|
||||
</details>
|
||||
|
||||
@@ -1333,6 +1382,22 @@ export async function initSettingsTab() {
|
||||
showToast(t("settingsSaved"), "success");
|
||||
});
|
||||
|
||||
// --- GenerateUI Model tab highlight color ---
|
||||
document.getElementById("wfm-model-active-color")?.addEventListener("input", (e) => {
|
||||
saveLocalSettings({ modelTabActiveColor: e.target.value });
|
||||
applyModelTabActiveColor(e.target.value);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-model-active-color-reset")?.addEventListener("click", () => {
|
||||
const cur = loadLocalSettings();
|
||||
delete cur.modelTabActiveColor;
|
||||
localStorage.setItem(SETTINGS_KEY, JSON.stringify(cur));
|
||||
applyModelTabActiveColor(undefined);
|
||||
const inp = document.getElementById("wfm-model-active-color");
|
||||
if (inp) inp.value = MODEL_ACTIVE_COLOR_DEFAULT;
|
||||
showToast(t("settingsSaved"), "success");
|
||||
});
|
||||
|
||||
// Save (local settings)
|
||||
document.getElementById("wfm-settings-save")?.addEventListener("click", () => {
|
||||
const taFontSize = parseInt(document.getElementById("wfm-ta-font-size")?.value) || TEXTAREA_FONT_SIZE_DEFAULT;
|
||||
@@ -1486,8 +1551,53 @@ export async function initSettingsTab() {
|
||||
statusEl.textContent = t("importError") + ": " + e.message;
|
||||
statusEl.style.color = "var(--wfm-error, #f44336)";
|
||||
}
|
||||
// Reset file input so same file can be re-selected
|
||||
e.target.value = "";
|
||||
});
|
||||
|
||||
// --- Full Backup Export (ZIP) ---
|
||||
document.getElementById("wfm-settings-export-full")?.addEventListener("click", async () => {
|
||||
const statusEl = document.getElementById("wfm-settings-data-status");
|
||||
try {
|
||||
const includeWorkflows = document.getElementById("wfm-settings-backup-include-workflows")?.checked ? "1" : "0";
|
||||
const includeWildcard = document.getElementById("wfm-settings-backup-include-wildcard")?.checked ? "1" : "0";
|
||||
const res = await fetch(`/api/wfm/settings/export-full?include_workflows=${includeWorkflows}&include_wildcard=${includeWildcard}`);
|
||||
if (!res.ok) throw new Error(await res.text());
|
||||
const blob = await res.blob();
|
||||
const url = URL.createObjectURL(blob);
|
||||
const a = document.createElement("a");
|
||||
a.href = url;
|
||||
a.download = "wfm-full-backup.zip";
|
||||
a.click();
|
||||
URL.revokeObjectURL(url);
|
||||
statusEl.textContent = t("exportSuccess");
|
||||
statusEl.style.color = "var(--wfm-success, #4caf50)";
|
||||
} catch (e) {
|
||||
statusEl.textContent = t("exportError") + ": " + e.message;
|
||||
statusEl.style.color = "var(--wfm-error, #f44336)";
|
||||
}
|
||||
});
|
||||
|
||||
// --- Full Backup Import (ZIP) ---
|
||||
document.getElementById("wfm-settings-import-full-file")?.addEventListener("change", async (e) => {
|
||||
const statusEl = document.getElementById("wfm-settings-data-status");
|
||||
const file = e.target.files?.[0];
|
||||
if (!file) return;
|
||||
try {
|
||||
const fd = new FormData();
|
||||
fd.append("file", file, file.name);
|
||||
const res = await fetch("/api/wfm/settings/import-full", {
|
||||
method: "POST",
|
||||
body: fd,
|
||||
});
|
||||
const result = await res.json();
|
||||
if (!res.ok) throw new Error(result.error || res.statusText);
|
||||
statusEl.textContent = `${t("importSuccess")}: ${result.extracted?.length ?? 0} ${t("fullBackupFilesUnit")}`;
|
||||
statusEl.style.color = "var(--wfm-success, #4caf50)";
|
||||
} catch (e) {
|
||||
statusEl.textContent = t("importError") + ": " + e.message;
|
||||
statusEl.style.color = "var(--wfm-error, #f44336)";
|
||||
} finally {
|
||||
e.target.value = "";
|
||||
}
|
||||
});
|
||||
|
||||
// Save G'MIC path setting
|
||||
|
||||
@@ -0,0 +1,373 @@
|
||||
/**
|
||||
* Style Catalog Gallery — Gallery タブのサブタブ。
|
||||
* ws_style_catalog フォルダ配下の画像（GenerateUIの「カタログ作成」で生成した、
|
||||
* ファイル名=スタイル名のプレビュー画像）を閲覧し、埋め込みPositive/Negative
|
||||
* プロンプトを表示・コピーする。「Select as Style」でGenerateUIの登録済み
|
||||
* Styleドロップダウンをファイル名と同名のStyleに切り替える。
|
||||
*/
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { escapeHtml, setupSearchClearBtn } from "./util.js";
|
||||
import { extractAllMetadata, loadFileIntoMetadataTab } from "./metadata-tab.js";
|
||||
import { selectStyleByName, loadWorkflowIntoEditor } from "./generate-tab.js";
|
||||
|
||||
const API = {
|
||||
root: () => `/wfm/gallery/style-catalog/root`,
|
||||
folders: (root) => `/wfm/gallery/folders?root=${encodeURIComponent(root)}`,
|
||||
images: (params) => `/wfm/gallery/images?${new URLSearchParams(params)}`,
|
||||
thumb: (path, w = 200) => `/wfm/gallery/image/thumb?path=${encodeURIComponent(path)}&w=${w}`,
|
||||
serveImage: (path) => `/wfm/gallery/image/serve?path=${encodeURIComponent(path)}`,
|
||||
workflow: (path) => `/wfm/gallery/image/workflow?path=${encodeURIComponent(path)}`,
|
||||
};
|
||||
|
||||
const state = {
|
||||
root: null,
|
||||
currentFolder: null,
|
||||
search: "",
|
||||
images: [],
|
||||
selectedImage: null,
|
||||
};
|
||||
|
||||
let _initialized = false;
|
||||
|
||||
async function apiFetch(url, opts = {}) {
|
||||
const res = await fetch(url, opts);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return res.json();
|
||||
}
|
||||
|
||||
function debounce(fn, wait) {
|
||||
let timer;
|
||||
return (...args) => {
|
||||
clearTimeout(timer);
|
||||
timer = setTimeout(() => fn(...args), wait);
|
||||
};
|
||||
}
|
||||
|
||||
// ── 初期化 ────────────────────────────────────────────────────
|
||||
|
||||
export function initStyleCatalogTab() {
|
||||
document.getElementById("wfm-stylecatalog-refresh-btn")?.addEventListener("click", () => {
|
||||
loadFolderTree();
|
||||
loadImages();
|
||||
});
|
||||
|
||||
setupSearchClearBtn("wfm-stylecatalog-search", "wfm-stylecatalog-search-clear-btn", () => {
|
||||
state.search = "";
|
||||
loadImages();
|
||||
});
|
||||
|
||||
const searchInput = document.getElementById("wfm-stylecatalog-search");
|
||||
searchInput?.addEventListener("input", debounce(() => {
|
||||
state.search = searchInput.value.trim();
|
||||
loadImages();
|
||||
}, 300));
|
||||
|
||||
document.getElementById("wfm-stylecatalog-copy-positive-btn")?.addEventListener("click", () => copyText("wfm-stylecatalog-positive-text"));
|
||||
document.getElementById("wfm-stylecatalog-copy-negative-btn")?.addEventListener("click", () => copyText("wfm-stylecatalog-negative-text"));
|
||||
document.getElementById("wfm-stylecatalog-select-style-btn")?.addEventListener("click", onSelectStyleClick);
|
||||
document.getElementById("wfm-stylecatalog-load-genui-btn")?.addEventListener("click", onLoadGenUIClick);
|
||||
document.getElementById("wfm-stylecatalog-open-metadata-btn")?.addEventListener("click", onOpenMetadataClick);
|
||||
}
|
||||
|
||||
export async function activateStyleCatalogTab() {
|
||||
if (_initialized) return;
|
||||
_initialized = true;
|
||||
try {
|
||||
const data = await apiFetch(API.root());
|
||||
state.root = data.root;
|
||||
await loadFolderTree();
|
||||
} catch (e) {
|
||||
const tree = document.getElementById("wfm-stylecatalog-tree");
|
||||
if (tree) tree.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
// ── フォルダツリー ────────────────────────────────────────────
|
||||
|
||||
async function loadFolderTree() {
|
||||
if (!state.root) return;
|
||||
const tree = document.getElementById("wfm-stylecatalog-tree");
|
||||
tree.innerHTML = `<p class="wfm-placeholder">${t("loading")}</p>`;
|
||||
try {
|
||||
const data = await apiFetch(API.folders(state.root));
|
||||
if (data.error) {
|
||||
tree.innerHTML = `<p class="wfm-placeholder">${escapeHtml(data.error)}</p>`;
|
||||
return;
|
||||
}
|
||||
tree.innerHTML = "";
|
||||
renderTreeNode(data, tree, 0, true);
|
||||
const firstLabel = tree.querySelector(".wfm-gallery-tree-label");
|
||||
if (firstLabel) firstLabel.click();
|
||||
} catch (e) {
|
||||
tree.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
function renderTreeNode(node, container, depth, isRoot) {
|
||||
const item = document.createElement("div");
|
||||
item.className = "wfm-gallery-tree-item";
|
||||
item.style.paddingLeft = `${depth * 12}px`;
|
||||
item.dataset.path = node.abs_path;
|
||||
|
||||
const hasChildren = node.children && node.children.length > 0;
|
||||
const absPath = node.abs_path;
|
||||
|
||||
const arrow = document.createElement("span");
|
||||
arrow.className = "wfm-gallery-tree-arrow";
|
||||
if (hasChildren) {
|
||||
arrow.textContent = "▶";
|
||||
arrow.dataset.expanded = "false";
|
||||
} else {
|
||||
arrow.textContent = " ";
|
||||
arrow.style.visibility = "hidden";
|
||||
}
|
||||
item.appendChild(arrow);
|
||||
|
||||
const label = document.createElement("span");
|
||||
label.className = "wfm-gallery-tree-label";
|
||||
label.textContent = isRoot ? "[root]" : node.name;
|
||||
const totalCount = node.image_count_total ?? node.image_count;
|
||||
if (totalCount > 0) {
|
||||
const badge = document.createElement("span");
|
||||
badge.className = "wfm-gallery-tree-count";
|
||||
badge.textContent = totalCount;
|
||||
label.appendChild(badge);
|
||||
}
|
||||
item.appendChild(label);
|
||||
|
||||
label.addEventListener("click", () => {
|
||||
document.querySelectorAll("#wfm-stylecatalog-tree .wfm-gallery-tree-item.selected").forEach(el => el.classList.remove("selected"));
|
||||
item.classList.add("selected");
|
||||
state.currentFolder = absPath;
|
||||
loadImages();
|
||||
});
|
||||
|
||||
if (hasChildren) {
|
||||
const childContainer = document.createElement("div");
|
||||
childContainer.className = "wfm-gallery-tree-children";
|
||||
childContainer.style.display = "none";
|
||||
|
||||
arrow.addEventListener("click", (e) => {
|
||||
e.stopPropagation();
|
||||
const expanded = arrow.dataset.expanded === "true";
|
||||
if (!expanded) {
|
||||
arrow.dataset.expanded = "true";
|
||||
arrow.textContent = "▼";
|
||||
childContainer.style.display = "";
|
||||
if (childContainer.children.length === 0) {
|
||||
node.children.forEach(child => renderTreeNode(child, childContainer, depth + 1, false));
|
||||
}
|
||||
} else {
|
||||
arrow.dataset.expanded = "false";
|
||||
arrow.textContent = "▶";
|
||||
childContainer.style.display = "none";
|
||||
}
|
||||
});
|
||||
|
||||
container.appendChild(item);
|
||||
container.appendChild(childContainer);
|
||||
} else {
|
||||
container.appendChild(item);
|
||||
}
|
||||
}
|
||||
|
||||
// ── 画像一覧 ─────────────────────────────────────────────────
|
||||
|
||||
async function loadImages() {
|
||||
if (!state.currentFolder) return;
|
||||
const grid = document.getElementById("wfm-stylecatalog-grid");
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("loading")}</p>`;
|
||||
|
||||
// 上位フォルダを選択した場合もサブフォルダの画像をまとめて表示する
|
||||
const params = { folder: state.currentFolder, sort: "name_asc", recursive: "true" };
|
||||
if (state.search) params.search = state.search;
|
||||
|
||||
try {
|
||||
const data = await apiFetch(API.images(params));
|
||||
state.images = data.images || [];
|
||||
document.getElementById("wfm-stylecatalog-count").textContent = `${state.images.length} images`;
|
||||
renderImages();
|
||||
} catch (e) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
|
||||
}
|
||||
}
|
||||
|
||||
function renderImages() {
|
||||
const grid = document.getElementById("wfm-stylecatalog-grid");
|
||||
if (state.images.length === 0) {
|
||||
grid.innerHTML = `<p class="wfm-placeholder">${t("galleryNoImages")}</p>`;
|
||||
return;
|
||||
}
|
||||
grid.innerHTML = "";
|
||||
const fragment = document.createDocumentFragment();
|
||||
state.images.forEach(img => fragment.appendChild(createThumbCard(img)));
|
||||
grid.appendChild(fragment);
|
||||
}
|
||||
|
||||
function createThumbCard(img) {
|
||||
const card = document.createElement("div");
|
||||
card.className = "wfm-gallery-thumb-card";
|
||||
card.title = img.filename;
|
||||
card.dataset.path = img.path;
|
||||
if (state.selectedImage && state.selectedImage.path === img.path) {
|
||||
card.classList.add("selected");
|
||||
}
|
||||
|
||||
const imgEl = document.createElement("img");
|
||||
imgEl.className = "wfm-gallery-thumb-img";
|
||||
imgEl.loading = "lazy";
|
||||
imgEl.src = API.thumb(img.path);
|
||||
imgEl.alt = img.filename;
|
||||
imgEl.onerror = () => {
|
||||
imgEl.style.display = "none";
|
||||
const placeholder = document.createElement("div");
|
||||
placeholder.className = "wfm-gallery-thumb-placeholder";
|
||||
placeholder.textContent = "?";
|
||||
card.insertBefore(placeholder, imgEl.nextSibling);
|
||||
};
|
||||
card.appendChild(imgEl);
|
||||
|
||||
card.addEventListener("click", () => selectImage(img));
|
||||
card.addEventListener("dblclick", () => openLightbox(img));
|
||||
|
||||
return card;
|
||||
}
|
||||
|
||||
// ── 選択画像 / Positive・Negative表示 ─────────────────────────
|
||||
|
||||
function styleNameFromFilename(filename) {
|
||||
return filename.replace(/\.[^.]+$/, "");
|
||||
}
|
||||
|
||||
async function selectImage(img) {
|
||||
state.selectedImage = img;
|
||||
document.querySelectorAll("#wfm-stylecatalog-grid .wfm-gallery-thumb-card").forEach(el => {
|
||||
el.classList.toggle("selected", el.dataset.path === img.path);
|
||||
});
|
||||
|
||||
document.getElementById("wfm-stylecatalog-selected-name").textContent = img.filename;
|
||||
const preview = document.getElementById("wfm-stylecatalog-selected-preview");
|
||||
preview.innerHTML = "";
|
||||
const previewImg = document.createElement("img");
|
||||
previewImg.src = API.serveImage(img.path);
|
||||
previewImg.alt = img.filename;
|
||||
previewImg.title = "Double-click to enlarge";
|
||||
previewImg.style.cssText = "max-width:100%;max-height:100%;object-fit:contain;cursor:zoom-in;";
|
||||
previewImg.addEventListener("dblclick", () => openLightbox(img));
|
||||
preview.appendChild(previewImg);
|
||||
|
||||
const positiveText = document.getElementById("wfm-stylecatalog-positive-text");
|
||||
const negativeText = document.getElementById("wfm-stylecatalog-negative-text");
|
||||
const copyPositiveBtn = document.getElementById("wfm-stylecatalog-copy-positive-btn");
|
||||
const copyNegativeBtn = document.getElementById("wfm-stylecatalog-copy-negative-btn");
|
||||
const selectStyleBtn = document.getElementById("wfm-stylecatalog-select-style-btn");
|
||||
const loadGenuiBtn = document.getElementById("wfm-stylecatalog-load-genui-btn");
|
||||
const openMetadataBtn = document.getElementById("wfm-stylecatalog-open-metadata-btn");
|
||||
|
||||
positiveText.textContent = "";
|
||||
negativeText.textContent = "";
|
||||
copyPositiveBtn.disabled = true;
|
||||
copyNegativeBtn.disabled = true;
|
||||
selectStyleBtn.disabled = false; // ファイル名からのStyle選択はメタデータ抽出に依存しない
|
||||
loadGenuiBtn.disabled = false;
|
||||
openMetadataBtn.disabled = false;
|
||||
|
||||
try {
|
||||
const res = await fetch(API.serveImage(img.path));
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const blob = await res.blob();
|
||||
const file = new File([blob], img.filename, { type: blob.type || "image/png" });
|
||||
const meta = await extractAllMetadata(file);
|
||||
|
||||
const positive = meta?.positives?.[0] || "";
|
||||
const negative = meta?.negatives?.[0] || "";
|
||||
|
||||
if (!positive && !negative) {
|
||||
positiveText.textContent = t("stylecatalogNoPromptFound");
|
||||
} else {
|
||||
positiveText.textContent = positive;
|
||||
negativeText.textContent = negative;
|
||||
copyPositiveBtn.disabled = !positive;
|
||||
copyNegativeBtn.disabled = !negative;
|
||||
}
|
||||
} catch (e) {
|
||||
positiveText.textContent = t("stylecatalogNoPromptFound");
|
||||
}
|
||||
}
|
||||
|
||||
// gallery-tab.jsのライトボックスと同じCSSクラスを使う（ESCで閉じる処理もgallery-tab.js側の
|
||||
// グローバルkeydownリスナーが共通で担う）。
|
||||
function openLightbox(img) {
|
||||
const overlay = document.createElement("div");
|
||||
overlay.className = "wfm-gallery-lightbox";
|
||||
overlay.innerHTML = `
|
||||
<div class="wfm-gallery-lightbox-inner">
|
||||
<img src="${API.serveImage(img.path)}" class="wfm-gallery-lightbox-img" alt="${escapeHtml(img.filename)}">
|
||||
<div class="wfm-gallery-lightbox-footer">${escapeHtml(img.filename)}</div>
|
||||
<button class="wfm-gallery-lightbox-close">×</button>
|
||||
</div>
|
||||
`;
|
||||
overlay.addEventListener("click", (e) => {
|
||||
if (e.target === overlay || e.target.classList.contains("wfm-gallery-lightbox-close")) {
|
||||
overlay.remove();
|
||||
}
|
||||
});
|
||||
document.body.appendChild(overlay);
|
||||
}
|
||||
|
||||
function copyText(elementId) {
|
||||
const el = document.getElementById(elementId);
|
||||
const text = el?.textContent || "";
|
||||
if (!text.trim()) return;
|
||||
navigator.clipboard.writeText(text)
|
||||
.then(() => showToast(t("imagePromptCopiedToast"), "success"))
|
||||
.catch((e) => showToast(t("errorWithMsg", e.message), "error"));
|
||||
}
|
||||
|
||||
function onSelectStyleClick() {
|
||||
const img = state.selectedImage;
|
||||
if (!img) return;
|
||||
const name = styleNameFromFilename(img.filename);
|
||||
const ok = selectStyleByName(name);
|
||||
if (ok) {
|
||||
showToast(t("stylecatalogStyleSelected", name), "success");
|
||||
} else {
|
||||
showToast(t("stylecatalogNoMatch"), "error");
|
||||
}
|
||||
}
|
||||
|
||||
// カタログ画像は生成元の出力画像と同一バイト列で保存されているため、埋め込みワークフローも
|
||||
// そのまま残っている。Outputフォルダ内の元画像を探す必要なく、この画像から直接読み込める。
|
||||
async function onLoadGenUIClick() {
|
||||
const img = state.selectedImage;
|
||||
if (!img) return;
|
||||
try {
|
||||
const data = await apiFetch(API.workflow(img.path));
|
||||
if (!data.has_workflow) {
|
||||
showToast(t("galleryNoEmbeddedWorkflow"), "warning");
|
||||
return;
|
||||
}
|
||||
const loaded = await loadWorkflowIntoEditor(data.workflow, img.filename);
|
||||
if (loaded !== false) {
|
||||
document.querySelector('.wfm-tab[data-tab="generate"]')?.click();
|
||||
}
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
}
|
||||
}
|
||||
|
||||
async function onOpenMetadataClick() {
|
||||
const img = state.selectedImage;
|
||||
if (!img) return;
|
||||
try {
|
||||
const res = await fetch(API.serveImage(img.path));
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
const blob = await res.blob();
|
||||
const file = new File([blob], img.filename, { type: blob.type || "image/png" });
|
||||
await loadFileIntoMetadataTab(file);
|
||||
} catch (e) {
|
||||
showToast(t("errorWithMsg", e.message), "error");
|
||||
}
|
||||
}
|
||||
@@ -2,6 +2,76 @@
|
||||
* Shared utilities
|
||||
*/
|
||||
|
||||
// ============================================
|
||||
// PNG metadata embedding (iTXt chunk)
|
||||
// ============================================
|
||||
// Embeds an arbitrary key/UTF-8-text pair into a canvas-generated PNG data URL as
|
||||
// an iTXt chunk, inserted right after IHDR — same insertion point/CRC approach as
|
||||
// ComfyUI's own workflow-embedding (web/comfyui/top_menu_extension.js's
|
||||
// embedWorkflowInPng), but iTXt instead of tEXt so non-ASCII text (Japanese notes,
|
||||
// prompts, etc.) round-trips correctly through this codebase's PNG chunk reader
|
||||
// (metadata-tab.js's readAllPNGTextChunks already decodes iTXt text as UTF-8;
|
||||
// tEXt text there is decoded as Latin-1, which would mangle non-ASCII payloads).
