chore(release): v0.7.2 - Workflowタブソート機能、Video Editプロジェクト永続化、Assetタイプフィルタ

Workflowタブにグループフィルタ右隣のソートドロップダウン(Date/Name昇順降順)を
追加。VideoタブではEditサブタブのトリムパネルの不具合修正(「現在位置を使用」
でスクラバー表示が更新されないバグ、バッジ重なり)とUI調整(左ペイン幅・スクラバー
高さ)を行い、Video Edit Phase 5としてタイムラインのSave/Save As/Load永続化
(VideoEditProjectService新設)とサイドバーProjectタブのPlan/Editモード切替を
実装。Asset subtabには動画/画像タイプフィルタドロップダウンを追加した。

DEVLOG.md/README.mdを更新し、pyproject.tomlのバージョンを0.7.2に更新。

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

.claude/hooks/on_doc_edit.py

@@ -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()

.claude/settings.json

@@ -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
+          }
+        ]
+      }
+    ]
+  }
+}

.claudeignore

@@ -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/

.env.example

@@ -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=

.github/workflows/publish.yml

@@ -17,6 +17,6 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@main
+        uses: Comfy-Org/publish-node-action@d2366e7abb6ab16f3bb03e3520ae25c8cf749bc9 # 1.0.1
         with:
           personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.gitignore

@@ -5,3 +5,10 @@ __pycache__/
 .venv
 *.egg-info/
 data/
+
+# graphify: local-only knowledge graph output, not shared with other clones
+graphify-out/
+
+# Working-notes plan docs — local scratch, not meant to ship with the repo
+VIDEO_EDIT_TAB_PLAN.md
+QWEN_CHAT_INTEGRATION_PLAN.md

.graphifyignore

@@ -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

CLAUDE.md

@@ -199,3 +199,29 @@ os.environ["HF_HOME"] = "D:\\hf_cache"
 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
+
+-->
+

DEVLOG.index.md

@@ -0,0 +1,182 @@
+# 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.
+
+- **v0.7.2（2026-09-18）** — Workflowタブにソート機能追加（Date/Name 昇順降順）
+- **v0.7.1（2026-09-17）** — セキュリティ修正: Tagger VLM の Unsloth 経路でAPIキーがSSRFにより外部送信され得る不備を修正
+- **v0.7.0（2026-09-17）** — プレビュー枠の高さ固定・AssetサブタブをPlan/Editと並ぶ中央下部へ移設・トリムパネルにClipchamp風スクラバー追加・Frame保存のVideo Assets登録とAsset→Plan画像連携
+- **v0.6.3** — Send CCボタンでMP4動画をComic Creatorの新設「動画ツール」へ送れるように対応
+- **v0.6.2** — Image Editタブにペンタブレット筆圧対応とFillツール（バケツ塗りつぶし）を追加
+- **v0.6.1** — Galleryタブの検索が「読み込み中」のまま止まりクリアでも中断できない不具合、および50枚程度のフォルダでも数秒〜十数秒かかる不具合を修正
+- **v0.6.0** — Gallery/Image Edit両タブにPSD(Photoshop)対応を追加 — 複数画像の一括PSD書き出し、レイヤー保存/読み込み、Gallery表示、Image Editのレイヤーパネル改善
+- **v0.5.8** — Tagger DBタブに一括削除機能を追加（外部コントリビューターPRのレビュー・検証・修正・マージ）
+- **v0.5.7** — 設定エクスポート/フルバックアップのドキュメント修正: v0.5.5のVideo Plan機能追加分の言及漏れ
+- **v0.5.6** — バグ修正: WD Taggerで不正確なタグが生成される問題を修正(GitHub Issue #1)
+- **v0.5.5** — Videoタブ全面刷新: Plan/Asset/Editバッチ生成 + Projectサブタブ + プレビュー2ペイン化 + Video Assets/Video Temp運用整理 + Galleryメタデータ消失バグ根本修正
+- **v0.5.4** — 機能追加: Video/GenerateUIタブのLTX-2.5・Wan2.2対応拡大 + AI Assistant設定のUnsloth対応・独立モーダル化・モデルアンロード機能
+- **v0.5.3** — バグ修正: Labタブのワークフロー切替時に前の内容が残留 + Galleryタブの読み込み高速化・root自動展開 + READMEスクリーンショット刷新
+- **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

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 ketle-man
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.index.md

@@ -0,0 +1,262 @@
+# 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)
+
+![Workflow Studio](https://img.shields.io/badge/ComfyUI-Custom_Node-blue)
+![Version](https://img.shields.io/badge/version-0.7.2-green)
+
+## 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), Sort
+
+### 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, CivitAI Host, CivitAI API Key, Default workflow, Default Checkpoint, Video Playback Volume, 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, PSD 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, VLM tagging, Single tab, Single output options, Batch tab, DB tab, Dependencies
+
+### Image Edit Tab (v0.3.65)
+Layer-based image editor, Loading images, Open PSD button, Close button, New button, Tools, Layer panel, Duplicate layer (⧉), Layer lock, Text quality, Export, Canvas navigation, Undo / Redo
+
+### Video Tab (v0.5.0 – v0.7.2)
+Dedicated video generation UI, independent of GenerateUI, Plan subtab, Base settings row, Edit subtab, Edit project persistence, Asset subtab, Project subtab, Two center previews, First Frame / Last Frame are both optional, Semantic node lookup, not hardcoded IDs, Field highlight color, Load in Video, Frame tab, GIF tab, Playback volume
+
+### 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, Model unload, 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
+- **[psd-tools](https://pypi.org/project/psd-tools/)** (v0.6.0) — enables all PSD (Photoshop) features: Gallery bulk "Create PSD" export, Gallery browsing/thumbnails for `.psd` files, and Image Edit's Save PSD / Open PSD buttons; `pip install -r requirements.txt` (ships prebuilt wheels, no C compiler required); without it, PSD-related actions show an error toast with the install command instead of failing silently
+
+---
+
+## 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
+

README.md

@@ -12,7 +12,7 @@ A comprehensive workflow, asset management, and generation UI plugin for [ComfyU
 
 - 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 Checkpoint/VAE/Prompt/KSampler
+- 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
 
@@ -22,7 +22,7 @@ A comprehensive workflow, asset management, and generation UI plugin for [ComfyU
 - Built-in AI tools (translation and more)
 
 ![Workflow Studio](https://img.shields.io/badge/ComfyUI-Custom_Node-blue)
-![Version](https://img.shields.io/badge/version-0.3.97-green)
+![Version](https://img.shields.io/badge/version-0.7.2-green)
 
 ## Screenshots
 
@@ -30,29 +30,29 @@ A comprehensive workflow, asset management, and generation UI plugin for [ComfyU
 | :-------------------------------------: | :---------------------------------: |
 | ![Workflow Tab](docs/1_workflowtab.png) | ![Models Tab](docs/2_modelstab.png) |
 
-|                   Prompt Input Assistance                   |               Gen UI Feeder               |
-| :----------------------------------------------------------: | :---------------------------------------: |
-| ![Prompt Input Assistance](docs/3_PromptInputAssistance.png) | ![Gen UI Feeder](docs/4_GenUI_feeder.png) |
+|                   Prompt Input Assistance                   |               GenUI Batch               |
+| :----------------------------------------------------------: | :-------------------------------------: |
+| ![Prompt Input Assistance](docs/3_PromptInputAssistance.png) | ![GenUI Batch](docs/10_GenUI_Batch.png) |
 
-|                GenUI LoRA Stack                |               GenUI Batch               |
-| :---------------------------------------------: | :-------------------------------------: |
-| ![GenUI LoRA Stack](docs/9_GenUI_LoraStack.png) | ![GenUI Batch](docs/10_GenUI_Batch.png) |
-
-|                   Models Multi-select Menu                   |            Top Bar            |
-| :-----------------------------------------------------------: | :---------------------------: |
-| ![Models Multi-select Menu](docs/11_multiple_select_menu.png) | ![Top Bar](docs/5_topbar.png) |
+|            Top Bar            |              Image Edit Tab              |
+| :----------------------------: | :--------------------------------------: |
+| ![Top Bar](docs/5_topbar.png) | ![Image Edit Tab](docs/13_ImageEdit.png) |
 
 |              WS Library              |                  Library Information                  |
 | :----------------------------------: | :---------------------------------------------------: |
 | ![WS Library](docs/6_ws_library.png) | ![Library Information](docs/7_library_Infomation.png) |
 
-|            Tagger Tab            |              Image Edit Tab              |
-| :-------------------------------: | :--------------------------------------: |
-| ![Tagger Tab](docs/12_Tagger.png) | ![Image Edit Tab](docs/13_ImageEdit.png) |
+|              Video Tab              |               AI TOOL Tab               |
+| :----------------------------------: | :--------------------------------------: |
+| ![Video Tab](docs/14_videotab.png) | ![AI TOOL Tab](docs/17_aitooltab.png) |
 
-|             Customize             |  |
-| :--------------------------------: | :-: |
-| ![Customize](docs/8_Customize.png) |  |
+|          Video Tab - Edit           |          Video Tab - Asset           |
+| :----------------------------------: | :-----------------------------------: |
+| ![Video Tab - Edit](docs/18_videotab_Edit.png) | ![Video Tab - Asset](docs/19_videotab_Asset.png) |
+
+|              Lab Tab              |               Lab Index Image               |
+| :--------------------------------: | :------------------------------------------: |
+| ![Lab Tab](docs/15_labtab.png) | ![Lab Index Image](docs/16_lab_index.png) |
 
 ---
 
@@ -69,9 +69,11 @@ A comprehensive workflow, asset management, and generation UI plugin for [ComfyU
 - **Badge management** — add, rename, delete badges with custom colors shared with the Models tab (⚙ Badge button)
 - **AI summary** — generate workflow descriptions using Ollama
 - **Import / Export** — import workflows from files or clipboard; **Send to Canvas** button sends the selected workflow directly to the ComfyUI canvas (UI and API formats both supported)
+- **Side panel toolbar buttons** (v0.5.0) — selecting a card shows three buttons in the top toolbar: **Load in GenerateUI**, **Load in Video** (loads a MiniMax H3 Image-to-Video, LTX-2.5 (Text/Image/First&Last-Frame to Video), or Wan2.2 (Text to Video) workflow into the Video tab (LTX-2.5/Wan2.2 support: v0.5.4); shows an error toast for workflows that don't contain a supported node), and **Send to Canvas**; the detail modal's duplicate copies of these three buttons were removed in favor of this single toolbar location
 - **Default view setting** — persist your preferred view mode (Thumbnail / Table)
 - **Search clear (✕)** — inline ✕ button appears inside the search box whenever text is entered; click to clear immediately
 - **Clear all filters (✕ Clear)** — rightmost toolbar button; resets search text, group filter, and badge filter (ALL) in one click
+- **Sort** (v0.7.2) — Date (Newest/Oldest) / Name (A-Z/Z-A) dropdown next to the group filter, persisted via localStorage — same pattern as the Gallery tab's sort control
 
 </details>
 
@@ -89,20 +91,24 @@ A comprehensive workflow, asset management, and generation UI plugin for [ComfyU
 <summary><h3>GenerateUI Tab (v0.3.5)</h3></summary>
 
 - **6-tab layout** — Input / Model / Settings / Feeder / Batch / **Lab** tabs; Input, Model, and Settings each include a Raw JSON column on the right for instant preview and direct editing
-- **Save button** — located at the right end of the subtab row; opens a filename dialog (default: current workflow name) and saves the current workflow as a `.json` file to the Workflow tab via the import API
+- **Save button** — located at the right end of the subtab row; opens a filename dialog (default: current workflow name) and saves the current workflow as a `.json` file to the Workflow tab via the import API; hidden while the Batch or Lab subtab is active (v0.4.6), since those work on workflow copies / their own Plan files rather than the currently loaded workflow
+  - **Default-workflow snapshot fix** (v0.4.8) — overwriting the loaded workflow under a filename that's currently set as the auto-load **Default workflow** (Settings tab, see below) now also refreshes that cached snapshot; previously only the file on disk was updated, so the old (pre-edit) version — including any checkpoint no longer installed — kept reappearing after every page reload until the default was re-set by hand
 - **Input tab** — Prompt and Image inner tabs (drag-and-drop upload); Prompt tab shows Positive Prompt and Negative Prompt textareas, plus an **Embeddings selector** at the bottom (Filter + Select + Weight input + Paste button); Paste inserts `(embedding:Name:weight)` at the cursor position of the last focused textarea (defaults to Positive when neither is focused); Raw JSON (540px) in the right column
   - **Revert button** (v0.3.70) — a ↺ button next to each Apply button reverts the textarea to the node's currently applied value (the reverse of Apply)
   - **Emphasis weight editing** (v0.3.70) — select text (or just place the cursor on a word/parenthesis block) and press Ctrl+↑/↓ to adjust `(text:weight)` by ±0.05, A1111/native-ComfyUI style; parentheses are removed automatically when the weight returns to 1.0
   - **Mask slot for inpainting** (v0.3.71) — LoadImage slots whose MASK output feeds an inpaint node (e.g. `VAEEncodeForInpaint`) show an additional Mask drop zone; when a mask is set, image + mask are composited into a single RGBA upload (mask baked into the alpha channel) matching ComfyUI's native alpha→MASK extraction — no extra nodes required
   - **Placeholder Image + Clear** (v0.3.97) — each LoadImage card gets a **Clear** button (resets that card's pending selection/preview back to empty; doesn't touch the workflow node itself) and a **Placeholder** button; a collapsible **Placeholder Image** section (collapsed by default) at the top of the Image sub-panel configures one shared default — **Color** mode (set Width/Height/Color, generates a solid-color PNG on the fly) or **Image** mode (drop one default image, reused as-is) — remembered across sessions; clicking a card's own Placeholder button immediately replaces just that card's image with the configured default, so multiple LoadImage cards can each be replaced individually with one click
-- **Model tab** — Checkpoint, VAE, LoRA, ControlNet, Diffusion Model (UNETLoader / UnetLoaderGGUF / LoaderGGUF), Text Encoder (CLIPLoader / DualCLIPLoader / ClipLoaderGGUF / DualClipLoaderGGUF — single clip: type + device; dual clip: two clip selectors + type + device), **Hypernetwork** (with Strength field) selectors with filter; Raw JSON on the right
+- **Model tab** — Checkpoint, VAE, Diffusion Model (UNETLoader / UnetLoaderGGUF / LoaderGGUF), and Text Encoder (CLIPLoader / DualCLIPLoader / ClipLoaderGGUF / DualClipLoaderGGUF — single clip: type + device; dual clip: two clip selectors + type + device) are always shown; **ControlNet** and **Hypernetwork** (with Strength field) are collapsed by default (v0.3.98) since they're used less often — click their header to expand; **LoRA** (Single/Stack tabs) has its own column; all selectors have a filter; Raw JSON on the right
+  - **Highlight color** (v0.3.98) — the label of each model type above, plus the LoRA column header, turns a distinct color (default bright green, customizable in the Settings tab) when the loaded workflow actually contains that type of node; stays visible even while ControlNet/Hypernetwork are collapsed, so relevance is visible without expanding
   - **LoRA Stack Group toggle** (v0.3.92) — for `Lora Loader (LoraManager)` nodes, a **Use Stack Group** checkbox next to the Single/Stack tabs switches what the Stack tab shows and edits: OFF (the default whenever a workflow is loaded) displays and edits the LoRAs actually configured in the workflow's own Lora Manager node — correctly showing every LoRA it holds, not just the first one; ON switches to the LoRA group registered in the Models tab instead; toggling either direction clears the node's LoRA list first, then loads it from the newly selected source, and switching back to OFF restores exactly the list that was present when the workflow was loaded
+  - **LoRA Bypass button** (v0.4.4) — a ⛔ toggle next to the Refresh Stack group button in the LoRA column header; forces the target LoRA node's strength to 0 (`strength_model`/`strength_clip` for a standard LoraLoader, or every entry's `strength`/`clipStrength` for a Lora Manager node — its LoRA list stays intact, not replaced) without touching the actual ComfyUI node's mode; click again to restore the exact values that were there before toggling; targets whichever node Single/Stack Apply default to (the workflow's Lora Manager node if it has one, else the first LoRA node); clicking a Single or Stack Apply button clears the bypass state so it can't silently reassert itself later
 - **Settings tab** — KSampler and Latent Image side by side at 50% width each; Raw JSON on the right
 - **Always-visible Raw JSON** — edit the API-format JSON directly from any tab with syntax highlighting; Apply button reloads the workflow; built-in **search bar** (always shown) finds all matches as you type with count display (`3/12`); navigate with ↑/↓ buttons or Enter / Shift+Enter; Escape or ✕ clears; current match highlighted in orange, other matches in yellow
 - **Bypass/Mute node handling** (v0.3.75) — loading a UI-format workflow with Bypass-mode nodes now reroutes their wires to the upstream source (matching same-type input/output slots, recursively through chained bypasses) instead of leaving a dangling reference that failed validation; Mute-mode nodes are excluded without rerouting, same as ComfyUI itself; when either is present, a note listing the affected nodes appears above the Raw JSON search bar in the Input/Model/Settings tabs
 - **One-click generation** — queue prompts to ComfyUI without leaving the studio
 - **Seed control** — randomize, lock, or manually set seeds; seed input and mode selector stacked vertically for readability
 - **Style selector** — checkbox and dropdown next to the Reset Workflow button; enable to apply a Fooocus-style JSON to positive and negative prompts at generation time; style files (`*.json`) are loaded from `user/default/Workflow-Studio/style/`; the style's `{prompt}` placeholder is replaced with the original prompt text, or the style is appended if no placeholder exists; `negative_prompt` is appended to the existing negative prompt; styles are applied to a per-generation copy and do not modify the loaded workflow
+  - **Wildcards inside a Style now expand** (v0.4.7) — an A1111-style `__wildcard__` reference written into a Style's Positive/Negative prompt is expanded at generation time just like one typed directly into GenerateUI; the Style text is merged into the prompt *before* wildcard expansion runs (previously the order was reversed, so a Style's own wildcard references were sent to ComfyUI unexpanded)
 - **Create Catalog / Catalog buttons** (v0.3.95) — next to the Style dropdown. **Create Catalog** generates one preview image per Style checked in the Batch tab's Style sub-tab (using the currently loaded workflow, typically T2I), then saves each result into a chosen `ws_style_catalog` subfolder named after the Style (existing files with the same name are overwritten); a modal lets you create a new destination folder or pick an existing one — separate folders are useful for grouping catalogs by checkpoint or by purpose. **Catalog** jumps straight to the Gallery tab's Style_Catalog sub-tab to browse the results (see below)
   - **Don't keep a copy in the Output folder** (v0.3.96) — checkbox in the Create Catalog modal; each generation still writes a normal file to the Output folder first (the workflow's own Save Image node always does that), but once its copy has been saved into the catalog folder, checking this deletes that Output-folder original right away — keeps a large catalog run (e.g. every Style at once) from flooding the Output folder with near-duplicate files; if a style's catalog copy fails to save, its Output file is left alone rather than deleted
 - **Batch type selector** — check one of the column header checkboxes in the Batch Queue pane (Checkpoint / Lora / Prompt / Workflow / Sampler / Scheduler / Style) to activate that batch type; only one type can be active at a time; the Batch panel below Generate shows the active type, progress, and Pause/Resume/Stop controls
@@ -112,6 +118,9 @@ A comprehensive workflow, asset management, and generation UI plugin for [ComfyU
   - **Right pane (Batch Queue)** — 7 columns: Checkpoint / Lora / Prompt / Workflow / Sampler / Scheduler / **Style**; each column header has an enable checkbox (radio behavior: only one at a time); count shown per column; Style batch applies each selected style sequentially to a workflow copy
   - **Lora batch prompt sync** (v0.3.69) — for each selected LoRA, its syntax (`<lora:name:strength_model:strength_clip>`) and CivitAI trained words are automatically appended to the Positive prompt (rebuilt from the original prompt each time, not accumulated), then restored to the pre-batch state when the batch completes or is stopped; if a selected LoRA is not yet recognized by ComfyUI's model list, a warning toast suggests a Refresh or restart
 - **UI-to-API conversion** — automatic conversion supporting subgraphs (nested workflows), COMBO types, and display-only node exclusion; improved analysis covers SDXL multi-hop CONDITIONING chains, CLIPTextEncodeSDXL, SDXLPromptStyler, KSamplerAdvanced, and Guider-based "Advanced Sampling" workflows (`SamplerCustomAdvanced`/`SamplerCustom` + `CFGGuider`/`DualCFGGuider`/`BasicGuider` + `RandomNoise` + `KSamplerSelect` + `*Scheduler` — Flux.2 Klein, LongCat, Boogu, HiDream E1, etc.)
+  - **MultiType widget-value bug fix** (v0.5.4) — a node input declaring a comma-joined MultiType (e.g. LTX-2.5's `LTXVEmptyLatentAudio.frame_rate: "FLOAT,INT"`) was silently dropped from the widget list instead of being recognized as a scalar widget, which shifted every subsequent widget's value onto the wrong input (surfaced as `LTXVEmptyLatentAudio.batch_size` receiving `frame_rate`'s value and video generation failing with "Expected size 1 but got size 25")
+- **Video workflow support** (v0.5.2) — loading a MiniMax H3 (or other all-in-one video-generation) workflow now works from GenerateUI too, not just the dedicated Video tab: the Input tab's Prompt field picks up its `prompt` widget, the Image tab picks up its LoadImage-based `first_frame` the same way any other I2I workflow does, and generation runs with a longer timeout since video generation takes much longer than a still image; the result preview shows the generated mp4 inline (`<video>`) instead of trying to render it as an image
+  - **LTX-2.5 / Wan2.2 support** (v0.5.4) — these follow ComfyUI's regular node-graph pattern rather than an all-in-one node, so they needed their own detection: Positive/Negative Prompt now traces through LTX-2.5's `LTXVDualCFGGuider`/`LTXVConditioning` nodes to the actual prompt text, and the Settings tab's Latent Image section recognizes Wan2.2's `EmptyHunyuanLatentVideo` and LTX-2.5's `EmptyLTXVLatentVideo` (width/height/batch size, plus an added **Length (frames)** field) instead of showing "No EmptyLatentImage node found"; the extended generation timeout now also triggers off a `SaveVideo`/`CreateVideo` node anywhere in the graph, not just an all-in-one node
 - **Eagle integration** — auto-save generated images to [Eagle](https://eagle.cool/) with metadata
 
 </details>
@@ -145,18 +154,20 @@ Two independent modes selectable via **[Image Loop] / [Gallery]** toggle buttons
 </details>
 
 <details>
-<summary><h3>Lab subtab (v0.3.87 – v0.3.97)</h3></summary>
+<summary><h3>Lab subtab (v0.3.87 – v0.5.2)</h3></summary>
 
-An experimental batch generator: runs the workflow currently loaded in GenerateUI N times, letting Checkpoint / VAE / Prompt / KSampler each change independently starting at a chosen iteration — unlike the Batch tab's single-axis queue, every column keeps its own list of change points. Never mutates the loaded workflow; every iteration runs on a fresh clone. Works with both I2I and T2I workflows (v0.3.90).
+An experimental batch generator: runs the workflow currently loaded in GenerateUI N times, letting Model (Checkpoint/LoRA Single/VAE) / Prompt / KSampler each change independently starting at a chosen iteration — unlike the Batch tab's single-axis queue, every column keeps its own list of change points. Never mutates the loaded workflow; every iteration runs on a fresh clone. Works with both I2I and T2I workflows (v0.3.90).
 
-- **Setting / Results / Plan JSON sub-panels** — Setting: image drop zone + 4 keyframe columns + run controls; Results: source image and up to 9 generated thumbnails (click to enlarge); Plan JSON: the current plan as raw editable JSON (v0.3.88)
-- **Per-column keyframes** — Checkpoint, VAE, Prompt, KSampler each hold an independent list of `{iteration, value}` keyframes; +/− per column adds/removes the last keyframe (iteration #1 is fixed and can't be removed); any keyframe other than #1 can also be deleted individually from inside its own edit modal (v0.3.93), not just the last one; click a cell to edit its value, the iteration it starts applying from, and a "revert to #1's setting" checkbox; the effective value at iteration N is the latest keyframe with iteration ≤ N, carried forward until the next one changes it; lowering the Batch count below an existing keyframe's iteration doesn't delete or renumber it — a warning toast appears and it just stays dormant until Batch count is raised again (v0.3.93)
+- **Setting / Results / Plan JSON sub-panels** — Setting: image drop zone + 3 keyframe columns + run controls; Results: source image and up to 9 generated thumbnails (click to enlarge); Plan JSON: the current plan as raw editable JSON (v0.3.88)
+- **Per-column keyframes** — Checkpoint, LoRA, VAE, Prompt, and KSampler each hold their own fully independent list of `{iteration, value}` keyframes; +/− per column adds/removes the last keyframe (iteration #1 is fixed and can't be removed); any keyframe other than #1 can also be deleted individually from inside its own edit modal (v0.3.93), not just the last one; click a cell to edit its value, the iteration it starts applying from, and a "revert to #1's setting" checkbox; the effective value at iteration N is the latest keyframe with iteration ≤ N, carried forward until the next one changes it; lowering the Batch count below an existing keyframe's iteration doesn't delete or renumber it — a warning toast appears and it just stays dormant until Batch count is raised again (v0.3.93)
+- **Model column — Checkpoint / LoRA Single / VAE** (v0.4.2, redesigned v0.4.3) — Checkpoint and VAE (and now LoRA) share one visual "Model" column; a `[C][L][V]` toggle in its header picks which of the three independent keyframe lists the shared cell list, its own +/-, and its edit modal currently operate on — each behaves exactly like its own column (same as Prompt/KSampler), just sharing the Model column's screen space, so adding, removing, editing, reverting, or bypassing a keyframe while viewing one never touches the other two. Each toggle button lights up the same highlight color as GenerateUI's Model tab (configurable in Settings) when the loaded workflow actually contains that node type. A field left blank in a later keyframe's edit modal shows "(inherits: ...)" instead of "(workflow default)" when an earlier keyframe in that same list already set something. LoRA support is Single only (no Stack, matching the experimental scope of this tab); if a LoRA is set but the loaded workflow has no LoRA node at all, Run warns once and skips it rather than auto-inserting one — unlike VAE (which does auto-inject a VAELoader when missing), wiring in a LoraLoader would require rewiring the Checkpoint's model/clip outputs through it
+- **Empty state when no matching node exists** (v0.4.5) — Checkpoint/LoRA/VAE each show "No matching node in this workflow" instead of a keyframe list when the loaded workflow has no node that column could actually override, and the column's +/− is disabled too, so no dead "(workflow default)" keyframe row can be created in the first place; VAE still counts as usable whenever a Checkpoint node exists, since its baked-in VAE can still be overridden via auto-injected VAELoader as above
 - **Wildcards** (v0.3.93) — Prompt keyframes' Positive/Negative text supports the same A1111-style `__name__` syntax as the main GenerateUI prompt fields; each keyframe is expanded fresh right before that iteration runs, so reusing the same keyframe across iterations can still draw a different random line each time
-- **Checkpoint / VAE cell filtering** (v0.3.88) — a Filter... box above the value dropdown narrows the list live, same as GenerateUI's Model tab; overlay ✕ clear button matches the Models/Nodes/Workflow/Gallery tab search boxes
 - **Prompt cell extras** (v0.3.88, refined in v0.3.89, v0.3.93, and v0.3.97) — **Get from GenerateUI** fills Positive/Negative from the workflow currently loaded in GenerateUI; **Get from Image** extracts a prompt from the image currently loaded in Lab's own Image drop zone (same detection as the Metadata tab; no separate file picker); **Get from Previous** (v0.3.97) copies both Positive and Negative wholesale from the keyframe immediately before this one in the same column (e.g. editing #3 copies #2's prompt; not shown on keyframe #1, which has no earlier row); **Clear** (v0.3.97) empties both fields; **Style** row — an **Apply** button merges whichever style is currently selected in GenerateUI's own top-bar Style dropdown into the Positive/Negative text below, comma-separated after any existing text (never clears it) and works regardless of that top-bar's own Style enable checkbox; a checkbox next to it tracks on/off and shows the applied style's name beside itself (not on the button) — unchecking only clears that name label, the already-merged text stays and is yours to edit/delete by hand
-- **Keyframe #1 live-reflect** (v0.3.97) — in every column, keyframe #1 shows the currently loaded workflow's own live setting (Checkpoint/VAE/Prompt/KSampler) instead of a blank placeholder, both in its grid cell (dashed border) and pre-filled into its edit modal, for as long as it hasn't been explicitly saved with an override; saving it — even unchanged — locks that value in as a real override and the dashed/live styling goes away; switching to the Lab subtab always refreshes this against whatever is currently loaded in GenerateUI
+- **Keyframe #1 live-reflect** (v0.3.97) — in every column, keyframe #1 shows the currently loaded workflow's own live setting instead of a blank placeholder — for the Model column this covers Checkpoint and VAE only, LoRA is never live-reflected since there's no reliable way to infer "the" active LoRA back out of a workflow node — both in its grid cell (dashed border) and pre-filled into its edit modal, for as long as it hasn't been explicitly saved with an override; saving it — even unchanged — locks that value in as a real override and the dashed/live styling goes away; switching to the Lab subtab always refreshes this against whatever is currently loaded in GenerateUI
 - **Bypass checkbox** (v0.3.97) — available on every keyframe including #1; skips that row entirely when picking which setting applies at a given iteration, as if it didn't exist, so whatever was active before it keeps running instead; Lab-internal only, never touches the actual ComfyUI node's mode; disables that row's value fields and its Revert checkbox while checked (mutually exclusive with Revert); the cell shows an amber "⏭ Bypassed" label, taking priority over both the Revert and live-reflect display
-- **T2I workflow support** (v0.3.90) — a **T2I workflow (no source image)** checkbox in the Image pane skips the source-image requirement and never touches a LoadImage node, so workflows with no image input (e.g. plain `EmptyLatentImage` txt2img) can also use the Checkpoint/VAE/Prompt/KSampler keyframes; the Image drop zone and "Use generated image for next" are greyed out (not cleared) while it's on; saved with the plan as `t2i_mode`
+- **LoRA node bypass & prompt injection** (v0.4.4) — inside the LoRA keyframe's own edit modal, two checkboxes independent of the row-level Bypass above: **Bypass LoRA node** forces that keyframe's `strength_model`/`strength_clip` to 0 at apply-time — the behavioral equivalent of ComfyUI's node Bypass (mode:4), since the workflow used here is already API-format with no `mode` concept; **Apply LoRA syntax + trigger words to Positive prompt** appends `<lora:name:strengthModel:strengthClip>` plus that LoRA's cached CivitAI trigger words to the Positive prompt, comma-separated, at apply-time — the same format GenerateUI's own Model tab Apply button writes. Switching keyframes (a different LoRA, this checkbox turned off, or the row Bypassed) always strips exactly what the previous iteration injected before adding anything new, so a batch never accumulates every LoRA it has used; the prompt checkbox is disabled automatically while Bypass LoRA node is checked
+- **T2I workflow support** (v0.3.90) — a **T2I workflow (no source image)** checkbox in the Image pane skips the source-image requirement and never touches a LoadImage node, so workflows with no image input (e.g. plain `EmptyLatentImage` txt2img) can also use the Model/Prompt/KSampler keyframes; the Image drop zone and "Use generated image for next" are greyed out (not cleared) while it's on; saved with the plan as `t2i_mode`
 - **Use generated image for next** — optional checkbox that chains iterations: from iteration 2 onward the previous iteration's first output image is fed back in as the I2I source instead of the original dropped image, using ComfyUI's `"name [type]"` annotated-filename reference (no re-upload needed); disabled together with the Image drop zone in T2I mode
 - **Workflow recall** (v0.3.90) — every saved plan records the filename of the workflow that was loaded in GenerateUI at save time (`workflow_filename` in the Plan JSON); after loading a plan, an **Open Workflow** button appears next to it — click to load that exact workflow file back into GenerateUI (asks for confirmation first, since it replaces whatever is currently loaded)
 - **Plan files** — Plan Save (silently overwrites the loaded plan) / Save As (always asks for a new name) / Plan Clear; plans are stored as `ws_labplan_<name>.json` in `user/default/Workflow-Studio/lab_plan/` (the `ws_labplan_` prefix is added automatically, v0.3.90 — type just the plain name when prompted), alongside an auto-generated `ws_labplan_<name>.png` index image (a 3-per-row contact sheet of up to 9 result thumbnails)
@@ -165,6 +176,10 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
 - **Save index image to Output on Run** (v0.3.90, on by default since v0.3.93) — checkbox in the run controls; when checked, every completed Run also builds the same contact-sheet index image used for Plan files and saves it directly into ComfyUI's own Output folder as `Lab_index_<counter>_.png` (auto-numbered like a normal generated image, right alongside the images that Run just generated); independent of Plan Save, which always writes its own copy next to the plan file regardless of this checkbox; saved with the plan as `save_index_on_run`
 - **Index images are self-contained plan files** (v0.3.91) — every index-image PNG, whether from Plan Save/Save As or from Save index image to Output on Run, carries the entire plan embedded directly in the file as a PNG `wfm_lab_plan` chunk; this is what lets the Output-folder copy above work as a Plan Load source even though it has no matching `.json` anywhere
 - **Eagle integration** — every image Lab generates is auto-saved to Eagle the same way as GenerateUI's own Generate button, if enabled in the Settings tab; the Output-folder index image above is included too (v0.3.90) once saved
+- **Select vs. edit, and keyframe reordering** (v0.4.8) — a single click on a keyframe cell now only selects it (blue outline); double-click to open its edit modal as before. Each column's header (Model / Prompt / KSampler) also gets **▲ / ▼** buttons that swap the selected keyframe with its immediate neighbor, reordering keyframes without retyping them; keyframe #1 (the live-reflecting baseline) can never move, and nothing can be swapped into its slot
+- **PG — apply a Prompt tab preset group** (v0.4.8) — a **PG** button on the Prompt column header opens a picker for the Prompt tab's Table-view preset groups (the same groups the Batch tab's checklist uses); choosing one replaces the entire Prompt column with one keyframe per preset in that group's order, starting at #1 (#1 is overwritten, not appended after). If the group has more presets than the current Batch count, Batch count is raised automatically to fit (capped at 50, the Batch input's own limit); anything still beyond that shows the existing "keyframes beyond Batch count" warning instead of being dropped
+- **Video workflow support** (v0.5.2) — loading a MiniMax H3 (or other all-in-one video-generation) workflow into GenerateUI and running Lab now works end to end: the Prompt column writes to its `prompt` widget, the KSampler column auto-hides (same "no matching node" treatment as Model sub-columns) since the sampler wired internally is a fixed part of the model's own preset rather than something meant to be tuned per keyframe, the Results grid plays mp4 results inline, and the contact-sheet index image draws each video result's last frame instead of failing silently onto a black square
+- **Use generated image for next, now video-aware** (v0.5.2) — when a Run's output is a video, the "Use generated image for next" chain extracts its last frame client-side (canvas) and uploads it as the next iteration's source image, instead of trying to feed the raw mp4 into a LoadImage node
 
 </details>
 
@@ -172,7 +187,8 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
 <summary><h3>Prompt Tab</h3></summary>
 
 - **3-column layout** — AI Assistant (left), Presets/Preset Manager tab-panel (center), Wildcard/Style support (right)
-- **AI chat assistant** — powered by [Ollama](https://ollama.com/), generate and refine prompts interactively
+- **AI chat assistant** — generate and refine prompts interactively using Ollama, LM Studio, Lemonade, or Unsloth (multi-backend: v0.5.4)
+  - **⚙ Settings modal** (v0.5.4) — replaces the old central Settings-tab panel; opens a dedicated modal (Backend / Connection / Model / Generation / Unload / Save) scoped to this tab only, saved under its own `localStorage` key so it stays independent of the Tagger tab's VLM settings; **Thinking mode** and **Max tokens** apply to every chat request, and an **Unload** button frees the selected model from Ollama/LM Studio/Lemonade (Unsloth has no such API and is shown as unsupported)
 - **Image attachment** — attach reference images for vision-capable models
 - **Translation** — JA/EN/ZH translation buttons for multilingual prompt creation
 - **Prompt presets** — save/load reusable prompt templates (positive & negative) with category support
@@ -188,21 +204,18 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
   - **New styles go to a dedicated file** — created via **+ New**, a style is appended to its own `custom.json` inside the style folder, so the bundled style packs are never modified directly
   - **+ Add to this file** — while editing an existing style, this button clears the form for a brand-new entry that gets appended to that *same* file instead of the default `custom.json` — the way to add a style into one of the bundled packs
   - Saving, renaming, or deleting a style immediately refreshes GenerateUI's Style dropdown, the Batch tab's checklist, and the Style Catalog's name list — no reload needed
-
-</details>
-
-<details>
-<summary><h3>Metadata Tab (v0.3.8)</h3></summary>
-
-- **3-column layout** — Drop zone (left) | Model info (center) | LoRA + Prompt (right)
-- **File drop** — drop a ComfyUI-generated PNG / WebP or workflow JSON onto the drop zone (or click to open a file picker); PNG/WebP images are shown as a preview
-- **Model extraction** — automatically extracts Checkpoint, VAE, Diffusion Model, and Text Encoder names from the workflow; supports both standard and subgraph-based workflows (Flux.2 Dev/Klein, Qwen-Image-Edit/2511/Layered, Z-Image Base/Turbo, Ernie Image, WAN2.2, Mage-Flow, LongCat, Boogu, HiDream E1, FireRed, Krea-2 Turbo); node types covered: UNETLoader, UnetLoaderGGUF, UNETLoaderGGUF (e.g. HiDream GGUF), CLIPLoader, DualCLIPLoader, TripleCLIPLoader, QuadrupleCLIPLoader (e.g. HiDream 4-CLIP)
-- **LoRA extraction** — lists all LoRA models with `strength_model / strength_clip` values
-- **Prompt extraction** — lists prompts with POS / NEG badges when positive/negative can be determined; when distinction is not possible (e.g. `SamplerCustomAdvanced`, intermediate nodes, cross-level connections), prompts are shown without a badge as plain **Text**; click any entry to view the full text below
-  - **CLIP Text Encode edit+ support** (v0.3.92) — `CLIPTextEncodeEditPlus` nodes are resolved using the same RAW / EDIT / front / back combination rule as the node itself, correctly merging the linked `text1` (e.g. a Lora Manager's trigger words) with the editable `text_edit` field instead of showing only one side
-- **Prompt actions** — Copy to clipboard, **GenUI:P/N** (set GenerateUI positive/negative prompt), **Prompt:P/N** (set Prompt tab preset positive/negative)
-- **Format support** — ComfyUI PNG/WebP/JSON (standard + Flux.2 / Qwen-Image / Z-Image / Ernie Image / WAN2.2 / Mage-Flow / LongCat / Boogu / HiDream E1 / FireRed / Krea-2 Turbo subgraph workflows), SD WebUI, SD Forge, Fooocus
-- **Format note** — supported formats and covered model types are always shown in the left column
+- **Form / Table toggle** (v0.4.6) — two top-level tabs at the top of Prompt Tab: **Form** is the one-item-at-a-time editor described above; **Table** shows the same Presets / Wildcards / Style data all at once as editable spreadsheets, useful once there are more than a handful of items to manage
+- **Table view — Presets / Presets Group / Wildcards / Style** (v0.4.6) — four sub-tabs, each an editable table built from the exact same data as the Form tab above (changes on either side stay in sync)
+  - **Row selection & bulk delete** — every table shares a left-most auto-numbered **#** column; click a number to select that row (multiple at once), then use the toolbar's **Delete** button to remove every selected row in one confirm, or **Deselect** to clear the selection — replacing per-row delete buttons, which were too easy to misclick
+  - **Double-click to edit in a modal** — double-clicking a row's number opens a larger prompt-editing modal instead of the small in-table textarea; Presets/Style show Positive/Negative toggle buttons above one shared textarea (switching preserves unsaved text on the other side), Wildcards shows just the file content; Save writes back and closes the modal, Close discards changes
+    - **Wildcard syntax toolbar in the Presets modal** (v0.4.7) — the same `{|}` / `__|__` / `<lora>` / file-picker toolbar as the Form tab's Wildcard composer appears above the Presets modal's textarea, since that's where a wildcard reference typically ends up; not shown in the Wildcards or Style modals (the Wildcard modal edits the raw file content itself, and Style prompts have no such wildcard-composer connection)
+  - **+ New row** — the toolbar's **+ New ...** button adds a blank editable row with small ✓ (Add) / ✕ (Cancel) icons in place of its number
+  - **Presets table** — **★** (favorite) and **B** (Batch group) columns work as toggle switches, same as the Form tab's Preset Manager; the toolbar's **BC** button clears the whole Batch group at once; **PP Copy** / **NP Copy** copy the single selected row's positive/negative prompt to the clipboard; a **group select + Add to Group** control bulk-adds every selected preset to a chosen group in one click — searching/selecting from the table itself (rather than a long per-group preset picker) keeps group assignment usable once there are many presets
+    - **★ / B sort toggles** (v0.4.7) — toolbar buttons next to the search box bring favorited and/or batch-selected presets to the top of the list; both can be enabled together (★ takes priority, B breaks ties), and everything else keeps its original relative order; toggling a row's own ★/B while a sort is active re-sorts immediately
+  - **Presets Group table** — create, rename, and delete groups, with each group's members shown as removable badges; the reserved **Batch** group is not listed here — it's managed entirely through the Presets table's B column and BC button instead
+  - **Wildcards table** — same files as the Form tab's Wildcard file manager above; editing the File path or the Ext dropdown renames the file (saves the content under the new name, then removes the old file)
+  - **Style table** — same styles as the Form tab's Style manager above, editable inline the same way as Presets
+    - **+ Add to this file** (v0.4.8) — select exactly one row whose File badge isn't `custom.json` to enable this toolbar button, bringing the Form tab's same-file-add capability (above) to the Table view too; it adds a blank row targeting that same file (shown as a badge in the File column even before saving); custom.json entries and multi-row selections leave it disabled — use the regular + New Style button for those instead
 
 </details>
 
@@ -216,14 +229,17 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
 - **Workflows directory** — configure which folder to scan for workflows
 - **Gallery output directory** — configure which output folder the Gallery tab scans for images
 - **Eagle connection** — set Eagle API endpoint for auto-save
-- **Ollama connection** — configure Ollama server URL
 - **CivitAI Host** — choose which site opens when clicking a model link: `civitai.com` (SFW only) or `civitai.red` (unrestricted); saved to `settings.json` and synced to `localStorage` for the Models tab to use without extra fetches
 - **CivitAI API Key** — optional Bearer token for authenticated CivitAI access; stored in `settings.json` (excluded from data exports); environment variable `CIVITAI_API_KEY` takes priority if set
 - **Default workflow** — set a workflow to auto-load on startup
 - **Default Checkpoint** (v0.3.73) — optional checkbox to force every checkpoint loader node's model to a chosen default whenever a workflow is loaded into GenerateUI; a safeguard against generation errors or garbled images from a stale/mismatched checkpoint saved in the workflow file
-- **Data Management** — export all plugin data (settings, metadata, prompts, etc.) to a single JSON file; import to restore data (useful when migrating or reinstalling); API keys are excluded from exports for security
+- ~~AI Assistant Settings~~ (removed in v0.5.4) — the central Ollama backend/URL/model panel that used to live here was replaced by a ⚙ Settings button + modal directly in the Prompt tab and the Tagger tab, each with its own independent configuration (backend, URL, model, Thinking mode, Max tokens, and a model Unload button) — see those tabs below; the AI TOOL tab's own Settings pane is unaffected
+- **Video Playback Volume** (v0.5.4) — the video preview's volume is remembered here and shared across every video player in the plugin (Video tab, Gallery tab), instead of resetting to maximum whenever a different video is opened
+- **Data Management** — export core settings/metadata/prompts/CivitAI cache to a single JSON file; import to restore data (useful when migrating or reinstalling); API keys are excluded from exports for security
+  - **Full Backup (ZIP)** (v0.5.2) — additionally covers what the JSON bundle can't represent: folder-based data (AI Skills, Lab Plans, Video Plans, Style presets) and the Tagger SQLite database; two opt-in checkboxes can also include ComfyUI's default workflows folder (`user/default/workflows`) and the Impact Pack's wildcard folder in the export — both of those are export-only and stored under an `_external/` prefix in the ZIP that Restore always skips, since auto-overwriting files a different plugin/ComfyUI core manages would be unsafe; copy them back by hand if needed. Restoring overwrites existing files; restart ComfyUI afterward for either restore path
 - **Text Size** — one slider (10–28 px) adjusts font size for all prompt and chat textareas at once: Generate UI positive/negative prompts, AI Assistant chat input, Preset prompts, Wildcard prompt and file editor, and Metadata prompt full preview; takes effect immediately and saved with Save Settings
 - **RAW JSON Colors** — customize the 6 syntax highlight colors for the Raw JSON editor in Generate UI: Default Text (base), Name/Scheduler (yellow), Title (pink), Width/Height (green), Prompt/Text (cyan), Image/File (red); changes apply immediately on color pick; Reset Defaults restores the original scheme; saved to `localStorage` under `wfm_settings.jsonColors` and applied on startup
+- **GenerateUI Model Tab Highlight** (v0.3.98) — pick the color used to mark Checkpoint/VAE/Diffusion Model/Text Encoder/ControlNet/Hypernetwork labels and the LoRA column header in GenerateUI's Model tab when the loaded workflow actually contains that type of node; default bright green; changes apply immediately, Reset Defaults restores it; saved to `localStorage` under `wfm_settings.modelTabActiveColor`; the same color and mechanism also drives the Video tab's field highlighting (v0.5.4, see above)
 - **Wildcard Integration** — link the WFS wildcard directory to ComfyUI-Impact-Pack's `wildcards/` directory (directory junction on Windows, symlink on other OS); existing WFS wildcard files are automatically migrated; requires [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack)
 - **G'MIC-Qt Integration** — set the path to `gmic_qt.exe` for the Image Edit Filter tool; download G'MIC-Qt Standalone from `gmic.eu/download.html` (Windows: `gmic_qt-win64.zip`), extract anywhere, then enter the full path to `gmic_qt.exe` and click Save
 - **Language** — English / Japanese / Chinese
@@ -233,31 +249,37 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
 <details>
 <summary><h3>Gallery Tab (v0.3.44)</h3></summary>
 
-- **Output / Style-Prompt sub-tabs** (v0.3.94) — the Gallery tab has two sub-tabs at the top: **Output** (the image browser described below) and **Style/Prompt** (a separate visual prompt-building library — see its own section below)
+- **Output / ImagePrompt / Style_Catalog / Metadata sub-tabs** (v0.3.94, v0.3.95, v0.4.6) — four sub-tabs at the top of the Gallery tab: **Output** (the image browser described below), **ImagePrompt** and **Style_Catalog** (separate visual libraries — see their own sections below), and **Metadata** (embeds the full Metadata tab — see its own section below)
 - **Image browser** — browse ComfyUI output images (Thumbnail / Table views) with server-side scanning optimized for 6,000+ image libraries
 - **Folder tree root label** (v0.3.90) — the root entry in the folder tree is labeled simply **[root]**, no longer suffixed with the scanned folder's own directory name (which could be a misleading, environment-specific name — e.g. StabilityMatrix installs where the output folder is a symlink named `Text2Img` — even though the folder holds every kind of generated output, not just text-to-image results)
 - **Thumbnail / Table views** — switch view modes; Favorites column shown leftmost in Table view
 - **Folder management** — create subfolders ("+ New") or delete the selected folder with all contents ("Del") from the folder tree header
 - **File operations** — move or delete individual images from the detail panel's Info tab; bulk Move To..., Export, and Delete File from the multi-select bar
-- **Download** — hover over the image preview in the detail panel to reveal a download icon (⬇); click to download the single image
-- **Multi-select** — Ctrl+click to select multiple images; Bulk Bar appears for batch operations: select group → "Add to Group" / "Remove from Group"; Favorite All / Unfavorite All; Compare (2–4 images); Move To...; **Export** (downloads all selected images as a ZIP file); Delete File
+- **Download** (v0.5.0) — a dedicated orange **Download** button in the detail panel's action-button row (next to Tagger); replaces the old preview-image hover overlay, which blocked video playback controls
+- **MP4 video support** (v0.5.0) — `.mp4` files are browsable, previewable (detail panel with native `<video controls>`, lightbox, and Compare), and included in favorites/tags/groups/move/delete/export alongside images; server-side thumbnails are generated from the first video frame via PyAV (falls back to a placeholder if PyAV/ffmpeg decoding isn't available); a small ▶ badge overlays video thumbnails in the grid
+- **MP4 embedded metadata** (v0.5.1) — the Prompt and JSON tabs now also work for `.mp4` files: reads the `workflow`/`prompt` container tags ComfyUI's `SaveVideo` writes into the mp4 (via PyAV server-side), including all-in-one video-generation nodes that carry their prompt directly on a `prompt` input rather than a separate `CLIPTextEncode`-style node (e.g. `MiniMaxH3ImageToVideo`)
+- **Info tab: dimensions & duration** (v0.5.1) — the detail panel's Info tab now also shows image dimensions (width×height) or video duration, in addition to filename, size, date, tags, and memo
+- **Multi-select** — Ctrl+click to select multiple images; Bulk Bar appears for batch operations: select group → "Add to Group" / "Remove from Group"; Favorite All / Unfavorite All; Compare (2–4 images); Move To...; **Export** (downloads all selected images as a ZIP file); **Create PSD** (v0.6.0, combines the selected images into one layered PSD file, positioned top-left on a canvas sized to the largest image); **Edit as Layers** (v0.6.0, opens all selected images in the Image Edit tab at once, each as its own layer); Delete File
 - **Image Compare** — select 2–4 images with Ctrl+click, then click "Compare" in the Bulk Bar to open a side-by-side lightbox; grid adapts to the number of selected images
 - **Prompt search** — text search covers filename, tags, memo, and prompt text (A1111 `parameters` field cached on first detail panel open)
 - **Server-side filtering** — filter by group, favorites, or tags with fast server-side set lookup (no full rescan)
 - **Group management** — create, rename, delete groups and assign/remove images using the same 4-section panel as Models tab; **`__Feeder__`** is a reserved group (🔒 prefix) that cannot be renamed or deleted; used by the Feeder Gallery mode to define the generation queue; **FC** button in the toolbar clears all members without deleting the group
 - **Thumbnail F button** — cyan overlay button (top-left of each image card) toggles the image's membership in the `__Feeder__` group; cyan and always-visible when the image is in the group; visible on hover only when inactive
 - **Favorites** — star images inline without reopening the detail panel
-- **Detail panel** — view filename, path, tags, groups, and metadata in a slide-out panel
+- **Detail panel** — view filename, path, tags, groups, and metadata in a slide-out panel; four tabs — **Info** / **JSON** / **Groups** / **Prompt**
+- **Prompt tab** (v0.4.7) — selected by default (the most frequently checked piece of info shouldn't need an extra click); lists every positive/negative prompt candidate found in the embedded workflow with POS/NEG badges, reusing the exact same list→click→full-text UI as the Metadata tab; extracts from the API-format embedding when available rather than the UI-format one, since a workflow with independent top-level and subgraph sampling chains (e.g. Krea-2) can otherwise have some of its prompts missed
 - **Workflow viewer** — Metadata tab displays workflow JSON from PNG embedded data (`prompt` / `workflow` keys) or from workflow saved by the Generate UI tab; **Copy & Send Canvas** button copies the JSON to the clipboard and sends the workflow directly to the ComfyUI canvas (UI and API formats both supported)
-- **Load GenUI button** — loads the embedded ComfyUI workflow from the selected image directly into the GenerateUI tab; shows a warning toast if no workflow is embedded or the format is unsupported; Metadata button is styled green, Load GenUI button uses the primary accent color
-- **Image Edit button** — toolbar button; sends the selected image directly to the Image Edit tab as the base layer
+- **GenUI button** (renamed from "Load GenUI" in v0.5.0 for readability) — loads the embedded ComfyUI workflow from the selected image directly into the GenerateUI tab; shows a warning toast if no workflow is embedded or the format is unsupported; Metadata button is styled green, GenUI button uses the primary accent color
+- **Image Edit button** — toolbar button; sends the selected image directly to the Image Edit tab as the base layer; PSD files (v0.6.0) are fully decomposed into their original layers instead of being flattened into one image
 - **Send GenUI Image button** — toolbar button (next to Image Edit); uploads the selected image to ComfyUI and sets it as the input for the first LoadImage node in the GenerateUI tab; automatically switches to GenerateUI → Input → Image; requires a workflow with at least one LoadImage node to be loaded
-- **Send CC button** (v0.3.69) — toolbar button, shown only when this Gallery tab is embedded as an iframe inside ComfyUI Comic Creator (a separate custom node); sends the selected image directly into Comic Creator's currently selected panel/overlay
+- **Send to LI node button** (v0.5.2) — toolbar button (next to Send GenUI Image); uploads the selected image and writes its filename into the `image` widget of the node currently selected on the ComfyUI canvas (falling back to the first LoadImage-type node found) — same destination and cross-window bridge as the Image Edit tab's **Send to LI node** button (renamed from "Send to Workflow" in this same release), so both entry points now share one consistent name; requires Workflow Studio to have been opened from ComfyUI's top menu button
+- **Send CC button** (v0.3.69) — toolbar button, shown only when this Gallery tab is embedded as an iframe inside ComfyUI Comic Creator (a separate custom node); sends the selected image directly into Comic Creator's currently selected panel/overlay; when the selected file is an MP4 video (v0.6.3), it is routed to Comic Creator's Layout-tab Video tool instead — inserted as a new video object in the selected panel (playable, capturable to a still frame) rather than as a flattened image; sending a video while the Image tab is the intended destination is not yet supported and shows an info toast instead
 - **Search clear (✕)** — inline ✕ button appears inside the search box whenever text is entered; click to clear immediately
-- **Clear all filters (✕ Clear)** — rightmost toolbar button; resets search text, tag filter, group filter, and favorites filter in one click (sort order is preserved)
+- **Clear all filters (✕ Clear)** — toolbar button next to the view-mode (Thumbnail/Table) switch (v0.5.2, moved from the toolbar's rightmost position so it sits with the other display-affecting controls); resets search text, tag filter, group filter, and favorites filter in one click (sort order is preserved)
 - **Workflow auto-save** — images generated from the Generate UI tab have their workflow automatically saved to gallery metadata
 - **Output folder configurable** — set the scanned output folder from Settings tab
 - **SVG file support** (v0.3.77) — `.svg` files are browsable, previewable (detail panel and lightbox), and included in favorites/tags/groups/move/delete/export alongside raster formats; served as-is with no rasterized thumbnail, since `<img>` scales vector graphics natively at any size
+- **PSD file support** (v0.6.0) — `.psd` files are browsable alongside other formats; thumbnails and full-size previews show every layer merged together (hidden layers excluded), rendered server-side via `psd-tools` and disk-cached; the underlying PSD file itself is never modified — only the Gallery preview is a flattened rendering
 - **Performance** — server-side 256px JPEG thumbnail generation with disk cache (`data/thumb_cache/`); infinite-scroll paging (50 images per page, IntersectionObserver); folder-level mtime cache (60s TTL); bulk operations use single-request API endpoints
 
 </details>
@@ -286,6 +308,23 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
 
 </details>
 
+<details>
+<summary><h3>Metadata Gallery subtab (v0.3.8, moved from a top-level tab in v0.4.6)</h3></summary>
+
+- **Now inside Gallery** (v0.4.6) — this used to be its own top-level tab; it's now the fourth Gallery sub-tab, to the right of Style_Catalog — everything below works exactly the same as before
+- **3-column layout** — Drop zone (left) | Model info (center) | LoRA + Prompt (right)
+- **File drop** — drop a ComfyUI-generated PNG / WebP / MP4 or workflow JSON onto the drop zone (or click to open a file picker); PNG/WebP images are shown as a preview
+- **MP4 embedded metadata** (v0.5.1) — reads the `workflow`/`prompt` container tags ComfyUI's `SaveVideo` writes into the mp4's `moov/udta/meta` box (parsed client-side, no server round-trip); also recognizes all-in-one video-generation nodes that carry their prompt directly on a `prompt` input instead of a separate `CLIPTextEncode`-style node (e.g. `MiniMaxH3ImageToVideo`)
+- **Model extraction** — automatically extracts Checkpoint, VAE, Diffusion Model, and Text Encoder names from the workflow; supports both standard and subgraph-based workflows (Flux.2 Dev/Klein, Qwen-Image-Edit/2511/Layered, Z-Image Base/Turbo, Ernie Image, WAN2.2, Mage-Flow, LongCat, Boogu, HiDream E1, FireRed, Krea-2 Turbo); node types covered: UNETLoader, UnetLoaderGGUF, UNETLoaderGGUF (e.g. HiDream GGUF), CLIPLoader, DualCLIPLoader, TripleCLIPLoader, QuadrupleCLIPLoader (e.g. HiDream 4-CLIP)
+- **LoRA extraction** — lists all LoRA models with `strength_model / strength_clip` values
+- **Prompt extraction** — lists prompts with POS / NEG badges when positive/negative can be determined; when distinction is not possible (e.g. `SamplerCustomAdvanced`, intermediate nodes, cross-level connections), prompts are shown without a badge as plain **Text**; click any entry to view the full text below
+  - **CLIP Text Encode edit+ support** (v0.3.92) — `CLIPTextEncodeEditPlus` nodes are resolved using the same RAW / EDIT / front / back combination rule as the node itself, correctly merging the linked `text1` (e.g. a Lora Manager's trigger words) with the editable `text_edit` field instead of showing only one side
+- **Prompt actions** — Copy to clipboard, **GenUI:P/N** (set GenerateUI positive/negative prompt), **Prompt:P/N** (set Prompt tab preset positive/negative)
+- **Format support** — ComfyUI PNG/WebP/MP4/JSON (standard + Flux.2 / Qwen-Image / Z-Image / Ernie Image / WAN2.2 / Mage-Flow / LongCat / Boogu / HiDream E1 / FireRed / Krea-2 Turbo subgraph workflows), SD WebUI, SD Forge, Fooocus
+- **Format note** — supported formats and covered model types are always shown in the left column
+
+</details>
+
 <details>
 <summary><h3>Nodes Tab (v0.1.7)</h3></summary>
 
@@ -335,11 +374,12 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
 - **3 sub-tabs** — Single / Batch / DB for single-image tagging, folder batch processing, and tag database management
 - **Model support** — WD Tagger (ONNX, NCHW/NHWC auto-detect), SwinV2 (ONNX), DeepDanbooru (.h5, requires TensorFlow); place each model in its own subfolder under `ComfyUI/models/tagger/<model-name>/` containing the `.onnx` + `selected_tags.csv` (or `.h5` + label file)
 - **Threshold sliders** — General threshold (default 0.35) and Character threshold for `character:` prefix tags (default 0.85); sliders update the display value in real time
-- **Ollama VLM** — optional vision model alongside WD Tagger; set API URL and select model (↻ to refresh list); results from both models are merged into one comma-separated tag string
+- **VLM tagging** (v0.3.38, multi-backend: v0.5.4) — optional vision model alongside WD Tagger, running on Ollama, LM Studio, Lemonade, or Unsloth through the same unified backend used by the Prompt tab; results from both models are merged into one comma-separated tag string
+  - **⚙ Settings modal** (v0.5.4) — a Settings button (Single and Batch tabs) opens the same modal component as the Prompt tab's AI Assistant (Backend / Connection / Model / Generation / Unload), but saved under its own `localStorage` key so Tagger's VLM backend/model choice is independent of the Prompt tab's; **Thinking mode** and **Max tokens** apply to VLM requests too, and the **Unload** button frees the selected model (Ollama/LM Studio/Lemonade; Unsloth unsupported) — replaces the old Tagger-only direct Ollama URL/model fields
 - **Single tab** — drag & drop an image onto the preview area or click Upload; Gallery detail panel **Tagger** button opens the selected image directly here
 - **Single output options** — (1) **GenUI:P** — appends tags to the GenerateUI tab's positive prompt and immediately applies to the loaded workflow; (2) **Send to Prompt** — appends tags to the Prompt tab's positive textarea; (3) **Save to Gallery** — saves tags to Gallery image metadata (requires image opened from Gallery); (4) **Write to File** — embeds tags into JPEG EXIF (`ImageDescription`) or PNG `tEXt` chunk (`Tags` key), other formats get a `.tags.json` sidecar; (5) **Save to DB** — stores in internal SQLite database
-- **Batch tab** — enter a folder path, configure WD Tagger and Ollama settings; output options: **Save to DB** (default on), **Write to File** (EXIF/PNG metadata), **Write .txt** (creates `<filename>.txt` alongside each image with all tags); real-time progress bar and log; Stop cancels after the current image
-- **DB tab** — searchable SQLite database of all tagged images; click a row to open the edit panel and modify WD Tags / VLM Tags; Save updates the record, Delete removes it; Export CSV downloads all records
+- **Batch tab** — enter a folder path, configure WD Tagger thresholds and (via the same ⚙ Settings modal as Single) the VLM backend; output options: **Save to DB** (default on), **Write to File** (EXIF/PNG metadata), **Write .txt** (creates `<filename>.txt` alongside each image with all tags); real-time progress bar and log; Stop cancels after the current image
+- **DB tab** — searchable SQLite database of all tagged images; click a row to open the edit panel and modify WD Tags / VLM Tags; Save updates the record, Delete removes it; Export CSV downloads all records; **bulk delete** (v0.5.8) — per-row checkboxes plus a header checkbox to select/deselect all, a confirmation dialog before deleting the selected records
 - **Dependencies** — install into ComfyUI's embedded Python: `python_embedded\python.exe -m pip install -r requirements.txt`; for GPU inference use `onnxruntime-gpu`; TensorFlow (DeepDanbooru) is optional and commented out in `requirements.txt`
 
 </details>
@@ -347,25 +387,50 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
 <details>
 <summary><h3>Image Edit Tab (v0.3.65)</h3></summary>
 