|
||||
|
||||
function _n2b(n) {
|
||||
return new Uint8Array([(n >> 24) & 0xff, (n >> 16) & 0xff, (n >> 8) & 0xff, n & 0xff]);
|
||||
}
|
||||
|
||||
function _joinBytes(...arrs) {
|
||||
const result = new Uint8Array(arrs.reduce((total, a) => total + a.byteLength, 0));
|
||||
arrs.reduce((offset, a) => { result.set(a, offset); return offset + a.byteLength; }, 0);
|
||||
return result;
|
||||
}
|
||||
|
||||
let _pngCrcTable = null;
|
||||
function _pngCrc32(data) {
|
||||
if (!_pngCrcTable) {
|
||||
_pngCrcTable = [];
|
||||
for (let n = 0; n < 256; n++) {
|
||||
let c = n;
|
||||
for (let k = 0; k < 8; k++) c = c & 1 ? 0xedb88320 ^ (c >>> 1) : c >>> 1;
|
||||
_pngCrcTable[n] = c;
|
||||
}
|
||||
}
|
||||
let crc = 0 ^ -1;
|
||||
for (let i = 0; i < data.byteLength; i++) crc = (crc >>> 8) ^ _pngCrcTable[(crc ^ data[i]) & 0xff];
|
||||
return (crc ^ -1) >>> 0;
|
||||
}
|
||||
|
||||
function _dataUrlToBytes(dataUrl) {
|
||||
const binary = atob(dataUrl.slice(dataUrl.indexOf(",") + 1));
|
||||
const bytes = new Uint8Array(binary.length);
|
||||
for (let i = 0; i < binary.length; i++) bytes[i] = binary.charCodeAt(i);
|
||||
return bytes;
|
||||
}
|
||||
|
||||
function _bytesToDataUrl(bytes, mime) {
|
||||
let binary = "";
|
||||
const chunkSize = 0x8000;
|
||||
for (let i = 0; i < bytes.length; i += chunkSize) {
|
||||
binary += String.fromCharCode.apply(null, bytes.subarray(i, i + chunkSize));
|
||||
}
|
||||
return `data:${mime};base64,${btoa(binary)}`;
|
||||
}
|
||||
|
||||
export function embedPngTextChunk(dataUrl, keyword, text) {
|
||||
const bytes = _dataUrlToBytes(dataUrl);
|
||||
const view = new DataView(bytes.buffer, bytes.byteOffset, bytes.byteLength);
|
||||
|
||||
const data = _joinBytes(
|
||||
new TextEncoder().encode(`iTXt${keyword}`),
|
||||
new Uint8Array([0, 0, 0, 0, 0]), // keyword-NUL, compression flag(0), compression method(0), empty lang tag+NUL, empty translated keyword+NUL
|
||||
new TextEncoder().encode(text),
|
||||
);
|
||||
const chunk = _joinBytes(_n2b(data.byteLength - 4), data, _n2b(_pngCrc32(data)));
|
||||
|
||||
// Insert after IHDR (8-byte signature + 4-byte length + 4-byte type + IHDR data + 4-byte CRC)
|
||||
const ihdrDataLen = view.getUint32(8);
|
||||
const insertAt = ihdrDataLen + 20;
|
||||
const result = _joinBytes(bytes.subarray(0, insertAt), chunk, bytes.subarray(insertAt));
|
||||
return _bytesToDataUrl(result, "image/png");
|
||||
}
|
||||
|
||||
// HTMLエスケープ（属性値・テキストコンテンツ両方で安全に使える）
|
||||
export function escapeHtml(s) {
|
||||
return String(s)
|
||||
@@ -32,12 +102,56 @@ export const AI_BACKEND_DEFAULT_URLS = {
|
||||
ollama: "http://localhost:11434",
|
||||
lmstudio: "http://localhost:1234",
|
||||
lemonade: "http://localhost:13305",
|
||||
unsloth: "http://localhost:8888",
|
||||
};
|
||||
|
||||
export function getAiBackendDefaultUrl(backend) {
|
||||
return AI_BACKEND_DEFAULT_URLS[backend] || AI_BACKEND_DEFAULT_URLS.ollama;
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Eagle Auto-Save
|
||||
// ============================================
|
||||
|
||||
export function getEagleSettings() {
|
||||
const s = getSettings();
|
||||
return {
|
||||
url: s.eagleUrl || "http://localhost:41595",
|
||||
autoSave: !!s.eagleAutoSave,
|
||||
};
|
||||
}
|
||||
|
||||
export async function saveToEagle(imageUrl, name, tags = [], fileInfo = null) {
|
||||
const eagle = getEagleSettings();
|
||||
if (!eagle.autoSave) return;
|
||||
try {
|
||||
const res = await fetch("/api/wfm/eagle/add", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
eagleUrl: eagle.url,
|
||||
url: imageUrl,
|
||||
name,
|
||||
tags: ["wfm-comfyui", ...tags],
|
||||
// SVG はサーバー側でローカルパス解決(addFromPath)するために必要
|
||||
filename: fileInfo?.filename || "",
|
||||
subfolder: fileInfo?.subfolder || "",
|
||||
type: fileInfo?.type || "output",
|
||||
// comfyui-tosvg の Save SVG String など、絶対パスが既知の場合に渡す
|
||||
localPath: fileInfo?.localPath || "",
|
||||
}),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.status === "success") {
|
||||
console.log("[Eagle] Saved:", name);
|
||||
} else {
|
||||
console.warn("[Eagle] Save failed:", data.message);
|
||||
}
|
||||
} catch (err) {
|
||||
console.warn("[Eagle] Save error:", err.message);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 検索inputにオーバーレイXボタンを設定する。
|
||||
* @param {string} inputId - 検索inputのID
|
||||
|
||||
@@ -0,0 +1,725 @@
|
||||
/**
|
||||
* Video Tab - MiniMax H3 (Image to Video) 専用の動画生成UI
|
||||
* GenerateUIタブとは完全に独立。UI形式ワークフローJSONを直接編集し、
|
||||
* comfyWorkflow.convertUiToApi() でAPI形式に変換して実行する。
|
||||
* 状態は「テンプレートJSON＋フォーム値」のみ保持し、生成の都度
|
||||
* structuredClone() したコピーに書き込む（複数回生成をまたいだID不整合を防ぐ）。
|
||||
*/
|
||||
|
||||
import { showToast } from "./app.js";
|
||||
import { t } from "./i18n.js";
|
||||
import { comfyUI } from "./comfyui-client.js";
|
||||
import { comfyWorkflow } from "./comfyui-workflow.js";
|
||||
import { escapeHtml } from "./util.js";
|
||||
|
||||
const DEFAULT_WORKFLOW_FILENAME = "minimax_test.json";
|
||||
|
||||
const state = {
|
||||
templateWorkflow: null,
|
||||
templateFilename: null,
|
||||
firstFrameFilename: null,
|
||||
lastFrameFilename: null,
|
||||
generating: false,
|
||||
// Whatever's currently shown in the center preview frame — a just-generated
|
||||
// result, or a video dropped/picked in the Video Source panel. Used by the
|
||||
// Frame/GIF property tabs so they work on any video, not just generated ones.
|
||||
// { kind: "output"|"input", filename, subfolder, type } for anything already on
|
||||
// the server, or { kind: "local", file } for a picked file not yet uploaded.
|
||||
previewSource: null,
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// ワークフロー取得
|
||||
// ============================================
|
||||
|
||||
async function getRawWorkflow(filename) {
|
||||
const res = await fetch(`/api/wfm/workflows/raw?filename=${encodeURIComponent(filename)}`);
|
||||
if (!res.ok) throw new Error(`HTTP ${res.status}`);
|
||||
return await res.json();
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// MiniMax H3ノードの動的特定（ノードIDをハードコードしない）
|
||||
// ============================================
|
||||
|
||||
// ノードのinputs[]のうちwidgetキーを持つものだけを抽出した並び順で、名前からインデックスを引く。
|
||||
// widgets_values配列はこの並び順と1:1対応する（link接続されていてもwidgetキーを持つ入力は
|
||||
// widgets_valuesにスロットを持つ「legacy full」形式のため）。
|
||||
function _widgetIndex(node, name) {
|
||||
const widgetInputs = (node?.inputs || []).filter((inp) => inp.widget);
|
||||
return widgetInputs.findIndex((inp) => inp.name === name);
|
||||
}
|
||||
|
||||
function locateMiniMaxNodes(workflow) {
|
||||
if (!Array.isArray(workflow?.nodes) || !Array.isArray(workflow?.links)) return null;
|
||||
|
||||
const sgDef = (workflow.definitions?.subgraphs || [])
|
||||
.find((sg) => (sg.nodes || []).some((n) => n.type === "MiniMaxH3ImageToVideo"));
|
||||
if (!sgDef) return null;
|
||||
|
||||
const instanceNode = workflow.nodes.find((n) => n.type === sgDef.id);
|
||||
if (!instanceNode || !Array.isArray(instanceNode.widgets_values)) return null;
|
||||
|
||||
const firstFrameSlot = instanceNode.inputs.findIndex((inp) => inp.name === "first_frame");
|
||||
const lastFrameSlot = instanceNode.inputs.findIndex((inp) => inp.name === "last_frame");
|
||||
const widthSlot = instanceNode.inputs.findIndex((inp) => inp.name === "width");
|
||||
if (firstFrameSlot === -1 || lastFrameSlot === -1 || widthSlot === -1) return null;
|
||||
|
||||
const findLinkedNode = (targetSlot) => {
|
||||
const link = workflow.links.find((l) => l[3] === instanceNode.id && l[4] === targetSlot);
|
||||
if (!link) return null;
|
||||
return workflow.nodes.find((n) => n.id === link[1]) || null;
|
||||
};
|
||||
|
||||
// first_frameはoptional入力のため、LoadImageノードが接続されていないワークフロー
|
||||
// (テキストのみで動作するT2V構成など)もサポート対象とする。未接続の場合は
|
||||
// loadImageNode: null のまま返し、必要ならrunGeneration側で動的にノードを注入する。
|
||||
const rawLoadImageNode = findLinkedNode(firstFrameSlot);
|
||||
const loadImageNode = (rawLoadImageNode && rawLoadImageNode.type === "LoadImage") ? rawLoadImageNode : null;
|
||||
const resolutionNode = findLinkedNode(widthSlot);
|
||||
if (!resolutionNode || resolutionNode.type !== "ResolutionSelector") return null;
|
||||
|
||||
const promptIdx = _widgetIndex(instanceNode, "prompt");
|
||||
const durationIdx = _widgetIndex(instanceNode, "value_1");
|
||||
if (promptIdx === -1 || durationIdx === -1) return null;
|
||||
|
||||
return {
|
||||
sgDef, instanceNode, loadImageNode, resolutionNode,
|
||||
firstFrameSlot, lastFrameSlot, promptIdx, durationIdx,
|
||||
};
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// ワークフロー読み込み
|
||||
// ============================================
|
||||
|
||||
function _showFramePreview(which, filename) {
|
||||
const previewImg = document.getElementById(`wfm-video-${which}-frame-preview`);
|
||||
const wrap = document.getElementById(`wfm-video-${which}-frame-wrap`);
|
||||
if (!previewImg || !wrap || !filename) return;
|
||||
previewImg.src = `${comfyUI.baseUrl}/view?filename=${encodeURIComponent(filename)}&type=input`;
|
||||
wrap.style.display = "";
|
||||
}
|
||||
|
||||
export async function loadWorkflowIntoVideoEditor(workflow, filename) {
|
||||
const nodes = locateMiniMaxNodes(workflow);
|
||||
if (!nodes) {
|
||||
showToast(t("videoUnsupportedWorkflow"), "error");
|
||||
return false;
|
||||
}
|
||||
|
||||
state.templateWorkflow = workflow;
|
||||
state.templateFilename = filename || "";
|
||||
state.firstFrameFilename = null;
|
||||
state.lastFrameFilename = null;
|
||||
|
||||
const nameEl = document.getElementById("wfm-video-wf-name");
|
||||
if (nameEl) nameEl.textContent = filename || "Loaded Workflow";
|
||||
|
||||
const { instanceNode, resolutionNode, loadImageNode, promptIdx, durationIdx } = nodes;
|
||||
|
||||
const promptEl = document.getElementById("wfm-video-prompt");
|
||||
if (promptEl) promptEl.value = instanceNode.widgets_values[promptIdx] ?? "";
|
||||
|
||||
const durationEl = document.getElementById("wfm-video-duration");
|
||||
if (durationEl) durationEl.value = instanceNode.widgets_values[durationIdx] ?? 2;
|
||||
|
||||
const arIdx = _widgetIndex(resolutionNode, "aspect_ratio");
|
||||
const mpIdx = _widgetIndex(resolutionNode, "megapixels");
|
||||
const multIdx = _widgetIndex(resolutionNode, "multiple");
|
||||
const aspectEl = document.getElementById("wfm-video-aspect-ratio");
|
||||
if (aspectEl && arIdx !== -1 && resolutionNode.widgets_values[arIdx] != null) {
|
||||
aspectEl.value = resolutionNode.widgets_values[arIdx];
|
||||
}
|
||||
const mpEl = document.getElementById("wfm-video-megapixels");
|
||||
if (mpEl && mpIdx !== -1) mpEl.value = resolutionNode.widgets_values[mpIdx] ?? mpEl.value;
|
||||
const multEl = document.getElementById("wfm-video-multiple");
|
||||
if (multEl && multIdx !== -1) multEl.value = resolutionNode.widgets_values[multIdx] ?? multEl.value;
|
||||
|
||||
const fnIdx = _widgetIndex(loadImageNode, "image");
|
||||
if (fnIdx !== -1 && loadImageNode.widgets_values[fnIdx]) {
|
||||
state.firstFrameFilename = loadImageNode.widgets_values[fnIdx];
|
||||
_showFramePreview("first", state.firstFrameFilename);
|
||||
}
|
||||
|
||||
showToast(t("videoWorkflowLoaded", filename || ""), "success");
|
||||
return true;
|
||||
}
|
||||
|
||||
async function loadDefaultWorkflow() {
|
||||
try {
|
||||
const wf = await getRawWorkflow(DEFAULT_WORKFLOW_FILENAME);
|
||||
await loadWorkflowIntoVideoEditor(wf, DEFAULT_WORKFLOW_FILENAME);
|
||||
} catch {
|
||||
// 初回自動ロード失敗は静かに無視する（環境によってファイルが存在しないこともある）。
|
||||
// Workflowタブの「Load in Video」ボタンから明示的に読み込める。
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Aspect Ratio 選択肢（object_infoから動的取得、ハードコードしない）
|
||||
// ============================================
|
||||
|
||||
async function _populateAspectRatioOptions() {
|
||||
const select = document.getElementById("wfm-video-aspect-ratio");
|
||||
if (!select) return;
|
||||
try {
|
||||
const info = await comfyUI.fetchObjectInfo("ResolutionSelector");
|
||||
const inputDef = info?.ResolutionSelector?.input?.required?.aspect_ratio;
|
||||
if (!inputDef) return;
|
||||
const first = inputDef[0];
|
||||
let values = null;
|
||||
if (Array.isArray(first)) values = first;
|
||||
else if (typeof first === "string" && Array.isArray(inputDef[1]?.values)) values = inputDef[1].values;
|
||||
else if (typeof first === "string" && Array.isArray(inputDef[1]?.options)) values = inputDef[1].options;
|
||||
if (!values?.length) return;
|
||||
select.innerHTML = values.map((v) => `<option value="${escapeHtml(v)}">${escapeHtml(v)}</option>`).join("");
|
||||
} catch {
|
||||
// ResolutionSelectorが未導入の環境などは選択肢が空のまま(手入力不可のselectなので実質何もできないが、
|
||||
// Videoタブ自体の初期化は継続させる)。
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// first_frame / last_frame アップロード
|
||||
// ============================================
|
||||
|
||||
function _wireDropZone(dropZoneId, fileInputId, onFile) {
|
||||
const dropZone = document.getElementById(dropZoneId);
|
||||
const fileInput = document.getElementById(fileInputId);
|
||||
if (!dropZone || !fileInput) return;
|
||||
fileInput.addEventListener("change", () => {
|
||||
if (fileInput.files.length > 0) onFile(fileInput.files[0]);
|
||||
});
|
||||
dropZone.addEventListener("dragover", (e) => { e.preventDefault(); dropZone.classList.add("drag-over"); });
|
||||
dropZone.addEventListener("dragleave", () => dropZone.classList.remove("drag-over"));
|
||||
dropZone.addEventListener("drop", (e) => {
|
||||
e.preventDefault();
|
||||
dropZone.classList.remove("drag-over");
|
||||
if (e.dataTransfer.files.length > 0) onFile(e.dataTransfer.files[0]);
|
||||
});
|
||||
}
|
||||
|
||||
async function _handleFrameUpload(which, file) {
|
||||
if (!file || !file.type.startsWith("image/")) return;
|
||||
const statusEl = document.getElementById(`wfm-video-${which}-frame-status`);
|
||||
const previewImg = document.getElementById(`wfm-video-${which}-frame-preview`);
|
||||
const wrap = document.getElementById(`wfm-video-${which}-frame-wrap`);
|
||||
if (previewImg) previewImg.src = URL.createObjectURL(file);
|
||||
if (wrap) wrap.style.display = "";
|
||||
if (statusEl) statusEl.textContent = "Uploading...";
|
||||
try {
|
||||
const result = await comfyUI.uploadImage(file, file.name);
|
||||
if (which === "first") state.firstFrameFilename = result.name;
|
||||
else state.lastFrameFilename = result.name;
|
||||
if (statusEl) {
|
||||
statusEl.textContent = `✓ ${result.name}`;
|
||||
statusEl.style.color = "var(--wfm-success)";
|
||||
}
|
||||
} catch (err) {
|
||||
if (statusEl) {
|
||||
statusEl.textContent = `✗ ${err.message}`;