-- **Layer-based image editor** — compose images with multiple layers (Image, Text, Draw, Mask types) and export the composite as PNG
-- **Loading images** — drag & drop onto the canvas, Upload button, or send from the Gallery tab via the **Image Edit** toolbar button; first image becomes Layer 1 (auto-locked), subsequent images are added as new layers scaled to fit the canvas
+- **Layer-based image editor** — compose images with multiple layers (Image, Text, Draw, Mask types) and export the composite as PNG or a fully layered PSD
+- **Loading images** — drag & drop onto the canvas, **File Open** button (renamed from Upload in v0.6.0), or send from the Gallery tab via the **Image Edit** toolbar button; first image becomes Layer 1 (auto-locked), subsequent images are added as new layers scaled to fit the canvas
+- **Open PSD button** (v0.6.0, next to File Open) — loads a layered PSD file from disk and decomposes it into individual layers, restoring each layer's position, size, visibility, opacity, and blend mode; groups and non-raster layers (adjustment layers, etc.) are flattened into their rendered appearance
+- **Close button** (v0.6.0, next to Open PSD) — closes the current document after a confirmation prompt, discarding all layers and unsaved edits and returning to the empty placeholder view
 - **New button** — create a blank canvas with custom dimensions (WxH prompt); clears all existing layers and automatically adds an empty Layer 1 (v0.3.70)
-- **Tools** — Select (V), Draw (B), Text (T), Shape (S), Mask (🎭), Blur (≈), BG Remove (⬚), Filter (★), Inpaint (🩹); tool options bar updates per active tool
+- **Tools** — Select (V), Draw (B), Text (T), Shape (S), Fill (G), Mask (🎭), Blur (≈), BG Remove (⬚), Filter (★), Inpaint (🩹); tool options bar updates per active tool
   - **Select** — click to select; drag to move; drag corner handles to resize; drag circle handle to rotate; Flip H / Flip V / Rotate angle in the options bar; double-click a text layer to re-edit its content
-  - **Draw** — freehand brush; options: color, brush size, blend mode; paints directly onto the active draw layer while all other layers remain visible; brush cursor shown as a size-accurate circle; strokes can start from outside the canvas margin and continue past the edge
+  - **Draw** — freehand brush; options: color, brush size, blend mode; paints directly onto the active draw layer while all other layers remain visible; brush cursor shown as a size-accurate circle; strokes can start from outside the canvas margin and continue past the edge; **pen tablet support** (v0.6.2) — brush size and opacity respond to pen pressure (mouse behaves exactly as before); the canvas listens for Pointer Events so pen, touch, and mouse all work through the same code path
   - **Text** — click to place; configure font, size, bold/italic, align, and color; placed as an exact-size text layer sized to the measured bounding box; double-click to re-edit
   - **Shape** — drag to draw geometric shapes (Rect / Ellipse / Line / FreeLine); options: shape type, Rounded toggle (Rect/Ellipse), Fill color, Stroke color + width, Opacity; each committed shape becomes an independent draw layer; Stroke None hidden for Line/FreeLine (stroke always active)
-  - **Mask** — 7 sub-tools for painting masks onto a dedicated mask layer; click **M** in the Layers header to add a mask layer (Mask tool activates automatically); clicking a mask layer in the list also switches to the Mask tool; **Tool Options bar**: sub-tool buttons (Paint / Color / Alpha / Text / Vector / Shape / SAM3), Invert checkbox, Overlay color picker (default red), Blur slider (0–50 px); the overlay is only shown for the currently active mask layer, other mask layers stay hidden until selected (v0.3.70); SAM3 requires Mask Editor One (disabled when not installed); Color / Alpha / Text / Vector / Shape work without Mask Editor One; **Properties pane — Paint**: Mode (Add / Erase), Size (1–200 px), Hardness (0–100%); **MASK EDITOR ONE section** (Paint sub-tool, visible when installed): Select opens the ABR stamp-brush library; ✕ clears back to Circle; when an image brush is active, Hardness is disabled and Spacing (5–100%), Angle (0–359°), Sz Jitter (0–100%), Rot. Jitter (on/off) appear; **Properties pane — Color**: click canvas to flood-select pixels by color similarity; Mode (Add / Subtract), Tolerance (0–255), Feather % (0–100); **Properties pane — Alpha**: extract the active layer's transparency as a mask; Threshold, Invert checkbox, Extract Alpha button; **Properties pane — Text**: click canvas to open a stamp overlay — enter text, set Font / Size / Bold / Italic / Align / Mode (Add / Erase), Stamp or Ctrl+Enter to apply; multi-line supported; **Properties pane — Vector**: click to add Catmull-Rom spline points; Enter (or click first point) closes and fills the polygon; Backspace removes the last point; Esc resets; Mode (Add / Erase); **Properties pane — Shape**: drag to draw Rect or Ellipse; hold Shift for square/circle; blue dashed outline preview while dragging; Mode (Add / Erase); **Properties pane — SAM3**: enter a text prompt, set Max candidates (3/6/9/12), click Segment; select candidate thumbnails (multi-select), Apply Selected (N) applies with current Mode; **Mask layer A/S operation**: A/S toggle — Add (green) composites normally, Subtract (red) cuts the mask from below; **✂ clipping mask button** clips the layer below to the painted area; brush cursor shown as size-accurate circle; strokes can start from outside the canvas margin and continue past the edge
+  - **Fill** (v0.6.2) — bucket fill; click a contiguous same-color region on the active layer to flood-fill it (scanline-based, ported from the Comic Creator plugin's Image tab); **Properties pane**: Mode (Solid / Gradient) — Solid: Color picker; Gradient: Shape (Linear / Radial), a draggable color-stop Ramp (click a stop to select it, drag to reposition, ＋/－ to add/remove stops, color swatch to edit the selected stop's color), and a Direction dial (drag to set angle/radius); both Tolerance (0–255, color-match threshold) and Opacity apply to either mode; Ramp and Direction dial are pointer-event based with pointer capture, so pen tablets work the same as mouse
+  - **Mask** — 7 sub-tools for painting masks onto a dedicated mask layer; click **Mask** on the second row of the Layers header (renamed from "M" and moved to its own row in v0.6.0) to add a mask layer (Mask tool activates automatically); clicking a mask layer in the list also switches to the Mask tool; **Tool Options bar**: sub-tool buttons (Paint / Color / Alpha / Text / Vector / Shape / SAM3), Invert checkbox, Overlay color picker (default red), Blur slider (0–50 px); the overlay is only shown for the currently active mask layer, other mask layers stay hidden until selected (v0.3.70); SAM3 requires Mask Editor One (disabled when not installed); Color / Alpha / Text / Vector / Shape work without Mask Editor One; **Properties pane — Paint**: Mode (Add / Erase), Size (1–200 px), Hardness (0–100%); brush size responds to pen pressure on a tablet (v0.6.2, same as the Draw tool); **MASK EDITOR ONE section** (Paint sub-tool, visible when installed): Select opens the ABR stamp-brush library; ✕ clears back to Circle; when an image brush is active, Hardness is disabled and Spacing (5–100%), Angle (0–359°), Sz Jitter (0–100%), Rot. Jitter (on/off) appear; **Edit in Mask Editor One →** (v0.4.0) — sends the current canvas composite (plus the active mask layer's content, if one is selected) to a MaskEditorOne node on the ComfyUI canvas — the currently selected node if it is one, otherwise the first MaskEditorOne node found in the graph (same selection logic as **Send to LI node**) — and opens its full interactive editor (paint, SAM3, BiRefNet, shapes, etc.) in the ComfyUI browser tab; clicking Apply there imports the result back here as a new mask layer; requires Workflow Studio to have been launched from ComfyUI's top-menu button and at least one MaskEditorOne node to exist on the ComfyUI canvas; **Properties pane — Color**: click canvas to flood-select pixels by color similarity; Mode (Add / Subtract), Tolerance (0–255), Feather % (0–100); **Properties pane — Alpha**: extract the active layer's transparency as a mask; Threshold, Invert checkbox, Extract Alpha button; **Properties pane — Text**: click canvas to open a stamp overlay — enter text, set Font / Size / Bold / Italic / Align / Mode (Add / Erase), Stamp or Ctrl+Enter to apply; multi-line supported; **Properties pane — Vector**: click to add Catmull-Rom spline points; Enter (or click first point) closes and fills the polygon; Backspace removes the last point; Esc resets; Mode (Add / Erase); **Properties pane — Shape**: drag to draw Rect or Ellipse; hold Shift for square/circle; blue dashed outline preview while dragging; Mode (Add / Erase); **Properties pane — SAM3**: enter a text prompt, set Max candidates (3/6/9/12), click Segment; select candidate thumbnails (multi-select), Apply Selected (N) applies with current Mode; **Mask layer A/S operation**: A/S toggle — Add (green) composites normally, Subtract (red) cuts the mask from below; **✂ clipping mask button** clips the layer below to the painted area; brush cursor shown as size-accurate circle; strokes can start from outside the canvas margin and continue past the edge
   - **Blur** — **Whole Blur**: Gaussian blur to the entire active layer with intensity slider (1–50 px); **Whole Mosaic**: pixelation mosaic with block-size slider (5–100 px); **Rect Blur / Rect Mosaic**: enable the toggle then drag a rectangle on the canvas to apply blur or mosaic to that region only (blue preview for blur, orange for mosaic); Rect Blur and Rect Mosaic are mutually exclusive; all operations support Undo
   - **BG Remove** — remove the background from the active layer; **Lightweight (@imgly)**: runs entirely in the browser via CDN (no server required; ~40 MB model downloaded on first use); **BiRefNet**: high-quality removal via Mask Editor One's Python backend (requires Mask Editor One installed and `birefnet.safetensors` in `ComfyUI/models/background_removal/`); **New Layer** option (default on) adds the result as a new layer above the original; when off, replaces the active layer in-place
   - **Filter (G'MIC)** — apply G'MIC-Qt filter effects to the active image layer; click **Edit with G'MIC** to launch G'MIC-Qt GUI; select a filter and click OK — a "G'MIC-Qt filter output" window appears; close it to save the result back as the active layer; requires G'MIC-Qt Standalone installed and path configured in Settings → G'MIC-Qt Executable Path
   - **Inpaint** (v0.3.71) — composites the visible layers plus a Mask layer into one RGBA image (mask baked into the alpha channel) and applies it to the first inpaint-capable LoadImage node in the workflow currently loaded in GenerateUI (a node whose MASK output feeds something like `VAEEncodeForInpaint`); also supports workflows built around the **Mask Editor One** custom node (`comfyui-mask-editor-one`) instead of LoadImage — image/mask are pushed via its node_id-keyed server cache and `layer_data` widget; **Properties pane**: Positive/Negative prompts, Grow Mask By (applied to the VAE Encode (for Inpainting) node), Denoise (applied to KSampler), Run button; **Use dedicated workflow** checkbox — when off, generates against the workflow currently loaded in GenerateUI; when on, pick a saved workflow from the dropdown to load, edit, and generate entirely in the background without disturbing what's shown in GenerateUI; the result image is shown back in the Properties pane when generation completes; requires a Mask layer painted first (Mask tool)
-- **Layer panel** — layer list with visibility (👁/🚫), lock (🔒/🔓), and clipping-mask (✂) toggles; opacity slider; type icons (🖼 image / T text / ✏ draw / ⬚ mask); Layer 1 is automatically locked on first image load
+- **Layer panel** — layer list with visibility (👁/🚫), lock (🔒/🔓), and clipping-mask (✂) toggles; opacity slider; type icons (🖼 image / T text / ✏ draw / ⬚ mask); Layer 1 is automatically locked on first image load; header is arranged in two rows (v0.6.0) — +, **⧉ Duplicate**, −, ↑, ↓ on top, **Mask** alone below
+- **Duplicate layer (⧉)** (v0.6.0) — copies the active layer, including its pixels, position, size, opacity, blend mode, and visibility, and inserts the copy directly above the original as the new active layer; works on any layer type (image, draw, text, mask)
 - **Layer lock** — locked layers show an orange bounding box and 🔒 icon on the canvas; move/resize/rotate are disabled while locked; click the 🔓 button in the layer row to unlock
 - **Text quality** — text layers are rendered at their measured bounding-box size; resizing with the Select tool regenerates the canvas at the new display resolution so text stays sharp
-- **Export** — Save PNG (download composite locally); **Save to Gallery** (saves to Gallery root folder with a timestamped default name `wfs-image-YYYYMMDDHHmmss`); **Send to ComfyUI** (uploads to ComfyUI input folder for use in Load Image nodes)
+- **Export** — Save PNG (download composite locally); **Save to Gallery** (saves to Gallery root folder with a timestamped default name `wfs-image-YYYYMMDDHHmmss`); **Save PSD** (v0.6.0, downloads a layered PSD file with every layer preserved separately — position, rotation, flip, and scale are baked into each layer's pixels since PSD layers cannot store a rotation angle, while opacity, blend mode, and visibility are kept as real, editable PSD layer attributes; clipping masks are merged into their target layer since PSD's native layer-mask mechanism isn't used); **Send to LI node** (renamed from "Send to Workflow" in v0.5.2) — uploads to ComfyUI's input folder via `/upload/image`, then writes the resulting filename directly into the `image` widget of the node currently selected on the ComfyUI canvas (falls back to the first LoadImage-type node found); requires Workflow Studio to have been launched from ComfyUI's top-menu button so the ComfyUI tab is reachable; the Gallery tab's toolbar has the same button (v0.5.2, see Gallery Tab below) so a selected Gallery image can be sent the same way without going through Image Edit first
 - **Canvas navigation** — scroll-wheel zoom, Space + drag to pan; zoom indicator in the bottom bar
-- **Undo / Redo** — Ctrl+Z / Ctrl+Y (or toolbar buttons); keyboard shortcuts: V (Select), B (Draw), T (Text), S (Shape), Delete (remove selected layer when 2+ exist)
+- **Undo / Redo** — Ctrl+Z / Ctrl+Y (or toolbar buttons); keyboard shortcuts: V (Select), B (Draw), T (Text), S (Shape), G (Fill), Delete (remove selected layer when 2+ exist)
+
+</details>
+
+<details>
+<summary><h3>Video Tab (v0.5.0 – v0.7.2)</h3></summary>
+
+- **Dedicated video generation UI, independent of GenerateUI** — a purpose-built tab for MiniMax H3 (Image to Video), LTX-2.5 (Text / Image / First & Last Frame to Video), and Wan2.2 (Text to Video) workflows (LTX-2.5/Wan2.2: v0.5.4); does not share state with the GenerateUI tab, and model selection (UNet/CLIP/VAE/Audio VAE) is fixed to the loaded workflow's own defaults rather than editable here
+- **Plan subtab** (center panel, batch redesign in v0.5.5) — a **plan** is a sequence of timeline blocks, each a fully self-contained generation job with its own Prompt, Duration, and First/Last Image; split the timeline with **＋ Split** / **＋ Block** / **Delete**, then **Run** to generate every block in order (Pause/Cancel work mid-run) — a plan with a single block behaves exactly like the old one-shot form. First Image can be None, an explicit image, or "use previous block's last frame" (chaining — an explicit Last Image on the previous block always wins over extracting one from its generated output). Save / Save As / Clear a plan (`ws_videoplan_*.json`, with a PNG contact-sheet thumbnail that embeds the full plan data so dropping the image back on works too)
+- **Base settings row** (topbar, v0.5.7) — Aspect Ratio / Megapixels / Multiple / Duration (seconds) / Noise Seed on one row — Duration always reflects the currently selected block (each block keeps its own value), the rest are plan-wide — plus a second row of model-specific fields: Width / Height (become directly editable for workflows with no Resolution Selector node, e.g. Wan2.2 and LTX-2.5's First & Last Frame to Video), Turbo Mode / Strength / Steps (MiniMax H3's turbo LoRA — Turbo Mode doubles as Wan2.2's own turbo toggle, sharing one field), and Prompt Enhance (LTX-2.5 only). Every field in both rows is enabled only when the loaded workflow actually has it — see Field highlight color below (one exception: Wan2.2's Text-to-Video variant doesn't expose Noise Seed at all, so it alone leaves that field disabled)
+- **Edit subtab** (v0.7.0) — a lightweight clip timeline for trimming and concatenating video/image clips outside of generation: add clips via the Video Source panel's **Add to Edit** button or the Asset subtab's **Send to Edit** button, drag blocks to reorder (or use the ◀/▶ toolbar buttons), Duplicate/Delete the selected clip, Clear to empty the timeline. The trim panel for a selected video clip shows a Clipchamp-style scrubber — a time ruler, a draggable playhead synced to the Source preview, and draggable in/out handles with time badges (image clips get a simple hold-duration field instead); the playhead's own time badge sits a row above the in/out handle badges (v0.7.2) so it stays legible even when the playhead lands right on a trim point, and the scrubber has extra headroom/height and spacing from the clip name above it (v0.7.2). **▶ Preview** plays every ready clip back-to-back (trimmed to its in/out points) in the result preview, and **Export** builds and runs the actual ComfyUI graph (trim → concatenate if more than one clip → save), tagging the result into Video Temp automatically — exporting more than one clip currently drops audio, since ComfyUI's concatenation node requires uniform audio across all inputs
+- **Edit project persistence** (v0.7.2) — **Save** / **Save As** / **Load** buttons in the Edit subtab's export panel persist the timeline itself (clip order, trim points, and which uploaded server-side file each clip refers to) as a `ws_videoeditproj_*.json` project file; Load re-fetches and re-probes each clip from its saved server reference, so the original files need to still exist
+- **Asset subtab** (center panel, next to Plan/Edit — moved here from the sidebar in v0.7.0) — browses generated videos/images through three groups: **Video Assets** (a manually-curated keeper group you organize yourself, on your own schedule), **Video Temp** (every plan run's output lands here automatically regardless of whether the plan itself was saved — a **Clear Video Temp** button in the Gallery tab's detail panel empties it in one click), and **All Video Assets** (the union of both); a filename search box, a video/image type filter dropdown (All Types / Video Only / Image Only, v0.7.2), and a thumbnail-grid/table (▦/☰) view toggle sit above the list (v0.7.0). Selecting an item shows it in the center preview and lets you tag/memo it, **Send to Edit** (queues it as a clip in the Edit subtab), jump straight to the full Gallery tab, or — for images only — **Set as First Image** / **Set as Last Image** (v0.7.0, uploads it straight into the currently-selected Plan block and switches to the Plan subtab)
+- **Project subtab** (sidebar) — lists saved plan files while the center Plan subtab is active, or saved Edit projects while the center Edit subtab is active (v0.7.2 — thumbnail/block-or-clip-count/last-updated) — click one to load it into the matching editor, or delete it with the × button; the panel collapses back down when its own tab is clicked again, and switching Plan↔Edit while it's open refreshes the list in place
+- **Two center previews** (v0.5.5) — the left preview shows either the video currently selected in the Asset subtab or whatever's dropped/picked in the **Video Source** panel to its left (**Clear** resets it to empty); the right preview always shows the most recently generated clip; both preview frames are a fixed height (v0.7.0) so switching sources never shifts the Plan/Edit/Asset panel below them. The Frame/GIF tools act on whichever preview was touched most recently
+- **First Frame / Last Frame are both optional** — leaving First Image empty runs the workflow text-only (the node is simply left unconnected — a "bypass"); workflows that don't even include a LoadImage node for First Frame are supported out of the box (Wan2.2's Text-to-Video graph has neither slot at all), and a LoadImage node is only injected into the graph on the fly if an image is actually supplied
+- **Semantic node lookup, not hardcoded IDs** (generalized in v0.5.4) — detects which of the three families is loaded from its subgraph's internal node types (or, for Wan2.2, the subgraph's own name — it has no distinctive internal node type), then locates its Resolution Selector and LoadImage node(s) by the subgraph's *public input label* (`prompt`, `duration`, `width`, `first_frame`, `last_frame`) rather than a fixed slot index — so it keeps working even though the three families expose those inputs under different internal names; a workflow no longer needs to match any bundled reference file's exact node IDs, and no fallback test workflow is auto-loaded on tab open
+- **Field highlight color** — Prompt / Duration / First Image / Last Image / Aspect Ratio / Megapixels / Multiple / Noise Seed / Width / Height / Turbo Mode / Strength / Steps / Prompt Enhance labels turn the same highlight color as the GenerateUI Model tab (shared setting, customizable in the Settings tab — see below) when the loaded workflow actually exposes that field; e.g. a Wan2.2 workflow (no Resolution Selector node) leaves Aspect Ratio / Megapixels / Multiple unhighlighted while Width / Height light up instead
+- **Load in Video** (Workflow tab) — load any workflow containing a MiniMax H3 Image-to-Video node, an LTX-2.5 Text/Image/First&Last-Frame-to-Video node, or a Wan2.2 Text-to-Video node straight into this tab from the Workflow tab's side-panel toolbar (next to Load in GenerateUI) — starts a fresh single-block plan seeded from the workflow's own current values; unsupported workflows show an error toast instead of loading
+- **Frame tab** (right panel) — captures the active preview's current playback position as a still frame: play/seek to the position you want, click **Capture Frame** (client-side canvas, no server round-trip), then **Save to Output** to write it as a PNG into ComfyUI's own Output folder (auto-numbered, same pattern as other generated images); an optional **Add to Video Assets group** checkbox (v0.7.0) also tags the saved frame into Gallery's Video Assets group
+- **GIF tab** (right panel) — converts the active preview to an animated GIF; set Start/End (seconds), FPS, and an optional Max Width, then **Convert to GIF** — the server decodes the video (PyAV) and encodes an animated GIF (Pillow), both dependencies ComfyUI itself already requires, so no ffmpeg binary or extra install is needed; the result is saved into ComfyUI's Output folder and previewed inline
+- **Playback volume** — the preview player's volume is saved to the Settings tab and shared across every video player in the plugin (Video tab, Gallery tab), so it no longer resets to maximum each time a different video is opened
 
 </details>
 
@@ -373,20 +438,25 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
 <summary><h3>AI TOOL Tab (v0.3.14)</h3></summary>
 
 - **4-pane layout** — Translation | Chat | TOOLS | Settings; all panes always visible simultaneously; no sub-tab switching required
-- **Translation pane** — translate text between Japanese, English, Chinese, or a custom Free language using Ollama, LM Studio, or Lemonade; language selectors with ⇄ swap button (swaps both language selectors and text content); selections saved automatically
-- **Chat pane** (v0.3.40) — multi-turn conversation with the LLM; full conversation history sent each turn for context; Enter to send, Shift+Enter for a newline; Clear button resets history; Ollama uses `/api/chat`, LM Studio and Lemonade use `/v1/chat/completions`
+- **Translation pane** — translate text between Japanese, English, Chinese, or a custom Free language using Ollama, LM Studio, Lemonade, or Unsloth; language selectors with ⇄ swap button (swaps both language selectors and text content); selections saved automatically
+  - **Reliability fix** (v0.3.98) — switched from a raw single-string prompt to a system-role chat request (Ollama `/api/chat`, LM Studio/Lemonade `/v1/chat/completions` with a `system` message) so small local models follow the "translation only" instruction more reliably; output is cleaned of common LLM preambles/quotes, and a warning toast appears if the model appears to have echoed the source text unchanged instead of translating it
+- **Chat pane** (v0.3.40) — multi-turn conversation with the LLM; full conversation history sent each turn for context; Enter to send, Shift+Enter for a newline; Clear button resets history; Ollama uses `/api/chat`, LM Studio, Lemonade, and Unsloth use `/v1/chat/completions`
 - **Chat pane — image generation via Tool Calling** (v0.3.73) — if the selected model supports tool calling and the user asks for an image, a `generate_image` tool is invoked and the request runs through the same generation pipeline as the GenerateUI Generate button, with the result shown inline in the chat; an "Allow image generation" checkbox in the input row toggles this on/off (default on); the workflow used is the one currently loaded in GenerateUI, or a dedicated saved workflow configured in the Settings pane; models verified to work well for tool calling and vision: **Gemma 4:e4b**, **Qwen 3.5:9b**, **Ministral 3:3b** (Ollama)
+- **Chat pane — generate_image extra parameters** (v0.4.4) — besides `prompt`/`negative_prompt`, the tool call also accepts optional `steps`, `cfg`, `sampler_name`, `scheduler`, `denoise`, `seed`, `width`, `height`, `batch_size` (number of images produced in that one call), and `aspect_ratio` (a ratio preset like `3:2`, resolved against the node's actual COMBO options via `/object_info` and applied only if the workflow has a **Resolution Selector** node feeding width/height — ignored otherwise); anything the model doesn't supply keeps the loaded workflow's own current setting; `batch_size` > 1 shows every generated image inline in the same chat bubble
 - **TOOLS pane (VLM)** — drop an image into the 110px drop zone, select a task (Describe Image / Create Prompt / Create Tags), and click Run to analyze with a vision model; result shown in the output area with a Copy button
 - **TOOLS pane — shared Chat attachment** (v0.3.74) — an image dropped in the TOOLS drop zone is also usable as the Chat pane's attachment: it's sent to the LLM as vision input, and used as the base image when `generate_image` runs image-to-image; a ✕ button clears it (from either the TOOLS preview or the Chat attachment indicator); it stays attached across turns until explicitly cleared
 - **TOOLS pane (Wildcards)** (v0.3.40) — select "Create wildcards" from the task dropdown; enter a category name and count; click Run to generate plain-text wildcard entries one per line (no markdown, no numbering); result can be copied directly into wildcard `.txt` files
 - **Chat pane — image-to-image (I2I)** (v0.3.74) — when an image is attached and `generate_image` is invoked, the attachment is uploaded and swapped into the target workflow's LoadImage node instead of running text-to-image; the target workflow is the one currently loaded in GenerateUI, or a dedicated I2I workflow configured in the Settings pane
 - **Chat pane — SVG generation, no ComfyUI workflow involved** (v0.3.77) — the LLM can also produce SVG graphics directly as text; if a reply contains SVG code (wrapped in a markdown code fence or written directly as a bare `<svg>...</svg>`), it's rendered inline as a preview and a "Save to Gallery" button writes it as a `.svg` file to the Gallery tab's output folder; unlike the Tool Calling image generation flow above, this is plain text pattern matching, so it works with any model regardless of tool-calling support
 - **Chat pane — Skills** (v0.3.79) — select a `.md` skill file from a dropdown above the message list to prepend its instructions to the conversation as a system prompt (not shown in the visible chat log); a pencil button opens an in-pane manager (list + inline editor, same pattern as the Prompt tab's wildcard file manager) to create, edit, or delete skill files stored in `user/default/Workflow-Studio/ai_skills/`; a file's `---`-delimited frontmatter (`name`/`description`) is shown in the dropdown; two starter skills are bundled — **SVG Icon Generator** (explicitly decomposes the subject into parts before drawing, which markedly improves recognizability from small local models) and **Skill Creator** (interviews you conversationally and drafts a new skill as a ```skill code block; a "Save as new skill" button appears under such replies and opens the manager pre-filled with the draft)
-- **Settings pane** — choose backend (Ollama / LM Studio / Lemonade), set the API URL, test connection, select a model (with refresh button), and configure Free language names for translation source and destination
+- **Settings pane** — choose backend (Ollama / LM Studio / Lemonade / Unsloth), set the API URL, test connection, select a model (with refresh button), and configure Free language names for translation source and destination
+- **Unsloth backend** (v0.3.99) — Unsloth Desktop's OpenAI-compatible API always requires an API key, even for local access; unlike the other backends it's proxied server-side (`py/routes/unsloth_routes.py`, `POST /api/wfm/unsloth/proxy`) so the key never reaches the browser: set `UNSLOTH_API_KEY` in a `.env` file in the plugin folder (copy `.env.example`, loaded via `python-dotenv` in `prestartup_script.py`) and restart ComfyUI; also requires **"Auto model switching (OpenAI API)"** to be turned on in the Unsloth Desktop app's own Settings screen — with it off, requests fail with HTTP 404 unless a model was already manually loaded in the Unsloth Studio UI
 - **Settings pane — Chat Image Generation** (v0.3.73) — "Use dedicated workflow" checkbox lets Chat-triggered image generation use a saved workflow of your choice instead of the one currently loaded in GenerateUI, without disturbing what's shown there (same pattern as Image Edit's Inpaint dedicated workflow)
 - **Settings pane — Chat I2I Generation** (v0.3.74) — same "Use dedicated workflow" pattern as above, but for image-to-image generation when an image is attached; independent of the text-to-image dedicated workflow setting
+- **Settings pane — Generation** (v0.3.98) — **Thinking mode** toggle (off by default; sends Ollama's `think` flag, and regardless of backend, any `<think>...</think>` block is stripped from the output when off so reasoning models don't clutter Translation/Chat/TOOLS results) and **Max tokens** (Ollama: `options.num_predict`; LM Studio/Lemonade/Unsloth: `max_tokens`; `0` = unlimited)
+- **Model unload** (v0.5.4) — an Unload button next to the model selector frees VRAM/memory by unloading the currently selected model from Ollama (`keep_alive:0` via `/api/generate`, the officially documented method — Ollama has no dedicated unload endpoint), LM Studio (`/api/v1/models/unload`), or Lemonade (`/api/v1/unload`); Unsloth exposes no equivalent API and the button is disabled for that backend
 - **Settings shared** — settings saved to `localStorage` under `wfm_ai_settings`; shared with the Library panel's AI TOOL tab so configuration is consistent across both interfaces
-- **Backend support** — Ollama (`/api/generate` for text, `/api/chat` for conversations, `/api/tags` for model list); LM Studio and Lemonade via the OpenAI-compatible API (`/v1/chat/completions`, `/v1/models`); VLM images sent as base64 (`images:[]` for Ollama, `image_url` content block for LM Studio/Lemonade)
+- **Backend support** — Ollama (`/api/generate` for text, `/api/chat` for conversations, `/api/tags` for model list); LM Studio and Lemonade via the OpenAI-compatible API directly from the browser (`/v1/chat/completions`, `/v1/models`); Unsloth via the same OpenAI-compatible API but relayed through a server-side proxy that attaches the `Authorization: Bearer` header; VLM images sent as base64 (`images:[]` for Ollama, `image_url` content block for LM Studio/Lemonade/Unsloth)
 - **URL security** — backend URL validated via `new URL()` to enforce `http://` or `https://` scheme
 - **Lemonade's other endpoints (not integrated)** — Lemonade Server also exposes OpenAI-compatible image generation (`/v1/images/generations`), text-to-speech (`/v1/audio/speech`), and transcription (`/v1/audio/transcriptions`) endpoints; these are not wired up — image generation would duplicate the existing ComfyUI-workflow-based `generate_image` tool call, and audio/TTS has no corresponding UI yet, so both are future considerations rather than a natural extension of the current backend switch
 
@@ -402,15 +472,16 @@ An experimental batch generator: runs the workflow currently loaded in GenerateU
   - **Package sub-tab** — dropdown to filter nodes by custom node package name
 - **M — Models tab** — browse installed models (All / ★ Favorites / Groups / By Type sub-tabs); LoRA groups show an **All N LoRAs** item — drag to canvas to place a `Lora Loader (LoraManager)` node with all LoRAs pre-loaded
 - **P — Prompts tab** — browse prompt presets with All / ★ Favorites / Categories sub-tabs; **Groups sub-tab** (row 2) — view presets by group (shared with the Batch tab's `wfm_prompt_preset_groups`)
-- **I — Information tab** — drop a ComfyUI-generated PNG/WebP or workflow JSON in the side panel to view its metadata; detects LoRAs from `LoraLoader`, `LoraLoaderModelOnly`, and `Lora Loader (LoraManager)` nodes (API format supported); supports `UnetLoaderGGUF` and `QuadrupleCLIPLoader` node types; preview area fixed at 110px
-- **A — AI TOOL tab** — Translation, Chat, TOOLS, and Settings sub-tabs powered by Ollama, LM Studio, or Lemonade; Chat supports multi-turn conversations (full history sent each turn); TOOLS includes VLM image analysis and wildcard generation; settings (backend, URL, model) shared with the SPA AI TOOL tab via `localStorage`
+- **I — Information tab** — drop a ComfyUI-generated PNG/WebP/MP4 or workflow JSON in the side panel to view its metadata; detects LoRAs from `LoraLoader`, `LoraLoaderModelOnly`, and `Lora Loader (LoraManager)` nodes (API format supported); supports `UnetLoaderGGUF` and `QuadrupleCLIPLoader` node types; preview area fixed at 110px
+  - **MP4 embedded metadata** (v0.5.1) — reads the `workflow`/`prompt` container tags ComfyUI's `SaveVideo` writes into the mp4's `moov/udta/meta` box, parsed entirely client-side (same box-walking logic as the Gallery tab's Metadata sub-tab); also recognizes all-in-one video-generation nodes that carry their prompt directly on a `prompt` input rather than a separate `CLIPTextEncode`-style node (e.g. `MiniMaxH3ImageToVideo`)
+- **A — AI TOOL tab** — Translation, Chat, TOOLS, and Settings sub-tabs powered by Ollama, LM Studio, Lemonade, or Unsloth; Chat supports multi-turn conversations (full history sent each turn); TOOLS includes VLM image analysis and wildcard generation; settings (backend, URL, model) shared with the SPA AI TOOL tab via `localStorage`
   - **model sub-tab** — Checkpoint, VAE, Diffusion Model, and Text Encoder; drag items to canvas to place the corresponding loader node (Checkpoint → `CheckpointLoaderSimple`, VAE → `VAELoader`, Diffusion Model → `UNETLoader`, Text Encoder → `CLIPLoader`); double-click also places at canvas center
   - **lora sub-tab** — detects LoRAs from `LoraLoader`, `LoraLoaderModelOnly`, and `Lora Loader (LoraManager)` nodes (API format `inputs.loras.__value__` supported); shows `strength_model / strength_clip` values; drag individual LoRA to place `LoraLoader`; **Multiple LORA** section (appears for 1+ LoRAs) drags all LoRAs into a single `Lora Loader (LoraManager)` node with LoRA syntax pre-filled
   - **Prompts sub-tab** — POS / NEG badge list; drag a prompt to place `CLIPTextEncode` with text pre-filled; click any entry to view full text + Copy button
 - **Drag & drop workflows** — drag a workflow onto the canvas to load it
 - **Drag & drop nodes** — drag nodes/node sets onto the canvas to place them
 - **Drag & drop prompts** — drag a preset onto the canvas to create a WFS_PromptText node with positive/negative prompts
-- **Send to Canvas** — clicking "Send to Canvas" (Workflow tab toolbar / detail modal) or "Copy & Send Canvas" (Gallery tab JSON panel) sends the workflow directly to the ComfyUI canvas via `window.opener` (UI and API formats both supported); if Workflow Studio was opened from a bookmark instead of the ComfyUI toolbar, UI-format workflows fall back to title drag (panel title highlights blue with a green ● — drag it onto the canvas to load)
+- **Send to Canvas** — clicking "Send to Canvas" (Workflow tab toolbar / detail modal) sends the workflow directly to the ComfyUI canvas via `window.opener` when available (UI and API formats both supported); if Workflow Studio was opened from a bookmark instead of the ComfyUI toolbar (or in another tab), it filters the Library's **W** tab down to that workflow's card instead (v0.4.7) — drag the card onto the canvas yourself; this replaced the old title-drag fallback, which had no thumbnail and didn't work for API-format workflows at all. "Copy & Send Canvas" (Gallery tab JSON panel) still uses only `window.opener` + title-drag for UI-format workflows, since the embedded workflow it sends isn't a saved Library file to filter down to
 - **Copy prompts** — copy individual positive (P) or negative (N) prompts from sidebar items
 - **Double-click** — load workflows or place nodes without dragging
 - **Search** — search within each sub-tab to quickly find items
@@ -500,8 +571,10 @@ Click the **camera icon** (next to the W button) in ComfyUI's top bar to capture
 - **[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
+- **[psd-tools](https://pypi.org/project/psd-tools/)** (v0.6.0) — enables all PSD (Photoshop) features: Gallery bulk "Create PSD" export, Gallery browsing/thumbnails for `.psd` files, and Image Edit's Save PSD / Open PSD buttons; `pip install -r requirements.txt` (ships prebuilt wheels, no C compiler required); without it, PSD-related actions show an error toast with the install command instead of failing silently
 