|
||||
statusEl.style.color = "var(--wfm-danger)";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function _clearFrame(which) {
|
||||
if (which === "first") state.firstFrameFilename = null;
|
||||
else state.lastFrameFilename = null;
|
||||
const wrap = document.getElementById(`wfm-video-${which}-frame-wrap`);
|
||||
const previewImg = document.getElementById(`wfm-video-${which}-frame-preview`);
|
||||
const statusEl = document.getElementById(`wfm-video-${which}-frame-status`);
|
||||
const fileInput = document.getElementById(`wfm-video-${which}-frame-file`);
|
||||
if (wrap) wrap.style.display = "none";
|
||||
if (previewImg) previewImg.src = "";
|
||||
if (statusEl) statusEl.textContent = "";
|
||||
if (fileInput) fileInput.value = "";
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// フォーム値の読み取り・ワークフローへの反映
|
||||
// ============================================
|
||||
|
||||
function _readFormValues() {
|
||||
return {
|
||||
prompt: document.getElementById("wfm-video-prompt")?.value || "",
|
||||
aspectRatio: document.getElementById("wfm-video-aspect-ratio")?.value || "",
|
||||
megapixels: Number(document.getElementById("wfm-video-megapixels")?.value) || 0.2,
|
||||
multiple: Number(document.getElementById("wfm-video-multiple")?.value) || 32,
|
||||
duration: Number(document.getElementById("wfm-video-duration")?.value) || 2,
|
||||
};
|
||||
}
|
||||
|
||||
function _applyFormToWorkflow(nodes, form) {
|
||||
const { instanceNode, resolutionNode, loadImageNode, promptIdx, durationIdx } = nodes;
|
||||
|
||||
instanceNode.widgets_values[promptIdx] = form.prompt;
|
||||
instanceNode.widgets_values[durationIdx] = form.duration;
|
||||
|
||||
const arIdx = _widgetIndex(resolutionNode, "aspect_ratio");
|
||||
const mpIdx = _widgetIndex(resolutionNode, "megapixels");
|
||||
const multIdx = _widgetIndex(resolutionNode, "multiple");
|
||||
if (arIdx !== -1) resolutionNode.widgets_values[arIdx] = form.aspectRatio;
|
||||
if (mpIdx !== -1) resolutionNode.widgets_values[mpIdx] = form.megapixels;
|
||||
if (multIdx !== -1) resolutionNode.widgets_values[multIdx] = form.multiple;
|
||||
|
||||
// first_frameは既存のLoadImageノードが接続されている場合のみここで書き換える。
|
||||
// 接続が無い場合(loadImageNode: null)は、first_frameが新規指定された時だけ
|
||||
// _injectFrameNode()でノードごと注入する（未指定ならバイパス=未接続のまま実行）。
|
||||
if (loadImageNode) {
|
||||
const fnIdx = _widgetIndex(loadImageNode, "image");
|
||||
if (fnIdx !== -1) loadImageNode.widgets_values[fnIdx] = state.firstFrameFilename;
|
||||
}
|
||||
}
|
||||
|
||||
// first_frame/last_frameが未接続のワークフローに対して、画像が指定された時だけ
|
||||
// LoadImageノードを新規注入する共通ヘルパー。ノードID/リンクIDはclone内の実際の
|
||||
// 最大値+1で採番するため、重複ID/リンクID不整合は起きない
|
||||
// (CLAUDE.md記載の既知の落とし穴への対策)。未指定時は何もしない
|
||||
// (テンプレート由来のlink:nullのまま=クリーンな状態を毎回cloneするので残留の心配がない=バイパス扱い)。
|
||||
function _injectFrameNode(clone, instanceNode, targetSlot, filename) {
|
||||
const maxNodeId = Math.max(clone.last_node_id || 0, ...clone.nodes.map((n) => n.id));
|
||||
const maxLinkId = Math.max(clone.last_link_id || 0, ...clone.links.map((l) => l[0]));
|
||||
const newNodeId = maxNodeId + 1;
|
||||
const newLinkId = maxLinkId + 1;
|
||||
|
||||
clone.nodes.push({
|
||||
id: newNodeId,
|
||||
type: "LoadImage",
|
||||
pos: [(instanceNode.pos?.[0] || 0) - 260, (instanceNode.pos?.[1] || 0) + 40],
|
||||
size: [280, 314],
|
||||
flags: {},
|
||||
order: clone.nodes.length,
|
||||
mode: 0,
|
||||
inputs: [
|
||||
{ localized_name: "image", name: "image", type: "COMBO", widget: { name: "image" }, link: null },
|
||||
{ localized_name: "choose file to upload", name: "upload", type: "IMAGEUPLOAD", widget: { name: "upload" }, link: null },
|
||||
],
|
||||
outputs: [
|
||||
{ localized_name: "IMAGE", name: "IMAGE", type: "IMAGE", links: [newLinkId] },
|
||||
{ localized_name: "MASK", name: "MASK", type: "MASK", links: null },
|
||||
],
|
||||
properties: { "Node name for S&R": "LoadImage" },
|
||||
widgets_values: [filename, "image"],
|
||||
});
|
||||
|
||||
clone.links.push([newLinkId, newNodeId, 0, instanceNode.id, targetSlot, "IMAGE"]);
|
||||
const targetInput = instanceNode.inputs[targetSlot];
|
||||
if (targetInput) targetInput.link = newLinkId;
|
||||
|
||||
clone.last_node_id = newNodeId;
|
||||
clone.last_link_id = newLinkId;
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// 実行
|
||||
// ============================================
|
||||
|
||||
async function runGeneration() {
|
||||
if (state.generating) return;
|
||||
if (!state.templateWorkflow) {
|
||||
showToast(t("videoNoWorkflowLoaded"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
const clone = structuredClone(state.templateWorkflow);
|
||||
const nodes = locateMiniMaxNodes(clone);
|
||||
if (!nodes) {
|
||||
showToast(t("videoUnsupportedWorkflow"), "error");
|
||||
return;
|
||||
}
|
||||
|
||||
_applyFormToWorkflow(nodes, _readFormValues());
|
||||
// first_frame: 既存のLoadImageノードが無いワークフロー(T2V構成等)で、
|
||||
// ユーザーが新たに画像を指定した場合のみノードを注入する。未指定ならバイパス(未接続のまま実行)。
|
||||
if (!nodes.loadImageNode && state.firstFrameFilename) {
|
||||
_injectFrameNode(clone, nodes.instanceNode, nodes.firstFrameSlot, state.firstFrameFilename);
|
||||
}
|
||||
if (state.lastFrameFilename) {
|
||||
_injectFrameNode(clone, nodes.instanceNode, nodes.lastFrameSlot, state.lastFrameFilename);
|
||||
}
|
||||
|
||||
const genBtn = document.getElementById("wfm-video-generate-btn");
|
||||
const progressBar = document.getElementById("wfm-video-progress-bar");
|
||||
const progressText = document.getElementById("wfm-video-progress-text");
|
||||
|
||||
state.generating = true;
|
||||
if (genBtn) genBtn.disabled = true;
|
||||
if (progressBar) progressBar.style.width = "0%";
|
||||
if (progressText) progressText.textContent = "Converting workflow...";
|
||||
|
||||
try {
|
||||
const apiWorkflow = await comfyWorkflow.convertUiToApi(clone);
|
||||
|
||||
const seedValue = Number(document.getElementById("wfm-video-noise-seed")?.value) || 0;
|
||||
const seedMode = document.getElementById("wfm-video-seed-randomize")?.checked ? "random" : "fixed";
|
||||
|
||||
const { images, seed } = await comfyUI.generate(apiWorkflow, {
|
||||
seedMode,
|
||||
seedValue,
|
||||
timeoutMs: 30 * 60 * 1000, // 動画生成は静止画より長時間になりうる
|
||||
onProgress: (pct) => {
|
||||
if (progressBar) progressBar.style.width = `${(pct * 100).toFixed(1)}%`;
|
||||
if (progressText) progressText.textContent = `${(pct * 100).toFixed(0)}%`;
|
||||
},
|
||||
});
|
||||
|
||||
const seedEl = document.getElementById("wfm-video-noise-seed");
|
||||
if (seedEl) seedEl.value = seed;
|
||||
|
||||
const outputMedia = images.filter((img) => img.type !== "temp");
|
||||
if (progressText) progressText.textContent = t("videoDoneCount", outputMedia.length);
|
||||
if (progressBar) progressBar.style.width = "100%";
|
||||
|
||||
_renderResult(outputMedia);
|
||||
await _saveGeneratedVideoMeta(outputMedia, apiWorkflow);
|
||||
showToast(t("videoGenerateSuccess"), "success");
|
||||
} catch (err) {
|
||||
showToast(t("errorWithMsg", err.message), "error");
|
||||
if (progressText) progressText.textContent = "Error";
|
||||
} finally {
|
||||
state.generating = false;
|
||||
if (genBtn) genBtn.disabled = false;
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// 結果表示
|
||||
// ============================================
|
||||
|
||||
// Sets (or clears) the center preview frame's video and records where it came from —
|
||||
// `source` is what the Frame/GIF property tabs act on (see state.previewSource above).
|
||||
// Revokes the previous blob: URL (if any) so picking a series of local files doesn't
|
||||
// leak memory.
|
||||
let _previewObjectUrl = null;
|
||||
|
||||
function _setPreviewMedia(url, source) {
|
||||
state.previewSource = source;
|
||||
const video = document.getElementById("wfm-video-preview-video");
|
||||
const placeholder = document.getElementById("wfm-video-preview-placeholder");
|
||||
if (!video || !placeholder) return;
|
||||
|
||||
if (_previewObjectUrl) {
|
||||
URL.revokeObjectURL(_previewObjectUrl);
|
||||
_previewObjectUrl = null;
|
||||
}
|
||||
if (url && url.startsWith("blob:")) _previewObjectUrl = url;
|
||||
|
||||
if (url) {
|
||||
video.src = url;
|
||||
video.style.display = "block";
|
||||
placeholder.style.display = "none";
|
||||
} else {
|
||||
video.pause();
|
||||
video.removeAttribute("src");
|
||||
video.load();
|
||||
video.style.display = "none";
|
||||
placeholder.style.display = "";
|
||||
}
|
||||
}
|
||||
|
||||
function _renderResult(images) {
|
||||
// MiniMax H3 always emits a single VIDEO output — but guard generically (prefer
|
||||
// an mp4 if present) in case a future video workflow's SaveVideo/PreviewVideo
|
||||
// ordering differs.
|
||||
const media = (images || []).find((img) => img.filename.toLowerCase().endsWith(".mp4")) || images?.[0];
|
||||
if (!media) {
|
||||
_setPreviewMedia(null, null);
|
||||
return;
|
||||
}
|
||||
const params = new URLSearchParams({ filename: media.filename, subfolder: media.subfolder || "", type: media.type || "output" });
|
||||
const src = `${comfyUI.baseUrl}/view?${params}`;
|
||||
_setPreviewMedia(src, {
|
||||
kind: media.type === "input" ? "input" : "output",
|
||||
filename: media.filename,
|
||||
subfolder: media.subfolder || "",
|
||||
type: media.type || "output",
|
||||
});
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Gallery連携（生成結果のワークフローをサイドカーメタとして保存）
|
||||
// ============================================
|
||||
|
||||
let _outputDir = "";
|
||||
|
||||
async function _fetchOutputDir() {
|
||||
try {
|
||||
const res = await fetch("/api/wfm/settings/output-dir");
|
||||
if (res.ok) {
|
||||
const data = await res.json();
|
||||
_outputDir = (data.current || "").replace(/\\/g, "/").replace(/\/$/, "");
|
||||
}
|
||||
} catch { /* non-critical */ }
|
||||
}
|
||||
|
||||
async function _saveGeneratedVideoMeta(images, workflow) {
|
||||
if (!_outputDir) await _fetchOutputDir();
|
||||
if (!_outputDir) return;
|
||||
for (const img of images) {
|
||||
if (img.type !== "output") continue;
|
||||
const parts = [_outputDir];
|
||||
if (img.subfolder) parts.push(img.subfolder);
|
||||
parts.push(img.filename);
|
||||
const path = parts.join("/");
|
||||
try {
|
||||
await fetch("/wfm/gallery/image/meta", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ path, workflow }),
|
||||
});
|
||||
} catch { /* non-critical */ }
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// i18nの適用（app.jsのapplyI18nToHtml()と同じ「個別要素にt()を適用」方式）
|
||||
// ============================================
|
||||
|
||||
function _applyVideoI18n() {
|
||||
const setText = (id, key) => {
|
||||
const el = document.getElementById(id);
|
||||
if (el) el.textContent = t(key);
|
||||
};
|
||||
setText("wfm-video-title-label", "videoTitle");
|
||||
setText("wfm-video-first-frame-label", "videoFirstFrame");
|
||||
setText("wfm-video-last-frame-label", "videoLastFrame");
|
||||
setText("wfm-video-first-frame-drop-label", "videoDropOrClick");
|
||||
setText("wfm-video-last-frame-drop-label", "videoDropOrClick");
|
||||
setText("wfm-video-first-frame-clear", "clear");
|
||||
setText("wfm-video-last-frame-clear", "clear");
|
||||
setText("wfm-video-prompt-label", "videoPrompt");
|
||||
setText("wfm-video-aspect-ratio-label", "videoAspectRatio");
|
||||
setText("wfm-video-megapixels-label", "videoMegapixels");
|
||||
setText("wfm-video-multiple-label", "videoMultiple");
|
||||
setText("wfm-video-duration-label", "videoDuration");
|
||||
setText("wfm-video-noise-seed-label", "videoNoiseSeed");
|
||||
setText("wfm-video-randomize-label", "videoRandomizeSeed");
|
||||
setText("wfm-video-generate-btn", "videoGenerate");
|
||||
setText("wfm-video-edit-placeholder-label", "videoEditPlaceholder");
|
||||
setText("wfm-video-source-label", "videoSourceLabel");
|
||||
setText("wfm-video-source-hint", "videoSourceHint");
|
||||
setText("wfm-video-source-drop-label", "videoSourceDropLabel");
|
||||
setText("wfm-video-source-clear", "clear");
|
||||
setText("wfm-video-preview-placeholder", "videoNoVideo");
|
||||
setText("wfm-video-prop-tab-frame", "videoPropFrame");
|
||||
setText("wfm-video-prop-tab-gif", "videoPropGif");
|
||||
setText("wfm-video-frame-hint", "videoFrameHint");
|
||||
setText("wfm-video-frame-capture-btn", "videoCaptureFrame");
|
||||
setText("wfm-video-frame-save-btn", "videoSaveToOutput");
|
||||
setText("wfm-video-gif-start-label", "videoGifStart");
|
||||
setText("wfm-video-gif-end-label", "videoGifEnd");
|
||||
setText("wfm-video-gif-fps-label", "videoGifFps");
|
||||
setText("wfm-video-gif-width-label", "videoGifWidth");
|
||||
setText("wfm-video-gif-convert-btn", "videoConvertToGif");
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Video Source panel — loads any local video into the preview frame, independent of
|
||||
// generation, so Frame/GIF tools work without running MiniMax H3 first.
|
||||
// ============================================
|
||||
|
||||
function _wireVideoSourcePanel() {
|
||||
_wireDropZone("wfm-video-source-drop", "wfm-video-source-file", (file) => {
|
||||
if (!file.type.startsWith("video/")) return;
|
||||
const statusEl = document.getElementById("wfm-video-source-status");
|
||||
_setPreviewMedia(URL.createObjectURL(file), { kind: "local", file });
|
||||
if (statusEl) { statusEl.textContent = file.name; statusEl.style.color = ""; }
|
||||
});
|
||||
|
||||
document.getElementById("wfm-video-source-clear")?.addEventListener("click", () => {
|
||||
_setPreviewMedia(null, null);
|
||||
const statusEl = document.getElementById("wfm-video-source-status");
|
||||
if (statusEl) statusEl.textContent = "";
|
||||
const fileInput = document.getElementById("wfm-video-source-file");
|
||||
if (fileInput) fileInput.value = "";
|
||||
});
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Properties panel tabs (Frame / GIF)
|
||||
// ============================================
|
||||
|
||||
function _initPropTabs() {
|
||||
const tabs = document.querySelectorAll(".wfm-video-prop-tab");
|
||||
tabs.forEach((btn) => {
|
||||
btn.addEventListener("click", () => {
|
||||
tabs.forEach((b) => b.classList.remove("active"));
|
||||
btn.classList.add("active");
|
||||
const target = btn.dataset.propTab;
|
||||
document.querySelectorAll(".wfm-video-prop-content").forEach((c) => {
|
||||
c.style.display = c.id === `wfm-video-prop-${target}` ? "" : "none";
|
||||
});
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Frame tab — captures the preview video's current playback position as a PNG,
|
||||
// client-side (canvas), and saves it into ComfyUI's own output folder.
|
||||
// ============================================
|
||||
|
||||
let _capturedFrameBlob = null;
|
||||
|
||||
function _blobToDataUrl(blob) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const reader = new FileReader();
|
||||
reader.onload = () => resolve(reader.result);
|
||||
reader.onerror = () => reject(new Error("Failed to read blob"));
|
||||
reader.readAsDataURL(blob);
|
||||
});
|
||||
}
|
||||
|
||||
function _initFrameTab() {
|
||||
const captureBtn = document.getElementById("wfm-video-frame-capture-btn");
|
||||
const saveBtn = document.getElementById("wfm-video-frame-save-btn");
|
||||
const previewWrap = document.getElementById("wfm-video-frame-preview-wrap");
|
||||
const previewImg = document.getElementById("wfm-video-frame-preview-img");
|
||||
const statusEl = document.getElementById("wfm-video-frame-status");
|
||||
|
||||
captureBtn?.addEventListener("click", () => {
|
||||
const video = document.getElementById("wfm-video-preview-video");
|
||||
if (!video || video.style.display === "none" || !video.videoWidth) {
|
||||
showToast(t("videoNoSourceLoaded"), "error");
|
||||
return;
|
||||
}
|
||||
try {
|
||||
const canvas = document.createElement("canvas");
|
||||
canvas.width = video.videoWidth;
|
||||
canvas.height = video.videoHeight;
|
||||
canvas.getContext("2d").drawImage(video, 0, 0);
|
||||
canvas.toBlob((blob) => {
|
||||
if (!blob) return;
|
||||
_capturedFrameBlob = blob;
|
||||
if (previewImg) previewImg.src = URL.createObjectURL(blob);
|
||||
if (previewWrap) previewWrap.style.display = "";
|
||||
if (saveBtn) saveBtn.disabled = false;
|
||||
if (statusEl) statusEl.textContent = "";
|
||||
}, "image/png");
|
||||
} catch (err) {
|
||||
showToast(t("videoFrameCaptureFailed", err.message), "error");
|
||||
}
|
||||
});
|
||||
|
||||
saveBtn?.addEventListener("click", async () => {
|
||||
if (!_capturedFrameBlob) return;
|
||||
saveBtn.disabled = true;
|
||||
if (statusEl) statusEl.textContent = "Saving...";
|
||||
try {
|
||||
const dataUrl = await _blobToDataUrl(_capturedFrameBlob);
|
||||
const res = await fetch("/api/wfm/video/frame/save-to-output", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ image_base64: dataUrl }),
|
||||
});
|
||||
const json = await res.json();
|
||||
if (!res.ok) throw new Error(json.error || `HTTP ${res.status}`);
|
||||
if (statusEl) { statusEl.textContent = `✓ ${json.filename}`; statusEl.style.color = "var(--wfm-success)"; }
|
||||
showToast(t("videoFrameSaved", json.filename), "success");
|
||||
} catch (err) {
|
||||
if (statusEl) { statusEl.textContent = `✗ ${err.message}`; statusEl.style.color = "var(--wfm-danger)"; }
|
||||
showToast(t("errorWithMsg", err.message), "error");
|
||||
} finally {
|
||||
saveBtn.disabled = false;
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// GIF tab — sends the preview video (uploading it first if it's a local file not yet
|
||||
// on the server) to the server, which decodes it with PyAV and encodes an animated
|
||||
// GIF with Pillow (both already required by ComfyUI itself — no ffmpeg dependency).
|
||||
// ============================================
|
||||
|
||||
function _initGifTab() {
|
||||
const convertBtn = document.getElementById("wfm-video-gif-convert-btn");
|
||||
const progressBar = document.getElementById("wfm-video-gif-progress-bar");
|
||||
const progressText = document.getElementById("wfm-video-gif-progress-text");
|
||||
const previewWrap = document.getElementById("wfm-video-gif-preview-wrap");
|
||||
const previewImg = document.getElementById("wfm-video-gif-preview-img");
|
||||
const statusEl = document.getElementById("wfm-video-gif-status");
|
||||
|
||||
convertBtn?.addEventListener("click", async () => {
|
||||
if (!state.previewSource) {
|
||||
showToast(t("videoNoSourceLoaded"), "error");
|
||||
return;
|
||||
}
|
||||
convertBtn.disabled = true;
|
||||
if (statusEl) statusEl.textContent = "";
|
||||
if (progressBar) progressBar.style.width = "20%";
|
||||
if (progressText) progressText.textContent = "Uploading...";
|
||||
|
||||
try {
|
||||
let ref = state.previewSource;
|
||||
if (ref.kind === "local") {
|
||||
const result = await comfyUI.uploadImage(ref.file, ref.file.name);
|
||||
ref = { kind: "input", filename: result.name, subfolder: result.subfolder || "", type: "input" };
|
||||
// Cache the uploaded reference so a repeat conversion doesn't re-upload.
|
||||
state.previewSource = ref;
|
||||
}
|
||||
|
||||
const start = Number(document.getElementById("wfm-video-gif-start")?.value) || 0;
|
||||
const endRaw = document.getElementById("wfm-video-gif-end")?.value;
|
||||
const fps = Number(document.getElementById("wfm-video-gif-fps")?.value) || 10;
|
||||
const widthRaw = document.getElementById("wfm-video-gif-width")?.value;
|
||||
|
||||
if (progressBar) progressBar.style.width = "60%";
|
||||
if (progressText) progressText.textContent = t("videoGifConverting");
|
||||
|
||||
const res = await fetch("/api/wfm/video/to-gif", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
filename: ref.filename,
|
||||
subfolder: ref.subfolder || "",
|
||||
type: ref.type,
|
||||
start_time: start,
|
||||
end_time: endRaw ? Number(endRaw) : null,
|
||||
fps,
|
||||
max_width: widthRaw ? Number(widthRaw) : null,
|
||||
}),
|
||||
});
|
||||
const json = await res.json();
|
||||
if (!res.ok) throw new Error(json.error || `HTTP ${res.status}`);
|
||||
|
||||
if (progressBar) progressBar.style.width = "100%";
|
||||
if (progressText) progressText.textContent = t("videoGifDoneCount", json.frame_count ?? 0);
|
||||
|
||||
const params = new URLSearchParams({ filename: json.filename, subfolder: json.subfolder || "", type: "output" });
|
||||
if (previewImg) previewImg.src = `${comfyUI.baseUrl}/view?${params}`;
|
||||
if (previewWrap) previewWrap.style.display = "";
|
||||
if (statusEl) { statusEl.textContent = `✓ ${json.filename}`; statusEl.style.color = "var(--wfm-success)"; }
|
||||
showToast(t("videoGifSaved", json.filename), "success");
|
||||
} catch (err) {
|
||||
if (statusEl) { statusEl.textContent = `✗ ${err.message}`; statusEl.style.color = "var(--wfm-danger)"; }
|
||||
if (progressText) progressText.textContent = "Error";
|
||||
showToast(t("errorWithMsg", err.message), "error");
|
||||
} finally {
|
||||
convertBtn.disabled = false;
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// 初期化
|
||||
// ============================================
|
||||
|
||||
export function initVideoTab() {
|
||||
_applyVideoI18n();
|
||||
_populateAspectRatioOptions();
|
||||
|
||||
_wireDropZone("wfm-video-first-frame-drop", "wfm-video-first-frame-file", (file) => _handleFrameUpload("first", file));
|
||||
_wireDropZone("wfm-video-last-frame-drop", "wfm-video-last-frame-file", (file) => _handleFrameUpload("last", file));
|
||||
|
||||
document.getElementById("wfm-video-first-frame-clear")?.addEventListener("click", () => _clearFrame("first"));
|
||||
document.getElementById("wfm-video-last-frame-clear")?.addEventListener("click", () => _clearFrame("last"));
|
||||
document.getElementById("wfm-video-generate-btn")?.addEventListener("click", runGeneration);
|
||||
|
||||
_wireVideoSourcePanel();
|
||||
_initPropTabs();
|
||||
_initFrameTab();
|
||||
_initGifTab();
|
||||
|
||||
loadDefaultWorkflow();
|
||||
}
|
||||
@@ -0,0 +1,73 @@
|
||||
/**
|
||||
* Video helper utilities shared across tabs that deal with generated mp4 output
|
||||
* (Lab tab's "use generated image for next" chaining, and the Video tab's future
|
||||
* batch features). Kept dependency-free (no comfyUI/comfyEditor imports) so it can
|
||||
* be reused from either tab without pulling in unrelated state.
|
||||
*/
|
||||
|
||||
export function isVideoFilename(filename) {
|
||||
return /\.(mp4|webm|mov)$/i.test(filename || "");
|
||||
}
|
||||
|
||||
// Extracts the last frame of a video at `videoUrl` as a PNG Blob, by seeking a
|
||||
// detached <video> element to just before its end and drawing that frame onto a
|
||||
// canvas. Runs entirely client-side (no server round-trip / PyAV dependency).
|
||||
//
|
||||
// Seeks to (duration - 0.05s) rather than duration itself: seeking exactly to the
|
||||
// end is unreliable across browsers (some clamp it back to the last keyframe's
|
||||
// timestamp, which can fire a "seeked" event before the frame is actually decoded).
|
||||
// The "seeked" listener also ignores events that don't land near that target time,
|
||||
// since loading a video can fire an initial seeked-to-0 before the real seek lands.
|
||||
export function extractLastFrameBlob(videoUrl) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const video = document.createElement("video");
|
||||
video.muted = true;
|
||||
video.playsInline = true;
|
||||
video.preload = "auto";
|
||||
|
||||
let target = null;
|
||||
let settled = false;
|
||||
|
||||
const cleanup = () => {
|
||||
video.removeAttribute("src");
|
||||
video.load();
|
||||
};
|
||||
const finish = (fn, arg) => {
|
||||
if (settled) return;
|
||||
settled = true;
|
||||
cleanup();
|
||||
fn(arg);
|
||||
};
|
||||
|
||||
video.addEventListener("error", () => {
|
||||
finish(reject, new Error("Failed to load video for last-frame extraction"));
|
||||
});
|
||||
|
||||
video.addEventListener("loadedmetadata", () => {
|
||||
if (!Number.isFinite(video.duration) || video.duration <= 0) {
|
||||
finish(reject, new Error("Video has no readable duration"));
|
||||
return;
|
||||
}
|
||||
target = Math.max(0, video.duration - 0.05);
|
||||
video.currentTime = target;
|
||||
});
|
||||
|
||||
video.addEventListener("seeked", () => {
|
||||
if (target == null || Math.abs(video.currentTime - target) > 0.5) return;
|
||||
try {
|
||||
const canvas = document.createElement("canvas");
|
||||
canvas.width = video.videoWidth;
|
||||
canvas.height = video.videoHeight;
|
||||
canvas.getContext("2d").drawImage(video, 0, 0);
|
||||
canvas.toBlob((blob) => {
|
||||
if (blob) finish(resolve, blob);
|
||||
else finish(reject, new Error("Failed to encode last frame"));
|
||||
}, "image/png");
|
||||
} catch (err) {
|
||||
finish(reject, err);
|
||||
}
|
||||
});
|
||||
|
||||
video.src = videoUrl;
|
||||
});
|
||||
}
|
||||
+27
-35
@@ -7,6 +7,7 @@
|
||||
import { showToast, openModal, closeModal } from "./app.js";
|
||||
import { comfyWorkflow } from "./comfyui-workflow.js";
|
||||
import { loadWorkflowIntoEditor } from "./generate-tab.js";
|
||||
import { loadWorkflowIntoVideoEditor } from "./video-tab.js";
|
||||
import { t, getSummaryPrompt } from "./i18n.js";
|
||||
import { highlightJSON } from "./json-highlight.js";
|
||||
import { openBadgeEditModal } from "./models-tab.js";
|
||||
@@ -46,7 +47,7 @@ function saveBadgePalette(palette) {
|
||||
// Open in ComfyUI / Send to Canvas
|
||||
// ============================================
|
||||
|
||||
function sendToCanvas(workflowData) {
|
||||
function sendToCanvas(workflowData, filename) {
|
||||
try {
|
||||
// window.opener経由でComfyUIキャンバスに直接ロード（推奨）
|
||||
if (window.opener && typeof window.opener.wfmReceiveWorkflow === "function") {
|
||||
@@ -54,14 +55,13 @@ function sendToCanvas(workflowData) {
|
||||
showToast(t("workflowSentToCanvasDirect"), "success");
|
||||
return;
|
||||
}
|
||||
// フォールバック: localStorage + タイトルドラッグ（UI形式のみ）
|
||||
const fmt = comfyWorkflow.detectFormat(workflowData);
|
||||
if (fmt === "api") {
|
||||
showToast(t("apiFormatCanvasNoOpener"), "error");
|
||||
return;
|
||||
}
|
||||
localStorage.setItem("wfm_pending_workflow", JSON.stringify(workflowData));
|
||||
showToast(t("workflowSentToCanvas"), "success");
|
||||
// フォールバック: Workflow Studio LibraryのWorkflows(W)タブを当該ワークフローで
|
||||
// 絞り込み表示する。以前はUI形式限定で「タイトルをドラッグ」させていたが、
|
||||
// サムネイルが見えず分かりにくい上、API形式のワークフローはそもそも非対応だった。
|
||||
// 絞り込み表示なら実物のワークフローカード（サムネイル付き）が出るので、
|
||||
// フォーマットを問わずユーザー自身がそこからドラッグできる。
|
||||
localStorage.setItem("wfm_library_filter_request", filename || "");
|
||||
showToast(t("workflowFilteredInLibrary"), "success");
|
||||
} catch (err) {
|
||||
showToast(t("errorWithMsg", err.message), "error");
|
||||
}
|
||||
@@ -616,6 +616,8 @@ async function showSidePanel(wf, cardEl) {
|
||||
// Enable toolbar load buttons
|
||||
const listLoadBtn = document.getElementById("wfm-list-load-btn");
|
||||
if (listLoadBtn) { listLoadBtn.disabled = false; listLoadBtn.title = ""; }
|
||||
const listLoadVideoBtn = document.getElementById("wfm-list-load-video-btn");
|
||||
if (listLoadVideoBtn) { listLoadVideoBtn.disabled = false; listLoadVideoBtn.title = ""; }
|
||||
const listOpenComfyBtn = document.getElementById("wfm-list-open-comfyui-btn");
|
||||
if (listOpenComfyBtn) { listOpenComfyBtn.disabled = false; listOpenComfyBtn.title = ""; }
|
||||
|
||||
@@ -661,6 +663,8 @@ function closeSidePanel() {
|
||||
if (titleEl) titleEl.textContent = "";
|
||||
const listLoadBtn = document.getElementById("wfm-list-load-btn");
|
||||
if (listLoadBtn) { listLoadBtn.disabled = true; listLoadBtn.title = t("selectCardFirst"); }
|
||||
const listLoadVideoBtn = document.getElementById("wfm-list-load-video-btn");
|
||||
if (listLoadVideoBtn) { listLoadVideoBtn.disabled = true; listLoadVideoBtn.title = t("selectCardFirst"); }
|
||||
const listOpenComfyBtn = document.getElementById("wfm-list-open-comfyui-btn");
|
||||
if (listOpenComfyBtn) { listOpenComfyBtn.disabled = true; listOpenComfyBtn.title = t("selectCardFirst"); }
|
||||
renderSideGroup(null);
|
||||
@@ -957,8 +961,6 @@ function openDetailModal(wf) {
|
||||
<textarea class="wfm-textarea" id="wfm-detail-memo" rows="4" placeholder="${t("memoPlaceholder")}">${meta.memo || ""}</textarea>
|
||||
</section>
|
||||
<div class="wfm-modal-actions">
|
||||
<button class="wfm-btn wfm-btn-primary" id="wfm-detail-load">${t("loadInGenerate")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-detail-open-comfyui">${t("sendToCanvas")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-detail-set-default">${t("setAsDefault")}</button>
|
||||
<button class="wfm-btn wfm-btn-sm" id="wfm-detail-change-thumb">${t("changeThumbnail")}</button>
|
||||
<input type="file" id="wfm-detail-thumb-file" accept="image/png,image/webp,image/jpeg" style="display:none">
|
||||
@@ -1095,29 +1097,6 @@ function openDetailModal(wf) {
|
||||
}
|
||||
});
|
||||
|
||||
// Load in GenerateUI
|
||||
document.getElementById("wfm-detail-load").addEventListener("click", async () => {
|
||||
try {
|
||||
const wfData = await getRawWorkflow(wf.filename);
|
||||
const loaded = await loadWorkflowIntoEditor(wfData, wf.filename);
|
||||
if (loaded === false) return;
|
||||
document.querySelector('.wfm-tab[data-tab="generate"]')?.click();
|
||||
closeModal();
|
||||
} catch (err) {
|
||||
showToast(t("loadError") + ": " + err.message, "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Send to Canvas
|
||||
document.getElementById("wfm-detail-open-comfyui")?.addEventListener("click", async () => {
|
||||
try {
|
||||
const wfData = await getRawWorkflow(wf.filename);
|
||||
sendToCanvas(wfData);
|
||||
} catch (err) {
|
||||
showToast(t("loadError") + ": " + err.message, "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Set as default workflow
|
||||
document.getElementById("wfm-detail-set-default")?.addEventListener("click", async () => {
|
||||
try {
|
||||
@@ -1399,12 +1378,25 @@ export function initWorkflowTab() {
|
||||
}
|
||||
});
|
||||
|
||||
// Toolbar: Load in Video button (MiniMax H3専用。非対応ワークフローはloadWorkflowIntoVideoEditor内でエラートースト表示済み)
|
||||
document.getElementById("wfm-list-load-video-btn")?.addEventListener("click", async () => {
|
||||
if (!state.selectedWf) return;
|
||||
try {
|
||||
const wfData = await getRawWorkflow(state.selectedWf.filename);
|
||||
const loaded = await loadWorkflowIntoVideoEditor(wfData, state.selectedWf.filename);
|
||||
if (loaded === false) return;
|
||||
document.querySelector('.wfm-tab[data-tab="video"]')?.click();
|
||||
} catch (err) {
|
||||
showToast(t("loadError") + ": " + err.message, "error");
|
||||
}
|
||||
});
|
||||
|
||||
// Toolbar: Send to Canvas button
|
||||
document.getElementById("wfm-list-open-comfyui-btn")?.addEventListener("click", async () => {
|
||||
if (!state.selectedWf) return;
|
||||
try {
|
||||
const wfData = await getRawWorkflow(state.selectedWf.filename);
|
||||
sendToCanvas(wfData);
|
||||
sendToCanvas(wfData, state.selectedWf.filename);
|
||||
} catch (err) {
|
||||
showToast(t("loadError") + ": " + err.message, "error");
|
||||
}
|
||||
|
||||
+958
-87
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,121 @@
|
||||
"""Regenerate README.index.md / DEVLOG.index.md from README.md / DEVLOG.md.
|
||||
|
||||
README.md and DEVLOG.md are excluded from graphify's semantic extraction
|
||||
(.graphifyignore) because they are too large for reliable local-Ollama
|
||||
extraction: big chunks make the model summarize in prose instead of emitting
|
||||
JSON, and time out on slower models/hardware. These condensed index files are
|
||||
extracted instead. Run this whenever README.md or DEVLOG.md changes, then
|
||||
`graphify --update` to refresh the graph.