 ---
 

prestartup_script.py

@@ -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}")

py/config.py

@@ -71,6 +71,8 @@ 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"
+VIDEO_PLAN_DIR = DATA_DIR / "video_plan"
+VIDEO_EDIT_PROJECT_DIR = DATA_DIR / "video_edit_project"
 
 # Tagger
 TAGGER_DB_FILE = DATA_DIR / "tagger.db"

py/routes/gallery_routes.py

@@ -88,6 +88,10 @@ def setup_routes(app: web.Application):
     app.router.add_post("/wfm/gallery/images/delete", delete_images_route)
     app.router.add_post("/wfm/gallery/images/move", move_images_route)
     app.router.add_post("/wfm/gallery/images/export-zip", export_images_zip)
+    app.router.add_post("/wfm/gallery/images/export-psd", export_images_psd)
+    app.router.add_post("/wfm/gallery/image/export-psd-layers", export_psd_from_layers)
+    app.router.add_post("/wfm/gallery/image/import-psd-layers", import_psd_layers)
+    app.router.add_get("/wfm/gallery/image/psd-layers", get_psd_layers)
 
 
 # ──────────────────────────────────────────────────────────────
@@ -163,9 +167,15 @@ 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)
 
@@ -175,7 +185,10 @@ async def serve_image(request: web.Request) -> web.Response:
     if not path:
         return web.Response(status=400)
 
-    img_path = _service.serve_image(path)
+    # PSDの合成処理はCPU負荷が高く同期実行するとイベントループ全体をブロックする
+    # (他の並行リクエスト、例えば別画像のサムネイル配信も止まってしまう)ためexecutorで実行する
+    loop = asyncio.get_event_loop()
+    img_path = await loop.run_in_executor(None, _service.serve_image, path)
     if img_path is None:
         return web.Response(status=404)
 
@@ -197,7 +210,10 @@ async def serve_thumb(request: web.Request) -> web.Response:
     except ValueError:
         width = 256
 
-    thumb_path = _service.serve_thumbnail(path, width)
+    # PSD/MP4のサムネイル生成はCPU負荷が高く同期実行するとイベントループ全体をブロックするため
+    # executorで実行する（他画像のサムネイル配信が巻き添えで遅延・停止するのを防ぐ）
+    loop = asyncio.get_event_loop()
+    thumb_path = await loop.run_in_executor(None, _service.serve_thumbnail, path, width)
     if thumb_path is None:
         return web.Response(status=404)
 
@@ -399,7 +415,7 @@ async def create_group(request: web.Request) -> web.Response:
         return web.json_response({"error": str(e)}, status=500)
 
 
-_RESERVED_GROUPS = {"__Feeder__"}
+_RESERVED_GROUPS = {"__Feeder__", "__Video Assets__", "__Video Temp__"}
 
 
 async def rename_group(request: web.Request) -> web.Response:
@@ -568,3 +584,97 @@ async def export_images_zip(request: web.Request) -> web.Response:
     except Exception as e:
         logger.error("Error exporting images to ZIP: %s", e)
         return web.json_response({"error": str(e)}, status=500)
+
+
+async def export_images_psd(request: web.Request) -> web.Response:
+    """POST /wfm/gallery/images/export-psd - 複数画像を1つのPSD(レイヤー分割)にしてダウンロード"""
+    try:
+        body = await request.json()
+        paths = body.get("paths", [])
+        if not paths:
+            return web.json_response({"error": "paths required"}, status=400)
+
+        psd_bytes = _service.build_psd_from_images(paths)
+
+        return web.Response(
+            body=psd_bytes,
+            content_type="image/vnd.adobe.photoshop",
+            headers={"Content-Disposition": "attachment; filename=gallery_export.psd"}
+        )
+    except RuntimeError as e:
+        logger.error("PSD export dependency missing: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+    except ValueError as e:
+        return web.json_response({"error": str(e)}, status=400)
+    except Exception as e:
+        logger.error("Error exporting images to PSD: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def export_psd_from_layers(request: web.Request) -> web.Response:
+    """POST /wfm/gallery/image/export-psd-layers - Image Editタブのレイヤー配列を1つのPSDにしてダウンロード"""
+    try:
+        body = await request.json()
+        width = int(body.get("width", 0))
+        height = int(body.get("height", 0))
+        layers = body.get("layers", [])
+        if not layers:
+            return web.json_response({"error": "layers required"}, status=400)
+
+        psd_bytes = _service.build_psd_from_layers(width, height, layers)
+
+        return web.Response(
+            body=psd_bytes,
+            content_type="image/vnd.adobe.photoshop",
+            headers={"Content-Disposition": "attachment; filename=image_edit_export.psd"}
+        )
+    except RuntimeError as e:
+        logger.error("PSD export dependency missing: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+    except ValueError as e:
+        return web.json_response({"error": str(e)}, status=400)
+    except Exception as e:
+        logger.error("Error exporting Image Edit layers to PSD: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def import_psd_layers(request: web.Request) -> web.Response:
+    """POST /wfm/gallery/image/import-psd-layers - アップロードされたPSDをレイヤー配列(JSON)に分解して返す"""
+    try:
+        reader = await request.multipart()
+        field = await reader.next()
+        if field is None:
+            return web.json_response({"error": "file required"}, status=400)
+        psd_bytes = await field.read(decode=False)
+
+        loop = asyncio.get_event_loop()
+        result = await loop.run_in_executor(None, _service.import_psd_layers, psd_bytes)
+        return web.json_response(result)
+    except RuntimeError as e:
+        logger.error("PSD import dependency missing: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+    except ValueError as e:
+        return web.json_response({"error": str(e)}, status=400)
+    except Exception as e:
+        logger.error("Error importing PSD layers: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def get_psd_layers(request: web.Request) -> web.Response:
+    """GET /wfm/gallery/image/psd-layers - サーバー上のPSDファイルをレイヤー配列(JSON)に分解して返す
+    (Galleryから直接Image Editへ送る際、アップロードの往復を挟まずに済ませるため)"""
+    path = request.rel_url.query.get("path", "")
+    if not path:
+        return web.json_response({"error": "path required"}, status=400)
+    try:
+        loop = asyncio.get_event_loop()
+        result = await loop.run_in_executor(None, _service.get_psd_layers_from_path, path)
+        return web.json_response(result)
+    except RuntimeError as e:
+        logger.error("PSD import dependency missing: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+    except ValueError as e:
+        return web.json_response({"error": str(e)}, status=400)
+    except Exception as e:
+        logger.error("Error reading PSD layers: %s", e)
+        return web.json_response({"error": str(e)}, status=500)

py/routes/gmic_routes.py

@@ -82,9 +82,9 @@ async def handle_open(request: web.Request) -> web.Response:
         # Retrieve setting for gmic path
         data = _settings.load()
         gmic_exe = data.get("gmic_qt_path", "")
-        if not os.path.exists(gmic_exe):
+        if not os.path.isabs(gmic_exe) or os.path.splitext(gmic_exe)[1].lower() != ".exe" or not os.path.isfile(gmic_exe):
             return web.json_response({
-                "error": f"G'MIC executable not found at: {gmic_exe}. Please configure the path in settings."
+                "error": f"G'MIC executable not found at: {gmic_exe}. Please configure an absolute .exe path in settings."
             }, status=400)
 
         ext = ".png"

py/routes/settings_routes.py

@@ -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",
 ]
 
 
@@ -37,6 +41,8 @@ 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)
@@ -248,6 +254,152 @@ async def handle_import(request: web.Request) -> web.Response:
         return web.json_response({"error": str(e)}, status=500)
 
 
+# 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/, video_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"
     if not style_dir.is_dir():

py/routes/tagger_routes.py

@@ -18,8 +18,8 @@ _db = TaggerDbService(TAGGER_DB_FILE)
 def setup_routes(app: web.Application):
     app.router.add_get("/wfm/tagger/models", handle_models)
     app.router.add_post("/wfm/tagger/predict", handle_predict)
-    app.router.add_get("/wfm/tagger/ollama/models", handle_ollama_models)
-    app.router.add_post("/wfm/tagger/ollama/predict", handle_ollama_predict)
+    app.router.add_get("/wfm/tagger/vlm/models", handle_vlm_models)
+    app.router.add_post("/wfm/tagger/vlm/predict", handle_vlm_predict)
     app.router.add_post("/wfm/tagger/batch/start", handle_batch_start)
     app.router.add_get("/wfm/tagger/batch/status", handle_batch_status)
     app.router.add_post("/wfm/tagger/batch/stop", handle_batch_stop)
@@ -27,6 +27,7 @@ def setup_routes(app: web.Application):
     app.router.add_get("/wfm/tagger/db/search", handle_db_search)
     app.router.add_put("/wfm/tagger/db/{id}", handle_db_update)
     app.router.add_delete("/wfm/tagger/db/{id}", handle_db_delete)
+    app.router.add_delete("/wfm/tagger/db", handle_db_delete_bulk)
     app.router.add_get("/wfm/tagger/db/export", handle_db_export)
     app.router.add_post("/wfm/tagger/db/save", handle_db_save)
     app.router.add_post("/wfm/tagger/write_meta", handle_write_meta)
@@ -61,25 +62,29 @@ async def handle_predict(request: web.Request) -> web.Response:
         return web.json_response({"error": str(e)}, status=500)
 
 
-async def handle_ollama_models(request: web.Request) -> web.Response:
+async def handle_vlm_models(request: web.Request) -> web.Response:
+    backend = request.rel_url.query.get("backend", "ollama")
     api_url = request.rel_url.query.get("api_url", "http://127.0.0.1:11434")
     try:
-        models = await asyncio.to_thread(_svc.ollama_models, api_url)
+        models = await asyncio.to_thread(_svc.vlm_models, backend, api_url)
         return web.json_response({"models": models})
     except Exception as e:
         return web.json_response({"error": str(e)}, status=500)
 
 
-async def handle_ollama_predict(request: web.Request) -> web.Response:
+async def handle_vlm_predict(request: web.Request) -> web.Response:
     try:
         body = await request.json()
         result = await asyncio.to_thread(
-            _svc.ollama_predict,
+            _svc.vlm_predict,
             body.get("image_b64", ""),
+            body.get("backend", "ollama"),
             body.get("api_url", "http://127.0.0.1:11434"),
             body.get("model", ""),
             body.get("prompt", ""),
             int(body.get("max_tags", 40)),
+            bool(body.get("thinking_mode", False)),
+            int(body.get("max_tokens", 0)),
         )
         return web.json_response(result)
     except Exception as e:
@@ -96,7 +101,8 @@ async def handle_batch_start(request: web.Request) -> web.Response:
             float(body.get("threshold", 0.35)),
             float(body.get("char_threshold", 0.85)),
             bool(body.get("use_ollama", False)),
-            body.get("ollama_api", "http://127.0.0.1:11434"),
+            body.get("vlm_backend", "ollama"),
+            body.get("vlm_api_url", "http://127.0.0.1:11434"),
             body.get("ollama_model", ""),
             body.get("ollama_prompt", ""),
             int(body.get("ollama_max_tags", 40)),
@@ -104,6 +110,8 @@ async def handle_batch_start(request: web.Request) -> web.Response:
             bool(body.get("write_file", False)),
             bool(body.get("write_txt", False)),
             _db,
+            bool(body.get("thinking_mode", False)),
+            int(body.get("max_tokens", 0)),
         )
         return web.json_response(result)
     except Exception as e:
@@ -161,6 +169,27 @@ async def handle_db_delete(request: web.Request) -> web.Response:
     except Exception as e:
         return web.json_response({"error": str(e)}, status=500)
 
+async def handle_db_delete_bulk(request: web.Request) -> web.Response:
+    try:
+        data = await request.json()
+        ids = data.get("ids", [])
+
+        if not ids:
+            return web.json_response(
+                {"error": "No IDs specified"},
+                status=400,
+            )
+
+        ids = [int(row_id) for row_id in ids]
+
+        deleted_count = await asyncio.to_thread(_db.delete_bulk, ids)
+
+        return web.json_response({
+            "ok": True,
+            "count": deleted_count,
+        })
+    except Exception as e:
+        return web.json_response({"error": str(e)}, status=500)
 
 async def handle_db_export(request: web.Request) -> web.Response:
     try:

py/routes/unsloth_routes.py

@@ -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)

py/routes/video_routes.py

@@ -0,0 +1,209 @@
+"""Video tab API routes: last-frame capture, animated GIF conversion, and
+Plan (batch timeline) file management."""
+
+import asyncio
+import logging
+
+from aiohttp import web
+
+from ..config import VIDEO_PLAN_DIR, VIDEO_EDIT_PROJECT_DIR
+from ..services.video_service import VideoService
+from ..services.video_plan_service import VideoPlanService
+from ..services.video_edit_project_service import VideoEditProjectService
+
+logger = logging.getLogger(__name__)
+
+_service = VideoService()
+_plan_service = VideoPlanService(VIDEO_PLAN_DIR)
+_edit_project_service = VideoEditProjectService(VIDEO_EDIT_PROJECT_DIR)
+
+
+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)
+    app.router.add_get("/api/wfm/video/edit/probe", handle_probe_video)
+
+    app.router.add_get("/api/wfm/video/plans", handle_list_plans)
+    app.router.add_get("/api/wfm/video/plans/content", handle_get_plan_content)
+    app.router.add_post("/api/wfm/video/plans/save", handle_save_plan)
+    app.router.add_post("/api/wfm/video/plans/delete", handle_delete_plan)
+    app.router.add_post("/api/wfm/video/index-image/save-to-output", handle_save_index_to_output)
+
+    app.router.add_get("/api/wfm/video/edit/projects", handle_list_edit_projects)
+    app.router.add_get("/api/wfm/video/edit/projects/content", handle_get_edit_project_content)
+    app.router.add_post("/api/wfm/video/edit/projects/save", handle_save_edit_project)
+    app.router.add_post("/api/wfm/video/edit/projects/delete", handle_delete_edit_project)
+
+
+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)
+
+
+async def handle_probe_video(request: web.Request) -> web.Response:
+    filename = request.rel_url.query.get("filename", "")
+    subfolder = request.rel_url.query.get("subfolder", "")
+    type_ = request.rel_url.query.get("type", "input")
+    if not filename:
+        return web.json_response({"error": "filename required"}, status=400)
+    try:
+        result = await asyncio.to_thread(_service.probe_video, filename, subfolder, type_)
+        return web.json_response(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 probing video: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def handle_list_plans(request: web.Request) -> web.Response:
+    try:
+        result = await asyncio.to_thread(_plan_service.list_plans)
+        return web.json_response(result)
+    except Exception as e:
+        logger.error("Error listing video plans: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def handle_get_plan_content(request: web.Request) -> web.Response:
+    filename = request.rel_url.query.get("filename", "")
+    try:
+        data = await asyncio.to_thread(_plan_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 video plan content: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def handle_save_plan(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(_plan_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 video plan: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def handle_delete_plan(request: web.Request) -> web.Response:
+    try:
+        body = await request.json()
+        filename = body.get("filename", "")
+        await asyncio.to_thread(_plan_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 video plan: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def handle_list_edit_projects(request: web.Request) -> web.Response:
+    try:
+        result = await asyncio.to_thread(_edit_project_service.list_projects)
+        return web.json_response(result)
+    except Exception as e:
+        logger.error("Error listing video edit projects: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def handle_get_edit_project_content(request: web.Request) -> web.Response:
+    filename = request.rel_url.query.get("filename", "")
+    try:
+        data = await asyncio.to_thread(_edit_project_service.get_project, 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 video edit project content: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def handle_save_edit_project(request: web.Request) -> web.Response:
+    try:
+        body = await request.json()
+        filename = body.get("filename", "")
+        data = body.get("data", {})
+        result = await asyncio.to_thread(_edit_project_service.save_project, filename, data)
+        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 video edit project: %s", e)
+        return web.json_response({"error": str(e)}, status=500)
+
+
+async def handle_delete_edit_project(request: web.Request) -> web.Response:
+    try:
+        body = await request.json()
+        filename = body.get("filename", "")
+        await asyncio.to_thread(_edit_project_service.delete_project, 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 video edit project: %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(_plan_service.save_index_image_to_output, image_base64)
+        return web.json_response({"status": "ok", **result})
+    except Exception as e:
+        logger.error("Error saving video index image to output: %s", e)
+        return web.json_response({"error": str(e)}, status=500)

py/services/gallery_metadata.py

@@ -1,6 +1,7 @@
 """
 Gallery Metadata Store - ギャラリー画像のメタデータ永続化
 """
+import copy
 import json
 import logging
 import threading
@@ -38,6 +39,9 @@ class GalleryMetadataStore:
     def __init__(self, file_path: Path):
         self.file_path = file_path
         self._lock = threading.RLock()
+        # ディスクへの実書き込み（JSONシリアライズ+ファイルI/O）を直列化するための
+        # 別ロック。_save_to_disk() を参照。
+        self._write_lock = threading.Lock()
         self._data: dict = {"images": {}, "groups": []}
         self._load()
 
@@ -53,17 +57,56 @@ class GalleryMetadataStore:
                 }
             except Exception as e:
                 logger.warning("GalleryMetadataStore: load error: %s", e)
+                # 破損ファイルをリネームしてバックアップしておく。ここでリネームせずに
+                # 空の self._data のまま次の save() が走ると、破損直前までの全メタデータ
+                # (タグ/メモ/グループ登録)が跡形もなく上書きされ、二度と復元できなくなる
+                # (実際に発生した不具合: _save_to_disk が非アトミック書き込みだったため、
+                # 大量画像フォルダのBGインデックス処理中の頻繁な保存とComfyUIの終了タイミング
+                # が重なるとファイルが不完全な状態のまま残ることがあった — _save_to_disk側は
+                # os.replace()によるアトミック書き込みに修正済みだが、過去に壊れたファイルや
+                # 他要因による破損に備えてここでも保護しておく)。
+                try:
+                    backup_path = self.file_path.with_name(self.file_path.name + ".corrupt")
+                    self.file_path.replace(backup_path)
+                    logger.warning("GalleryMetadataStore: corrupt file backed up to %s", backup_path)
+                except Exception as backup_err:
+                    logger.warning("GalleryMetadataStore: failed to back up corrupt file: %s", backup_err)
 
     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:
-                json.dump(self._data, f, ensure_ascii=False, indent=2)
-            return True
-        except Exception as e:
-            logger.error("GalleryMetadataStore: save error: %s", e)
-            return False
+        # 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。
+        # ここではself._lock配下でスナップショット(deepcopy)を取るだけに留め、実際のJSON
+        # シリアライズ+ファイルI/Oはロックの外、別途self._write_lockで直列化して行う。
+        # 理由: gallery_metadata.jsonは画像数が多い環境では1MBを超えることがあり、
+        # self._lockを保持したままjson.dump+ファイル書き込みまで行うと、その間
+        # 他スレッドのget()（検索リクエストなど）が丸ごとブロックされてしまう
+        # （実際に発生した不具合: バックグラウンドインデックス処理が10枚ごとに保存する際、
+        # 検索リクエストのget()呼び出しがロック待ちになり、50枚程度のフォルダに対する
+        # 検索でも数秒〜十数秒かかっていた）。deepcopy自体はメモリ内操作でファイルI/Oより
+        # 大幅に軽いため、ロック内に残しても実用上のブロック時間は無視できるレベルになる。
+        snapshot = copy.deepcopy(self._data)
+        return self._write_snapshot(snapshot)
+
+    def _write_snapshot(self, snapshot: dict) -> bool:
+        """self._lockの外から呼ばれる。一時ファイルに書き出してから置き換える
+        (os.replace相当のPath.replace)ことで書き込みをアトミックにする。素朴な
+        open(path, "w") での直接上書きだと、書き込み完了前に他プロセスが読むと
+        不完全なJSONを掴む(torn read)うえ、書き込み中にComfyUIプロセスが終了すると
+        ファイルが壊れたまま残る — 大量画像フォルダのBGインデックス処理で書き込み
+        頻度が高い環境ほど発生しやすく、次回起動時のロード失敗→空データでの全上書き
+        という形でユーザーの登録済みグループ/タグ/メモが消失する原因になっていた。
+        self._write_lockで直列化し、複数スレッドの書き込みが競合して古いスナップショット
+        が新しいものを上書きしないようにする。"""
+        with self._write_lock:
+            try:
+                self.file_path.parent.mkdir(parents=True, exist_ok=True)
+                tmp_path = self.file_path.with_name(self.file_path.name + ".tmp")
+                with open(tmp_path, "w", encoding="utf-8") as f:
+                    json.dump(snapshot, f, ensure_ascii=False, indent=2)
+                tmp_path.replace(self.file_path)
+                return True
+            except Exception as e:
+                logger.error("GalleryMetadataStore: save error: %s", e)
+                return False
 
     def _normalize_path(self, image_path: str) -> str:
         return str(Path(image_path)).replace("\\", "/")
@@ -83,6 +126,24 @@ class GalleryMetadataStore:
             self._data["images"][key] = existing
             return self._save_to_disk()
 
+    def save_batch(self, entries: dict[str, dict]) -> bool:
+        """複数画像のメタデータを1回のディスク書き込みでまとめて保存する。
+        バックグラウンドインデックス処理のように多数の画像を連続して保存する場合、
+        1件ずつsave()を呼ぶと画像の数だけJSONシリアライズ+ファイルI/Oが発生してしまう
+        （実際に発生した不具合: gallery_metadata.jsonが1MBを超える環境で10枚ごとに
+        個別save()していたため、検索リクエストが完了するまで数秒〜十数秒かかっていた）。"""
+        if not entries:
+            return True
+        allowed = {"favorite", "tags", "memo", "groups", "workflow"}
+        with self._lock:
+            for image_path, data in entries.items():
+                key = self._normalize_path(image_path)
+                existing = self._data["images"].get(key, {})
+                existing.update(data)
+                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]:
@@ -191,17 +252,33 @@ class GalleryMetadataStore:
                 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 += "/"
+
+        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 key.startswith(folder_key) and key not in existing_paths
+                if _in_scope(key) and key not in existing_paths
             ]
             if not stale:
                 return 0

py/services/gallery_service.py

@@ -16,8 +16,34 @@ 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。
+# .psdはブラウザが直接レンダリングできないため、配信時に合成済みPNGへ変換する）
+IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp", ".gif", ".svg", ".mp4", ".psd"}
+
+# Canvas2D の globalCompositeOperation 文字列 -> psd_tools.constants.BlendMode 名
+# (Image Edit タブの Layer.blendMode はCanvas2Dの合成モード文字列をそのまま保持している)
+_PSD_BLEND_MODE_MAP = {
+    "source-over": "NORMAL",
+    "multiply": "MULTIPLY",
+    "screen": "SCREEN",
+    "overlay": "OVERLAY",
+    "darken": "DARKEN",
+    "lighten": "LIGHTEN",
+    "color-dodge": "COLOR_DODGE",
+    "color-burn": "COLOR_BURN",
+    "hard-light": "HARD_LIGHT",
+    "soft-light": "SOFT_LIGHT",
+    "difference": "DIFFERENCE",
+    "exclusion": "EXCLUSION",
+    "hue": "HUE",
+    "saturation": "SATURATION",
+    "color": "COLOR",
+    "luminosity": "LUMINOSITY",
+}
+# 逆引き: psd_tools.constants.BlendMode 名 -> Canvas2D 文字列（PSDインポート用）。
+# CSSに対応物が無いPSD側のブレンドモード(Dissolve, Linear Burn/Dodge, Vivid/Linear/Pin Light,
+# Hard Mix, Subtract, Divide, Darker/Lighter Color, Pass Through)は "source-over" にフォールバックする。
+_CSS_BLEND_MODE_REVERSE = {v: k for k, v in _PSD_BLEND_MODE_MAP.items()}
 
 # data URL の MIME タイプ -> 拡張子（save_image_to_gallery / save_image_to_folder共用）
 _SAVE_EXT_BY_MIME = {
@@ -160,8 +186,14 @@ class GalleryService:
         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]] = {}
+        # ponyxlWildcardsVault形式フォールバック用キャッシュ: category_root文字列 -> (キャッシュ時刻, {leaf: tags})
+        self._vault_leaf_cache: dict[str, tuple[float, dict]] = {}
+        # 画像の親フォルダ文字列 -> (キャッシュ時刻, category_root or None)。
+        # _find_vault_category_root は祖先ディレクトリを最大8階層×2回stat()するため、
+        # 検索フィルタで全画像に対して毎回呼ぶと（特にVaultでない通常フォルダでも
+        # 無駄に）遅くなる。同一フォルダ内の画像は結果が同じなのでフォルダ単位で
+        # キャッシュする。
+        self._vault_root_cache: dict[str, tuple[float, Path | None]] = {}
 
     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）"""
@@ -225,13 +257,14 @@ class GalleryService:
                 batch[abs_path] = {"prompt_cache": prompt_cache}
                 processed += 1
                 if len(batch) >= 10:
-                    for p, d in batch.items():
-                        self.metadata_store.save(p, d)
+                    # 1件ずつsave()を呼ぶとバッチ内の枚数分だけJSONシリアライズ+
+                    # ファイルI/Oが発生してしまう（大量画像フォルダのメタデータでは
+                    # 1回あたり数百ms〜になりうる）ため、まとめて1回で保存する。
+                    self.metadata_store.save_batch(batch)
                     batch = {}
                     time.sleep(0.05)  # 50ms 他の処理に譲る
             if batch and not cancel.is_set():
-                for p, d in batch.items():
-                    self.metadata_store.save(p, d)
+                self.metadata_store.save_batch(batch)
             if processed:
                 logger.debug("BG index done: %s (%d indexed)", folder_path, processed)
         except Exception as e:
@@ -312,15 +345,22 @@ class GalleryService:
         try:
             with os.scandir(folder) as it:
                 for entry in it:
-                    if not entry.is_file(follow_symlinks=True):
+                    # follow_symlinks=True forces stat() to make an extra syscall per file
+                    # to resolve the link target even for plain files. Output folders don't
+                    # contain symlinked images, so False is safe and much cheaper.
+                    if not entry.is_file(follow_symlinks=False):
                         continue
                     if Path(entry.name).suffix.lower() not in IMAGE_EXTENSIONS:
                         continue
                     try:
-                        st = entry.stat(follow_symlinks=True)
+                        st = entry.stat(follow_symlinks=False)
+                        # entry.path is already absolute+resolved because callers always
+                        # pass in an already-resolved `folder`. Re-resolving it here via
+                        # Path(entry.path).resolve() costs an extra filesystem round-trip
+                        # per file — on a 9786-file folder that alone was 6.7s vs 0.07s.
                         entries.append((
                             entry.name,
-                            str(Path(entry.path).resolve()).replace("\\", "/"),
+                            entry.path.replace("\\", "/"),
                             st.st_size,
                             st.st_mtime,
                         ))
@@ -333,30 +373,45 @@ class GalleryService:
         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）。
+        """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）。
+        os.walk()はDirEntryを介さずPath.stat()（follow_symlinks=True相当）を呼ぶため、
+        _scan_folder()と同じ理由でフォルダ内ファイル数に対して極端に遅くなる。
+        os.scandir()を自前で再帰し、follow_symlinks=Falseでstatする。
         """
         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
+
+        def _walk(d):
+            try:
+                with os.scandir(d) as it:
+                    dir_entries = list(it)
+            except (PermissionError, OSError):
+                return
+            for entry in dir_entries:
+                if entry.is_dir(follow_symlinks=False):
+                    if not entry.name.startswith("."):
+                        _walk(entry.path)
+                    continue
+                if not entry.is_file(follow_symlinks=False):
+                    continue
+                if Path(entry.name).suffix.lower() not in IMAGE_EXTENSIONS:
+                    continue
+                try:
+                    st = entry.stat(follow_symlinks=False)
+                    # entry.path is already absolute since the top-level `folder` passed
+                    # in by callers is always pre-resolved — see _scan_folder() for why
+                    # re-resolving per file here would be costly.
+                    entries.append((
+                        entry.name,
+                        entry.path.replace("\\", "/"),
+                        st.st_size,
+                        st.st_mtime,
+                    ))
+                except OSError:
+                    continue
+
+        _walk(folder)
         return entries
 
     def list_images(
@@ -385,9 +440,16 @@ class GalleryService:
         # 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_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）
-        existing_paths = {abs_path for _, abs_path, _, _ in raw_entries}
-        self.metadata_store.cleanup_stale_images(str(folder), existing_paths)
+        # 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）。
+        # 検索文字列入力中はキー入力のたびに呼ばれるため、ここではスキップする
+        # （全メタデータ走査＋該当時はJSON全体のディスク書き込みを伴い重く、検索の
+        # 度に繰り返す必要はない。通常の検索なしロード時に実行されれば十分）。
+        if not search:
+            existing_paths = {abs_path for _, abs_path, _, _ in raw_entries}
+            self.metadata_store.cleanup_stale_images(str(folder), existing_paths, recursive=recursive)
 
         results = []
         for name, abs_path, size, mtime in raw_entries:
@@ -511,32 +573,45 @@ class GalleryService:
 
     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"""
+        (=ponyxlWildcardsVaultのカテゴリルート) を探す。
+        同一フォルダ内の画像は結果が同じなので、フォルダ単位でTTLキャッシュする。"""
+        parent_key = str(image_path.parent)
+        cached = self._vault_root_cache.get(parent_key)
+        if cached is not None and time.monotonic() - cached[0] <= _CACHE_TTL:
+            return cached[1]
+
         cur = image_path.parent
+        result: Path | None = None
         for _ in range(_VAULT_ROOT_SEARCH_DEPTH):
             if any((cur / name).is_dir() for name in _VAULT_THUMB_DIR_NAMES):
-                return cur
+                result = cur
+                break
             if cur.parent == cur:
                 break
             cur = cur.parent
-        return None
+
+        self._vault_root_cache[parent_key] = (time.monotonic(), result)
+        return result
 
     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 = ()
+        """category_root直下のyamlをパースしたleaf辞書をTTLキャッシュする。
+        以前はキャッシュの妥当性確認のため毎回 category_root.glob("*.yaml") を実行して
+        いたが、category_root は「thumbnails(_option2)という名前のフォルダを直下に持つ
+        祖先」というゆるい条件で決まるため、Vaultとは無関係な通常の画像出力フォルダ
+        （例: 直下にComfyUIが作る空のthumbnailsキャッシュフォルダがあるだけ）が誤検出
+        されることがある。そのフォルダに数千〜数万枚のファイルがあると、glob自体が
+        1回数十ms〜になり、検索フィルタで画像ごとに呼ばれると数秒単位まで積み上がって
+        いた（実際に発生した不具合: 1万枚超のルート直下に誤検出されたフォルダがあり、
+        53枚のサブフォルダを検索するだけで約3秒かかっていた）。TTL内はglobを省略する。"""
         cache_key = str(category_root)
         cached = self._vault_leaf_cache.get(cache_key)
-        if cached is not None and cached[0] == signature:
+        if cached is not None and time.monotonic() - cached[0] <= _CACHE_TTL:
             return cached[1]
+        yaml_files = sorted(category_root.glob("*.yaml")) + sorted(category_root.glob("*.yml"))
         leaves: dict[str, str] = {}
         for yf in yaml_files:
             leaves.update(_parse_vault_yaml_leaves(yf))
-        self._vault_leaf_cache[cache_key] = (signature, leaves)
+        self._vault_leaf_cache[cache_key] = (time.monotonic(), leaves)
         return leaves
 
     def _read_vault_prompt(self, image_path: Path) -> str | None:
@@ -584,7 +659,10 @@ 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）
@@ -603,6 +681,9 @@ 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", ""),
@@ -682,62 +763,129 @@ 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 == ".psd":
+                from psd_tools import PSDImage
+                psd = PSDImage.open(path)
+                info["width"], info["height"] = psd.width, psd.height
+            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()):
+        resolved = Path(image_path).resolve()
+        if not self._check_path_allowed(resolved):
             return False
-        meta = self.metadata_store.get(image_path)
+        # get_image_metadata()/list_images() 側はresolve()済みパスをキーにして取得する
+        # ため、ここでも統一する(save_image_metaと同じ理由。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)。
+        resolved_str = str(resolved)
+        meta = self.metadata_store.get(resolved_str)
         new_val = not meta.get("favorite", False)
-        self.metadata_store.save(image_path, {"favorite": new_val})
+        self.metadata_store.save(resolved_str, {"favorite": new_val})
         return new_val
 
     def bulk_set_favorite(self, paths: list[str], value: bool) -> dict:
         """複数画像のお気に入りを一括設定する"""
         ok = fail = 0
         for p in paths:
-            if not self._check_path_allowed(Path(p).resolve()):
+            resolved = Path(p).resolve()
+            if not self._check_path_allowed(resolved):
                 fail += 1
                 continue
-            if self.metadata_store.save(p, {"favorite": value}):
+            if self.metadata_store.save(str(resolved), {"favorite": value}):
                 ok += 1
             else:
                 fail += 1
@@ -771,21 +919,28 @@ class GalleryService:
         return self.metadata_store.delete_group(name)
 
     def add_to_group(self, image_path: str, group_name: str) -> bool:
-        if not self._check_path_allowed(Path(image_path).resolve()):
+        resolved = Path(image_path).resolve()
+        if not self._check_path_allowed(resolved):
             return False
-        meta = self.metadata_store.get(image_path)
+        # get_image_metadata()/list_images() 側はresolve()済みパスをキーにして取得する
+        # ため、ここでも統一する(save_image_metaと同じ理由。キー不一致による
+        # 「グループに追加はできるが一覧に反映されない」というサイレントな食い違いを防ぐ)。
+        resolved_str = str(resolved)
+        meta = self.metadata_store.get(resolved_str)
         groups = meta.get("groups", [])
         if group_name not in groups:
             groups.append(group_name)
-            return self.metadata_store.save(image_path, {"groups": groups})
+            return self.metadata_store.save(resolved_str, {"groups": groups})
         return True
 
     def remove_from_group(self, image_path: str, group_name: str) -> bool:
-        if not self._check_path_allowed(Path(image_path).resolve()):
+        resolved = Path(image_path).resolve()
+        if not self._check_path_allowed(resolved):
             return False
-        meta = self.metadata_store.get(image_path)
+        resolved_str = str(resolved)
+        meta = self.metadata_store.get(resolved_str)
         groups = [g for g in meta.get("groups", []) if g != group_name]
-        return self.metadata_store.save(image_path, {"groups": groups})
+        return self.metadata_store.save(resolved_str, {"groups": groups})
 
     def list_images_in_group(self, group_name: str) -> list[str]:
         all_paths = self.metadata_store.list_images_in_group(group_name)
@@ -906,8 +1061,215 @@ class GalleryService:
                 errors.append(f"{p.name}: {e}")
         return {"moved": moved, "errors": errors}
 
+    def build_psd_from_images(self, paths: list) -> bytes:
+        """複数画像を各1レイヤーとして1つのPSDにまとめ、バイナリを返す。
+        画像サイズが異なる場合はキャンバスを最大サイズに合わせ、各レイヤーは左上(0,0)基準で配置する。
+        """
+        try:
+            from psd_tools import PSDImage
+            from psd_tools.api.layers import PixelLayer
+        except ImportError as e:
+            raise RuntimeError(
+                "psd-tools is not installed. Run: "
+                "python_embeded\\python.exe -m pip install psd-tools"
+            ) from e
+        from PIL import Image
+        import io
+
+        layers_data = []
+        for img_path in paths:
+            p = Path(img_path).resolve()
+            if not p.is_file() or p.suffix.lower() not in IMAGE_EXTENSIONS:
+                continue
+            if p.suffix.lower() == ".mp4":
+                continue  # 動画はPSDレイヤーにできない
+            if not self._check_path_allowed(p):
+                continue
+            try:
+                im = Image.open(p).convert("RGBA")
+            except Exception as e:
+                logger.warning("build_psd_from_images: failed to open %s: %s", p, e)
+                continue
+            layers_data.append((p.stem, im))
+
+        if not layers_data:
+            raise ValueError("No valid images to export")
+
+        max_w = max(im.width for _, im in layers_data)
+        max_h = max(im.height for _, im in layers_data)
+
+        base = Image.new("RGBA", (max_w, max_h), (0, 0, 0, 0))
+        psd = PSDImage.frompil(base)
+        for name, im in layers_data:
+            PixelLayer.frompil(im, psd, name=name, top=0, left=0)
+
+        buf = io.BytesIO()
+        psd.save(buf)
+        return buf.getvalue()
+
+    def build_psd_from_layers(self, width: int, height: int, layers: list) -> bytes:
+        """Image Edit タブのレイヤー配列(dataURL+メタデータ)から1つのPSDを組み立てる。
+        各レイヤー画像は既にフロントエンド側で回転・反転・拡縮をベイクしたキャンバス全体サイズの
+        PNGとして渡される前提のため位置は常に(0,0)。opacity/blendModeはPSDレイヤー属性として渡す。
+        """
+        try:
+            from psd_tools import PSDImage
+            from psd_tools.api.layers import PixelLayer
+            from psd_tools.constants import BlendMode
+        except ImportError as e:
+            raise RuntimeError(
+                "psd-tools is not installed. Run: "
+                "python_embeded\\python.exe -m pip install psd-tools"
+            ) from e
+        from PIL import Image
+        import base64
+        import io
+        import re
+
+        if not layers:
+            raise ValueError("No layers to export")
+        if width <= 0 or height <= 0:
+            raise ValueError("Invalid canvas size")
+
+        base = Image.new("RGBA", (width, height), (0, 0, 0, 0))
+        psd = PSDImage.frompil(base)
+
+        added = 0
+        for layer_data in layers:
+            data_url = layer_data.get("imageData", "")
+            m = re.match(r"^data:image/\w+;base64,(.+)$", data_url)
+            if not m:
+                continue
+            try:
+                im = Image.open(io.BytesIO(base64.b64decode(m.group(1)))).convert("RGBA")
+            except Exception as e:
+                logger.warning("build_psd_from_layers: failed to decode layer image: %s", e)
+                continue
+
+            name = str(layer_data.get("name") or "Layer")[:255]
+            opacity = int(max(0.0, min(1.0, float(layer_data.get("opacity", 1.0)))) * 255)
+            visible = bool(layer_data.get("visible", True))
+            blend_key = _PSD_BLEND_MODE_MAP.get(layer_data.get("blendMode", "source-over"), "NORMAL")
+            blend_mode = getattr(BlendMode, blend_key)
+
+            pixel_layer = PixelLayer.frompil(im, psd, name=name, top=0, left=0)
+            pixel_layer.opacity = opacity
+            pixel_layer.blend_mode = blend_mode
+            pixel_layer.visible = visible
+            added += 1
+
+        if added == 0:
+            raise ValueError("No valid layer images to export")
+
+        buf = io.BytesIO()
+        psd.save(buf)
+        return buf.getvalue()
+
+    def _extract_psd_layers(self, psd) -> dict:
+        """PSDImageインスタンスからレイヤー配列(dataURL+メタデータ)を抽出する共通ロジック。
+        グループ自体・ラスタ化できないレイヤー(調整レイヤー等)はスキップする。
+        戻り値のlayersはフロントエンドのLayer配列規約(先頭=最前面)に合わせて並べる。
+        """
+        from psd_tools.constants import BlendMode
+        import base64
+        import io
+
+        layers_out = []
+        for layer in psd.descendants():
+            if layer.is_group():
+                continue
+            try:
+                im = layer.topil()
+            except Exception as e:
+                logger.warning("_extract_psd_layers: failed to rasterize layer %r: %s", layer.name, e)
+                continue
+            if im is None:
+                continue
+            im = im.convert("RGBA")
+
+            left, top, right, bottom = layer.bbox
+            w, h = right - left, bottom - top
+            if w <= 0 or h <= 0:
+                continue
+
+            buf = io.BytesIO()
+            im.save(buf, format="PNG")
+            data_url = "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode()
+
+            blend_value = layer.blend_mode
+            try:
+                blend_name = BlendMode(blend_value.value if hasattr(blend_value, "value") else blend_value).name
+            except ValueError:
+                blend_name = "NORMAL"
+            css_blend = _CSS_BLEND_MODE_REVERSE.get(blend_name, "source-over")
+
+            layers_out.append({
+                "name": layer.name or "Layer",
+                "x": left,
+                "y": top,
+                "width": w,
+                "height": h,
+                "opacity": (layer.opacity if layer.opacity is not None else 255) / 255.0,
+                "visible": bool(layer.visible),
+                "blendMode": css_blend,
+                "imageData": data_url,
+            })
+
+        if not layers_out:
+            raise ValueError("No importable layers found in this PSD")
+
+        # descendants()は背面→前面(下から上)の順で返るため、
+        # フロントエンドのLayer配列規約(先頭=最前面)に合わせて反転する
+        layers_out.reverse()
+
+        return {"width": psd.width, "height": psd.height, "layers": layers_out}
+
+    def import_psd_layers(self, psd_bytes: bytes) -> dict:
+        """アップロードされたPSDファイル(バイト列)をレイヤー配列(dataURL+メタデータ)に分解する。"""
+        try:
+            from psd_tools import PSDImage
+        except ImportError as e:
+            raise RuntimeError(
+                "psd-tools is not installed. Run: "
+                "python_embeded\\python.exe -m pip install psd-tools"
+            ) from e
+        import io
+
+        try:
+            psd = PSDImage.open(io.BytesIO(psd_bytes))
+        except Exception as e:
+            raise ValueError(f"Failed to read PSD file: {e}") from e
+
+        return self._extract_psd_layers(psd)
+
+    def get_psd_layers_from_path(self, image_path: str) -> dict:
+        """サーバー上に既にあるPSDファイル(Gallery上のパス)をレイヤー配列に分解する。
+        Galleryから直接Image Editへ送る際、アップロードの往復無しで済ませるために使う。
+        """
+        p = Path(image_path).resolve()
+        if not p.is_file() or p.suffix.lower() != ".psd":
+            raise ValueError("Not a PSD file")
+        if not self._check_path_allowed(p):
+            raise ValueError("Access denied")
+
+        try:
+            from psd_tools import PSDImage
+        except ImportError as e:
+            raise RuntimeError(
+                "psd-tools is not installed. Run: "
+                "python_embeded\\python.exe -m pip install psd-tools"
+            ) from e
+
+        try:
+            psd = PSDImage.open(p)
+        except Exception as e:
+            raise ValueError(f"Failed to read PSD file: {e}") from e
+
+        return self._extract_psd_layers(psd)
+
     def serve_image(self, image_path: str):
-        """画像のPathオブジェクトを返す（ルートで使用）"""
+        """画像のPathオブジェクトを返す（ルートで使用）。
+        PSDはブラウザが直接レンダリングできないため、合成済みPNGにキャッシュ変換して返す。"""
         p = Path(image_path).resolve()
         if not p.is_file():
             return None
@@ -916,13 +1278,42 @@ class GalleryService:
         if not self._check_path_allowed(p):
             logger.warning("serve_image: path not allowed: %s", p)
             return None
+        if p.suffix.lower() == ".psd":
+            return self._get_psd_composite_cache(p)
         return p
 
+    def _get_psd_composite_cache(self, p: Path) -> Path | None:
+        """PSDの全レイヤー合成結果をフル解像度PNGとしてディスクキャッシュし、そのPathを返す。"""
+        try:
+            mtime = int(p.stat().st_mtime * 1000)
+        except OSError:
+            return None
+        cache_key = hashlib.md5(f"{p}:{mtime}:full".encode()).hexdigest()
+        cache_dir = self.data_dir / "thumb_cache"
+        cache_dir.mkdir(exist_ok=True)
+        out_path = cache_dir / f"{cache_key}_psdfull.png"
+        if out_path.exists():
+            return out_path
+        try:
+            from psd_tools import PSDImage
+            psd = PSDImage.open(p)
+            img = psd.composite()
+            if img is None:
+                return None
+            img.convert("RGBA").save(out_path, "PNG")
+            return out_path
+        except Exception as e:
+            logger.warning("serve_image: psd composite failed for %s: %s", p, e)
+            return None
+
     def serve_thumbnail(self, image_path: str, width: int = 256) -> Path | None:
         """縮小サムネイルのPathを返す。
         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）。
+        PSDはpsd_toolsでレイヤーを合成してからJPEGとしてキャッシュする（同様にフォールバック不可）。
         """
         p = Path(image_path).resolve()
         if not p.is_file():
@@ -955,6 +1346,42 @@ 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
+
+        # PSDはPillowで直接開けない(内部のマージ画像がPhotoshop側の実装依存で信頼できない)ため
+        # psd_toolsでレイヤーを正しく合成してからサムネイル化する。
+        if p.suffix.lower() == ".psd":
+            try:
+                from psd_tools import PSDImage
+                from PIL import Image
+                psd = PSDImage.open(p)
+                img = psd.composite()
+                if img is None:
+                    return None
+                img = img.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: psd composite failed for %s: %s", p, e)
+                return None
+
         try:
             from PIL import Image
             with Image.open(p) as img:

py/services/tagger_db_service.py

@@ -5,6 +5,7 @@ import io
 import logging
 import sqlite3
 from pathlib import Path
+from typing import List
 
 logger = logging.getLogger(__name__)
 
@@ -85,6 +86,20 @@ class TaggerDbService:
             conn.execute("DELETE FROM images WHERE id=?", (row_id,))
             conn.commit()
 
+    def delete_bulk(self, row_ids: List[int]) -> int:
+        if not row_ids:
+            return 0
+
+        placeholders = ",".join("?" for _ in row_ids)
+
+        with self._conn() as conn:
+            cur = conn.execute(
+                f"DELETE FROM images WHERE id IN ({placeholders})",
+                row_ids,
+            )
+            conn.commit()
+            return cur.rowcount
+
     def export_csv(self) -> str:
         with self._conn() as conn:
             rows = conn.execute(

py/services/tagger_service.py

@@ -5,11 +5,14 @@ import csv
 import io
 import json
 import logging
+import os
+import re
 import tempfile
 import threading
 import urllib.request
 from pathlib import Path
 from typing import Optional
+from urllib.parse import urlparse
 
 import numpy as np
 from PIL import Image, ImageOps, PngImagePlugin
@@ -18,6 +21,20 @@ logger = logging.getLogger(__name__)
 
 _model_lock = threading.Lock()
 
+# Unsloth requires an Authorization: Bearer <UNSLOTH_API_KEY> header even for
+# local access. api_url is client-supplied per request, so without a host
+# check a client could point it at an arbitrary server and exfiltrate the key
+# (SSRF). Mirrors unsloth_routes.py's _is_allowed_base_url/_ALLOWED_HOSTS.
+_UNSLOTH_ALLOWED_HOSTS = {"localhost", "127.0.0.1", "::1"}
+
+
+def _is_allowed_unsloth_url(api_url: str) -> bool:
+    try:
+        parsed = urlparse(api_url)
+        return parsed.scheme in ("http", "https") and parsed.hostname in _UNSLOTH_ALLOWED_HOSTS
+    except Exception:
+        return False
+
 
 class TaggerService:
     def __init__(self, models_dir: Path, settings_file: Path):
@@ -185,19 +202,33 @@ class TaggerService:
     def _predict_onnx(self, img: Image.Image, data: dict, thr: float, char_thr: float) -> list:
         shape = data["input_shape"]
         fmt = "NHWC"
-        size = (224, 224)
+        size = 224
         if len(shape) == 4:
             if shape[1] == 3 and isinstance(shape[2], int) and shape[2] > 0:
                 fmt = "NCHW"
-                size = (int(shape[3]), int(shape[2]))
+                size = int(shape[2])
             elif shape[3] == 3 and isinstance(shape[1], int) and shape[1] > 0:
                 fmt = "NHWC"
-                size = (int(shape[2]), int(shape[1]))
+                size = int(shape[1])
 
         img = ImageOps.exif_transpose(img)
-        if img.mode != "RGB":
-            img = img.convert("RGB")
-        arr = np.array(img.resize(size, Image.Resampling.LANCZOS), dtype=np.float32) / 255.0
+        # WD Tagger系標準の前処理: 透過は白背景で合成 → アスペクト比を維持したまま
+        # 白背景で正方形にパディング → リサイズ → RGB→BGR → 0-255のfloat32のまま(NHWC)
+        if img.mode != "RGBA":
+            img = img.convert("RGBA")
+        canvas = Image.new("RGBA", img.size, (255, 255, 255))
+        canvas.paste(img, mask=img)
+        img = canvas.convert("RGB")
+
+        w, h = img.size
+        max_dim = max(w, h)
+        padded = Image.new("RGB", (max_dim, max_dim), (255, 255, 255))
+        padded.paste(img, ((max_dim - w) // 2, (max_dim - h) // 2))
+        if max_dim != size:
+            padded = padded.resize((size, size), Image.Resampling.BICUBIC)
+
+        arr = np.array(padded, dtype=np.float32)
+        arr = np.ascontiguousarray(arr[:, :, ::-1])  # RGB -> BGR
         if fmt == "NCHW":
             arr = arr.transpose((2, 0, 1))
         arr = np.expand_dims(arr, 0)
@@ -223,39 +254,112 @@ class TaggerService:
                 tags.append(label.replace("character:", ""))
         return tags
 
-    # ── Ollama VLM ────────────────────────────────────────────
+    # ── VLM Tagging (Ollama / LM Studio / Lemonade / Unsloth) ──
 
-    def ollama_models(self, api_url: str) -> list:
+    @staticmethod
+    def _unsloth_api_key() -> Optional[str]:
+        """UNSLOTH_API_KEY を環境変数(.env)から取得。Unslothはローカルでも常に必須。"""
+        return os.environ.get("UNSLOTH_API_KEY", "").strip() or None
+
+    @staticmethod
+    def _strip_thinking_tags(text: str) -> str:
+        text = re.sub(r"<think>[\s\S]*?</think>", "", text, flags=re.IGNORECASE)
+        text = re.sub(r"<thinking>[\s\S]*?</thinking>", "", text, flags=re.IGNORECASE)
+        return text.strip()
+
+    def vlm_models(self, backend: str, api_url: str) -> list:
+        backend = (backend or "ollama").lower()
         try:
-            req = urllib.request.Request(api_url.rstrip("/") + "/api/tags")
+            if backend == "ollama":
+                req = urllib.request.Request(api_url.rstrip("/") + "/api/tags")
+                with urllib.request.urlopen(req, timeout=5) as resp:
+                    data = json.loads(resp.read().decode("utf-8"))
+                return sorted(m["name"] for m in data.get("models", []))
+
+            headers = {}
+            if backend == "unsloth":
+                if not _is_allowed_unsloth_url(api_url):
+                    logger.error("vlm_models: Unsloth backend URL must point to localhost/127.0.0.1/::1")
+                    return []
+                key = self._unsloth_api_key()
+                if not key:
+                    logger.error("vlm_models: UNSLOTH_API_KEY is not set")
+                    return []
+                headers["Authorization"] = f"Bearer {key}"
+            req = urllib.request.Request(api_url.rstrip("/") + "/v1/models", headers=headers)
             with urllib.request.urlopen(req, timeout=5) as resp:
                 data = json.loads(resp.read().decode("utf-8"))
-            return sorted(m["name"] for m in data.get("models", []))
+            return sorted(m["id"] for m in data.get("data", []))
         except Exception as e:
-            logger.error("ollama_models: %s", e)
+            logger.error("vlm_models: %s", e)
             return []
 
-    def ollama_predict(self, image_b64: str, api_url: str, model: str, prompt: str, max_tags: int = 40) -> dict:
-        payload = json.dumps({
-            "model": model,
-            "messages": [{"role": "user", "content": prompt, "images": [image_b64]}],
-            "stream": False,
-            "options": {"temperature": 0.7, "num_ctx": 4096},
-        }).encode("utf-8")
+    def vlm_predict(self, image_b64: str, backend: str, api_url: str, model: str, prompt: str, max_tags: int = 40,
+                     thinking_mode: bool = False, max_tokens: int = 0) -> dict:
+        backend = (backend or "ollama").lower()
         try:
-            req = urllib.request.Request(
-                api_url.rstrip("/") + "/api/chat",
-                data=payload,
-                headers={"Content-Type": "application/json"},
-                method="POST",
-            )
-            with urllib.request.urlopen(req, timeout=180) as resp:
-                data = json.loads(resp.read().decode("utf-8"))
-            content = data.get("message", {}).get("content", "")
+            if backend == "ollama":
+                body = {
+                    "model": model,
+                    "messages": [{"role": "user", "content": prompt, "images": [image_b64]}],
+                    "stream": False,
+                    "think": bool(thinking_mode),
+                    "options": {"temperature": 0.7, "num_ctx": 4096},
+                }
+                if max_tokens > 0:
+                    body["options"]["num_predict"] = max_tokens
+                payload = json.dumps(body).encode("utf-8")
+                req = urllib.request.Request(
+                    api_url.rstrip("/") + "/api/chat",
+                    data=payload,
+                    headers={"Content-Type": "application/json"},
+                    method="POST",
+                )
+                with urllib.request.urlopen(req, timeout=180) as resp:
+                    data = json.loads(resp.read().decode("utf-8"))
+                content = data.get("message", {}).get("content", "")
+            else:
+                headers = {"Content-Type": "application/json"}
+                if backend == "unsloth":
+                    if not _is_allowed_unsloth_url(api_url):
+                        return {"error": "Unsloth backend URL must point to localhost/127.0.0.1/::1"}
+                    key = self._unsloth_api_key()
+                    if not key:
+                        return {"error": "UNSLOTH_API_KEY is not set. Copy .env.example to .env in the "
+                                          "plugin folder, fill in the key, and restart ComfyUI."}
+                    headers["Authorization"] = f"Bearer {key}"
+                body = {
+                    "model": model,
+                    "messages": [{"role": "user", "content": [
+                        {"type": "text", "text": prompt},
+                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}},
+                    ]}],
+                    "stream": False,
+                }
+                if max_tokens > 0:
+                    body["max_tokens"] = max_tokens
+                payload = json.dumps(body).encode("utf-8")
+                req = urllib.request.Request(
+                    api_url.rstrip("/") + "/v1/chat/completions",
+                    data=payload,
+                    headers=headers,
+                    method="POST",
+                )
+                with urllib.request.urlopen(req, timeout=180) as resp:
+                    data = json.loads(resp.read().decode("utf-8"))
+                message = data.get("choices", [{}])[0].get("message", {})
+                content = message.get("content", "")
+                # Unslothはreasoningを別フィールドで返すため<think>タグに畳んで統一的に扱う
+                reasoning = message.get("reasoning_content")
+                if reasoning:
+                    content = f"<think>{reasoning}</think>{content}"
+
+            if not thinking_mode:
+                content = self._strip_thinking_tags(content)
             tags = [t.strip() for t in content.split(",") if t.strip()][:max_tags]
             return {"tags": ", ".join(tags), "count": len(tags)}
         except Exception as e:
-            logger.error("ollama_predict: %s", e)
+            logger.error("vlm_predict: %s", e)
             return {"error": str(e)}
 
     # ── ファイルメタデータ書込 ────────────────────────────────
@@ -334,8 +438,9 @@ class TaggerService:
         self._batch_state["stop"] = True
 
     def batch_start(self, folder: str, model_name: str, threshold: float, char_threshold: float,
-                    use_ollama: bool, ollama_api: str, ollama_model: str, ollama_prompt: str,
-                    ollama_max_tags: int, save_db: bool, write_file: bool, write_txt: bool, db_service) -> dict:
+                    use_ollama: bool, vlm_backend: str, vlm_api_url: str, ollama_model: str, ollama_prompt: str,
+                    ollama_max_tags: int, save_db: bool, write_file: bool, write_txt: bool, db_service,
+                    thinking_mode: bool = False, max_tokens: int = 0) -> dict:
         if self._batch_state["running"]:
             return {"error": "Batch already running"}
         folder_path = Path(folder)
@@ -370,7 +475,8 @@ class TaggerService:
 
                     vlm_tags = ""
                     if use_ollama and ollama_model:
-                        res2 = self.ollama_predict(b64, ollama_api, ollama_model, ollama_prompt, ollama_max_tags)
+                        res2 = self.vlm_predict(b64, vlm_backend, vlm_api_url, ollama_model, ollama_prompt, ollama_max_tags,
+                                                 thinking_mode, max_tokens)
                         vlm_tags = res2.get("tags", "")
 
                     all_tags = ", ".join(filter(None, [interrogator_tags, vlm_tags]))

py/services/video_edit_project_service.py

@@ -0,0 +1,98 @@
+"""Video tab Edit subtab project file management (JSON timeline persistence).
+
+Same CRUD shape as VideoPlanService (list/get/save/delete with a safe-path
+helper), kept as its own class rather than sharing a base because the two
+JSON schemas are unrelated (batch keyframe blocks vs. an ordered clip
+timeline) — see VideoPlanService's own docstring for the same reasoning.
+No index-image thumbnail support (unlike VideoPlanService) since the Edit
+project list is plain text/JSON only for now.
+"""
+
+import json
+import logging
+from datetime import datetime, timezone
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+class VideoEditProjectService:
+    def __init__(self, project_dir: Path):
+        self.project_dir = project_dir
+        self.project_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.project_dir / name).resolve()
+        project_dir_resolved = self.project_dir.resolve()
+        if resolved != project_dir_resolved and not str(resolved).startswith(str(project_dir_resolved) + "\\") and not str(resolved).startswith(str(project_dir_resolved) + "/"):
+            return None
+        return resolved
+
+    def _now_iso(self) -> str:
+        return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")
+
+    # ------------------------------------------------------------------
+    # CRUD
+    # ------------------------------------------------------------------
+
+    def list_projects(self) -> list[dict]:
+        if not self.project_dir.is_dir():
+            return []
+        result = []
+        for path in sorted(self.project_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 video edit project %s: %s", path.name, e)
+                continue
+
+            result.append({
+                "filename": path.name,
+                "name": data.get("name", path.stem),
+                "clip_count": len(data.get("clips", [])),
+                "updated_at": data.get("updated_at", ""),
+            })
+        result.sort(key=lambda p: p.get("updated_at", ""), reverse=True)
+        return result
+
+    def get_project(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"Project not found: {filename}")
+        with open(path, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    def save_project(self, filename: str, data: dict) -> 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)
+
+        return {"filename": path.name, "updated_at": data["updated_at"]}
+
+    def delete_project(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()

py/services/video_plan_service.py

@@ -0,0 +1,143 @@
+"""Video tab plan file management service (JSON plan + optional PNG index thumbnail).
+
+Duplicated from LabPlanService rather than shared — the two plan formats are
+unrelated (batch keyframe columns vs. sequential timeline blocks) and would
+gain nothing from a forced common base beyond coincidental line-count savings.
+"""
+
+import base64
+import json
+import logging
+from datetime import datetime, timezone
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+class VideoPlanService:
+    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 video 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/video_plan/{stem}.png"
+
+            result.append({
+                "filename": path.name,
+                "name": data.get("name", stem),
+                "note": data.get("note", ""),
+                "block_count": len(data.get("blocks", [])),
+                "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 = "Video_index") -> dict:
+        """Saves the contact-sheet index image into ComfyUI's own output folder,
+        alongside the videos 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()

py/services/video_service.py

@@ -0,0 +1,159 @@
+"""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 probe_video(self, filename: str, subfolder: str, type_: str) -> dict:
+        """Reads duration/dimensions/fps/audio presence via PyAV only (no ffmpeg
+        subprocess) — used by the Video Edit tab right after a clip is added, so
+        the timeline UI can show its length and the export-time resolution-match
+        check has real numbers to compare against."""
+        import av  # type: ignore
+
+        src_path = self._resolve_media_path(filename, subfolder, type_)
+        container = av.open(str(src_path))
+        try:
+            stream = container.streams.video[0]
+            fps = float(stream.average_rate) if stream.average_rate else 0.0
+            if stream.duration is not None and stream.time_base is not None:
+                duration = float(stream.duration * stream.time_base)
+            elif container.duration is not None:
+                duration = float(container.duration) / 1_000_000
+            else:
+                duration = 0.0
+            width = stream.codec_context.width
+            height = stream.codec_context.height
+            has_audio = len(container.streams.audio) > 0
+        finally:
+            container.close()
+
+        return {
+            "duration": duration,
+            "width": width,
+            "height": height,
+            "fps": fps,
+            "has_audio": has_audio,
+        }
+
+    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)}

py/wfm.py

@@ -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, LAB_PLAN_DIR
+from .config import TEMPLATES_DIR, STATIC_DIR, DATA_DIR, WORKFLOWS_DIR, LAB_PLAN_DIR, VIDEO_PLAN_DIR
 
 logger = logging.getLogger(__name__)
 
@@ -57,6 +57,22 @@ async def serve_lab_plan_file(request: web.Request) -> web.Response:
     })
 
 
+async def serve_video_plan_file(request: web.Request) -> web.Response:
+    """Serve files (index thumbnails) from the Video 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 = VIDEO_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."""
 