|
||||
|
||||
Usage:
|
||||
python tools/generate_doc_index.py [readme|devlog|all]
|
||||
(defaults to "all")
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
|
||||
|
||||
def generate_readme_index() -> Path:
|
||||
text = (ROOT / "README.md").read_text(encoding="utf-8")
|
||||
|
||||
details_re = re.compile(
|
||||
r"<details>\s*<summary><h3>(.*?)</h3></summary>\s*(.*?)</details>",
|
||||
re.S,
|
||||
)
|
||||
bullet_re = re.compile(r"^- \*\*(.+?)\*\*", re.M)
|
||||
|
||||
feature_lines: list[str] = []
|
||||
for title, body in details_re.findall(text):
|
||||
names = bullet_re.findall(body)
|
||||
feature_lines.append(f"### {title.strip()}")
|
||||
if names:
|
||||
feature_lines.append(", ".join(names))
|
||||
feature_lines.append("")
|
||||
|
||||
tail_start = text.find("## Installation")
|
||||
tail = text[tail_start:] if tail_start != -1 else ""
|
||||
|
||||
intro_end = text.find("## Screenshots")
|
||||
intro = text[:intro_end].strip() if intro_end != -1 else ""
|
||||
|
||||
out = [
|
||||
"# README Index — ComfyUI-Workflow-Studio",
|
||||
"",
|
||||
"Condensed index of README.md: intro summary, feature names per tab (full "
|
||||
"descriptions live in the excluded README.md), and the installation/usage/"
|
||||
"requirements/project-structure sections verbatim. Exists so graphify can "
|
||||
"extract this without feeding the full README (with its screenshot table) to "
|
||||
"the local Ollama model. Regenerate with "
|
||||
"`python tools/generate_doc_index.py readme` whenever README.md changes.",
|
||||
"",
|
||||
intro,
|
||||
"",
|
||||
"## Features (tab -> feature names)",
|
||||
"",
|
||||
*feature_lines,
|
||||
tail,
|
||||
]
|
||||
|
||||
content = "\n".join(out)
|
||||
out_path = ROOT / "README.index.md"
|
||||
out_path.write_text(content, encoding="utf-8")
|
||||
return out_path
|
||||
|
||||
|
||||
def generate_devlog_index() -> Path:
|
||||
text = (ROOT / "DEVLOG.md").read_text(encoding="utf-8")
|
||||
|
||||
entries = re.split(r"^## ", text, flags=re.M)[1:]
|
||||
lines: list[str] = []
|
||||
for entry in entries:
|
||||
parts = entry.split("\n", 1)
|
||||
version_line = parts[0].strip()
|
||||
rest = parts[1] if len(parts) > 1 else ""
|
||||
m = re.search(r"^### (.+)$", rest, re.M)
|
||||
subtitle = m.group(1).strip() if m else ""
|
||||
lines.append(
|
||||
f"- **{version_line}** — {subtitle}" if subtitle else f"- **{version_line}**"
|
||||
)
|
||||
|
||||
out = [
|
||||
"# DEVLOG Index — ComfyUI-Workflow-Studio",
|
||||
"",
|
||||
"Condensed version-by-version index of DEVLOG.md (each line: version + "
|
||||
'one-line summary of what changed and why). Full rationale, code details, '
|
||||
'and "How to apply" lessons live in the excluded DEVLOG.md itself; this '
|
||||
"index exists so graphify can extract a queryable semantic node per release "
|
||||
"without feeding the full log to the local Ollama model. Regenerate with "
|
||||
"`python tools/generate_doc_index.py devlog` whenever DEVLOG.md changes.",
|
||||
"",
|
||||
*lines,
|
||||
"",
|
||||
]
|
||||
|
||||
content = "\n".join(out)
|
||||
out_path = ROOT / "DEVLOG.index.md"
|
||||
out_path.write_text(content, encoding="utf-8")
|
||||
return out_path
|
||||
|
||||
|
||||
def main() -> None:
|
||||
target = sys.argv[1] if len(sys.argv) > 1 else "all"
|
||||
written: list[Path] = []
|
||||
if target in ("readme", "all"):
|
||||
written.append(generate_readme_index())
|
||||
if target in ("devlog", "all"):
|
||||
written.append(generate_devlog_index())
|
||||
if not written:
|
||||
print(f"unknown target {target!r}; use readme|devlog|all", file=sys.stderr)
|
||||
raise SystemExit(2)
|
||||
for p in written:
|
||||
print(f"wrote {p.relative_to(ROOT)} ({p.stat().st_size:,} bytes)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,128 @@
|
||||
"""
|
||||
Style/Prompt ギャラリー用シードデータ投入スクリプト。
|
||||
|
||||
comfyui_prompt_gallery / prompt_builder_proto 形式の wildcard データ
|
||||
(pack: set: category: leaf: [tag_string] という4階層YAML + thumbnails/ 配下の対応画像)
|
||||
から、画像とタグ文字列の組が揃っているエントリだけを ws_style_prompt 用フォルダへコピーする。
|
||||
|
||||
- テキストのみのワイルドカード（画像が無いもの）は対象外
|
||||
- サムネイル画像はあってもYAML側にタグが無いもの（プレースホルダー画像等）も対象外
|
||||
- コピー先には画像と同名の .txt サイドカー（タグ文字列そのまま）を書き込む
|
||||
|
||||
Usage:
|
||||
python tools/import_style_prompt_seed.py --source <wildcard_dataディレクトリ> --dest <ws_style_promptディレクトリ>
|
||||
"""
|
||||
import argparse
|
||||
import re
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
IMAGE_EXTS = {".png", ".jpg", ".jpeg", ".webp"}
|
||||
|
||||
|
||||
def clean_tags(text: str) -> str:
|
||||
text = text.strip()
|
||||
text = re.sub(r"^[,\s]+|[,\s]+$", "", text)
|
||||
text = re.sub(r"\s*,\s*", ", ", text)
|
||||
return text
|
||||
|
||||
|
||||
def parse_yaml_leaves(yaml_path: Path) -> dict[str, str]:
|
||||
"""comfyui_prompt_gallery の parseYamlForImages と同じ簡易パーサ（PyYAML不要）。
|
||||
「key:」行の直後が「- タグ文字列」で始まる行なら key をリーフとみなす。
|
||||
戻り値: {leaf_name(小文字): タグ文字列}
|
||||
"""
|
||||
leaves: dict[str, str] = {}
|
||||
lines = yaml_path.read_text(encoding="utf-8", errors="replace").split("\n")
|
||||
for i, line in enumerate(lines):
|
||||
trimmed = line.strip()
|
||||
if not trimmed or trimmed.startswith("#"):
|
||||
continue
|
||||
if not trimmed.endswith(":"):
|
||||
continue
|
||||
if i + 1 >= len(lines):
|
||||
continue
|
||||
next_trimmed = lines[i + 1].strip()
|
||||
if not next_trimmed.startswith("-"):
|
||||
continue
|
||||
key = trimmed[:-1].strip()
|
||||
tags = next_trimmed[1:].strip()
|
||||
if not tags or key.lower() == "skip":
|
||||
continue
|
||||
leaves[key.lower()] = clean_tags(tags)
|
||||
return leaves
|
||||
|
||||
|
||||
def import_category(cat_dir: Path, dest_root: Path) -> tuple[int, int, int]:
|
||||
"""1カテゴリ分を投入する。戻り値: (copied, images_without_prompt, leaves_total)"""
|
||||
category = cat_dir.name
|
||||
yaml_files = sorted(cat_dir.glob("*.yaml")) + sorted(cat_dir.glob("*.yml"))
|
||||
|
||||
leaves: dict[str, str] = {}
|
||||
for yf in yaml_files:
|
||||
leaves.update(parse_yaml_leaves(yf))
|
||||
|
||||
copied = 0
|
||||
images_without_prompt = 0
|
||||
|
||||
# "thumbnails" と、preview違いの "thumbnails_option2"（例: alchemist.preview3.jpeg）の両方を対象にする
|
||||
for thumb_dir_name in ("thumbnails", "thumbnails_option2"):
|
||||
thumbnails_dir = cat_dir / thumb_dir_name
|
||||
if not thumbnails_dir.is_dir():
|
||||
continue
|
||||
for img_path in sorted(thumbnails_dir.rglob("*")):
|
||||
if not img_path.is_file() or img_path.suffix.lower() not in IMAGE_EXTS:
|
||||
continue
|
||||
# "alchemist.preview3.jpeg" のような二重拡張子にも対応するため、最初の "." より前をリーフ名候補にする
|
||||
leaf_candidate = img_path.name.split(".")[0].lower()
|
||||
tags = leaves.get(leaf_candidate)
|
||||
if tags is None:
|
||||
images_without_prompt += 1
|
||||
continue
|
||||
|
||||
rel_parts = img_path.relative_to(thumbnails_dir).parts
|
||||
# 先頭1階層（パック名。例: "ponyxl"）は全カテゴリ共通で冗長なため省く
|
||||
dest_rel = Path(*rel_parts[1:]) if len(rel_parts) > 1 else Path(rel_parts[0])
|
||||
dest_path = dest_root / category / dest_rel
|
||||
dest_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
shutil.copy2(img_path, dest_path)
|
||||
dest_path.with_suffix(".txt").write_text(tags, encoding="utf-8")
|
||||
copied += 1
|
||||
|
||||
return copied, images_without_prompt, len(leaves)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--source", required=True, help="wildcard_data ディレクトリ")
|
||||
parser.add_argument("--dest", required=True, help="投入先 ws_style_prompt ディレクトリ")
|
||||
args = parser.parse_args()
|
||||
|
||||
source = Path(args.source)
|
||||
dest = Path(args.dest)
|
||||
if not source.is_dir():
|
||||
raise SystemExit(f"source not found: {source}")
|
||||
dest.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
categories = [
|
||||
d for d in sorted(source.iterdir())
|
||||
if d.is_dir() and (d / "thumbnails").is_dir()
|
||||
]
|
||||
if not categories:
|
||||
raise SystemExit(f"No category with a thumbnails/ subfolder found under {source}")
|
||||
|
||||
total_copied = 0
|
||||
total_skipped = 0
|
||||
for cat_dir in categories:
|
||||
copied, skipped, leaf_total = import_category(cat_dir, dest)
|
||||
print(f"[{cat_dir.name}] copied={copied} images_without_prompt={skipped} yaml_leaves={leaf_total}")
|
||||
total_copied += copied
|
||||
total_skipped += skipped
|
||||
|
||||
print(f"\nDone. {len(categories)} categories, {total_copied} images copied, {total_skipped} skipped (no matching prompt).")
|
||||
print(f"Destination: {dest}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+564
-55
@@ -583,6 +583,136 @@ window.wfmReceiveWorkflow = async (data) => {
|
||||
await loadDataOnCanvas(data);
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Cross-window bridge for SPA Mask Editor One integration
|
||||
// ============================================
|
||||
// WFS Image Edit タブから呼ばれる。「Send to Workflow」の image ウィジェット探索
|
||||
// （_wfmFindImageWidget）と同じ「選択中ノード優先・フォールバック」方式で
|
||||
// MaskEditorOne ノードをComfyUIキャンバス上から探す。
|
||||
function _wfmFindMaskEditorOneNode() {
|
||||
const graph = app.graph;
|
||||
if (!graph) return null;
|
||||
|
||||
let selected = [];
|
||||
try {
|
||||
const sel = app.canvas?.selected_nodes;
|
||||
if (sel) selected = Object.values(sel);
|
||||
} catch { /* ignore */ }
|
||||
|
||||
const pools = [selected, graph._nodes || []];
|
||||
for (const pool of pools) {
|
||||
for (const node of pool) {
|
||||
if (node?.type === "MaskEditorOne") return node;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
// 見つけた MaskEditorOne ノードへ画像を送り込み、そのノード自身の「Edit Mask」ウィジェット
|
||||
// （node._editMaskWidget.callback）を疑似クリックしてモーダルを開く。モーダルの overlay DOM
|
||||
// (.me-overlay) が非表示に戻ったタイミングで Apply 後のマスク（node._maskDataUrl、グレースケール
|
||||
// PNG data URL）を呼び出し元へ返す。Cancel/×で閉じた場合はマスクが更新されていないため null を返す。
|
||||
window.wfmOpenMaskEditorForNode = async (imageDataUrl, maskDataUrl) => {
|
||||
const node = _wfmFindMaskEditorOneNode();
|
||||
if (!node) {
|
||||
throw new Error("No Mask Editor One node found (select one on the canvas, or add one to the workflow)");
|
||||
}
|
||||
if (typeof node._editMaskWidget?.callback !== "function") {
|
||||
throw new Error("MaskEditorOne node is missing its Edit Mask control (unsupported version?)");
|
||||
}
|
||||
const nodeId = node.id;
|
||||
|
||||
const resp = await fetch("/mask_editor/store_image", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ node_id: String(nodeId), bg_image_b64: imageDataUrl }),
|
||||
});
|
||||
if (!resp.ok) throw new Error("Failed to send image to Mask Editor One");
|
||||
|
||||
// WFS側で既にマスクレイヤーを描いていた場合、それを初期マスクとしてそのまま引き継ぐ
|
||||
// （applyImageAndMaskToMaskEditorOneNode の layer_data 形式と同じスキーマ）
|
||||
if (maskDataUrl) {
|
||||
const layerWidget = node.widgets?.find((w) => w.name === "layer_data");
|
||||
if (layerWidget) {
|
||||
layerWidget.value = JSON.stringify({
|
||||
layers: [{ visible: true, opacity: 1, operation: "add", imageData: maskDataUrl }],
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// ノード側のプレビュー/モーダル背景を、送信した画像に同期させる
|
||||
node._bgDataUrl = imageDataUrl;
|
||||
node._bgImg = await new Promise((r) => {
|
||||
const img = new Image();
|
||||
img.onload = () => r(img);
|
||||
img.onerror = () => r(null);
|
||||
img.src = imageDataUrl;
|
||||
});
|
||||
node._previewMode = "image";
|
||||
node.setDirtyCanvas?.(true, true);
|
||||
|
||||
const prevMaskUrl = node._maskDataUrl;
|
||||
node._editMaskWidget.callback();
|
||||
|
||||
// openMaskEditor() は同期的に overlay DOM を構築・表示するため、callback() 直後に取得できる
|
||||
const overlays = [...document.querySelectorAll(".me-overlay")];
|
||||
const overlay = overlays.find((o) => o.style.display !== "none") || overlays[overlays.length - 1];
|
||||
if (!overlay) throw new Error("Mask Editor One modal did not open");
|
||||
|
||||
window.focus();
|
||||
|
||||
return new Promise((resolve) => {
|
||||
const observer = new MutationObserver(() => {
|
||||
if (overlay.style.display === "none") {
|
||||
observer.disconnect();
|
||||
resolve(node._maskDataUrl && node._maskDataUrl !== prevMaskUrl ? node._maskDataUrl : null);
|
||||
}
|
||||
});
|
||||
observer.observe(overlay, { attributes: true, attributeFilter: ["style"] });
|
||||
});
|
||||
};
|
||||
|
||||
// ============================================
|
||||
// Cross-window bridge for SPA "Send to LI node"
|
||||
// ============================================
|
||||
// WFS Image Edit / Galleryタブから呼ばれる。アップロード済みの画像ファイル名を、現在ComfyUIキャンバス上で
|
||||
// 選択中のノードの "image" ウィジェット（LoadImage / LoadImage 互換ノード）へ書き込む。
|
||||
// 選択ノードに該当ウィジェットが無ければグラフ内の最初の該当ノードにフォールバックする。
|
||||
// chat_TE カスタムノードの "Send to workflow" ボタンと同じロジック。
|
||||
function _wfmFindImageWidget() {
|
||||
const graph = app.graph;
|
||||
if (!graph) return null;
|
||||
|
||||
let selected = [];
|
||||
try {
|
||||
const sel = app.canvas?.selected_nodes;
|
||||
if (sel) selected = Object.values(sel);
|
||||
} catch { /* ignore */ }
|
||||
|
||||
const pools = [selected, graph._nodes || []];
|
||||
for (const pool of pools) {
|
||||
for (const node of pool) {
|
||||
const widget = node?.widgets?.find((w) => w.name === "image");
|
||||
if (widget) return widget;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
window.wfmSendImageToSelectedNode = (filename) => {
|
||||
const widget = _wfmFindImageWidget();
|
||||
if (!widget) {
|
||||
throw new Error("No image widget found (select a LoadImage-type node)");
|
||||
}
|
||||
if (widget.options?.values && !widget.options.values.includes(filename)) {
|
||||
widget.options.values.unshift(filename);
|
||||
}
|
||||
widget.value = filename;
|
||||
widget.callback?.(widget.value);
|
||||
app.graph.setDirtyCanvas(true, true);
|
||||
return true;
|
||||
};
|
||||
|
||||
const loadWorkflowOnCanvas = async (filename) => {
|
||||
const displayName = filename.replace(/\.json$/i, "");
|
||||
showToast(`Loading "${displayName}"...`, "info");
|
||||
@@ -2246,6 +2376,35 @@ const createDraggableItem = (label, type, data, subtitle) => {
|
||||
// Toggle & exports
|
||||
// ============================================
|
||||
|
||||
// Opens the Library panel on the Workflows (W) tab, filtered down to a single
|
||||
// workflow by filename. Used as the "Send to Canvas" fallback when window.opener
|
||||
// isn't available (or the workflow is API-format, which the title-drag mechanism
|
||||
// can't handle at all) — the user gets the actual workflow card, thumbnail
|
||||
// included, to drag onto the canvas themselves instead of a bare title element.