@@ -76,15 +92,19 @@ class WorkflowStudio:
         # Dynamic Lab plan file serving (index thumbnails)
         app.router.add_get("/wfm_data/lab_plan/{filename}", serve_lab_plan_file)
 
+        # Dynamic Video plan file serving (index thumbnails)
+        app.router.add_get("/wfm_data/video_plan/{filename}", serve_video_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, lab_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)
@@ -95,5 +115,6 @@ class WorkflowStudio:
         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")

pyproject.toml

@@ -1,8 +1,8 @@
 [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 = "0.3.97"
-license = "MIT"
+version = "0.7.2"
+license = { file = "LICENSE" }
 requires-python = ">= 3.10"
 dependencies = ["jinja2"]
 

requirements.txt

@@ -19,3 +19,17 @@ 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
+
+# -- Multi-layer PSD export (Gallery tab bulk action) --
+# Pure-Python, ships prebuilt wheels (no C compiler required on Windows).
+psd-tools>=1.10
+
+# -- 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

static/css/gallery-tab.css

@@ -290,6 +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;
@@ -465,43 +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;
@@ -836,6 +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 {

static/css/image-edit-tab.css

@@ -311,8 +311,8 @@
 
 .ie-layers-header {
     display: flex;
-    align-items: center;
-    gap: 2px;
+    flex-direction: column;
+    gap: 4px;
     padding: 5px 6px;
     border-bottom: 1px solid var(--wfm-border);
     font-size: 11px;
@@ -323,7 +323,13 @@
     flex-shrink: 0;
 }
 
-.ie-layers-header > span {
+.ie-layers-header-row {
+    display: flex;
+    align-items: center;
+    gap: 2px;
+}
+
+.ie-layers-header-row > span {
     flex: 1;
 }
 

static/css/lab-tab.css

@@ -163,8 +163,10 @@
 .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;
@@ -175,6 +177,39 @@
 
 .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;
@@ -235,6 +270,23 @@
     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;
@@ -243,6 +295,16 @@
     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;

static/css/main.css

@@ -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;
@@ -2374,6 +2387,248 @@ body {
     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;
@@ -5018,15 +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;
 }
 

static/css/tagger-tab.css

@@ -250,6 +250,14 @@
     z-index: 1;
 }
 
+#wfm-tagger-db-th-select, td.wfm-tagger-db-select {
+    text-align: center;
+}
+
+#wfm-tagger-db-th-select input, .wfm-tagger-db-select input {
+    vertical-align: middle;
+}
+
 .wfm-tagger-db-table tbody tr {
     cursor: pointer;
     transition: background 0.1s;

static/css/video-tab.css

@@ -0,0 +1,811 @@
+/* =============================================
+   Video Tab (MiniMax H3 / LTX-2.5 / Wan2.2対応・GenerateUIタブとは独立)
+   3ペイン構成: 左=Plan/Asset/Editサブタブ / 中央=プレビュー+Video Source / 右=プロパティ
+   中央・右ペインは左のサブタブ切替と無関係に常時表示される。
+   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: 253px;
+    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-wf-name {
+    color: var(--wfm-text-secondary);
+    font-size: 13px;
+}
+
+/* ── Plan/Asset/Edit サブタブナビ（Lab/Galleryと同型） ── */
+.wfm-video-subtab-nav {
+    display: flex;
+    border-bottom: 1px solid var(--wfm-border);
+    flex-shrink: 0;
+    margin: -16px -16px 12px;
+    padding: 0 16px;
+}
+
+.wfm-video-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-video-subtab-btn:hover { color: var(--wfm-text-primary); }
+.wfm-video-subtab-btn.active { color: var(--wfm-primary); border-bottom-color: var(--wfm-primary); }
+
+/* 中央ペイン用のタブナビ: プレビュー行の"下"に来るため、サイドバー版と違って
+   上マージンは潰さない(プレビュー行と重ならないようにする)。左右のみ潰して
+   center-panelのpadding分フルブリードにする。 */
+.wfm-video-center-subtab-nav {
+    margin: 0 -16px 12px;
+}
+
+.wfm-video-subtab-panel {
+    display: flex;
+    flex-direction: column;
+    gap: 10px;
+}
+
+/* ── Plan: タイムライン（各ブロックのdurationに応じてflex-growで幅配分） ── */
+.wfm-video-timeline-wrap {
+    display: flex;
+    flex-direction: column;
+    gap: 6px;
+}
+
+.wfm-video-timeline-track {
+    display: flex;
+    height: 40px;
+    border-radius: 4px;
+    overflow: hidden;
+    border: 1px solid var(--wfm-border);
+}
+
+.wfm-video-timeline-block {
+    flex-basis: 0;
+    min-width: 24px;
+    cursor: pointer;
+    border-right: 1px solid rgba(0, 0, 0, 0.3);
+    transition: outline 0.1s;
+}
+
+.wfm-video-timeline-block:last-child { border-right: none; }
+.wfm-video-timeline-block.selected { outline: 2px solid var(--wfm-primary); outline-offset: -2px; }
+
+.wfm-video-timeline-toolbar {
+    display: flex;
+    gap: 6px;
+}
+
+/* ── Plan: 中央下部に配置されるコンテンツ全体（Planサブタブ選択時のみ表示） ── */
+.wfm-video-plan-topbar {
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+}
+
+/* 現在ロード中のワークフロー名 + Planに記録されたワークフローのOpen Workflow行を
+   同じ1行にまとめる（以前は縦に2行だった）。 */
+.wfm-video-wf-row {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    flex-wrap: wrap;
+}
+
+.wfm-video-wf-row .wfm-lab-plan-workflow-row { margin-top: 0; }
+
+.wfm-video-plan-basesettings {
+    display: grid;
+    grid-template-columns: repeat(5, 150px);
+    gap: 10px;
+}
+
+/* ── Plan: 2行目の追加設定(Width/Height/Turbo Mode/Strength/Steps/Prompt Enhance)。
+   basesettingsと同じ見た目だが、ワークフローによって対応する項目が変わる可変集合の
+   ため別クラスに分けている ── */
+.wfm-video-plan-extrasettings {
+    display: grid;
+    grid-template-columns: repeat(6, 130px);
+    gap: 10px;
+}
+
+.wfm-video-plan-basesettings label,
+.wfm-video-plan-extrasettings label {
+    display: block;
+    font-size: 12px;
+    color: var(--wfm-text-secondary);
+    margin-bottom: 4px;
+}
+
+/* Noise Seed欄: 数値入力とRandomizeチェックボックスを1セル内で横並びにする */
+.wfm-video-seed-inline {
+    display: flex;
+    gap: 6px;
+    align-items: center;
+}
+
+.wfm-video-seed-inline .wfm-input { flex: 1; min-width: 0; }
+
+.wfm-video-seed-randomize-inline {
+    display: flex;
+    align-items: center;
+    gap: 3px;
+    font-size: 11px;
+    white-space: nowrap;
+    margin-bottom: 0;
+}
+
+/* Turbo Mode / Prompt Enhance欄: ラベル行の下にチェックボックスを1つだけ置く
+   (他のnumber/select欄と縦位置を揃えるため、input要素の高さを合わせる) */
+.wfm-video-checkbox-inline {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    height: 30px;
+    font-size: 12px;
+    margin-bottom: 0;
+}
+
+/* タイムラインの下: ブロックエディタ(左、横並びの3カラム) + Run/Planタブ(右) */
+.wfm-video-plan-main-row {
+    display: flex;
+    gap: 16px;
+    align-items: flex-start;
+}
+
+/* ── Plan: ブロックエディタ（選択中ブロックのFirst/Last Image・Prompt。Durationはtopbarの
+   basesettings行に移動済み。中央の広いエリアに配置されるため横並びの3カラムで表示する） ── */
+.wfm-video-block-editor {
+    flex: 1;
+    min-width: 0;
+    display: flex;
+    gap: 16px;
+}
+
+.wfm-video-block-col label {
+    display: block;
+    font-size: 12px;
+    color: var(--wfm-text-secondary);
+    margin-bottom: 4px;
+}
+
+.wfm-video-block-first-col,
+.wfm-video-block-last-col {
+    width: 190px;
+    flex: none;
+    display: flex;
+    flex-direction: column;
+}
+
+.wfm-video-block-prompt-col {
+    flex: 1;
+    min-width: 220px;
+    display: flex;
+    flex-direction: column;
+}
+
+.wfm-video-block-prompt-col textarea {
+    flex: 1;
+    resize: none;
+}
+
+.wfm-video-block-color-row {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    font-size: 11px;
+    color: var(--wfm-text-secondary);
+    margin-bottom: 6px;
+}
+
+.wfm-video-color-swatch {
+    display: inline-block;
+    width: 14px;
+    height: 14px;
+    border-radius: 3px;
+    border: 1px solid var(--wfm-border);
+}
+
+/* ── Plan: Run/Planタブ(実行系・プラン管理系を切替表示するコンパクトなウィジェット) ── */
+.wfm-video-runplan-panel {
+    width: 260px;
+    flex: none;
+}
+
+.wfm-video-runplan-tabnav {
+    display: flex;
+    gap: 4px;
+    margin-bottom: 10px;
+    border-bottom: 1px solid var(--wfm-border);
+}
+
+.wfm-video-runplan-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-runplan-tab.active {
+    color: var(--wfm-text);
+    border-bottom-color: var(--wfm-primary);
+}
+
+.wfm-video-runplan-content {
+    display: flex;
+    flex-direction: column;
+}
+
+/* ── Asset: 中央下部サブタブ。左=一覧(サムネイル/テーブル切替) / 右=詳細 ── */
+.wfm-video-asset-content { padding: 0 16px 16px; }
+
+.wfm-video-asset-layout {
+    display: flex;
+    gap: 16px;
+    align-items: flex-start;
+}
+
+.wfm-video-asset-list-col {
+    flex: 2;
+    min-width: 0;
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+}
+
+.wfm-video-asset-detail-col {
+    flex: 1;
+    min-width: 220px;
+    max-width: 320px;
+    border-left: 1px solid var(--wfm-border);
+    padding-left: 16px;
+}
+
+.wfm-video-asset-toolbar {
+    display: flex;
+    gap: 6px;
+    align-items: center;
+    flex-wrap: wrap;
+}
+
+.wfm-video-asset-view-btn.active {
+    background: var(--wfm-primary);
+    color: #fff;
+}
+
+.wfm-video-asset-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(110px, 1fr));
+    gap: 8px;
+    max-height: 420px;
+    overflow-y: auto;
+}
+
+.wfm-video-asset-table-wrap {
+    max-height: 420px;
+    overflow-y: auto;
+    border: 1px solid var(--wfm-border);
+    border-radius: 4px;
+}
+
+.wfm-video-asset-table {
+    width: 100%;
+    border-collapse: collapse;
+    font-size: 12px;
+}
+
+.wfm-video-asset-table th,
+.wfm-video-asset-table td {
+    padding: 6px 8px;
+    text-align: left;
+    border-bottom: 1px solid var(--wfm-border);
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+    max-width: 240px;
+}
+
+.wfm-video-asset-table thead th {
+    position: sticky;
+    top: 0;
+    background: var(--wfm-bg-secondary);
+}
+
+.wfm-video-asset-row { cursor: pointer; }
+.wfm-video-asset-row:hover { background: var(--wfm-bg-hover); }
+.wfm-video-asset-row.selected { outline: 2px solid var(--wfm-primary); outline-offset: -2px; }
+
+.wfm-video-asset-card {
+    cursor: pointer;
+    border: 1px solid var(--wfm-border);
+    border-radius: 4px;
+    overflow: hidden;
+    transition: outline 0.1s;
+}
+
+.wfm-video-asset-card.selected { outline: 2px solid var(--wfm-primary); outline-offset: -2px; }
+
+.wfm-video-asset-card-img {
+    width: 100%;
+    aspect-ratio: 1;
+    object-fit: cover;
+    display: block;
+    background: var(--wfm-bg-hover, #111);
+}
+
+.wfm-video-asset-card-name {
+    font-size: 10px;
+    padding: 2px 4px;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+    color: var(--wfm-text-secondary);
+}
+
+.wfm-video-asset-detail {
+    display: flex;
+    flex-direction: column;
+    gap: 6px;
+}
+
+/* ── Edit: クリップタイムライン(Planタブと同じ横トラック+共有ツールバー方式)・
+   トリムパネル・書き出しパネル ──
+   Planの.wfm-video-timeline-trackはブロックをflex-grow(duration比率)で幅一杯に
+   引き伸ばすのに対し、Editのブロックは実秒数×px/秒の固定幅で「左詰め」に並べる
+   (空きトラックはそのまま空く)ため、専用クラスに分けている。 */
+.wfm-video-edit-panel { padding: 0 16px 16px; }
+
+.wfm-video-edit-timeline-track {
+    display: flex;
+    align-items: stretch;
+    gap: 4px;
+    width: 100%;
+    box-sizing: border-box;
+    height: 48px;
+    padding: 4px;
+    border-radius: 4px;
+    border: 1px solid var(--wfm-border);
+    overflow-x: auto;
+    position: relative;
+    flex: none; /* stay a fixed-height full-width bar; never shrink/stretch with clip content */
+}
+
+.wfm-video-edit-timeline-placeholder {
+    display: flex;
+    align-items: center;
+}
+
+.wfm-video-edit-timeline-block {
+    flex: none;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    padding: 2px 6px;
+    border: 1px solid var(--wfm-border);
+    border-radius: 3px;
+    background: var(--wfm-bg-hover, rgba(255, 255, 255, 0.04));
+    cursor: grab;
+    overflow: hidden;
+    transition: outline 0.1s;
+}
+
+.wfm-video-edit-timeline-block:active { cursor: grabbing; }
+.wfm-video-edit-timeline-block.selected { outline: 2px solid var(--wfm-primary); outline-offset: -2px; }
+
+.wfm-video-edit-timeline-block-name {
+    font-size: 11px;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.wfm-video-edit-timeline-block-meta {
+    font-size: 10px;
+    color: var(--wfm-text-secondary);
+}
+
+.wfm-video-edit-main-row {
+    display: flex;
+    gap: 16px;
+    align-items: flex-start;
+    margin-top: 10px;
+}
+
+.wfm-video-edit-trim-panel {
+    flex: 1;
+    min-width: 0;
+}
+
+.wfm-video-edit-trim-row {
+    display: grid;
+    grid-template-columns: repeat(2, minmax(0, 160px));
+    gap: 10px;
+}
+
+.wfm-video-edit-trim-row label {
+    display: block;
+    font-size: 12px;
+    color: var(--wfm-text-secondary);
+    margin-bottom: 4px;
+}
+
+/* 開始/終了の入力欄+「現在位置を使用」ボタンを縦積みにする。以前は横並びで
+   ボタンのラベルがはみ出し、かつ入力欄とボタンの見分けがつきにくかった。 */
+.wfm-video-edit-trim-field {
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+}
+
+.wfm-video-edit-trim-field input { width: 100%; box-sizing: border-box; }
+
+.wfm-video-edit-playhead-btn {
+    align-self: flex-start;
+    font-size: 10px;
+    padding: 2px 6px;
+}
+
+.wfm-video-edit-total-duration {
+    font-size: 12px;
+    color: var(--wfm-text-secondary);
+    white-space: nowrap;
+}
+
+/* ── トリムスクラバー: clipchamp風の時間目盛り+シークバー+トリムハンドル。
+   すべて%位置指定(clip.duration基準)にすることでpx/秒換算を持たずに済ませている。 */
+.wfm-video-trim-scrubber {
+    margin-top: 10px;
+    /* start/end/playheadのバッジ(track上端から浮く)が上のクリップ名や
+       目盛りと重ならないよう、あらかじめ専用の余白を確保しておく。
+       playheadバッジはstart/endバッジより高い位置に2段目として出すため
+       (再生位置がトリム開始/終了に近いとき同じ高さだと文字が重なるため)、
+       その分も含めた高さを確保する。 */
+    padding-top: 44px;
+    user-select: none;
+}
+
+.wfm-video-trim-ruler {
+    position: relative;
+    height: 16px;
+    font-size: 10px;
+    color: var(--wfm-text-secondary);
+}
+
+.wfm-video-trim-ruler-tick {
+    position: absolute;
+    top: 0;
+    transform: translateX(-50%);
+    white-space: nowrap;
+}
+
+.wfm-video-trim-track {
+    position: relative;
+    height: 52px;
+    margin-top: 4px;
+    background: var(--wfm-bg-hover, #111);
+    border: 1px solid var(--wfm-border);
+    border-radius: 4px;
+    cursor: pointer;
+}
+
+.wfm-video-trim-range {
+    position: absolute;
+    top: 0;
+    bottom: 0;
+    background: rgba(70, 130, 230, 0.3);
+    border-top: 2px solid var(--wfm-primary);
+    border-bottom: 2px solid var(--wfm-primary);
+    pointer-events: none;
+}
+
+.wfm-video-trim-handle {
+    position: absolute;
+    top: 0;
+    bottom: 0;
+    width: 10px;
+    margin-left: -5px;
+    background: var(--wfm-primary);
+    border-radius: 2px;
+    cursor: ew-resize;
+    z-index: 3;
+}
+
+.wfm-video-trim-playhead {
+    position: absolute;
+    top: -4px;
+    bottom: -4px;
+    width: 2px;
+    margin-left: -1px;
+    background: #fff;
+    /* Below the trim handles (z-index:3) so a handle sitting at the same
+       position (e.g. right after "Set from playhead") stays grabbable instead
+       of the playhead's hit area stealing the pointer. */
+    z-index: 2;
+    cursor: ew-resize;
+}
+
+.wfm-video-trim-playhead::before {
+    content: "";
+    position: absolute;
+    top: -4px;
+    left: 50%;
+    transform: translateX(-50%);
+    width: 10px;
+    height: 10px;
+    border-radius: 50%;
+    background: #fff;
+}
+
+.wfm-video-trim-badge {
+    position: absolute;
+    top: -24px;
+    left: 50%;
+    transform: translateX(-50%);
+    background: #111;
+    color: #fff;
+    font-size: 10px;
+    padding: 1px 5px;
+    border-radius: 3px;
+    white-space: nowrap;
+    pointer-events: none;
+}
+
+.wfm-video-trim-badge-playhead {
+    background: #000;
+    /* start/endバッジ(-24px)より上の2段目に出し、再生位置がトリム範囲の
+       端に重なっても文字同士がぶつからないようにする */
+    top: -42px;
+    border: 1px solid var(--wfm-primary);
+}
+
+.wfm-video-edit-export-panel {
+    width: 220px;
+    flex: none;
+}
+
+.wfm-video-asset-detail label {
+    font-size: 12px;
+    color: var(--wfm-text-secondary);
+}
+
+.wfm-video-asset-name {
+    font-size: 12px;
+    font-weight: 600;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.wfm-video-asset-tags {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 4px;
+}
+
+.wfm-video-asset-tag {
+    display: inline-flex;
+    align-items: center;
+    gap: 2px;
+    background: var(--wfm-bg-secondary);
+    border: 1px solid var(--wfm-border);
+    border-radius: 10px;
+    padding: 1px 8px;
+    font-size: 11px;
+}
+
+.wfm-video-asset-tag button {
+    background: none;
+    border: none;
+    color: var(--wfm-text-secondary);
+    cursor: pointer;
+    padding: 0;
+    font-size: 11px;
+}
+
+/* ── 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);
+}
+
+/* 3カラム構成: 左=Video Source選択UI(固定幅) / 中央=Asset選択・Sourceドロップ兼用
+   プレビュー / 右=生成結果専用プレビュー。.wfm-video-preview-frameは共通クラスなので
+   両プレビュー枠とも自動的にflex:1で均等幅になる。 */
+.wfm-video-preview-row {
+    flex: none;
+    display: flex;
+    flex-direction: row;
+    gap: 16px;
+}
+
+.wfm-video-preview-frame {
+    flex: 1;
+    min-width: 0;
+    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);
+    text-align: center;
+    overflow: hidden;
+}
+
+/* ソース動画/画像のネイティブサイズがフレームより大きい場合のみ縮小し、
+   小さい場合は拡大しない(枠のheightを固定にしてPlan/Editの位置がずれないようにするため) */
+.wfm-video-preview-el {
+    max-width: 100%;
+    max-height: 100%;
+    width: auto;
+    height: auto;
+    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;
+}
+
+
+/* ── 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;
+}
+
+/* ── 左ペイン Project サブタブ: 保存済みPlan一覧 ── */
+.wfm-video-project-list {
+    display: flex;
+    flex-direction: column;
+    gap: 6px;
+    max-height: 60vh;
+    overflow-y: auto;
+}
+
+.wfm-video-project-item {
+    display: flex;
+    gap: 8px;
+    align-items: center;
+    border: 1px solid var(--wfm-border);
+    border-radius: 4px;
+    padding: 6px;
+    cursor: pointer;
+}
+
+.wfm-video-project-item:hover { border-color: var(--wfm-primary); }
+.wfm-video-project-item.active { outline: 2px solid var(--wfm-primary); outline-offset: -2px; }
+
+.wfm-video-project-item-thumb {
+    width: 40px;
+    height: 40px;
+    flex: none;
+    object-fit: cover;
+    border-radius: 3px;
+    background: var(--wfm-bg-hover, #111);
+}
+
+.wfm-video-project-item-info {
+    flex: 1;
+    min-width: 0;
+    display: flex;
+    flex-direction: column;
+    gap: 2px;
+}
+
+.wfm-video-project-item-name {
+    font-size: 12px;
+    font-weight: 600;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.wfm-video-project-item-meta {
+    font-size: 10px;
+    color: var(--wfm-text-secondary);
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.wfm-video-project-item-delete {
+    flex: none;
+    background: none;
+    border: none;
+    color: var(--wfm-text-secondary);
+    cursor: pointer;
+    font-size: 14px;
+    padding: 2px 6px;
+}
+
+.wfm-video-project-item-delete:hover { color: var(--wfm-danger); }

static/js/ai-settings-modal.js

@@ -0,0 +1,205 @@
+/**
+ * Shared AI backend settings modal (Backend / Connection / Model / Generation / Unload / Save).
+ * Used by the Prompt tab and Tagger tab, each with its own localStorage key so their
+ * settings stay independent — mirrors the AI TOOL tab's own settings pane (ai-tab.js).
+ */
+
+import { openModal, closeModal, showToast } from "./app.js";
+import { t } from "./i18n.js";
+import { readJsonStorage, getAiBackendDefaultUrl, unslothProxy, unloadAiModel } from "./util.js";
+
+function loadCfg(storageKey) {
+    return readJsonStorage(storageKey);
+}
+
+function saveCfg(storageKey, patch) {
+    const data = { ...loadCfg(storageKey), ...patch };
+    localStorage.setItem(storageKey, JSON.stringify(data));
+    return data;
+}
+
+// 保存済み設定を {backend, url, model, thinkingMode, maxTokens} の形で返す
+export function getAiBackendConfig(storageKey) {
+    const s = loadCfg(storageKey);
+    const backend = s.backend || "ollama";
+    const url = (s.backendUrl || getAiBackendDefaultUrl(backend)).replace(/\/$/, "");
+    return {
+        backend,
+        url,
+        model: s.model || "",
+        thinkingMode: !!s.thinkingMode,
+        maxTokens: Math.max(0, parseInt(s.maxTokens, 10) || 0),
+    };
+}
+
+function isValidBackendUrl(url) {
+    try {
+        const u = new URL(url);
+        return u.protocol === "http:" || u.protocol === "https:";
+    } catch {
+        return false;
+    }
+}
+
+async function fetchBackendModels(url, backend) {
+    if (backend === "ollama") {
+        const res = await fetch(`${url}/api/tags`);
+        if (!res.ok) throw new Error(`HTTP ${res.status}`);
+        return (await res.json()).models?.map(m => m.name) || [];
+    } else if (backend === "unsloth") {
+        const data = await unslothProxy(url, "/v1/models", "GET");
+        return (data.data || []).map(m => m.id);
+    } else {
+        const res = await fetch(`${url}/v1/models`);
+        if (!res.ok) throw new Error(`HTTP ${res.status}`);
+        return ((await res.json()).data || []).map(m => m.id);
+    }
+}
+
+/**
+ * @param {string} storageKey localStorage key holding {backend, backendUrl, model, thinkingMode, maxTokens}
+ * @param {string} title modal title
+ * @param {() => void} [onSaved] called after Save persists the config (e.g. to refresh a status label)
+ */
+export function openAiBackendSettingsModal(storageKey, title, onSaved) {
+    const saved = loadCfg(storageKey);
+    const backend = saved.backend || "ollama";
+
+    const html = `
+        <div class="wfm-ai-settings-container">
+            <div class="wfm-ai-settings-section">
+                <div class="wfm-ai-settings-title">${t("aiSettingsBackend")}</div>
+                <div class="wfm-ai-backend-row">
+                    ${[["ollama", "Ollama"], ["lmstudio", "LM Studio"], ["lemonade", "Lemonade"], ["unsloth", "Unsloth"]].map(([v, label]) => `
+                        <label class="wfm-ai-radio-label">
+                            <input type="radio" name="wfm-aism-backend" value="${v}" ${v === backend ? "checked" : ""}> ${label}
+                        </label>`).join("")}
+                </div>
+            </div>
+            <div class="wfm-ai-settings-section">
+                <div class="wfm-ai-settings-title">${t("aiSettingsConnection")}</div>
+                <div class="wfm-ai-settings-row">
+                    <label class="wfm-ai-label">URL</label>
+                    <input type="text" id="wfm-aism-url" class="wfm-input wfm-ai-url-input" value="${saved.backendUrl || getAiBackendDefaultUrl(backend)}">
+                </div>
+                <div class="wfm-ai-settings-row">
+                    <button id="wfm-aism-test-btn" class="wfm-btn">${t("aiSettingsTestBtn")}</button>
+                    <button id="wfm-aism-unload-btn" class="wfm-btn">${t("aiUnloadModel")}</button>
+                </div>
+                <div class="wfm-ai-settings-row">
+                    <span id="wfm-aism-test-result" class="wfm-ai-test-result"></span>
+                </div>
+            </div>
+            <div class="wfm-ai-settings-section">
+                <div class="wfm-ai-settings-title">${t("aiSettingsModelSection")}</div>
+                <div class="wfm-ai-settings-row">
+                    <select id="wfm-aism-model" class="wfm-select wfm-ai-model-select">
+                        <option value="">${t("selectModel")}</option>
+                    </select>
+                    <button id="wfm-aism-refresh-btn" class="wfm-btn">${t("aiSettingsRefreshBtn")}</button>
+                </div>
+            </div>
+            <div class="wfm-ai-settings-section">
+                <div class="wfm-ai-settings-title">${t("aiSettingsGenerationTitle")}</div>
+                <div class="wfm-ai-settings-row">
+                    <label class="wfm-ai-label" style="display:flex;align-items:center;gap:6px;cursor:pointer;">
+                        <input type="checkbox" id="wfm-aism-thinking"> ${t("aiSettingsThinkingMode")}
+                    </label>
+                </div>
+                <div class="wfm-ai-settings-row">
+                    <label class="wfm-ai-label">${t("aiSettingsMaxTokens")}</label>
+                    <input type="number" id="wfm-aism-max-tokens" class="wfm-input" min="0" step="1" placeholder="${t("aiSettingsMaxTokensPlaceholder")}">
+                </div>
+            </div>
+            <div class="wfm-ai-settings-section">
+                <button id="wfm-aism-save-btn" class="wfm-btn wfm-btn-primary">${t("aiSettingsSaveBtn")}</button>
+            </div>
+        </div>`;
+
+    openModal(title, html);
+
+    const urlInput = document.getElementById("wfm-aism-url");
+    const modelSelect = document.getElementById("wfm-aism-model");
+    const thinkingCheckbox = document.getElementById("wfm-aism-thinking");
+    const maxTokensInput = document.getElementById("wfm-aism-max-tokens");
+    const testResultEl = document.getElementById("wfm-aism-test-result");
+
+    thinkingCheckbox.checked = !!saved.thinkingMode;
+    if (saved.maxTokens) maxTokensInput.value = saved.maxTokens;
+
+    function currentBackend() {
+        return document.querySelector("input[name='wfm-aism-backend']:checked")?.value || "ollama";
+    }
+
+    async function populateModels(selectedModel) {
+        const url = urlInput.value.trim();
+        try {
+            const models = await fetchBackendModels(url, currentBackend());
+            modelSelect.innerHTML = models.length
+                ? models.map(name => `<option value="${name}" ${name === selectedModel ? "selected" : ""}>${name}</option>`).join("")
+                : `<option value="">${t("noModelsFound")}</option>`;
+        } catch {
+            modelSelect.innerHTML = `<option value="">${t("noModelsFound")}</option>`;
+        }
+    }
+    populateModels(saved.model || "");
+
+    document.querySelectorAll("input[name='wfm-aism-backend']").forEach(r => {
+        r.addEventListener("change", () => {
+            urlInput.value = getAiBackendDefaultUrl(r.value);
+            testResultEl.textContent = "";
+        });
+    });
+
+    document.getElementById("wfm-aism-refresh-btn")?.addEventListener("click", () => populateModels(modelSelect.value));
+
+    document.getElementById("wfm-aism-test-btn")?.addEventListener("click", async () => {
+        const url = urlInput.value.trim();
+        if (!isValidBackendUrl(url)) {
+            testResultEl.textContent = t("aiToastInvalidUrlInput");
+            testResultEl.className = "wfm-ai-test-result wfm-ai-status-error";
+            return;
+        }
+        testResultEl.textContent = t("aiStatusConnecting");
+        testResultEl.className = "wfm-ai-test-result wfm-ai-status-working";
+        try {
+            const models = await fetchBackendModels(url, currentBackend());
+            testResultEl.textContent = `${t("aiStatusConnectOk")} (${models.length} ${t("aiModels")})`;
+            testResultEl.className = "wfm-ai-test-result wfm-ai-status-ok";
+            await populateModels(modelSelect.value);
+        } catch (err) {
+            testResultEl.textContent = `${t("aiStatusConnectFail")}${err.message}`;
+            testResultEl.className = "wfm-ai-test-result wfm-ai-status-error";
+        }
+    });
+
+    document.getElementById("wfm-aism-unload-btn")?.addEventListener("click", async () => {
+        const url = urlInput.value.trim();
+        const model = modelSelect.value;
+        if (!model) { showToast(t("aiUnloadNoModel"), "error"); return; }
+        try {
+            await unloadAiModel(url, currentBackend(), model);
+            showToast(t("aiUnloadDone"), "success");
+        } catch (err) {
+            showToast(err.message === "UNSUPPORTED" ? t("aiUnloadUnsupported") : t("aiUnloadFailed") + err.message, "error");
+        }
+    });
+
+    document.getElementById("wfm-aism-save-btn")?.addEventListener("click", () => {
+        const url = urlInput.value.trim();
+        if (url && !isValidBackendUrl(url)) {
+            showToast(t("aiToastInvalidUrl"), "error");
+            return;
+        }
+        saveCfg(storageKey, {
+            backend: currentBackend(),
+            backendUrl: url,
+            model: modelSelect.value,
+            thinkingMode: !!thinkingCheckbox.checked,
+            maxTokens: Math.max(0, parseInt(maxTokensInput.value, 10) || 0),
+        });
+        showToast(t("aiToastSettingsSaved"), "success");
+        closeModal();
+        if (onSaved) onSaved();
+    });
+}

static/js/ai-tab.js

@@ -5,7 +5,7 @@
 
 import { showToast } from "./app.js";
 import { t } from "./i18n.js";
-import { readJsonStorage, getAiBackendDefaultUrl, escapeHtml } from "./util.js";
+import { readJsonStorage, getAiBackendDefaultUrl, escapeHtml, unloadAiModel } from "./util.js";
 import { comfyUI } from "./comfyui-client.js";
 import { comfyEditor } from "./comfyui-editor.js";
 import { comfyWorkflow } from "./comfyui-workflow.js";
@@ -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", () => {
@@ -334,6 +456,20 @@ function initSettingsTab() {
         }
     });
 
+    // Unload model
+    document.getElementById("wfm-ai-unload-btn")?.addEventListener("click", async () => {
+        const backend = document.querySelector("input[name='wfm-ai-backend']:checked")?.value || "ollama";
+        const url = urlInput?.value?.trim() || "";
+        const model = document.getElementById("wfm-ai-model-select")?.value || "";
+        if (!model) { showToast(t("aiUnloadNoModel"), "error"); return; }
+        try {
+            await unloadAiModel(url, backend, model);
+            showToast(t("aiUnloadDone"), "success");
+        } catch (err) {
+            showToast(err.message === "UNSUPPORTED" ? t("aiUnloadUnsupported") : t("aiUnloadFailed") + err.message, "error");
+        }
+    });
+
     // Model refresh
     document.getElementById("wfm-ai-model-refresh-btn")?.addEventListener("click", () => refreshModels());
 
@@ -395,6 +531,8 @@ 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 +541,7 @@ function initSettingsTab() {
 
         saveAiSettings({
             backend, backendUrl: url, model, freeSrcLang, freeDstLang,
+            thinkingMode, maxTokens,
             chatGenDedicatedEnabled, chatGenDedicatedFilename,
             chatGenI2IDedicatedEnabled, chatGenI2IDedicatedFilename,
         });
@@ -423,12 +562,22 @@ 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 +602,10 @@ 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 +614,17 @@ 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 +633,8 @@ 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 +659,7 @@ 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 +684,24 @@ 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 +1002,14 @@ 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 +1087,7 @@ 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 +1099,8 @@ 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 +1247,7 @@ 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 +1273,7 @@ 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";

static/js/app.js

@@ -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");
@@ -217,6 +245,10 @@ function applyI18nToHtml() {
     if (galleryBulkMove) galleryBulkMove.textContent = t("galleryBulkMoveTo");
     const galleryBulkExport = document.getElementById("wfm-gallery-bulk-export");
     if (galleryBulkExport) galleryBulkExport.textContent = t("galleryBulkExport");
+    const galleryBulkPsd = document.getElementById("wfm-gallery-bulk-psd");
+    if (galleryBulkPsd) galleryBulkPsd.textContent = t("galleryBulkPsd");
+    const galleryBulkEditLayers = document.getElementById("wfm-gallery-bulk-edit-layers");
+    if (galleryBulkEditLayers) galleryBulkEditLayers.textContent = t("galleryBulkEditLayers");
     const galleryBulkDelete = document.getElementById("wfm-gallery-bulk-delete");
     if (galleryBulkDelete) galleryBulkDelete.textContent = t("galleryBulkDelete");
     const galleryOpenTaggerBtn = document.getElementById("wfm-gallery-open-tagger-btn");
@@ -229,6 +261,8 @@ function applyI18nToHtml() {
     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");
@@ -289,10 +323,8 @@ function applyI18nToHtml() {
     if (assistantHeader) assistantHeader.textContent = t("assistantSubtab");
     const presetsHeader = document.querySelector(".wfm-prompt-split-right .wfm-prompt-split-header");
     if (presetsHeader) presetsHeader.textContent = t("presetsSubtab");
-    const ollamaRefreshBtn = document.getElementById("wfm-ollama-refresh-btn");
-    if (ollamaRefreshBtn) ollamaRefreshBtn.textContent = t("refresh");
-    const ollamaTestBtn = document.getElementById("wfm-ollama-test-btn");
-    if (ollamaTestBtn) ollamaTestBtn.textContent = t("test");
+    const ollamaSettingsBtn = document.getElementById("wfm-ollama-settings-btn");
+    if (ollamaSettingsBtn) ollamaSettingsBtn.title = t("settings");
     const ollamaAttachBtn = document.getElementById("wfm-ollama-attach-btn");
     if (ollamaAttachBtn) ollamaAttachBtn.textContent = t("attachImage");
     const ollamaClearBtn = document.getElementById("wfm-ollama-clear-btn");
@@ -364,6 +396,14 @@ 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");
@@ -446,6 +486,10 @@ function applyI18nToHtml() {
         "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-gen-31": "helpGen31",
         "wfm-help-feeder-title": "helpFeederTitle",
         "wfm-help-feeder-desc": "helpFeederDesc",
         "wfm-help-feeder-imgloop-title": "helpFeederImgloopTitle",
@@ -469,7 +513,10 @@ function applyI18nToHtml() {
         "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-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",
@@ -489,13 +536,23 @@ function applyI18nToHtml() {
         "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",
@@ -515,11 +572,14 @@ 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-28": "helpGallery28", "wfm-help-gallery-29": "helpGallery29",
+        "wfm-help-gallery-30": "helpGallery30",
         "wfm-help-gallery-card-styleprompt-title": "helpGalleryCardStyleprompt",
         "wfm-help-gallery-card-stylecatalog-title": "helpGalleryCardStylecatalog",
         "wfm-help-tagger-title": "helpTaggerTitle",
@@ -567,13 +627,16 @@ 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",
         "wfm-help-imageedit-26": "helpImageEdit26", "wfm-help-imageedit-27": "helpImageEdit27",
         "wfm-help-imageedit-28": "helpImageEdit28", "wfm-help-imageedit-29": "helpImageEdit29",
         "wfm-help-imageedit-30": "helpImageEdit30",
+        "wfm-help-imageedit-31": "helpImageEdit31", "wfm-help-imageedit-32": "helpImageEdit32",
+        "wfm-help-imageedit-33": "helpImageEdit33", "wfm-help-imageedit-34": "helpImageEdit34",
         "wfm-help-imageedit-7b": "helpImageEdit7b", "wfm-help-imageedit-7c": "helpImageEdit7c",
         "wfm-help-imageedit-7d": "helpImageEdit7d", "wfm-help-imageedit-7e": "helpImageEdit7e",
         "wfm-help-imageedit-8b": "helpImageEdit8b",
@@ -593,6 +656,7 @@ 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",
@@ -674,6 +738,14 @@ 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", "wfm-help-video-8": "helpVideo8",
+        "wfm-help-video-9": "helpVideo9", "wfm-help-video-10": "helpVideo10",
+        "wfm-help-video-11": "helpVideo11", "wfm-help-video-12": "helpVideo12",
     };
     for (const [id, key] of Object.entries(helpIdMap)) {
         const el = document.getElementById(id);
@@ -756,13 +828,15 @@ 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());
@@ -772,6 +846,7 @@ try {
     const _s = getSettings();
     if (_s.textareaFontSize) applyTextareaFontSize(_s.textareaFontSize);
     applyJsonColors(_s.jsonColors);
+    applyModelTabActiveColor(_s.modelTabActiveColor);
 } catch {}
 
 function initHelpTab() {
@@ -789,6 +864,9 @@ 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();
+            }
         });
     });
 }
@@ -804,11 +882,13 @@ document.addEventListener("DOMContentLoaded", () => {
     initModelsTab();
     initGenerateTab();
     initPromptTab();
+    initPromptTableTab();
     initMetadataTab();
     initGalleryTab();
     initAiTab();
     initTaggerTab();
     imageEditTab.init();
+    initVideoTab();
 
     console.log("Workflow Studio: App initialized");
 });

static/js/comfyui-editor.js

@@ -6,6 +6,7 @@ import { comfyUI } from "./comfyui-client.js";
 import { syncJsonHighlight } from "./json-highlight.js";
 import { t } from "./i18n.js";
 import { escapeHtml, setupSearchClearBtn } from "./util.js";
+import { showToast } from "./app.js";
 