|
||||
export const showWorkflowInLibrary = async (filename) => {
|
||||
const panel = createPanel();
|
||||
state.visible = true;
|
||||
panel.style.display = "flex";
|
||||
|
||||
state.topTab = "workflows";
|
||||
panel.querySelectorAll(".wfm-nlp-top-tab").forEach(b => b.classList.toggle("active", b.dataset.toptab === "workflows"));
|
||||
state.wfSubTab = "wf-all";
|
||||
state.wfSubTab2 = null;
|
||||
|
||||
if (!state.wfLoaded) await loadWfData();
|
||||
|
||||
state.searchText = (filename || "").toLowerCase().trim();
|
||||
const searchInput = panel.querySelector(".wfm-nlp-search-input");
|
||||
if (searchInput) {
|
||||
searchInput.value = filename || "";
|
||||
searchInput.placeholder = "Search workflows...";
|
||||
}
|
||||
|
||||
rebuildSubTabs();
|
||||
renderContent();
|
||||
installCanvasDropHandler();
|
||||
};
|
||||
|
||||
export const togglePanel = async () => {
|
||||
const panel = createPanel();
|
||||
state.visible = !state.visible;
|
||||
@@ -2394,6 +2553,81 @@ async function _readAllPNGTextChunks(file) {
|
||||
return chunks;
|
||||
}
|
||||
|
||||
// ── MP4 container metadata (moov/udta/meta の mdta keys/ilst) ──
|
||||
// ComfyUIのSaveVideoはPNGのtEXtチャンクと同じキー名(workflow=UI形式, prompt=API形式)で
|
||||
// JSON文字列をisobmff(mp4)のudta/meta(mdta)にコンテナレベルタグとして書き込む(FFmpegの
|
||||
// movflags=+use_metadata_tags)。static/js/metadata-tab.js の readMP4MetaTags() と同一ロジック。
|
||||
function _findMP4Box(view, bytes, start, end, targetType) {
|
||||
let offset = start;
|
||||
while (offset + 8 <= end) {
|
||||
const size = view.getUint32(offset);
|
||||
const type = String.fromCharCode(bytes[offset + 4], bytes[offset + 5], bytes[offset + 6], bytes[offset + 7]);
|
||||
let boxSize = size;
|
||||
let bodyStart = offset + 8;
|
||||
if (size === 1) {
|
||||
if (offset + 16 > end) break;
|
||||
const hi = view.getUint32(offset + 8);
|
||||
const lo = view.getUint32(offset + 12);
|
||||
boxSize = hi * 4294967296 + lo;
|
||||
bodyStart = offset + 16;
|
||||
} else if (size === 0) {
|
||||
boxSize = end - offset;
|
||||
}
|
||||
if (boxSize < 8) break;
|
||||
if (type === targetType) return { bodyStart, end: offset + boxSize };
|
||||
offset += boxSize;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
async function _readMP4MetaTags(file) {
|
||||
const buffer = await file.arrayBuffer();
|
||||
const bytes = new Uint8Array(buffer);
|
||||
const view = new DataView(buffer);
|
||||
if (bytes.length < 12) return null;
|
||||
const ftyp = String.fromCharCode(bytes[4], bytes[5], bytes[6], bytes[7]);
|
||||
if (ftyp !== "ftyp") return null;
|
||||
|
||||
const moov = _findMP4Box(view, bytes, 0, bytes.length, "moov");
|
||||
if (!moov) return null;
|
||||
const udta = _findMP4Box(view, bytes, moov.bodyStart, moov.end, "udta");
|
||||
let meta = udta ? _findMP4Box(view, bytes, udta.bodyStart, udta.end, "meta") : null;
|
||||
if (!meta) meta = _findMP4Box(view, bytes, moov.bodyStart, moov.end, "meta");
|
||||
if (!meta) return null;
|
||||
const metaBody = meta.bodyStart + 4; // meta は FullBox (version+flags 4バイト)
|
||||
const keysBox = _findMP4Box(view, bytes, metaBody, meta.end, "keys");
|
||||
const ilstBox = _findMP4Box(view, bytes, metaBody, meta.end, "ilst");
|
||||
if (!keysBox || !ilstBox) return null;
|
||||
|
||||
const utf8 = new TextDecoder("utf-8", { fatal: false });
|
||||
let off = keysBox.bodyStart + 4; // version+flags をスキップ
|
||||
const count = view.getUint32(off); off += 4;
|
||||
const keyNames = [];
|
||||
for (let i = 0; i < count && off + 8 <= keysBox.end; i++) {
|
||||
const ksize = view.getUint32(off);
|
||||
if (ksize < 8) break;
|
||||
keyNames.push(utf8.decode(bytes.slice(off + 8, off + ksize)));
|
||||
off += ksize;
|
||||
}
|
||||
|
||||
const result = {};
|
||||
let ioff = ilstBox.bodyStart;
|
||||
while (ioff + 8 <= ilstBox.end) {
|
||||
const itemSize = view.getUint32(ioff);
|
||||
const keyIndex = view.getUint32(ioff + 4);
|
||||
if (itemSize < 8) break;
|
||||
const itemEnd = Math.min(ioff + itemSize, ilstBox.end);
|
||||
const data = _findMP4Box(view, bytes, ioff + 8, itemEnd, "data");
|
||||
if (data) {
|
||||
const payload = bytes.slice(data.bodyStart + 8, data.end); // type(4)+locale(4)をスキップ
|
||||
const name = keyNames[keyIndex - 1];
|
||||
if (name) { try { result[name] = utf8.decode(payload); } catch { /* skip */ } }
|
||||
}
|
||||
ioff = itemEnd;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// UI→API変換（static/js/comfyui-workflow.js の convertUiToApi() 相当ロジックの複製）
|
||||
// web/comfyui/ は static/js/ と配信URLが異なりESモジュールをimportできないため、
|
||||
@@ -2939,6 +3173,58 @@ function _isTextEncoderNode(ct) { return ct === "CLIPTextEncode" || ct.includes(
|
||||
function _isSamplerNode(ct) { return ct === "KSampler" || ct === "KSamplerAdvanced" || ct.includes("KSampler") || ct.includes("Sampler"); }
|
||||
function _isPromptStylerNode(ct) { return ct.includes("PromptStyler"); }
|
||||
|
||||
// CLIPTextEncodeEditPlus (model-and-prompt-from-metadata) の encode() と同じ結合ルール。
|
||||
// RAW: text1のみ。EDIT: text_editのみ。front/back: text2(未接続ならtext_edit)をtext1の前/後に結合。
|
||||
function _resolveEditPlusText(mode, textEdit, text1, text2) {
|
||||
const t1 = typeof text1 === "string" ? text1 : "";
|
||||
const edit = typeof textEdit === "string" ? textEdit : "";
|
||||
const insert = typeof text2 === "string" && text2 !== "" ? text2 : edit;
|
||||
switch (mode) {
|
||||
case "RAW": return t1;
|
||||
case "EDIT": return edit;
|
||||
case "front": return t1 ? `${insert}, ${t1}` : insert;
|
||||
case "back": return t1 ? `${t1}, ${insert}` : insert;
|
||||
default: return t1 || edit;
|
||||
}
|
||||
}
|
||||
|
||||
// LiteGraph形式: リンクを辿ってテキストを解決（ComfySwitchNode等の中継ノードにも対応）
|
||||
function _resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, srcId, slot, depth = 0) {
|
||||
if (depth > 6) return null;
|
||||
const srcNode = nodeMap.get(srcId);
|
||||
if (!srcNode) return null;
|
||||
const srcType = srcNode.type ?? "";
|
||||
if (_isPromptStylerNode(srcType)) {
|
||||
const v = srcNode.widgets_values?.[slot];
|
||||
return (v && typeof v === "string") ? v : null;
|
||||
}
|
||||
if (srcType === "ComfySwitchNode" && Array.isArray(srcNode.inputs)) {
|
||||
for (const name of ["on_false", "on_true"]) {
|
||||
const inp = srcNode.inputs.find(i => i.name === name);
|
||||
if (inp?.link == null) continue;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
const originSlot = linkSlot.get(inp.link) ?? 0;
|
||||
if (originId == null) continue;
|
||||
const text = _resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot, depth + 1);
|
||||
if (text) return text;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
const v = srcNode.widgets_values?.[slot] ?? srcNode.widgets_values?.[0];
|
||||
if (v && typeof v === "string") return v;
|
||||
// "source" = PreviewAny（値タップ中継）, "string_a" = StringConcatenate（LoRAトリガーワード
|
||||
// 連結等）— Krea-2のプロンプト強化配線で使われる中継ノード
|
||||
if (Array.isArray(srcNode.inputs)) {
|
||||
const nextInput = srcNode.inputs.find(i => ["text", "value", "prompt", "string", "source", "string_a"].includes(i.name));
|
||||
if (nextInput?.link != null) {
|
||||
const originId = linkOrigin.get(nextInput.link);
|
||||
const originSlot = linkSlot.get(nextInput.link) ?? 0;
|
||||
if (originId != null) return _resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot, depth + 1);
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
function _extractPromptsFromNodeSet(nodes, links) {
|
||||
const nodeMap = new Map();
|
||||
for (const n of nodes) nodeMap.set(n.id, n);
|
||||
@@ -2951,7 +3237,22 @@ function _extractPromptsFromNodeSet(nodes, links) {
|
||||
}
|
||||
const textMap = new Map();
|
||||
for (const n of nodes) {
|
||||
if (!_isTextEncoderNode(n.type ?? "")) continue;
|
||||
const type = n.type ?? "";
|
||||
if (type === "CLIPTextEncodeEditPlus") {
|
||||
const resolveNamedInput = (name) => {
|
||||
if (!Array.isArray(n.inputs)) return null;
|
||||
const inp = n.inputs.find(i => i.name === name);
|
||||
if (inp?.link == null) return null;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
const originSlot = linkSlot.get(inp.link) ?? 0;
|
||||
if (originId == null) return null;
|
||||
return _resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot);
|
||||
};
|
||||
const combined = _resolveEditPlusText(n.widgets_values?.[1], n.widgets_values?.[0], resolveNamedInput("text1"), resolveNamedInput("text2"));
|
||||
if (combined) textMap.set(n.id, combined);
|
||||
continue;
|
||||
}
|
||||
if (!_isTextEncoderNode(type)) continue;
|
||||
const text = n.widgets_values?.[0];
|
||||
if (text && typeof text === "string") { textMap.set(n.id, text); }
|
||||
else if (Array.isArray(n.inputs)) {
|
||||
@@ -2959,12 +3260,8 @@ function _extractPromptsFromNodeSet(nodes, links) {
|
||||
if (textInput?.link != null) {
|
||||
const originId = linkOrigin.get(textInput.link);
|
||||
const originSlot = linkSlot.get(textInput.link) ?? 0;
|
||||
const srcNode = originId != null ? nodeMap.get(originId) : null;
|
||||
if (srcNode) {
|
||||
const srcType = srcNode.type ?? "";
|
||||
if (_isPromptStylerNode(srcType)) { const v = srcNode.widgets_values?.[originSlot]; if (v && typeof v === "string") textMap.set(n.id, v); }
|
||||
else { const v = srcNode.widgets_values?.[originSlot] ?? srcNode.widgets_values?.[0]; if (v && typeof v === "string") textMap.set(n.id, v); }
|
||||
}
|
||||
const text2 = _resolveLinkedTextInNodeSet(nodeMap, linkOrigin, linkSlot, originId, originSlot);
|
||||
if (text2) textMap.set(n.id, text2);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -2973,24 +3270,44 @@ function _extractPromptsFromNodeSet(nodes, links) {
|
||||
for (const n of nodes) {
|
||||
if (!_isSamplerNode(n.type ?? "") || !Array.isArray(n.inputs)) continue;
|
||||
foundSampler = true;
|
||||
for (const inp of n.inputs) {
|
||||
// SamplerCustomAdvanced doesn't hold positive/negative directly — resolve through its
|
||||
// "guider" input (CFGGuider/DualCFGGuider/BasicGuider) instead.
|
||||
let inputsToScan = n.inputs;
|
||||
const guiderInput = n.inputs.find(inp => inp.name === "guider");
|
||||
if (guiderInput?.link != null) {
|
||||
const guiderId = linkOrigin.get(guiderInput.link);
|
||||
const guiderNode = guiderId != null ? nodeMap.get(guiderId) : null;
|
||||
if (Array.isArray(guiderNode?.inputs)) inputsToScan = guiderNode.inputs;
|
||||
}
|
||||
for (const inp of inputsToScan) {
|
||||
if (!inp || inp.link == null) continue;
|
||||
const originId = linkOrigin.get(inp.link);
|
||||
if (originId == null) continue;
|
||||
const name = inp.name ?? "";
|
||||
const isPos = name === "positive" || name.startsWith("positive");
|
||||
// DualCFGGuider (HiDream E1): cond1 carries the positive-derived conditioning.
|
||||
const isPos = name === "positive" || name === "cond1" || name.startsWith("positive");
|
||||
const isNeg = name === "negative" || name.startsWith("negative");
|
||||
if (!isPos && !isNeg) continue;
|
||||
// TextEncodeMageFlowEdit — 1ノードでprompt/negative_prompt両方を直接持つため、
|
||||
// textMap(1ノード1テキスト)では両ロールを表現できない
|
||||
// TextEncodeMageFlowEdit / TextEncodeBooguEdit — 1ノードでprompt/negative_prompt両方を
|
||||
// 直接持つため、textMap(1ノード1テキスト)では両ロールを表現できない
|
||||
const srcNode = nodeMap.get(originId);
|
||||
if (srcNode?.type === "TextEncodeMageFlowEdit") {
|
||||
if (srcNode?.type === "TextEncodeMageFlowEdit" || srcNode?.type === "TextEncodeBooguEdit") {
|
||||
const widgetIdx = isPos ? 0 : 1; // widgets_values: [prompt, negative_prompt, ...]
|
||||
const txt = srcNode.widgets_values?.[widgetIdx];
|
||||
if (txt && typeof txt === "string") { if (isPos) pos.add(txt); else neg.add(txt); }
|
||||
continue;
|
||||
}
|
||||
const txt = textMap.get(originId);
|
||||
// InstructPixToPixConditioning (HiDream E1): forwards the actual text-encoder
|
||||
// conditioning through its own positive/negative inputs — follow one more hop.
|
||||
let resolvedOriginId = originId;
|
||||
if (srcNode?.type === "InstructPixToPixConditioning" && Array.isArray(srcNode.inputs)) {
|
||||
const innerInput = srcNode.inputs.find(i => i.name === (isPos ? "positive" : "negative"));
|
||||
if (innerInput?.link != null) {
|
||||
const innerId = linkOrigin.get(innerInput.link);
|
||||
if (innerId != null) resolvedOriginId = innerId;
|
||||
}
|
||||
}
|
||||
const txt = textMap.get(resolvedOriginId);
|
||||
if (!txt) continue;
|
||||
if (isPos) pos.add(txt); else neg.add(txt);
|
||||
}
|
||||
@@ -3040,13 +3357,29 @@ function _extractMarkdownNoteModels(wf) {
|
||||
return hasAny ? result : null;
|
||||
}
|
||||
|
||||
function _resolveLinkedText(wf, srcId, slot) {
|
||||
function _resolveLinkedText(wf, srcId, slot, depth = 0) {
|
||||
if (depth > 6) return null;
|
||||
const src = wf[String(srcId)];
|
||||
if (!src || typeof src !== "object") return null;
|
||||
const ct = src.class_type ?? "";
|
||||
if (_isPromptStylerNode(ct)) { const v = slot === 0 ? src.inputs?.text_positive : src.inputs?.text_negative; return (v && typeof v === "string") ? v : null; }
|
||||
const keys = slot === 0 ? ["text_positive", "text", "text_g", "prompt"] : ["text_negative", "text_l"];
|
||||
for (const k of keys) { const v = src.inputs?.[k]; if (v && typeof v === "string") return v; }
|
||||
// ComfySwitchNode ("If/Else Switch") — 片方(TextGenerateなどLLMノード)は静的解決不能なため
|
||||
// on_false/on_true の両方を試し、リテラルへ解決できた方を採用する。
|
||||
if (ct === "ComfySwitchNode") {
|
||||
for (const key of ["on_false", "on_true"]) {
|
||||
const v = src.inputs?.[key];
|
||||
if (Array.isArray(v)) { const text = _resolveLinkedText(wf, v[0], v[1] ?? 0, depth + 1); if (text) return text; }
|
||||
else if (typeof v === "string" && v) return v;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
// "source" = PreviewAny の値タップ中継、"string_a" = StringConcatenate の主オペランド
|
||||
const keys = slot === 0 ? ["text_positive", "text", "text_g", "prompt", "value", "source", "string_a"] : ["text_negative", "text_l", "source", "string_a"];
|
||||
for (const k of keys) {
|
||||
const v = src.inputs?.[k];
|
||||
if (typeof v === "string" && v) return v;
|
||||
if (Array.isArray(v)) { const text = _resolveLinkedText(wf, v[0], v[1] ?? 0, depth + 1); if (text) return text; }
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
@@ -3059,8 +3392,30 @@ function _extractPromptsAPI(wf) {
|
||||
}
|
||||
const textMap = new Map();
|
||||
for (const [id, n] of Object.entries(wf)) {
|
||||
if (!n || !_isTextEncoderNode(n.class_type ?? "")) continue;
|
||||
const raw = n.inputs?.text ?? n.inputs?.text_g ?? null;
|
||||
if (!n) continue;
|
||||
const ct = n.class_type ?? "";
|
||||
if (ct === "CLIPTextEncodeEditPlus") {
|
||||
const resolveField = (key) => {
|
||||
const v = n.inputs?.[key];
|
||||
if (typeof v === "string") return v;
|
||||
if (Array.isArray(v)) return _resolveLinkedText(wf, v[0], v[1] ?? 0);
|
||||
return null;
|
||||
};
|
||||
const combined = _resolveEditPlusText(n.inputs?.mode, n.inputs?.text_edit, resolveField("text1"), resolveField("text2"));
|
||||
if (combined) textMap.set(id, combined);
|
||||
continue;
|
||||
}
|
||||
// MiniMaxH3ImageToVideo等の動画生成オールインワンノード。CLIPTextEncode等を経由せず
|
||||
// 自ノードの"prompt"入力に直接テキストを持つ(positive/negativeの区別も無い)ため、
|
||||
// _isTextEncoderNode判定を通さずtextMapへ直接投入する(fallbackのtextsに載る)。
|
||||
if (typeof n.inputs?.prompt === "string" && /ImageToVideo|TextToVideo/i.test(ct)) {
|
||||
textMap.set(id, n.inputs.prompt);
|
||||
continue;
|
||||
}
|
||||
if (!_isTextEncoderNode(ct)) continue;
|
||||
// "prompt" — TextEncodeQwenImageEdit(Plus)/TextEncodeBooguEdit and similar Image Edit
|
||||
// model text encoders use this key instead of "text"/"text_g".
|
||||
const raw = n.inputs?.text ?? n.inputs?.text_g ?? n.inputs?.prompt ?? null;
|
||||
if (raw && typeof raw === "string") { textMap.set(id, raw); }
|
||||
else if (Array.isArray(raw)) { const txt = _resolveLinkedText(wf, raw[0], raw[1] ?? 0); if (txt) textMap.set(id, txt); }
|
||||
}
|
||||
@@ -3069,20 +3424,34 @@ function _extractPromptsAPI(wf) {
|
||||
for (const n of Object.values(wf)) {
|
||||
if (!n || !_isSamplerNode(n.class_type ?? "")) continue;
|
||||
foundSampler = true;
|
||||
for (const [key, val] of Object.entries(n.inputs ?? {})) {
|
||||
// SamplerCustomAdvanced doesn't hold positive/negative directly — it drives a separate
|
||||
// Guider node (CFGGuider/DualCFGGuider/BasicGuider) via its "guider" input. Scan that
|
||||
// node's inputs instead so the loop below can find the positive/negative-role keys.
|
||||
let inputsToScan = n.inputs ?? {};
|
||||
if (Array.isArray(n.inputs?.guider)) {
|
||||
const guiderNode = wf[String(n.inputs.guider[0])];
|
||||
if (guiderNode?.inputs) inputsToScan = guiderNode.inputs;
|
||||
}
|
||||
for (const [key, val] of Object.entries(inputsToScan)) {
|
||||
if (!Array.isArray(val)) continue;
|
||||
const isPos = key === "positive" || key.startsWith("positive");
|
||||
// DualCFGGuider (HiDream E1): cond1 carries the positive-derived conditioning.