 // ── Latent Image preset state ─────────────────────────────
 const _LATENT_PRESET_KEY = "wfm_latent_presets";
@@ -156,6 +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 };
 
@@ -616,10 +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",
@@ -627,46 +642,65 @@ 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>${s.label}</label>
-                    <div class="wfm-search-wrap" style="margin-bottom:4px;width:100%;">
-                        <input type="text" class="wfm-input wfm-search-input wfm-model-filter" id="wfm-model-${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="${s.inputKey}" title="Apply (Alt+Click: Apply &amp; Generate)">Apply</button>
-                    </div>
+            return `
+                <div class="wfm-search-wrap" style="margin-bottom:4px;width:100%;">
+                    <input type="text" class="wfm-input wfm-search-input wfm-model-filter" id="wfm-model-${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="${s.inputKey}" title="Apply (Alt+Click: Apply &amp; Generate)">Apply</button>
                 </div>
             `;
-            })
-            .join("") + '<div class="wfm-form-group" id="wfm-te-section" style="border-bottom:1px solid var(--wfm-border);padding-bottom:12px;"><label>Text Encoder</label></div>';
+        };
+
+        const mainHtml = mainSections.map((s) => {
+            const hasNodes = (s.nodes?.length || 0) > 0;
+            return `
+            <div class="wfm-form-group" style="border-bottom:1px solid var(--wfm-border);padding-bottom:12px;">
+                <label class="${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) => {
@@ -719,14 +753,81 @@ export const comfyEditor = {
         _initTextEncoderSection(analysis, this.models.textEncoders || [], el).catch(() => {});
     },
 
+    // 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);
@@ -735,6 +836,10 @@ export const comfyEditor = {
         // 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;
@@ -1060,6 +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
@@ -1192,6 +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
@@ -1331,6 +1446,12 @@ export const comfyEditor = {
                         <label>Batch Size</label>
                         <input type="number" class="wfm-input" id="wfm-settings-batch" value="${latent.batch_size ?? 1}" min="1" max="64">
                     </div>
+                    ${latent.isVideoLatent ? `
+                    <div class="wfm-form-group">
+                        <label>Length (frames)</label>
+                        <input type="number" class="wfm-input" id="wfm-settings-latent-length" value="${latent.length ?? ""}" placeholder="${latent.length === undefined ? "linked" : ""}" step="1" min="1">
+                    </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;">
@@ -1387,6 +1508,8 @@ export const comfyEditor = {
             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;
+            const lenEl = document.getElementById("wfm-settings-latent-length");
+            if (lenEl && lenEl.value !== "") inputs.length = parseInt(lenEl.value) || 1;
             _syncRawJson();
         });
 
@@ -1966,6 +2089,75 @@ 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");
@@ -2092,7 +2284,7 @@ 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>` : ""}

static/js/comfyui-workflow.js

@@ -75,6 +75,35 @@ async function _loadObjectInfo() {
  * Get ordered widget (non-link) input names for a node class from object_info.
  * These correspond to widgets_values in UI-format nodes.
  */
+// Scalar widget types object_info can declare for a required/optional input.
+const _WIDGET_TYPE_SET = new Set(["INT", "FLOAT", "STRING", "BOOLEAN", "COMBO", "COMFY_DYNAMICCOMBO_V3"]);
+
+/**
+ * Resolve a possibly-compound object_info type spec (e.g. LTXVEmptyLatentAudio's frame_rate:
+ * "FLOAT,INT", a MultiType declaration meaning "widget defaults to FLOAT but also accepts an INT
+ * link") down to the single scalar widget type it actually occupies a widgets_values slot as.
+ * Preferring the explicit widgetType hint some MultiType specs carry, else the first comma-
+ * separated segment. Returns null for link-only types (MODEL, CLIP, LATENT, etc.) or arrays that
+ * aren't combo option lists.
+ *
+ * Without this, a plain `upper === "INT" || ...` exact-match check silently drops any
+ * comma-joined MultiType widget from the name/type lists entirely — which corrupts every widget
+ * declared after it in the node's schema order in _simulateWidgetValues (see its own docstring),
+ * exactly the class of bug that produced e.g. LTXVEmptyLatentAudio's batch_size reading
+ * frame_rate's stale value instead of its own.
+ */
+function _resolveWidgetType(type, spec) {
+    if (Array.isArray(type)) return "COMBO";
+    if (typeof type !== "string") return null;
+    const explicit = spec?.[1]?.widgetType;
+    if (typeof explicit === "string" && _WIDGET_TYPE_SET.has(explicit.toUpperCase())) return explicit.toUpperCase();
+    for (const part of type.split(",")) {
+        const upper = part.trim().toUpperCase();
+        if (_WIDGET_TYPE_SET.has(upper)) return upper;
+    }
+    return null;
+}
+
 function _getWidgetInputNames(objectInfo, classType) {
     const info = objectInfo[classType];
     if (!info) return [];
@@ -89,31 +118,16 @@ function _getWidgetInputNames(objectInfo, classType) {
         // (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)) {
-            // Combo/enum widget
-            names.push(name);
-        } else if (typeof type === "string") {
-            const upper = type.toUpperCase();
-            if (upper === "INT" || upper === "FLOAT" || upper === "STRING" || upper === "BOOLEAN" || upper === "COMBO" || upper === "COMFY_DYNAMICCOMBO_V3") {
-                names.push(name);
-            }
-            // Other uppercase types (MODEL, CLIP, etc.) are link inputs, skip
-        }
+        // Widget types are: "INT", "FLOAT", "STRING", "BOOLEAN", an array of choices, or a
+        // comma-joined MultiType union of those (e.g. "FLOAT,INT") — see _resolveWidgetType.
+        if (_resolveWidgetType(type, spec)) names.push(name);
     }
 
     // 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") {
-            const upper = type.toUpperCase();
-            if (upper === "INT" || upper === "FLOAT" || upper === "STRING" || upper === "BOOLEAN" || upper === "COMFY_DYNAMICCOMBO_V3") {
-                names.push(name);
-            }
-        }
+        if (_resolveWidgetType(type, spec)) names.push(name);
     }
 
     return names;
@@ -133,26 +147,14 @@ 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") {
-            const upper = type.toUpperCase();
-            if (upper === "INT" || upper === "FLOAT" || upper === "STRING" || upper === "BOOLEAN" || upper === "COMBO" || upper === "COMFY_DYNAMICCOMBO_V3") {
-                types.push(upper);
-            }
-        }
+        const resolved = _resolveWidgetType(type, spec);
+        if (resolved) types.push(resolved);
     }
     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") {
-            const upper = type.toUpperCase();
-            if (upper === "INT" || upper === "FLOAT" || upper === "STRING" || upper === "BOOLEAN" || upper === "COMBO" || upper === "COMFY_DYNAMICCOMBO_V3") {
-                types.push(upper);
-            }
-        }
+        const resolved = _resolveWidgetType(type, spec);
+        if (resolved) types.push(resolved);
     }
     return types;
 }
@@ -614,6 +616,18 @@ export const comfyWorkflow = {
         return _lastMutedNodes;
     },
 
+    // Forces the next convertUiToApi() call to refetch /object_info instead of
+    // reusing the cached snapshot. Needed whenever a file gets uploaded mid-session
+    // after the cache was already populated (e.g. Video Plan's block-chaining
+    // uploads an extracted frame between batch iterations) — a stale cache's
+    // LoadImage "image" COMBO choices wouldn't include the just-uploaded filename
+    // yet, so the COMBO-mismatch fallback a few lines below (silently substituting
+    // choices[0] when a value isn't in the known list — see the file-level comment
+    // at the top) would replace it with an unrelated pre-existing file instead.
+    invalidateObjectInfoCache() {
+        _objectInfoCache = null;
+    },
+
     async convertUiToApi(workflow) {
         _lastCheckpointSubstitutions = [];
         _lastBypassedNodes = [];
@@ -886,6 +900,7 @@ export const comfyWorkflow = {
             prompt_nodes: [],
             sampler_nodes: [],
             latent_nodes: [],
+            resolution_selector_nodes: [],
             lora_nodes: [],
             vae_nodes: [],
             save_nodes: [],
@@ -961,6 +976,14 @@ export const comfyWorkflow = {
                 if (Array.isArray(inputs.positive)) samplerPositiveRef[inputs.positive[0]] = true;
                 if (Array.isArray(inputs.negative)) samplerNegativeRef[inputs.negative[0]] = true;
             }
+            // LTXVDualCFGGuider (LTX-2.5's video+audio guider) — positive/negative both
+            // commonly point at the *same* LTXVConditioning node (different output slots),
+            // so this only seeds the role on that node; LTXVConditioning below splits it
+            // back out per-slot onto its own positive/negative inputs.
+            if (ct === "LTXVDualCFGGuider") {
+                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) ===
@@ -1037,6 +1060,13 @@ export const comfyWorkflow = {
                     if (isPos && Array.isArray(inputs.positive)) samplerPositiveRef[inputs.positive[0]] = true;
                     if (isNeg && Array.isArray(inputs.negative)) samplerNegativeRef[inputs.negative[0]] = true;
                 }
+                // LTXVConditioning (LTX-2.5) — same shape as InstructPixToPixConditioning:
+                // separate positive/negative inputs feeding separate outputs consumed by
+                // LTXVDualCFGGuider, so each role must route only to its own input.
+                if (ct === "LTXVConditioning") {
+                    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
@@ -1335,6 +1365,19 @@ 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 ---
             // EmptyFlux2LatentImage (Flux.2 templates) often has width/height wired to a
             // GetImageSize node (auto-follows the reference image) rather than a direct number —
@@ -1349,6 +1392,35 @@ export const comfyWorkflow = {
                 });
             }
 
+            // Video latent nodes (Wan2.2's EmptyHunyuanLatentVideo, LTX-2.5's
+            // EmptyLTXVLatentVideo) — same width/height/batch_size shape as EmptyLatentImage
+            // plus a frame-count "length" input, usually linked to a duration*fps expression
+            // (see video-tab.js's own Wan2.2/LTX-2.5 handling) rather than a literal number —
+            // left undefined in that case so the settings-tab UI shows "linked".
+            if (ct === "EmptyHunyuanLatentVideo" || ct === "EmptyLTXVLatentVideo") {
+                result.latent_nodes.push({
+                    id, type: ct, title, isVideoLatent: true,
+                    width: typeof inputs.width === "number" ? inputs.width : undefined,
+                    height: typeof inputs.height === "number" ? inputs.height : undefined,
+                    batch_size: inputs.batch_size,
+                    length: typeof inputs.length === "number" ? inputs.length : undefined,
+                });
+            }
+
+            // 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
@@ -1504,6 +1576,19 @@ 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) {
+        if ((analysis?.prompt_nodes || []).some((n) => /ImageToVideo|TextToVideo/.test(n.type))) return true;
+        // LTX-2.5/Wan2.2: no all-in-one *ToVideo node — detected instead via their
+        // EmptyHunyuanLatentVideo/EmptyLTXVLatentVideo latent node (see analyzeWorkflow)
+        // or a SaveVideo/CreateVideo node anywhere in the flattened graph.
+        if ((analysis?.latent_nodes || []).some((n) => n.isVideoLatent)) return true;
+        return (analysis?.all_nodes || []).some((n) => n.type === "SaveVideo" || n.type === "CreateVideo");
+    },
+
     applyParams(workflow, params) {
         for (const [key, value] of Object.entries(params)) {
             // Parse key format: nodeId.paramName

static/js/gallery-tab.js

@@ -5,7 +5,7 @@
 
 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, getAiBackendDefaultUrl } from "./util.js";
 import { comfyWorkflow } from "./comfyui-workflow.js";
@@ -14,6 +14,7 @@ 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";
+import { applyStoredVideoVolume } from "./settings-tab.js";
 
 // ── 定数 ─────────────────────────────────────────────────────
 
@@ -42,9 +43,27 @@ const API = {
     imagesDelete:               `/wfm/gallery/images/delete`,
     imagesMove:                 `/wfm/gallery/images/move`,
     imagesExportZip:            `/wfm/gallery/images/export-zip`,
+    imagesExportPsd:            `/wfm/gallery/images/export-psd`,
 };
 
 export const FEEDER_GROUP = "__Feeder__";
+export const VIDEO_GROUP = "__Video Assets__";
+// Video Planを実行するたびに生成された動画が自動的に追加される作業用グループ
+// （Plan保存の有無に関わらず常にここへ入る）。__Video Assets__（ユーザーが手動で
+// キュレーションする素材グループ）への整理はユーザー任意のタイミングで行う。
+export const VTEMP_GROUP = "__Video Temp__";
+
+const _RESERVED_GROUPS = [FEEDER_GROUP, VIDEO_GROUP, VTEMP_GROUP];
+function _isReservedGroup(name) {
+    return _RESERVED_GROUPS.includes(name);
+}
+
+// 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。
+export 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
@@ -214,6 +233,9 @@ window._wfmReceiveGenerateRequest = async (prompt, width, height, negative, work
 const PAGE_SIZE = 50;
 let _renderedCount = 0;
 let _scrollObserver = null;
+let _loadImagesAbortController = null;
+let _searchDebounceTimer = null;
+const SEARCH_DEBOUNCE_MS = 300;
 
 // ── 状態 ─────────────────────────────────────────────────────
 
@@ -253,6 +275,13 @@ 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));
@@ -308,6 +337,30 @@ async function exportSelectedImagesToZip(paths) {
     }
 }
 
+async function exportSelectedImagesToPsd(paths) {
+    if (paths.length === 0) return;
+    try {
+        const res = await fetch(API.imagesExportPsd, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ paths }),
+        });
+        if (!res.ok) throw new Error((await res.json()).error || `HTTP ${res.status}`);
+        const blob = await res.blob();
+        const url = URL.createObjectURL(blob);
+        const a = document.createElement("a");
+        a.href = url;
+        a.download = `gallery_export_${Date.now()}.psd`;
+        document.body.appendChild(a);
+        a.click();
+        document.body.removeChild(a);
+        URL.revokeObjectURL(url);
+        showToast(t("psdExportCompleted"), "success");
+    } catch (e) {
+        showToast(t("errorWithMsg", e.message), "error");
+    }
+}
+
 // ── フォルダツリー ────────────────────────────────────────────
 
 function _getExpandedPaths() {
@@ -453,6 +506,16 @@ function renderTreeNode(node, container, depth, isRoot) {
 
         container.appendChild(item);
         container.appendChild(childContainer);
+
+        // root フォルダはデフォルトで展開状態にする（フォルダスキャン高速化により
+        // サブフォルダの内容もすぐ確認できるようになったため、折りたたみ初期状態だと
+        // 気付きにくい）
+        if (isRoot) {
+            arrow.dataset.expanded = "true";
+            arrow.textContent = "▼";
+            childContainer.style.display = "";
+            node.children.forEach(child => renderTreeNode(child, childContainer, depth + 1, false));
+        }
     } else {
         container.appendChild(item);
     }
@@ -463,6 +526,16 @@ function renderTreeNode(node, container, depth, isRoot) {
 async function loadImages() {
     if (!state.currentFolder) return;
 
+    // 前回のリクエストがまだ処理中なら中断する。中断せず並行させると、後から
+    // 返ってきた古いレスポンス（例: クリア直前に打っていた検索文字列の結果）が
+    // 最新の表示を上書きしてしまい、「クリアしても検索結果のまま/読み込み中の
+    // まま」に見える不具合の原因になっていた。
+    if (_loadImagesAbortController) {
+        _loadImagesAbortController.abort();
+    }
+    const controller = new AbortController();
+    _loadImagesAbortController = controller;
+
     const grid = document.getElementById("wfm-gallery-grid");
     grid.innerHTML = `<p class="wfm-placeholder">${t("loading")}</p>`;
 
@@ -474,18 +547,29 @@ async function loadImages() {
     if (state.favoriteOnly) params.favorite = "true";
     if (state.tagFilter) params.tag = state.tagFilter;
 
-    // グループフィルタはサーバーサイドで処理
-    if (state.groupFilter) params.group = state.groupFilter;
+    // グループフィルタはサーバーサイドで処理。グループのメンバーは現在のフォルダの
+    // サブフォルダに散らばっている可能性がある(例: 動画は"video"サブフォルダ配下)ため、
+    // 通常のフォルダ単位ブラウズ(非再帰)とは異なり、フィルタ時はフォルダ配下を再帰的に
+    // 検索する。
+    if (state.groupFilter) {
+        params.group = state.groupFilter;
+        params.recursive = "true";
+    }
 
     try {
-        const images = (await apiFetch(API.images(params))).images || [];
+        const images = (await apiFetch(API.images(params), { signal: controller.signal })).images || [];
         state.images = images;
         state.lastSelectionIndex = -1;
         document.getElementById("wfm-gallery-count").textContent = `${state.images.length} images`;
         renderImages();
         updateTagFilter(state.images);
     } catch (e) {
+        if (e.name === "AbortError") return;
         grid.innerHTML = `<p class="wfm-placeholder">Error: ${escapeHtml(e.message)}</p>`;
+    } finally {
+        if (_loadImagesAbortController === controller) {
+            _loadImagesAbortController = null;
+        }
     }
 }
 
@@ -578,6 +662,14 @@ 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" : ""}`;
@@ -877,18 +969,20 @@ 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));
+    const previewMediaEl = preview.querySelector(isVideo ? "video" : "img");
+    previewMediaEl.addEventListener("dblclick", () => openLightbox(img));
+    if (isVideo) applyStoredVideoVolume(previewMediaEl);
 
     // ファイル名
     document.getElementById("wfm-gallery-detail-filename").textContent = img.filename;
@@ -914,16 +1008,32 @@ async function loadImageDetail(img) {
         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");
@@ -964,6 +1074,44 @@ 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) {
@@ -971,7 +1119,8 @@ 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);
@@ -980,24 +1129,38 @@ 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;
     }
 }
 
@@ -1031,6 +1194,33 @@ export async function ensureFeederGroup() {
     }
 }
 
+/** __Video Assets__ と __Video Temp__ の両方が存在しない場合に作成する。add_to_groupは
+ * 対象グループがレジストリに登録されているかを確認せず画像側にグループ名を書き込むだけの
+ * ため、一度レジストリから消える(例: 予約保護が効く前の版で削除された)と、以後の手動追加は
+ * 画像には記録されてもグループ一覧・フィルタには一切出てこなくなる。VideoタブのAsset
+ * タブ初期化時に呼び、レジストリを確実に存在させる。
+ * （__Video Temp__側もここでensureする — 以前はensureVideoGroupがVIDEO_GROUPしか
+ * 保証しておらず、__Video Temp__のレジストリが何らかの理由で消えると誰も復活させられない
+ * バグがあった。バッチRun開始時のensure（video-plan-tab.jsの_ensureVideoAssetGroups）は
+ * Runするまで呼ばれないため、それだけでは不十分。） */
+export async function ensureVideoGroup() {
+    try {
+        await fetch(API.groupEnsure, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ name: VIDEO_GROUP }),
+        });
+        await fetch(API.groupEnsure, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ name: VTEMP_GROUP }),
+        });
+        await loadGroups();
+    } catch (e) {
+        console.warn("[Gallery] ensureVideoGroup error:", e);
+    }
+}
+
 /** Feederグループ内の全画像を除外する（FC ボタン） */
 async function clearFeederGroup() {
     try {
@@ -1045,6 +1235,19 @@ async function clearFeederGroup() {
     }
 }
 
+/** __Video Temp__グループ内の全画像を除外する（詳細パネルの「Clear __Video Temp__」ボタン） */
+async function clearVtempGroup() {
+    try {
+        await fetch(API.groupClear(VTEMP_GROUP), { method: "POST" });
+        showToast(t("vtempGroupCleared"), "success");
+        if (state.groupFilter === VTEMP_GROUP) {
+            await loadImages();
+        }
+    } catch (e) {
+        showToast(t("errorWithMsg", e.message), "error");
+    }
+}
+
 /** ツールバーのグループフィルタと一括バーのセレクトを更新 */
 function _updateGroupSelects() {
     // ツールバー: グループフィルタ
@@ -1124,15 +1327,16 @@ function renderDetailGroup(img) {
                         ${allGroups.length === 0
                             ? `<option value="">${t("modelsNoGroupAvailable")}</option>`
                             : allGroups.map(g => {
-                                const label = g === FEEDER_GROUP ? `🔒 ${escapeHtml(g)}` : escapeHtml(g);
+                                const label = _isReservedGroup(g) ? `🔒 ${escapeHtml(g)}` : escapeHtml(g);
                                 return `<option value="${escapeHtml(g)}">${label}</option>`;
                             }).join("")}
                     </select>
                     <button class="wfm-btn wfm-btn-sm" id="wfm-gallery-grp-rename-btn"
-                        ${allGroups.length === 0 || allGroups[0] === FEEDER_GROUP ? "disabled" : ""} title="${t("modelsRename")}">&#9998;</button>
+                        ${allGroups.length === 0 || _isReservedGroup(allGroups[0]) ? "disabled" : ""} title="${t("modelsRename")}">&#9998;</button>
                     <button class="wfm-btn wfm-btn-sm wfm-btn-danger" id="wfm-gallery-grp-delete-btn"
-                        ${allGroups.length === 0 || allGroups[0] === FEEDER_GROUP ? "disabled" : ""} title="${t("modelsDelete")}">&times;</button>
+                        ${allGroups.length === 0 || _isReservedGroup(allGroups[0]) ? "disabled" : ""} title="${t("modelsDelete")}">&times;</button>
                 </div>
+                <button class="wfm-btn wfm-btn-sm" id="wfm-gallery-vtemp-clear-btn" style="width:100%;margin-top:6px;" title="${t("vtempClearBtnTitle")}">${t("vtempClearBtnLabel")}</button>
             </div>
         </div>
     `;
@@ -1211,15 +1415,18 @@ function renderDetailGroup(img) {
         }
     });
 
-    // 管理セレクト変更時: __Feeder__ は rename/delete を無効化
+    // 管理セレクト変更時: 予約グループ(__Feeder__/__Video Assets__/__Video Temp__)は rename/delete を無効化
     el.querySelector("#wfm-gallery-grp-manage-sel")?.addEventListener("change", (e) => {
-        const isReserved = e.target.value === FEEDER_GROUP;
+        const isReserved = _isReservedGroup(e.target.value);
         const renameBtn = el.querySelector("#wfm-gallery-grp-rename-btn");
         const deleteBtn = el.querySelector("#wfm-gallery-grp-delete-btn");
         if (renameBtn) renameBtn.disabled = isReserved;
         if (deleteBtn) deleteBtn.disabled = isReserved;
     });
 
+    // Clear __Video Temp__ ボタン: __Video Temp__ グループをクリア(Video Plan実行時に自動登録される生成物置き場)
+    el.querySelector("#wfm-gallery-vtemp-clear-btn")?.addEventListener("click", clearVtempGroup);
+
     // グループ名変更
     el.querySelector("#wfm-gallery-grp-rename-btn")?.addEventListener("click", async () => {
         const sel = el.querySelector("#wfm-gallery-grp-manage-sel");
@@ -1540,9 +1747,12 @@ 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">&times;</button>
         </div>
@@ -1553,6 +1763,7 @@ function openLightbox(img) {
         }
     });
     document.body.appendChild(overlay);
+    if (isVideoFile(img)) applyStoredVideoVolume(overlay.querySelector("video"));
 }
 
 // ── 画像比較ライトボックス ─────────────────────────────────────
@@ -1564,12 +1775,17 @@ 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">
@@ -1583,6 +1799,7 @@ function openCompare(paths) {
         }
     });
     document.body.appendChild(overlay);
+    overlay.querySelectorAll("video").forEach(applyStoredVideoVolume);
 }
 
 // ── outputパス取得 ────────────────────────────────────────────
@@ -1636,7 +1853,7 @@ function onGalleryTabActivated() {
     loadGroups();
 }
 
-// ── サブタブ切替 (Output / ImagePrompt / Style_Catalog) ────────────────────────
+// ── サブタブ切替 (Output / ImagePrompt / Style_Catalog / Metadata) ────────────
 
 function _initGallerySubtabToggle() {
     document.querySelectorAll(".wfm-gallery-subtab-btn").forEach((btn) => {
@@ -1647,6 +1864,7 @@ function _initGallerySubtabToggle() {
             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();
         });
@@ -1661,17 +1879,32 @@ function bindEvents() {
     });
 
     // 選択画像を Image Edit タブへ送信
-    document.getElementById("wfm-gallery-send-image-edit-btn")?.addEventListener("click", () => {
+    document.getElementById("wfm-gallery-send-image-edit-btn")?.addEventListener("click", async () => {
         if (!state.selectedImage) {
             showToast("Please select an image first", "info");
             return;
         }
-        const url  = API.serveImage(state.selectedImage.path);
+        if (!window._wfmImageEditTab) return;
+        const path = state.selectedImage.path;
         const name = (state.selectedImage.filename || "gallery-image").replace(/\.[^.]+$/, "");
-        if (window._wfmImageEditTab) {
-            document.querySelector('[data-tab="image-edit"]')?.click();
-            window._wfmImageEditTab.loadFromUrl(url, name);
+
+        // PSDはフラット画像1枚ではなくレイヤー分解して送る(Open PSDボタンと同じ経路)
+        if (/\.psd$/i.test(path)) {
+            try {
+                const r = await fetch(`/wfm/gallery/image/psd-layers?path=${encodeURIComponent(path)}`);
+                if (!r.ok) throw new Error((await r.json().catch(() => ({}))).error || r.statusText);
+                const data = await r.json();
+                document.querySelector('[data-tab="image-edit"]')?.click();
+                await window._wfmImageEditTab.loadPsdLayersData(data, name);
+            } catch (err) {
+                showToast(`PSD import failed: ${err.message}`, "error");
+            }
+            return;
         }
+
+        const url = API.serveImage(path);
+        document.querySelector('[data-tab="image-edit"]')?.click();
+        window._wfmImageEditTab.loadFromUrl(url, name);
     });
 
     // 選択画像を GenerateUI Image タブへ送信
@@ -1707,6 +1940,34 @@ 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");
@@ -1725,6 +1986,7 @@ function bindEvents() {
                 return;
             }
             const url = API.serveImage(state.selectedImage.path);
+            const isVideo = isVideoFile(state.selectedImage);
 
             // I2I送信元がImageタブだった場合はそちらへ画像を読み込ませる（_ccI2ITargetModeはComic Creater側が
             // 「I2Iへ送る」ボタン押下時にセットするフラグ。undefined/'layout'なら従来通りコマ/オーバーレイへ挿入）
@@ -1735,6 +1997,11 @@ function bindEvents() {
                 targetMode = null;
             }
             if (targetMode === "image") {
+                // Imageタブは動画未対応（Comic Creator側の動画ツールはレイアウトタブのみ対応）
+                if (isVideo) {
+                    showToast(t("galleryVideoNotSupportedInImageTab"), "info");
+                    return;
+                }
                 let imageTab = null;
                 try {
                     if (window.parent && window.parent !== window && typeof window.parent._ccImageTab?.loadFromUrl === "function") {
@@ -1752,6 +2019,26 @@ function bindEvents() {
                 return;
             }
 
+            // レイアウトタブのコマ/オーバーレイへ（デフォルト経路）。動画（.mp4）はComic Creator側の
+            // 動画ツール専用の受け口（insertVideoFromUrl）へ、それ以外は従来通り画像として送る
+            if (isVideo) {
+                let insertVideoFn = null;
+                try {
+                    if (window.parent && window.parent !== window && typeof window.parent.insertVideoFromUrl === "function") {
+                        insertVideoFn = window.parent.insertVideoFromUrl;
+                    }
+                } catch (e) {
+                    insertVideoFn = null;
+                }
+                if (!insertVideoFn) {
+                    showToast(t("galleryCCNotAvailable"), "info");
+                    return;
+                }
+                insertVideoFn(url, state.selectedImage.filename || "video.mp4");
+                showToast(t("gallerySentCC"), "success");
+                return;
+            }
+
             let insertFn = null;
             try {
                 if (window.parent && window.parent !== window && typeof window.parent.insertImageFromUrl === "function") {
@@ -1769,12 +2056,15 @@ function bindEvents() {
         });
     }
 
-    // 検索
+    // 検索（連続キー入力のたびにリクエストを飛ばすと、50枚程度のフォルダでも
+    // ファイルI/Oの重いサーバー処理が積み重なって読み込みが遅延するためdebounceする）
     document.getElementById("wfm-gallery-search")?.addEventListener("input", (e) => {
         state.search = e.target.value;
-        loadImages();
+        clearTimeout(_searchDebounceTimer);
+        _searchDebounceTimer = setTimeout(loadImages, SEARCH_DEBOUNCE_MS);
     });
     setupSearchClearBtn("wfm-gallery-search", "wfm-gallery-search-clear-btn", () => {
+        clearTimeout(_searchDebounceTimer);
         state.search = "";
         loadImages();
     });
@@ -1840,7 +2130,7 @@ 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
@@ -1928,6 +2218,36 @@ function bindEvents() {
         exportSelectedImagesToZip([...state.selectedImages]);
     });
 
+    document.getElementById("wfm-gallery-bulk-psd")?.addEventListener("click", () => {
+        if (state.selectedImages.size === 0) return;
+        exportSelectedImagesToPsd([...state.selectedImages]);
+    });
+
+    document.getElementById("wfm-gallery-bulk-edit-layers")?.addEventListener("click", () => {
+        if (state.selectedImages.size === 0) return;
+        if (!window._wfmImageEditTab) return;
+
+        const allPaths = [...state.selectedImages];
+        const paths    = allPaths.filter(p => !/\.mp4$/i.test(p));
+        if (paths.length === 0) {
+            showToast(t("editLayersNoImages"), "error");
+            return;
+        }
+
+        const items = paths.map(p => {
+            const img      = state.images.find(i => i.path === p);
+            const filename = img?.filename || p.split(/[\\/]/).pop() || "layer";
+            return { url: API.serveImage(p), name: filename.replace(/\.[^.]+$/, "") };
+        });
+
+        document.querySelector('[data-tab="image-edit"]')?.click();
+        window._wfmImageEditTab.loadLayersFromUrls(items);
+
+        if (paths.length < allPaths.length) {
+            showToast(t("editLayersSkippedVideo", allPaths.length - paths.length), "info");
+        }
+    });
+
     document.getElementById("wfm-gallery-bulk-delete")?.addEventListener("click", () => {
         if (state.selectedImages.size === 0) return;
         const count = state.selectedImages.size;
@@ -1973,6 +2293,16 @@ 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) {
@@ -2012,6 +2342,7 @@ function bindEvents() {
     if (galleryClearBtn) {
         galleryClearBtn.textContent = t("clearFilters");
         galleryClearBtn.addEventListener("click", () => {
+            clearTimeout(_searchDebounceTimer);
             state.search = "";
             state.favoriteOnly = false;
             state.tagFilter = "";

static/js/generate-tab.js

@@ -11,6 +11,7 @@ import { syncJsonHighlight, syncScroll } from "./json-highlight.js";
 import { initFeederTab, refreshFeederNodeList } from "./feeder-tab.js";
 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
@@ -164,6 +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");
@@ -281,6 +293,13 @@ export async function loadWorkflowIntoEditor(workflow, filename) {
 
     refreshFeederNodeList();
 