|
||||
const isPos = key === "positive" || key === "cond1" || key.startsWith("positive");
|
||||
const isNeg = key === "negative" || key.startsWith("negative");
|
||||
if (!isPos && !isNeg) continue;
|
||||
// TextEncodeMageFlowEdit — 1ノードでprompt/negative_prompt両方を直接持つため、
|
||||
// textMap(1ノード1テキスト)では両ロールを表現できない
|
||||
// TextEncodeMageFlowEdit / TextEncodeBooguEdit — 1ノードでprompt/negative_prompt両方を
|
||||
// 直接持つため、textMap(1ノード1テキスト)では両ロールを表現できない
|
||||
const srcNode = wf[String(val[0])];
|
||||
if (srcNode?.class_type === "TextEncodeMageFlowEdit") {
|
||||
if (srcNode?.class_type === "TextEncodeMageFlowEdit" || srcNode?.class_type === "TextEncodeBooguEdit") {
|
||||
const txt = isPos ? srcNode.inputs?.prompt : srcNode.inputs?.negative_prompt;
|
||||
if (txt && typeof txt === "string") { if (isPos) pos.add(txt); else neg.add(txt); }
|
||||
continue;
|
||||
}
|
||||
const txt = textMap.get(String(val[0]));
|
||||
// InstructPixToPixConditioning (HiDream E1): forwards the actual text-encoder
|
||||
// conditioning through its own positive/negative inputs — follow one more hop.
|
||||
const resolvedId = srcNode?.class_type === "InstructPixToPixConditioning" && Array.isArray(srcNode.inputs?.[isPos ? "positive" : "negative"])
|
||||
? String(srcNode.inputs[isPos ? "positive" : "negative"][0])
|
||||
: String(val[0]);
|
||||
const txt = textMap.get(resolvedId);
|
||||
if (!txt) continue;
|
||||
if (isPos) pos.add(txt); else neg.add(txt);
|
||||
}
|
||||
@@ -3170,6 +3539,7 @@ async function _extractAllMetadata(file) {
|
||||
const name = file.name.toLowerCase();
|
||||
const isJSON = file.type === "application/json" || name.endsWith(".json");
|
||||
const isWebP = file.type === "image/webp" || name.endsWith(".webp");
|
||||
const isMP4 = file.type === "video/mp4" || name.endsWith(".mp4");
|
||||
|
||||
async function fromWorkflow(originalWf, source) {
|
||||
let wf = originalWf;
|
||||
@@ -3205,6 +3575,17 @@ async function _extractAllMetadata(file) {
|
||||
const wf = _extractWorkflowFromEXIF(exif);
|
||||
return wf ? await fromWorkflow(wf, "comfyui") : null;
|
||||
}
|
||||
if (isMP4) {
|
||||
let tags; try { tags = await _readMP4MetaTags(file); } catch { return null; }
|
||||
if (!tags) return null;
|
||||
for (const key of ["prompt", "workflow"]) {
|
||||
const raw = tags[key];
|
||||
if (!raw) continue;
|
||||
let wf; try { wf = JSON.parse(_sanitizeJSON(raw)); } catch { continue; }
|
||||
if (wf) return await fromWorkflow(wf, "comfyui");
|
||||
}
|
||||
return null;
|
||||
}
|
||||
// PNG
|
||||
const chunks = await _readAllPNGTextChunks(file);
|
||||
if (!chunks) return null;
|
||||
@@ -3314,7 +3695,7 @@ const setupInfoDropHandlers = (container) => {
|
||||
const renderInfoModels = (container, meta) => {
|
||||
container.innerHTML = "";
|
||||
if (!meta) {
|
||||
container.innerHTML = `<div class="wfm-nlp-info-empty">Drop a PNG/WebP/JSON file to view model info</div>`;
|
||||
container.innerHTML = `<div class="wfm-nlp-info-empty">Drop a PNG/WebP/MP4/JSON file to view model info</div>`;
|
||||
return;
|
||||
}
|
||||
const sections = [
|
||||
@@ -3353,7 +3734,7 @@ const renderInfoModels = (container, meta) => {
|
||||
const renderInfoLoras = (container, meta) => {
|
||||
container.innerHTML = "";
|
||||
if (!meta || !meta.loras || meta.loras.length === 0) {
|
||||
container.innerHTML = `<div class="wfm-nlp-info-empty">${meta ? "No LoRA found" : "Drop a PNG/WebP/JSON file to view LoRA info"}</div>`;
|
||||
container.innerHTML = `<div class="wfm-nlp-info-empty">${meta ? "No LoRA found" : "Drop a PNG/WebP/MP4/JSON file to view LoRA info"}</div>`;
|
||||
return;
|
||||
}
|
||||
for (const lora of meta.loras) {
|
||||
@@ -3402,7 +3783,7 @@ const renderInfoPrompts = (container, meta) => {
|
||||
const promptFull = panelEl?.querySelector("#wfm-nlp-info-prompt-full");
|
||||
const promptFullLabel = panelEl?.querySelector("#wfm-nlp-info-prompt-full-label");
|
||||
if (!meta) {
|
||||
container.innerHTML = `<div class="wfm-nlp-info-empty">Drop a PNG/WebP/JSON file to view prompts</div>`;
|
||||
container.innerHTML = `<div class="wfm-nlp-info-empty">Drop a PNG/WebP/MP4/JSON file to view prompts</div>`;
|
||||
return;
|
||||
}
|
||||
const allPrompts = [
|
||||
@@ -3467,8 +3848,8 @@ const renderInfoTab = (container) => {
|
||||
<div class="wfm-nlp-info-layout">
|
||||
<div class="wfm-nlp-info-drop" id="wfm-nlp-info-drop">
|
||||
<img id="wfm-nlp-info-preview-img" style="display:none;max-width:100%;max-height:100%;object-fit:contain;pointer-events:none;">
|
||||
<span id="wfm-nlp-info-drop-label" style="font-size:11px;color:var(--descrip-text,#999);pointer-events:none;">Drop PNG / WebP / JSON</span>
|
||||
<input type="file" id="wfm-nlp-info-file-input" accept=".png,.webp,.json,image/png,image/webp,application/json" style="display:none;">
|
||||
<span id="wfm-nlp-info-drop-label" style="font-size:11px;color:var(--descrip-text,#999);pointer-events:none;">Drop PNG / WebP / MP4 / JSON</span>
|
||||
<input type="file" id="wfm-nlp-info-file-input" accept=".png,.webp,.mp4,.json,image/png,image/webp,video/mp4,application/json" style="display:none;">
|
||||
</div>
|
||||
<div id="wfm-nlp-info-fileinfo" class="wfm-nlp-info-fileinfo">—</div>
|
||||
<div id="wfm-nlp-info-subcontent" class="wfm-nlp-info-subcontent"></div>
|
||||
@@ -3494,11 +3875,38 @@ const AI_BACKEND_DEFAULT_URLS = {
|
||||
ollama: "http://localhost:11434",
|
||||
lmstudio: "http://localhost:1234",
|
||||
lemonade: "http://localhost:13305",
|
||||
unsloth: "http://localhost:8888",
|
||||
};
|
||||
function getAiBackendDefaultUrl(backend) {
|
||||
return AI_BACKEND_DEFAULT_URLS[backend] || AI_BACKEND_DEFAULT_URLS.ollama;
|
||||
}
|
||||
|
||||
// Server relays the request, attaching the Unsloth API key from .env so it
|
||||
// never reaches the frontend — see py/routes/unsloth_routes.py.
|
||||
async function aiUnslothProxy(baseUrl, path, method, payload) {
|
||||
const r = await fetch("/api/wfm/unsloth/proxy", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ baseUrl, path, method, payload }),
|
||||
});
|
||||
const data = await r.json().catch(() => ({}));
|
||||
if (!r.ok) throw new Error(data.message || `HTTP ${r.status}`);
|
||||
return data;
|
||||
}
|
||||
|
||||
// Unlike Ollama/LM Studio/Lemonade (which inline reasoning as <think> tags
|
||||
// inside content), Unsloth's OpenAI-compatible API returns it in a separate
|
||||
// `reasoning_content` field. Fold it back into a <think> block so the
|
||||
// existing Thinking-mode show/strip logic (_aiApplyGenOptions/aiStripThinkingTags)
|
||||
// still applies uniformly. Without this, a low max_tokens can make the model
|
||||
// spend its whole budget reasoning and return an empty `content` with no
|
||||
// visible explanation.
|
||||
function _aiUnslothContent(message) {
|
||||
const reasoning = message?.reasoning_content;
|
||||
const content = message?.content || "";
|
||||
return reasoning ? `<think>${reasoning}</think>${content}` : content;
|
||||
}
|
||||
|
||||
function isValidAiUrl(url) {
|
||||
try {
|
||||
const u = new URL(url);
|
||||
@@ -3517,22 +3925,51 @@ function saveAiCfg(patch) {
|
||||
return d;
|
||||
}
|
||||
|
||||
async function aiCallLLM(url, backend, model, prompt) {
|
||||
// Ollamaは `think` (bool) と `options.num_predict` で制御できるが、
|
||||
// LM Studio/LemonadeはOpenAI互換APIのため max_tokens のみ標準対応。
|
||||
// thinking mode切替は非対応バックエンド／モデル向けに、出力からの
|
||||
// <think>タグ除去でも担保する（aiStripThinkingTagsを参照）。
|
||||
function _aiApplyGenOptions(body, backend, settings) {
|
||||
const maxTokens = parseInt(settings?.maxTokens, 10);
|
||||
if (backend === "ollama") {
|
||||
body.think = !!settings?.thinkingMode;
|
||||
if (maxTokens > 0) body.options = { ...(body.options || {}), num_predict: maxTokens };
|
||||
} else if (maxTokens > 0) {
|
||||
body.max_tokens = maxTokens;
|
||||
}
|
||||
return body;
|
||||
}
|
||||
|
||||
function aiStripThinkingTags(text) {
|
||||
return (text || "")
|
||||
.replace(/<think>[\s\S]*?<\/think>/gi, "")
|
||||
.replace(/<thinking>[\s\S]*?<\/thinking>/gi, "")
|
||||
.trim();
|
||||
}
|
||||
|
||||
async function aiCallLLM(url, backend, model, prompt, settings = {}) {
|
||||
if (backend === "ollama") {
|
||||
const r = await fetch(`${url}/api/generate`, {
|
||||
method: "POST", headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, prompt, stream: false }),
|
||||
body: JSON.stringify(_aiApplyGenOptions({ model, prompt, stream: false }, backend, settings)),
|
||||
});
|
||||
if (!r.ok) throw new Error(`HTTP ${r.status}`);
|
||||
return (await r.json()).response || "";
|
||||
const text = (await r.json()).response || "";
|
||||
return settings.thinkingMode ? text : aiStripThinkingTags(text);
|
||||
} else if (backend === "unsloth") {
|
||||
const body = _aiApplyGenOptions({ model, messages: [{ role: "user", content: prompt }], stream: false }, backend, settings);
|
||||
const d = await aiUnslothProxy(url, "/v1/chat/completions", "POST", body);
|
||||
const text = _aiUnslothContent(d.choices?.[0]?.message);
|
||||
return settings.thinkingMode ? text : aiStripThinkingTags(text);
|
||||
} else {
|
||||
const r = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST", headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages: [{ role: "user", content: prompt }], stream: false }),
|
||||
body: JSON.stringify(_aiApplyGenOptions({ model, messages: [{ role: "user", content: prompt }], stream: false }, backend, settings)),
|
||||
});
|
||||
if (!r.ok) throw new Error(`HTTP ${r.status}`);
|
||||
const d = await r.json();
|
||||
return d.choices?.[0]?.message?.content || "";
|
||||
const text = d.choices?.[0]?.message?.content || "";
|
||||
return settings.thinkingMode ? text : aiStripThinkingTags(text);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -3548,54 +3985,99 @@ function aiFileToBase64(file) {
|
||||
});
|
||||
}
|
||||
|
||||
async function aiCallVLM(url, backend, model, prompt, base64Image, mimeType) {
|
||||
async function aiCallVLM(url, backend, model, prompt, base64Image, mimeType, settings = {}) {
|
||||
if (backend === "ollama") {
|
||||
const r = await fetch(`${url}/api/generate`, {
|
||||
method: "POST", headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, prompt, images: [base64Image], stream: false }),
|
||||
body: JSON.stringify(_aiApplyGenOptions({ model, prompt, images: [base64Image], stream: false }, backend, settings)),
|
||||
});
|
||||
if (!r.ok) throw new Error(`HTTP ${r.status}`);
|
||||
return (await r.json()).response || "";
|
||||
const text = (await r.json()).response || "";
|
||||
return settings.thinkingMode ? text : aiStripThinkingTags(text);
|
||||
} else if (backend === "unsloth") {
|
||||
const body = _aiApplyGenOptions({
|
||||
model,
|
||||
messages: [{ role: "user", content: [
|
||||
{ type: "text", text: prompt },
|
||||
{ type: "image_url", image_url: { url: `data:${mimeType};base64,${base64Image}` } },
|
||||
]}],
|
||||
stream: false,
|
||||
}, backend, settings);
|
||||
const d = await aiUnslothProxy(url, "/v1/chat/completions", "POST", body);
|
||||
const text = _aiUnslothContent(d.choices?.[0]?.message);
|
||||
return settings.thinkingMode ? text : aiStripThinkingTags(text);
|
||||
} else {
|
||||
const r = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST", headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
body: JSON.stringify(_aiApplyGenOptions({
|
||||
model,
|
||||
messages: [{ role: "user", content: [
|
||||
{ type: "text", text: prompt },
|
||||
{ type: "image_url", image_url: { url: `data:${mimeType};base64,${base64Image}` } },
|
||||
]}],
|
||||
stream: false,
|
||||
}),
|
||||
}, backend, settings)),
|
||||
});
|
||||
if (!r.ok) throw new Error(`HTTP ${r.status}`);
|
||||
return (await r.json()).choices?.[0]?.message?.content || "";
|
||||
const text = (await r.json()).choices?.[0]?.message?.content || "";
|
||||
return settings.thinkingMode ? text : aiStripThinkingTags(text);
|
||||
}
|
||||
}
|
||||
|
||||
async function aiCallChat(url, backend, model, messages) {
|
||||
async function aiCallChat(url, backend, model, messages, settings = {}) {
|
||||
if (backend === "ollama") {
|
||||
const r = await fetch(`${url}/api/chat`, {
|
||||
method: "POST", headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages, stream: false }),
|
||||
body: JSON.stringify(_aiApplyGenOptions({ model, messages, stream: false }, backend, settings)),
|
||||
});
|
||||
if (!r.ok) throw new Error(`HTTP ${r.status}`);
|
||||
return (await r.json()).message?.content || "";
|
||||
const text = (await r.json()).message?.content || "";
|
||||
return settings.thinkingMode ? text : aiStripThinkingTags(text);
|
||||
} else if (backend === "unsloth") {
|
||||
const body = _aiApplyGenOptions({ model, messages, stream: false }, backend, settings);
|
||||
const d = await aiUnslothProxy(url, "/v1/chat/completions", "POST", body);
|
||||
const text = _aiUnslothContent(d.choices?.[0]?.message);
|
||||
return settings.thinkingMode ? text : aiStripThinkingTags(text);
|
||||
} else {
|
||||
const r = await fetch(`${url}/v1/chat/completions`, {
|
||||
method: "POST", headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ model, messages, stream: false }),
|
||||
body: JSON.stringify(_aiApplyGenOptions({ model, messages, stream: false }, backend, settings)),
|
||||
});
|
||||
if (!r.ok) throw new Error(`HTTP ${r.status}`);
|
||||
return (await r.json()).choices?.[0]?.message?.content || "";
|
||||
const text = (await r.json()).choices?.[0]?.message?.content || "";
|
||||
return settings.thinkingMode ? text : aiStripThinkingTags(text);
|
||||
}
|
||||
}
|
||||
|
||||
// Strips common LLM wrapping/preamble artifacts that survive despite the system prompt.
|
||||
function cleanAiTranslationOutput(raw) {
|
||||
let s = (raw || "").trim();
|
||||
s = s.replace(/^(sure[,.]?\s*)?(here('|)s|here is)?\s*(the\s+)?translation\s*:?\s*/i, "");
|
||||
s = s.replace(/^(翻訳|译文|翻译)\s*[::]\s*/, "");
|
||||
if (s.length >= 2) {
|
||||
const first = s[0], last = s[s.length - 1];
|
||||
const pairs = { '"': '"', "'": "'", "“": "”", "「": "」" };
|
||||
if (pairs[first] === last) s = s.slice(1, -1).trim();
|
||||
}
|
||||
return s;
|
||||
}
|
||||
|
||||
// Heuristic: if the cleaned output is (near-)identical to the input, the
|
||||
// model most likely echoed the source instead of translating it.
|
||||
function aiLooksUntranslated(input, output) {
|
||||
const normalize = (s) => s.trim().toLowerCase().replace(/[\s.,!?"'。、！？「」]/g, "");
|
||||
const a = normalize(input), b = normalize(output);
|
||||
return a.length > 0 && a === b;
|
||||
}
|
||||
|
||||
async function aiFetchModels(url, backend) {
|
||||
if (backend === "ollama") {
|
||||
const r = await fetch(`${url}/api/tags`);
|
||||
if (!r.ok) throw new Error(`HTTP ${r.status}`);
|
||||
return ((await r.json()).models || []).map(m => m.name);
|
||||
} else if (backend === "unsloth") {
|
||||
const d = await aiUnslothProxy(url, "/v1/models", "GET");
|
||||
return (d.data || []).map(m => m.id);
|
||||
} else {
|
||||
const r = await fetch(`${url}/v1/models`);
|
||||
if (!r.ok) throw new Error(`HTTP ${r.status}`);
|
||||
@@ -3747,6 +4229,7 @@ const renderAiTab = (container) => {
|
||||
<label class="wfm-nlp-ai-radio"><input type="radio" name="wfm-nlp-ai-backend" value="ollama"> Ollama</label>
|
||||
<label class="wfm-nlp-ai-radio"><input type="radio" name="wfm-nlp-ai-backend" value="lmstudio"> LM Studio</label>
|
||||
<label class="wfm-nlp-ai-radio"><input type="radio" name="wfm-nlp-ai-backend" value="lemonade"> Lemonade</label>
|
||||
<label class="wfm-nlp-ai-radio"><input type="radio" name="wfm-nlp-ai-backend" value="unsloth"> Unsloth</label>
|
||||
</div>
|
||||
</div>
|
||||
<div class="wfm-nlp-ai-sec">
|
||||
@@ -3766,6 +4249,18 @@ const renderAiTab = (container) => {
|
||||
<button id="wfm-nlp-ai-model-refresh" class="wfm-nlp-ai-btn">↻</button>
|
||||
</div>
|
||||
</div>
|
||||
<div class="wfm-nlp-ai-sec">
|
||||
<div class="wfm-nlp-ai-sec-title">Generation</div>
|
||||
<div class="wfm-nlp-ai-row">
|
||||
<label class="wfm-nlp-ai-lbl" style="display:flex;align-items:center;gap:6px;cursor:pointer;">
|
||||
<input type="checkbox" id="wfm-nlp-ai-thinking-mode"> Thinking mode
|
||||
</label>
|
||||
</div>
|
||||
<div class="wfm-nlp-ai-row">
|
||||
<span class="wfm-nlp-ai-lbl">Max tokens</span>
|
||||
<input type="number" id="wfm-nlp-ai-max-tokens" class="wfm-nlp-ai-input" min="0" step="1" placeholder="0 = unlimited" style="flex:1;">
|
||||
</div>
|
||||
</div>
|
||||
<div class="wfm-nlp-ai-sec">
|
||||
<div class="wfm-nlp-ai-sec-title">Free language</div>
|
||||
<div class="wfm-nlp-ai-row">
|
||||
@@ -3819,6 +4314,11 @@ const setupAiHandlers = (container) => {
|
||||
if (freeSrcInput && cfg.freeSrcLang) freeSrcInput.value = cfg.freeSrcLang;
|
||||
if (freeDstInput && cfg.freeDstLang) freeDstInput.value = cfg.freeDstLang;
|
||||
|
||||
const thinkingModeInput = container.querySelector("#wfm-nlp-ai-thinking-mode");
|
||||
if (thinkingModeInput) thinkingModeInput.checked = !!cfg.thinkingMode;
|
||||
const maxTokensInput = container.querySelector("#wfm-nlp-ai-max-tokens");
|
||||
if (maxTokensInput && cfg.maxTokens) maxTokensInput.value = cfg.maxTokens;
|
||||
|
||||
const srcSel = container.querySelector("#wfm-nlp-ai-src");
|
||||
const dstSel = container.querySelector("#wfm-nlp-ai-dst");
|
||||
if (srcSel && cfg.srcLang) srcSel.value = cfg.srcLang;
|
||||
@@ -3850,10 +4350,13 @@ const setupAiHandlers = (container) => {
|
||||
|
||||
const srcLang = srcSel?.value || "ja";
|
||||
const dstLang = dstSel?.value || "en";
|
||||
const srcName = srcLang === "free" ? (c.freeSrcLang || "Auto") : AI_LANG_NAMES[srcLang];
|
||||
const dstName = dstLang === "free" ? (c.freeDstLang || "English") : AI_LANG_NAMES[dstLang];
|
||||
const fromPart = srcLang === "free" ? "" : `from ${srcName} `;
|
||||
const prompt = `Translate the following text ${fromPart}to ${dstName}. Output only the translated text, nothing else.\n\n${text}`;
|
||||
const srcName = srcLang === "free" ? (c.freeSrcLang || "").trim() : AI_LANG_NAMES[srcLang];
|
||||
const dstName = dstLang === "free" ? (c.freeDstLang || "English").trim() || "English" : AI_LANG_NAMES[dstLang];
|
||||
const fromPart = srcName ? ` from ${srcName}` : "";
|
||||
const systemPrompt = `You are a professional translator. Translate the text the user sends${fromPart} into ${dstName}. `
|
||||
+ `Reply with ONLY the translated text and nothing else: no explanations, no notes, no preamble like "Here is the translation:", `
|
||||
+ `no surrounding quotes, and do not repeat or echo the original text.`;
|
||||
const messages = [{ role: "system", content: systemPrompt }, { role: "user", content: text }];
|
||||
|
||||
const transBtn = container.querySelector("#wfm-nlp-ai-trans-btn");
|
||||
const statusEl = container.querySelector("#wfm-nlp-ai-status");
|
||||
@@ -3864,10 +4367,14 @@ const setupAiHandlers = (container) => {
|
||||
outputEl.value = "";
|
||||
|
||||
try {
|
||||
const result = await aiCallLLM(url, backend, model, prompt);
|
||||
outputEl.value = result.trim();
|
||||
const result = await aiCallChat(url, backend, model, messages, c);
|
||||
const cleaned = cleanAiTranslationOutput(result);
|
||||
outputEl.value = cleaned;
|
||||
statusEl.textContent = "完了";
|
||||
statusEl.className = "wfm-nlp-ai-status wfm-nlp-ai-ok";
|
||||
if (srcLang !== dstLang && aiLooksUntranslated(text, cleaned)) {
|
||||
showToast("モデルが原文をそのまま返しました。翻訳されていない可能性があります。別のモデルをお試しください。", "error");
|
||||
}
|
||||
} catch (err) {
|
||||
statusEl.textContent = `エラー: ${err.message}`;
|
||||
statusEl.className = "wfm-nlp-ai-status wfm-nlp-ai-err";
|
||||
@@ -3941,11 +4448,13 @@ const setupAiHandlers = (container) => {
|
||||
const model = container.querySelector("#wfm-nlp-ai-model")?.value || "";
|
||||
const freeSrcLang = container.querySelector("#wfm-nlp-ai-free-src")?.value?.trim() || "";
|
||||
const freeDstLang = container.querySelector("#wfm-nlp-ai-free-dst")?.value?.trim() || "";
|
||||
const thinkingMode = !!container.querySelector("#wfm-nlp-ai-thinking-mode")?.checked;
|
||||
const maxTokens = Math.max(0, parseInt(container.querySelector("#wfm-nlp-ai-max-tokens")?.value, 10) || 0);
|
||||
if (url && !isValidAiUrl(url)) {
|
||||
showToast("URLは http:// または https:// で始まる必要があります", "error");
|
||||
return;
|
||||
}
|
||||
saveAiCfg({ backend, backendUrl: url, model, freeSrcLang, freeDstLang });
|
||||
saveAiCfg({ backend, backendUrl: url, model, freeSrcLang, freeDstLang, thinkingMode, maxTokens });
|
||||
showToast("Settings saved", "success");
|
||||
});
|
||||
|
||||
@@ -3991,7 +4500,7 @@ const setupAiHandlers = (container) => {
|
||||
const messagesToSend = skillPrompt
|
||||
? [{ role: "system", content: skillPrompt }, ...chatHistory]
|
||||
: chatHistory;
|
||||
const reply = await aiCallChat(url, backend, model, messagesToSend);
|
||||
const reply = await aiCallChat(url, backend, model, messagesToSend, c);
|
||||
chatHistory.push({ role: "assistant", content: reply });
|
||||
appendChatBubble("assistant", reply);
|
||||
chatStatusEl.textContent = "";
|
||||
@@ -4223,7 +4732,7 @@ const setupAiHandlers = (container) => {
|
||||
|
||||
const prompt = `Generate ${count} wildcard entries for the category "${name}". Output only plain text in English, one entry per line, no numbers, no markdown, no asterisks, no bold, nothing else.`;
|
||||
try {
|
||||
const result = await aiCallLLM(url, backend, model, prompt);
|
||||
const result = await aiCallLLM(url, backend, model, prompt, c);
|
||||
vlmResult.value = result.trim()
|
||||
.split("\n")
|
||||
.map(l => l.replace(/\*\*/g, "").replace(/^\*\s*/, "").replace(/^\d+\.\s*/, "").trim())
|
||||
@@ -4248,7 +4757,7 @@ const setupAiHandlers = (container) => {
|
||||
vlmResult.value = "";
|
||||
|
||||
try {
|
||||
const result = await aiCallVLM(url, backend, model, VLM_PROMPTS[task], vlmImage.base64, vlmImage.mimeType);
|
||||
const result = await aiCallVLM(url, backend, model, VLM_PROMPTS[task], vlmImage.base64, vlmImage.mimeType, c);
|
||||
vlmResult.value = result.trim();
|
||||
vlmStatus.textContent = "完了";
|
||||
vlmStatus.className = "wfm-nlp-ai-status wfm-nlp-ai-ok";
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import { app } from "../../scripts/app.js";
|
||||
import { togglePanel, getNodeSetsIcon, NODE_SETS_TOOLTIP, saveSelectedAsNodeSet } from "./node_sets_menu.js";
|
||||
import { togglePanel, getNodeSetsIcon, NODE_SETS_TOOLTIP, saveSelectedAsNodeSet, showWorkflowInLibrary } from "./node_sets_menu.js";
|
||||
|
||||
const BUTTON_TOOLTIP = "Launch Workflow Studio (Shift+Click opens in new window)";
|
||||
const SNAPSHOT_TOOLTIP = "Save workflow canvas image as thumbnail";
|
||||
@@ -572,6 +572,23 @@ app.registerExtension({
|
||||
};
|
||||
},
|
||||
async setup() {
|
||||
// Send to Canvas fallback: when the Workflow Studio SPA can't reach this page
|
||||
// directly via window.opener (e.g. it isn't the opener that spawned it, or
|
||||
// the workflow is API-format and window.opener.wfmReceiveWorkflow can't
|
||||
// handle it either), the SPA writes the workflow's filename here instead of
|
||||
// the old title-drag mechanism. localStorage "storage" events fire in every
|
||||
// same-origin tab except the one that wrote the value, so this listener
|
||||
// picks it up here and opens the Library panel pre-filtered to that
|
||||
// workflow's card for the user to drag onto the canvas themselves.
|
||||
window.addEventListener("storage", (e) => {
|
||||
if (e.key === "wfm_library_filter_request" && e.newValue != null) {
|
||||
showWorkflowInLibrary(e.newValue);
|
||||
// Consume it so a future page load (which reads the stale value but
|
||||
// never sees a "storage" event for it) doesn't re-trigger the filter.
|
||||
localStorage.removeItem("wfm_library_filter_request");
|
||||
}
|
||||
});
|
||||
|
||||
const injectStyles = () => {
|
||||
const styleId = "wfm-top-menu-button-styles";
|
||||
if (document.getElementById(styleId)) return;
|
||||
|
||||
@@ -0,0 +1,627 @@
|
||||
{
|
||||
"id": "8e2c1a4f-6b3d-4a9e-9c7f-1d5e8f3a2b6c",
|
||||
"revision": 0,
|
||||
"last_node_id": 10,
|
||||
"last_link_id": 11,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 7,
|
||||
"type": "CLIPTextEncode",
|
||||
"pos": [
|
||||
413,
|
||||
389
|
||||
],
|
||||
"size": [
|
||||
425.27801513671875,
|
||||
180.6060791015625
|
||||
],
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "clip",
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"link": 5
|
||||
},
|
||||
{
|
||||
"localized_name": "text",
|
||||
"name": "text",
|
||||
"type": "STRING",
|
||||
"widget": {
|
||||
"name": "text"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "CONDITIONING",
|
||||
"name": "CONDITIONING",
|
||||
"type": "CONDITIONING",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
6
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CLIPTextEncode",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.8",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
},
|
||||
"widgets_values": [
|
||||
"text, watermark"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 3,
|
||||
"type": "KSampler",
|
||||
"pos": [
|
||||
863,
|
||||
186
|
||||
],
|
||||
"size": [
|
||||
315,
|
||||
474
|
||||
],
|
||||
"flags": {},
|
||||
"order": 5,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "model",
|
||||
"name": "model",
|
||||
"type": "MODEL",
|
||||
"link": 1
|
||||
},
|
||||
{
|
||||
"localized_name": "positive",
|
||||
"name": "positive",
|
||||
"type": "CONDITIONING",
|
||||
"link": 4
|
||||
},
|
||||
{
|
||||
"localized_name": "negative",
|
||||
"name": "negative",
|
||||
"type": "CONDITIONING",
|
||||
"link": 6
|
||||
},
|
||||
{
|
||||
"localized_name": "latent_image",
|
||||
"name": "latent_image",
|
||||
"type": "LATENT",
|
||||
"link": 2
|
||||
},
|
||||
{
|
||||
"localized_name": "seed",
|
||||
"name": "seed",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "seed"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "steps",
|
||||
"name": "steps",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "steps"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "cfg",
|
||||
"name": "cfg",
|
||||
"type": "FLOAT",
|
||||
"widget": {
|
||||
"name": "cfg"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "sampler_name",
|
||||
"name": "sampler_name",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "sampler_name"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "scheduler",
|
||||
"name": "scheduler",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "scheduler"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "denoise",
|
||||
"name": "denoise",
|
||||
"type": "FLOAT",
|
||||
"widget": {
|
||||
"name": "denoise"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "LATENT",
|
||||
"name": "LATENT",
|
||||
"type": "LATENT",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
7
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "KSampler",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.8",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
},
|
||||
"widgets_values": [
|
||||
378124453179007,
|
||||
"randomize",
|
||||
20,
|
||||
8,
|
||||
"euler",
|
||||
"normal",
|
||||
1
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 8,
|
||||
"type": "VAEDecode",
|
||||
"pos": [
|
||||
1209,
|
||||
188
|
||||
],
|
||||
"size": [
|
||||
210,
|
||||
46
|
||||
],
|
||||
"flags": {},
|
||||
"order": 6,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "samples",
|
||||
"name": "samples",
|
||||
"type": "LATENT",
|
||||
"link": 7
|
||||
},
|
||||
{
|
||||
"localized_name": "vae",
|
||||
"name": "vae",
|
||||
"type": "VAE",
|
||||
"link": 10
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "IMAGE",
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
9
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "VAEDecode",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.8",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
},
|
||||
"widgets_values": []
|
||||
},
|
||||
{
|
||||
"id": 9,
|
||||
"type": "SaveImage",
|
||||
"pos": [
|
||||
1451,
|
||||
189
|
||||
],
|
||||
"size": [
|
||||
210,
|
||||
270
|
||||
],
|
||||
"flags": {},
|
||||
"order": 7,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "images",
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 9
|
||||
},
|
||||
{
|
||||
"localized_name": "filename_prefix",
|
||||
"name": "filename_prefix",
|
||||
"type": "STRING",
|
||||
"widget": {
|
||||
"name": "filename_prefix"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"properties": {
|
||||
"Node name for S&R": "SaveImage",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.8",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
},
|
||||
"widgets_values": [
|
||||
"ComfyUI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 5,
|
||||
"type": "EmptyLatentImage",
|
||||
"pos": [
|
||||
473,
|
||||
609
|
||||
],
|
||||
"size": [
|
||||
315,
|
||||
106
|
||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "width",
|
||||
"name": "width",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "width"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "height",
|
||||
"name": "height",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "height"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "batch_size",
|
||||
"name": "batch_size",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "batch_size"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "LATENT",
|
||||
"name": "LATENT",
|
||||
"type": "LATENT",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
2
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "EmptyLatentImage",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.8",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
},
|
||||
"widgets_values": [
|
||||
1024,
|
||||
1024,
|
||||
1
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 4,
|
||||
"type": "CheckpointLoaderSimple",
|
||||
"pos": [
|
||||
51.3,
|
||||
182.49999999999997
|
||||
],
|
||||
"size": [
|
||||
315,
|
||||
98
|
||||
],
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "ckpt_name",
|
||||
"name": "ckpt_name",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "ckpt_name"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "MODEL",
|
||||
"name": "MODEL",
|
||||
"type": "MODEL",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
1,
|
||||
11
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "CLIP",
|
||||
"name": "CLIP",
|
||||
"type": "CLIP",
|
||||
"slot_index": 1,
|
||||
"links": [
|
||||
3,
|
||||
5
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "VAE",
|
||||
"name": "VAE",
|
||||
"type": "VAE",
|
||||
"slot_index": 2,
|
||||
"links": []
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CheckpointLoaderSimple",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.8",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
},
|
||||
"widgets_values": [
|
||||
"v1-5-pruned-emaonly-fp16.safetensors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"type": "VAELoader",
|
||||
"pos": [
|
||||
61.897062649999256,
|
||||
347.8818042399999
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
58
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "vae_name",
|
||||
"name": "vae_name",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "vae_name"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "VAE",
|
||||
"name": "VAE",
|
||||
"type": "VAE",
|
||||
"links": [
|
||||
10
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {}
|
||||
},
|
||||
"Node name for S&R": "VAELoader"
|
||||
},
|
||||
"widgets_values": [
|
||||
"vae-ft-mse-840000-ema-pruned.safetensors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 6,
|
||||
"type": "ImpactWildcardEncode",
|
||||
"pos": [
|
||||
415,
|
||||
150
|
||||
],
|
||||
"size": [
|
||||
420,
|
||||
322
|
||||
],
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "model",
|
||||
"name": "model",
|
||||
"type": "MODEL",
|
||||
"link": 11
|
||||
},
|
||||
{
|
||||
"localized_name": "clip",
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"link": 3
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "model",
|
||||
"name": "model",
|
||||
"type": "MODEL",
|
||||
"slot_index": 0,
|
||||
"links": []
|
||||
},
|
||||
{
|
||||
"localized_name": "clip",
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"slot_index": 1,
|
||||
"links": []
|
||||
},
|
||||
{
|
||||
"localized_name": "conditioning",
|
||||
"name": "conditioning",
|
||||
"type": "CONDITIONING",
|
||||
"slot_index": 2,
|
||||
"links": [
|
||||
4
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "populated_text",
|
||||
"name": "populated_text",
|
||||
"type": "STRING",
|
||||
"slot_index": 3,
|
||||
"links": []
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ImpactWildcardEncode"
|
||||
},
|
||||
"widgets_values": [
|
||||
"a photo of __sample_season/spring__, with __sample_lighting/natural__, highly detailed",
|
||||
"",
|
||||
"populate",
|
||||
"Select the LoRA to add to the text",
|
||||
"Select the Wildcard to add to the text",
|
||||
123456789,
|
||||
"randomize"
|
||||
]
|
||||
}
|
||||
],
|
||||
"links": [
|
||||
[
|
||||
1,
|
||||
4,
|
||||
0,
|
||||
3,
|
||||
0,
|
||||
"MODEL"
|
||||
],
|
||||
[
|
||||
2,
|
||||
5,
|
||||
0,
|
||||
3,
|
||||
3,
|
||||
"LATENT"
|
||||
],
|
||||
[
|
||||
3,
|
||||
4,
|
||||
1,
|
||||
6,
|
||||
1,
|
||||
"CLIP"
|
||||
],
|
||||
[
|
||||
4,
|
||||
6,
|
||||
2,
|
||||
3,
|
||||
1,
|
||||
"CONDITIONING"
|
||||
],
|
||||
[
|
||||
5,
|
||||
4,
|
||||
1,
|
||||
7,
|
||||
0,
|
||||
"CLIP"
|
||||
],
|
||||
[
|
||||
6,
|
||||
7,
|
||||
0,
|
||||
3,
|
||||
2,
|
||||
"CONDITIONING"
|
||||
],
|
||||
[
|
||||
7,
|
||||
3,
|
||||
0,
|
||||
8,
|
||||
0,
|
||||
"LATENT"
|
||||
],
|
||||
[
|
||||
9,
|
||||
8,
|
||||
0,
|
||||
9,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
10,
|
||||
10,
|
||||
0,
|
||||
8,
|
||||
1,
|
||||
"VAE"
|
||||
],
|
||||
[
|
||||
11,
|
||||
4,
|
||||
0,
|
||||
6,
|
||||
0,
|
||||
"MODEL"
|
||||
]
|
||||
],
|
||||
"groups": [],
|
||||
"config": {},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"offset": [
|
||||
0,
|
||||
0
|
||||
],
|
||||
"scale": 1
|
||||
},
|
||||
"ue_links": [],
|
||||
"links_added_by_ue": []
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
Reference in New Issue
Block a user