+    // Lab subtab may already be the active one (e.g. it was active when this workflow
+    // was loaded) — the subtab-click handler that normally triggers this refresh never
+    // fires in that case, leaving Setting 1's live-reflected cells showing the previous
+    // workflow until the user clicks a cell or revisits the subtab.
+    const activeSubtab = document.querySelector(".wfm-gen-subtab-btn.active")?.dataset.subtab;
+    if (activeSubtab === "lab") refreshLabLiveDefaults();
+
     if (ckptWarning) {
         showToast(ckptWarning, "error");
     } else {
@@ -1443,26 +1462,38 @@ export 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)}%`;
@@ -1508,23 +1539,36 @@ 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");
                 });
@@ -1972,9 +2016,11 @@ 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) {
@@ -2003,7 +2049,7 @@ 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";
@@ -2146,6 +2192,10 @@ export async function initGenerateTab() {
             // 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" : "";
         });
     });
 
@@ -2156,6 +2206,14 @@ 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", () => {
@@ -2233,10 +2291,11 @@ export async function initGenerateTab() {
             });
             searchOverlay.innerHTML = html + "\n";
 
-            // Scroll editor to current match
+            // Scroll editor to current match. フォーカス/selectionRangeは意図的に設定しない
+            // (テキストエリアにカーソルが当たった状態になり、検索直後のキー入力が選択範囲を
+            // 上書きして不意にJSONを編集してしまうため)。ハイライトはsearchOverlayの<mark>
+            // 表示のみで行い、編集はユーザーが明示的にエディタをクリックした場合に限る。
             const pos = matchPositions[currentMatchIndex];
-            editor.focus();
-            editor.setSelectionRange(pos, pos + term.length);
             const lineNum = (text.slice(0, pos).match(/\n/g) || []).length;
             const lineH = parseFloat(getComputedStyle(editor).lineHeight) || 18;
             editor.scrollTop = Math.max(0, lineH * lineNum - editor.clientHeight / 2);

static/js/image-edit/BgRemove.js

@@ -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");
+    }
+}

static/js/image-edit/BlurTool.js

@@ -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();
+    }
+}

static/js/image-edit/DrawTool.js

@@ -20,6 +20,11 @@ export class DrawTool {
         this.sizeJitterAmount = 0.5;
         this.rotationJitter   = false;
 
+        // Pen pressure (0–1, from PointerEvent.pressure; mouse reports 0.5).
+        // pressure=0.5 maps to a 1.0x multiplier so mouse behavior is unchanged.
+        this.pressureEnabled = true;
+        this._lastPressure   = 0.5;
+
         // Image brush from ABR library (null = default circle brush)
         this.brushImage  = null;
         this.brushName   = "Circle";
@@ -80,29 +85,31 @@ export class DrawTool {
         if (this.canvas) this.canvas.style.cursor = "";
     }
 
-    onMouseDown(x, y) {
-        this._drawing = true;
-        this._lastX   = x;
-        this._lastY   = y;
+    onMouseDown(x, y, pressure = 0.5) {
+        this._drawing      = true;
+        this._lastX         = x;
+        this._lastY         = y;
+        this._lastPressure  = pressure;
         if (this.brushImage) {
             this._initStrokeBuffer();
-            this._paintToStroke(x, y);
+            this._paintToStroke(x, y, pressure);
             this._mergeStroke();
         } else {
-            this._paint(x, y);
+            this._paint(x, y, pressure);
         }
     }
 
-    onMouseMove(x, y) {
+    onMouseMove(x, y, pressure = 0.5) {
         if (!this._drawing) return;
         if (this.brushImage) {
-            this._paintLineToStroke(this._lastX, this._lastY, x, y);
+            this._paintLineToStroke(this._lastX, this._lastY, x, y, this._lastPressure, pressure);
             this._mergeStroke();
         } else {
-            this._paintLine(this._lastX, this._lastY, x, y);
+            this._paintLine(this._lastX, this._lastY, x, y, this._lastPressure, pressure);
         }
-        this._lastX = x;
-        this._lastY = y;
+        this._lastX        = x;
+        this._lastY        = y;
+        this._lastPressure = pressure;
     }
 
     onMouseUp() {
@@ -117,6 +124,20 @@ export class DrawTool {
         if (this._drawing) this.onMouseUp();
     }
 
+    // ── Pressure response ──────────────────────────────────────────────
+
+    _effectiveSize(pressure) {
+        if (!this.pressureEnabled) return this.brushSize;
+        const p = Math.max(0, Math.min(1, pressure ?? 0.5));
+        return Math.max(1, this.brushSize * (0.3 + p * 1.4)); // p=0.5 -> 1.0x
+    }
+
+    _effectiveOpacity(pressure) {
+        if (!this.pressureEnabled) return this.opacity;
+        const p = Math.max(0, Math.min(1, pressure ?? 0.5));
+        return Math.max(0, Math.min(1, this.opacity * (0.4 + p * 1.2))); // p=0.5 -> 1.0x
+    }
+
     // ── Stroke buffer (image brush only) ─────────────────────────────
 
     _initStrokeBuffer() {
@@ -155,54 +176,48 @@ export class DrawTool {
         ctx.restore();
     }
 
-    _paintToStroke(x, y) {
+    _paintToStroke(x, y, pressure = 0.5) {
         const saved = this.ctx;
         this.ctx = this._strokeCtx;
 
-        if (this.sizeJitter || this.rotationJitter) {
-            const savedSize      = this.brushSize;
-            const savedAngle     = this.angle;
-            const savedStamp     = this._imgStamp;
-            const savedStampSize = this._imgStampSize;
-            if (this.sizeJitter) {
-                this.brushSize = Math.max(1, savedSize * (1 - Math.random() * this.sizeJitterAmount));
-            }
-            if (this.rotationJitter) {
-                this.angle = Math.random() * 360;
-            }
-            this._paintImageBrush(x, y);
-            this.brushSize     = savedSize;
-            this.angle         = savedAngle;
-            this._imgStamp     = savedStamp;
-            this._imgStampSize = savedStampSize;
+        let size = this._effectiveSize(pressure);
+        if (this.sizeJitter) {
+            size = Math.max(1, size * (1 - Math.random() * this.sizeJitterAmount));
+        }
+
+        if (this.rotationJitter) {
+            const savedAngle = this.angle;
+            this.angle = Math.random() * 360;
+            this._paintImageBrush(x, y, size);
+            this.angle = savedAngle;
         } else {
-            this._paintImageBrush(x, y);
+            this._paintImageBrush(x, y, size);
         }
 
         this.ctx = saved;
     }
 
-    _paintLineToStroke(x0, y0, x1, y1) {
+    _paintLineToStroke(x0, y0, x1, y1, p0 = 0.5, p1 = 0.5) {
         const dist  = Math.hypot(x1 - x0, y1 - y0);
         const step  = Math.max(1, this.brushSize * this.spacing);
         const steps = Math.ceil(dist / step);
         for (let i = 1; i <= steps; i++) {
             const t = i / steps;
-            this._paintToStroke(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t);
+            this._paintToStroke(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t, p0 + (p1 - p0) * t);
         }
     }
 
     // ── Circle brush ─────────────────────────────────────────────────
 
-    _getStamp() {
+    _getStamp(size) {
+        size = Math.max(1, Math.round(size));
         if (
             this._stamp &&
-            this._stampSize  === this.brushSize &&
+            this._stampSize  === size &&
             this._stampHard  === this.hardness &&
             this._stampColor === this.color
         ) return this._stamp;
 
-        const size = Math.max(1, Math.round(this.brushSize));
         const sc   = document.createElement("canvas");
         sc.width   = size;
         sc.height  = size;
@@ -225,11 +240,12 @@ export class DrawTool {
         return sc;
     }
 
-    _paint(x, y) {
-        const stamp = this._getStamp();
+    _paint(x, y, pressure = 0.5) {
+        const size  = this._effectiveSize(pressure);
+        const stamp = this._getStamp(size);
         const s     = stamp.width;
         this.ctx.save();
-        this.ctx.globalAlpha = this.opacity;
+        this.ctx.globalAlpha = this._effectiveOpacity(pressure);
         if (this.mode === "erase") {
             this.ctx.globalCompositeOperation = "destination-out";
         }
@@ -237,13 +253,13 @@ export class DrawTool {
         this.ctx.restore();
     }
 
-    _paintLine(x0, y0, x1, y1) {
+    _paintLine(x0, y0, x1, y1, p0 = 0.5, p1 = 0.5) {
         const dist    = Math.hypot(x1 - x0, y1 - y0);
         const spacing = Math.max(1, this.brushSize * 0.2);
         const steps   = Math.max(1, Math.ceil(dist / spacing));
         for (let i = 1; i <= steps; i++) {
             const t = i / steps;
-            this._paint(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t);
+            this._paint(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t, p0 + (p1 - p0) * t);
         }
     }
 
@@ -319,8 +335,8 @@ export class DrawTool {
         return sc;
     }
 
-    _paintImageBrush(x, y) {
-        const stamp = this._getImageStamp(Math.round(this.brushSize));
+    _paintImageBrush(x, y, size = this.brushSize) {
+        const stamp = this._getImageStamp(Math.round(size));
         this.ctx.globalCompositeOperation = "source-over";
         if (this.angle) {
             this.ctx.save();

static/js/image-edit/FileExport.js

@@ -0,0 +1,222 @@
+/**
+ * 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();
+            }
+        }
+    }
+
+    // 保存用レイヤー配列を構築: 各レイヤーを個別のcanvas(ドキュメント全体サイズ)に
+    // 回転・反転・拡縮のみベイクして描画する。opacity/blendModeはピクセルに焼き込まず
+    // PSDレイヤー属性として渡すためメタデータのまま残す。
+    // maskApply=true のクリッピングマスクは対象レイヤーと合成して1枚にベイクする
+    // (PSDの本物のレイヤーマスクとしては書き出さない簡略化)。
+    _buildLayerExportList(layerMgr, w, h) {
+        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);
+            }
+        }
+
+        const exportLayers = [];
+        // 背面→前面の順(PSD側で後から追加したレイヤーほど上に重なるため、この順で送る)
+        for (let i = layers.length - 1; i >= 0; i--) {
+            const layer = layers[i];
+            if (maskedIndices.has(i)) continue; // マスク側の処理でまとめて扱う
+
+            if (layer.type === "mask") {
+                if (!(layer.maskApply && layer.visible && i + 1 < layers.length)) continue;
+                const target = layers[i + 1];
+                const canvas = document.createElement("canvas");
+                canvas.width = w;
+                canvas.height = h;
+                this._cb.renderMaskedLayer(canvas.getContext("2d"), canvas, layer, target, false);
+                exportLayers.push({
+                    name: target.name || "Layer",
+                    visible: target.visible,
+                    opacity: 1,
+                    blendMode: "source-over",
+                    imageData: canvas.toDataURL("image/png"),
+                });
+                continue;
+            }
+
+            const canvas = document.createElement("canvas");
+            canvas.width = w;
+            canvas.height = h;
+            const ctx = canvas.getContext("2d");
+            ctx.save();
+            Layer.applyTransform(ctx, layer);
+            ctx.drawImage(layer.canvas, -layer.canvas.width / 2, -layer.canvas.height / 2);
+            ctx.restore();
+            exportLayers.push({
+                name: layer.name || "Layer",
+                visible: layer.visible,
+                opacity: layer.opacity,
+                blendMode: layer.blendMode,
+                imageData: canvas.toDataURL("image/png"),
+            });
+        }
+        return exportLayers;
+    }
+
+    async saveAsPsd() {
+        if (!this._cb.getLayerManager()) { showToast("No image loaded", "error"); return; }
+        const layerMgr = this._cb.getLayerManager();
+        const { w, h } = this._cb.getCanvasSize();
+        const exportLayers = this._buildLayerExportList(layerMgr, w, h);
+        if (exportLayers.length === 0) { showToast("No layers to export", "error"); return; }
+
+        try {
+            const r = await fetch("/wfm/gallery/image/export-psd-layers", {
+                method: "POST",
+                headers: { "Content-Type": "application/json" },
+                body: JSON.stringify({ width: w, height: h, layers: exportLayers }),
+            });
+            if (!r.ok) {
+                const e = await r.json().catch(() => ({}));
+                throw new Error(e.error || r.statusText);
+            }
+            const blob = await r.blob();
+            const url  = URL.createObjectURL(blob);
+            const a = document.createElement("a");
+            a.href     = url;
+            a.download = (this._cb.getBaseName() || "wfs-edit") + ".psd";
+            a.click();
+            URL.revokeObjectURL(url);
+            showToast("PSD saved", "success");
+        } catch (err) {
+            showToast(`PSD save failed: ${err.message}`, "error");
+        }
+    }
+
+    savePng() {
+        if (!this._cb.getLayerManager()) { showToast("No image loaded", "error"); return; }
+        const canvas = this._buildCompositeCanvas();
+        const a = document.createElement("a");
+        a.href     = canvas.toDataURL("image/png");
+        a.download = (this._cb.getBaseName() || "wfs-edit") + "-output.png";
+        a.click();
+        showToast("PNG saved", "success");
+    }
+
+    async saveToGallery() {
+        if (!this._cb.getLayerManager()) { showToast("No image loaded", "error"); return; }
+
+        // デフォルトファイル名: wfs-image-YYYYMMDDHHmmss
+        const now = new Date();
+        const pad = n => String(n).padStart(2, "0");
+        const ts  = `${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");
+        }
+    }
+}

static/js/image-edit/FillTool.js

@@ -0,0 +1,228 @@
+/**
+ * Image Edit Tab - Fill Tool (bucket fill)
+ * Flood-fills a contiguous same-color region on the active layer canvas,
+ * either with a solid color or a linear/radial gradient.
+ * Gradient ramp math (evalGradient / hex<->rgb) is ported from
+ * comfyUI-particle-pixijs's particle_engine.js.
+ */
+
+function _hexToRgb01(hex) {
+    const m = /^#?([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})$/i.exec(hex || "#000000");
+    if (!m) return { r: 0, g: 0, b: 0 };
+    return { r: parseInt(m[1], 16) / 255, g: parseInt(m[2], 16) / 255, b: parseInt(m[3], 16) / 255 };
+}
+
+function _rgb01ToHex({ r, g, b }) {
+    const h = v => Math.round(Math.max(0, Math.min(1, v)) * 255).toString(16).padStart(2, "0");
+    return `#${h(r)}${h(g)}${h(b)}`;
+}
+
+function _lerp(a, b, t) { return a + (b - a) * t; }
+
+export class FillTool {
+    constructor() {
+        // fill settings
+        this.tolerance    = 32;        // 0-255
+        this.fillMode     = "solid";   // "solid" | "gradient"
+        this.color        = "#ff0000"; // solid fill color
+        this.opacity      = 1.0;
+
+        // gradient settings
+        this.gradientShape    = "linear"; // "linear" | "radial"
+        this.gradientAngleDeg = 0;        // linear direction
+        this.gradientStrength = 1.0;      // 0.2-3.0, length/radius multiplier
+        this.gradientStops    = [
+            { pos: 0.0, color: "#ffffff" },
+            { pos: 1.0, color: "#888888" },
+        ];
+        this.selectedStopIdx  = 0;
+
+        // internal state
+        this._active = false;
+        this._canvas = null;
+        this._ctx    = null;
+
+        this._onChange = null;
+    }
+
+    setCanvas(canvas) {
+        this._canvas = canvas;
+        this._ctx    = canvas ? canvas.getContext("2d") : null;
+    }
+
+    onChange(cb) { this._onChange = cb; }
+
+    activate()   { this._active = true; }
+    deactivate() { this._active = false; }
+
+    // ── color ramp evaluation (ported from particle_engine.js evalGradient) ──
+
+    static evalGradient(stops, t) {
+        if (!stops || stops.length === 0) return { r: 0, g: 0, b: 0 };
+        const sorted = [...stops].sort((a, b) => a.pos - b.pos);
+        if (t <= sorted[0].pos) return _hexToRgb01(sorted[0].color);
+        const last = sorted[sorted.length - 1];
+        if (t >= last.pos) return _hexToRgb01(last.color);
+        for (let i = 0; i < sorted.length - 1; i++) {
+            const a = sorted[i], b = sorted[i + 1];
+            if (t >= a.pos && t <= b.pos) {
+                const span = b.pos - a.pos;
+                const localT = span > 0 ? (t - a.pos) / span : 0;
+                const ca = _hexToRgb01(a.color), cb = _hexToRgb01(b.color);
+                return {
+                    r: _lerp(ca.r, cb.r, localT),
+                    g: _lerp(ca.g, cb.g, localT),
+                    b: _lerp(ca.b, cb.b, localT),
+                };
+            }
+        }
+        return _hexToRgb01(last.color);
+    }
+
+    /** Preview swatch color (hex) of the ramp at position t. */
+    evalGradientHex(t) {
+        return _rgb01ToHex(FillTool.evalGradient(this.gradientStops, t));
+    }
+
+    // ── stop editing helpers (ported from particle_widget.js addStop/removeStop/selectStop) ──
+
+    addStop() {
+        const sorted = [...this.gradientStops].sort((a, b) => a.pos - b.pos);
+        let bestGapStart = 0, bestGapSize = 0;
+        for (let i = 0; i < sorted.length - 1; i++) {
+            const gap = sorted[i + 1].pos - sorted[i].pos;
+            if (gap > bestGapSize) { bestGapSize = gap; bestGapStart = sorted[i].pos; }
+        }
+        const pos = sorted.length < 2 ? 0.5 : bestGapStart + bestGapSize / 2;
+        const color = _rgb01ToHex(FillTool.evalGradient(this.gradientStops, pos));
+        this.gradientStops.push({ pos, color });
+        this.selectedStopIdx = this.gradientStops.length - 1;
+    }
+
+    removeStop(idx = this.selectedStopIdx) {
+        if (this.gradientStops.length <= 1) return;
+        this.gradientStops.splice(idx, 1);
+        this.selectedStopIdx = Math.max(0, Math.min(this.selectedStopIdx, this.gradientStops.length - 1));
+    }
+
+    selectStop(idx) {
+        this.selectedStopIdx = idx;
+    }
+
+    // ── flood fill ────────────────────────────────────────
+
+    /**
+     * Flood-fills the contiguous region matching the pixel at (x, y).
+     * (x, y) are canvas-pixel coordinates (already converted via DrawTool.getCanvasPos).
+     */
+    onMouseDown(x, y) {
+        if (!this._active || !this._ctx) return;
+        const w = this._canvas.width, h = this._canvas.height;
+        const px = Math.floor(x), py = Math.floor(y);
+        if (px < 0 || py < 0 || px >= w || py >= h) return;
+
+        const imgData = this._ctx.getImageData(0, 0, w, h);
+        const data = imgData.data;
+
+        const mask = this._buildFloodMask(data, w, h, px, py, this.tolerance);
+        if (!mask) return;
+
+        if (this.fillMode === "gradient") {
+            this._fillMaskWithGradient(data, w, h, mask.pixels, mask.bbox);
+        } else {
+            this._fillMaskWithSolid(data, mask.pixels, this.color, this.opacity);
+        }
+
+        this._ctx.putImageData(imgData, 0, 0);
+    }
+
+    /** Scanline stack-based flood fill. Returns { pixels: Uint8Array(w*h), bbox } or null. */
+    _buildFloodMask(data, w, h, startX, startY, tolerance) {
+        const idx0 = (startY * w + startX) * 4;
+        const tr = data[idx0], tg = data[idx0 + 1], tb = data[idx0 + 2], ta = data[idx0 + 3];
+        const tol2 = tolerance * tolerance;
+
+        const matches = (i) => {
+            const dr = data[i] - tr, dg = data[i + 1] - tg, db = data[i + 2] - tb, da = data[i + 3] - ta;
+            return (dr * dr + dg * dg + db * db + da * da) <= tol2 * 4;
+        };
+
+        const pixels = new Uint8Array(w * h);
+        const stack = [[startX, startY]];
+        let minX = startX, maxX = startX, minY = startY, maxY = startY;
+
+        while (stack.length) {
+            let [x, y] = stack.pop();
+            while (x >= 0 && matches((y * w + x) * 4)) x--;
+            x++;
+            let spanUp = false, spanDown = false;
+            while (x < w && matches((y * w + x) * 4) && !pixels[y * w + x]) {
+                pixels[y * w + x] = 1;
+                if (x < minX) minX = x; if (x > maxX) maxX = x;
+                if (y < minY) minY = y; if (y > maxY) maxY = y;
+
+                if (y > 0) {
+                    const above = matches((( y - 1) * w + x) * 4) && !pixels[(y - 1) * w + x];
+                    if (above && !spanUp) { stack.push([x, y - 1]); spanUp = true; }
+                    else if (!above) spanUp = false;
+                }
+                if (y < h - 1) {
+                    const below = matches(((y + 1) * w + x) * 4) && !pixels[(y + 1) * w + x];
+                    if (below && !spanDown) { stack.push([x, y + 1]); spanDown = true; }
+                    else if (!below) spanDown = false;
+                }
+                x++;
+            }
+        }
+        return { pixels, bbox: { minX, minY, maxX, maxY } };
+    }
+
+    _fillMaskWithSolid(data, pixels, colorHex, opacity) {
+        const { r, g, b } = _hexToRgb01(colorHex);
+        const cr = r * 255, cg = g * 255, cb = b * 255;
+        const alpha = Math.max(0, Math.min(1, opacity));
+        for (let i = 0; i < pixels.length; i++) {
+            if (!pixels[i]) continue;
+            const di = i * 4;
+            data[di]     = _lerp(data[di],     cr, alpha);
+            data[di + 1] = _lerp(data[di + 1], cg, alpha);
+            data[di + 2] = _lerp(data[di + 2], cb, alpha);
+            data[di + 3] = Math.max(data[di + 3], alpha * 255);
+        }
+    }
+
+    /** Reusable with any {pixels, bbox} mask (flood fill today; rect/ellipse selections in the future). */
+    _fillMaskWithGradient(data, w, h, pixels, bbox) {
+        const cx = (bbox.minX + bbox.maxX) / 2, cy = (bbox.minY + bbox.maxY) / 2;
+        const diagW = bbox.maxX - bbox.minX, diagH = bbox.maxY - bbox.minY;
+        const diag = Math.max(1, Math.hypot(diagW, diagH));
+        const strength = Math.max(0.2, Math.min(3.0, this.gradientStrength));
+        const alpha = Math.max(0, Math.min(1, this.opacity));
+
+        const angleRad = this.gradientAngleDeg * Math.PI / 180;
+        const dirX = Math.cos(angleRad), dirY = Math.sin(angleRad);
+        const halfLen = (diag / 2) * strength;
+        const radius = (diag / 2) * strength;
+
+        for (let y = bbox.minY; y <= bbox.maxY; y++) {
+            for (let x = bbox.minX; x <= bbox.maxX; x++) {
+                const i = y * w + x;
+                if (!pixels[i]) continue;
+                const dx = x - cx, dy = y - cy;
+                let t;
+                if (this.gradientShape === "radial") {
+                    t = Math.hypot(dx, dy) / radius;
+                } else {
+                    t = (dx * dirX + dy * dirY) / (halfLen * 2) + 0.5;
+                }
+                t = Math.max(0, Math.min(1, t));
+                const c = FillTool.evalGradient(this.gradientStops, t);
+                const di = i * 4;
+                data[di]     = _lerp(data[di],     c.r * 255, alpha);
+                data[di + 1] = _lerp(data[di + 1], c.g * 255, alpha);
+                data[di + 2] = _lerp(data[di + 2], c.b * 255, alpha);
+                data[di + 3] = Math.max(data[di + 3], alpha * 255);
+            }
+        }
+    }
+}

static/js/image-edit/GmicIntegration.js

@@ -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;
+        }
+    }
+}

static/js/image-edit/InpaintI2IActions.js

@@ -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;
+        }
+    }
+}

static/js/image-edit/MaskEditorOneBridge.js

@@ -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;
+        });
+    }
+}

static/js/image-edit/MaskTool.js

@@ -25,6 +25,11 @@ export class MaskTool {
         this.sizeJitterAmount = 0.5;    // 0–1: fraction of brushSize that can be removed
         this.rotationJitter   = false;
 
+        // Pen pressure (0–1, from PointerEvent.pressure; mouse reports 0.5).
+        // pressure=0.5 maps to a 1.0x multiplier so mouse behavior is unchanged.
+        this.pressureEnabled = true;
+        this._lastPressure   = 0.5;
+
         this._drawing    = false;
         this._lastX      = 0;
         this._lastY      = 0;
@@ -80,29 +85,31 @@ export class MaskTool {
         if (this.canvas) this.canvas.style.cursor = "";
     }
 
-    onMouseDown(x, y) {
-        this._drawing = true;
-        this._lastX   = x;
-        this._lastY   = y;
+    onMouseDown(x, y, pressure = 0.5) {
+        this._drawing      = true;
+        this._lastX         = x;
+        this._lastY         = y;
+        this._lastPressure  = pressure;
         if (this.brushImage) {
             this._initStrokeBuffer();
-            this._paintToStroke(x, y);
+            this._paintToStroke(x, y, pressure);
             this._mergeStroke();
         } else {
-            this._paint(x, y);
+            this._paint(x, y, pressure);
         }
     }
 
-    onMouseMove(x, y) {
+    onMouseMove(x, y, pressure = 0.5) {
         if (!this._drawing) return;
         if (this.brushImage) {
-            this._paintLineToStroke(this._lastX, this._lastY, x, y);
+            this._paintLineToStroke(this._lastX, this._lastY, x, y, this._lastPressure, pressure);
             this._mergeStroke();
         } else {
-            this._paintLine(this._lastX, this._lastY, x, y);
+            this._paintLine(this._lastX, this._lastY, x, y, this._lastPressure, pressure);
         }
-        this._lastX = x;
-        this._lastY = y;
+        this._lastX        = x;
+        this._lastY        = y;
+        this._lastPressure = pressure;
     }
 
     onMouseUp() {
@@ -117,6 +124,14 @@ export class MaskTool {
         if (this._drawing) this.onMouseUp();
     }
 
+    // ── Pressure response ──────────────────────────────────────────────
+
+    _effectiveSize(pressure) {
+        if (!this.pressureEnabled) return this.brushSize;
+        const p = Math.max(0, Math.min(1, pressure ?? 0.5));
+        return Math.max(1, this.brushSize * (0.3 + p * 1.4)); // p=0.5 -> 1.0x
+    }
+
     // ── Stroke buffer (image brush only) ─────────────────────────────
 
     _initStrokeBuffer() {
@@ -149,54 +164,48 @@ export class MaskTool {
         ctx.globalCompositeOperation = "source-over";
     }
 
-    _paintToStroke(x, y) {
+    _paintToStroke(x, y, pressure = 0.5) {
         const saved = this.ctx;
         this.ctx = this._strokeCtx;
 
-        if (this.sizeJitter || this.rotationJitter) {
-            const savedSize      = this.brushSize;
-            const savedAngle     = this.angle;
-            const savedStamp     = this._imgStamp;
-            const savedStampSize = this._imgStampSize;
-            if (this.sizeJitter) {
-                this.brushSize = Math.max(1, savedSize * (1 - Math.random() * this.sizeJitterAmount));
-            }
-            if (this.rotationJitter) {
-                this.angle = Math.random() * 360;
-            }
-            this._paintImageBrush(x, y);
-            this.brushSize     = savedSize;
-            this.angle         = savedAngle;
-            this._imgStamp     = savedStamp;
-            this._imgStampSize = savedStampSize;
+        let size = this._effectiveSize(pressure);
+        if (this.sizeJitter) {
+            size = Math.max(1, size * (1 - Math.random() * this.sizeJitterAmount));
+        }
+
+        if (this.rotationJitter) {
+            const savedAngle = this.angle;
+            this.angle = Math.random() * 360;
+            this._paintImageBrush(x, y, size);
+            this.angle = savedAngle;
         } else {
-            this._paintImageBrush(x, y);
+            this._paintImageBrush(x, y, size);
         }
 
         this.ctx = saved;
     }
 
-    _paintLineToStroke(x0, y0, x1, y1) {
+    _paintLineToStroke(x0, y0, x1, y1, p0 = 0.5, p1 = 0.5) {
         const dist  = Math.hypot(x1 - x0, y1 - y0);
         const step  = Math.max(1, this.brushSize * this.spacing);
         const steps = Math.ceil(dist / step);
         for (let i = 1; i <= steps; i++) {
             const t = i / steps;
-            this._paintToStroke(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t);
+            this._paintToStroke(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t, p0 + (p1 - p0) * t);
         }
     }
 
     // ── Circle brush ─────────────────────────────────────────────────
 
-    _getStamp() {
+    _getStamp(size) {
+        size = Math.max(1, Math.round(size));
         if (
             this._stamp &&
-            this._stampSize === this.brushSize &&
+            this._stampSize === size &&
             this._stampHard === this.hardness &&
             this._stampMode === this.mode
         ) return this._stamp;
 
-        const size  = Math.max(1, Math.round(this.brushSize));
         const sc    = document.createElement("canvas");
         sc.width    = size;
         sc.height   = size;
@@ -222,8 +231,9 @@ export class MaskTool {
         return sc;
     }
 
-    _paint(x, y) {
-        const stamp = this._getStamp();
+    _paint(x, y, pressure = 0.5) {
+        const size  = this._effectiveSize(pressure);
+        const stamp = this._getStamp(size);
         const s     = stamp.width;
         this.ctx.save();
         if (this.mode === "erase") {
@@ -233,13 +243,13 @@ export class MaskTool {
         this.ctx.restore();
     }
 
-    _paintLine(x0, y0, x1, y1) {
+    _paintLine(x0, y0, x1, y1, p0 = 0.5, p1 = 0.5) {
         const dist    = Math.hypot(x1 - x0, y1 - y0);
         const spacing = Math.max(1, this.brushSize * 0.2);
         const steps   = Math.max(1, Math.ceil(dist / spacing));
         for (let i = 1; i <= steps; i++) {
             const t = i / steps;
-            this._paint(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t);
+            this._paint(x0 + (x1 - x0) * t, y0 + (y1 - y0) * t, p0 + (p1 - p0) * t);
         }
     }
 
@@ -307,8 +317,8 @@ export class MaskTool {
         return sc;
     }
 
-    _paintImageBrush(x, y) {
-        const stamp = this._getImageStamp(Math.round(this.brushSize));
+    _paintImageBrush(x, y, size = this.brushSize) {
+        const stamp = this._getImageStamp(Math.round(size));
         this.ctx.globalCompositeOperation = "source-over";
         if (this.angle) {
             this.ctx.save();

static/js/image-edit/Sam3Segmentation.js

@@ -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;
+        });
+    }
+}

static/js/lab-tab.js

@@ -1,14 +1,15 @@
 /**
  * Lab Tab - Experimental I2I batch generation.
  *
- * Runs the currently loaded workflow N times, letting Checkpoint / VAE / Prompt /
- * KSampler each be overridden independently starting at a chosen iteration
+ * Runs the currently loaded workflow N times, letting Model (Checkpoint/LoRA/VAE) /
+ * Prompt / KSampler each be overridden independently starting at a chosen iteration
  * ("keyframe"). Never mutates comfyUI.currentWorkflow — everything is applied to a
  * fresh deep clone per iteration so it can't interfere with the Input/Model/Settings/
  * Batch subtabs.
  */
 
 import { comfyUI } from "./comfyui-client.js";
+import { comfyWorkflow } from "./comfyui-workflow.js";
 import { comfyEditor } from "./comfyui-editor.js";
 import { showToast, openModal, closeModal } from "./app.js";
 import { t } from "./i18n.js";
@@ -16,28 +17,94 @@ import { escapeHtml, embedPngTextChunk, getEagleSettings, saveToEagle, setupSear
 import { extractAllMetadata, readAllPNGTextChunks } from "./metadata-tab.js";
 import { syncJsonHighlight, syncScroll } from "./json-highlight.js";
 import { _expandWildcardsInWorkflow } from "./generate-tab.js";
+import { pmGroups, getPresetsInGroup } from "./prompt-presets.js";
+import { isVideoFilename, extractLastFrameBlob } from "./video-utils.js";
 
-const COLUMN_KEYS = ["checkpoint", "vae", "prompt", "ksampler"];
+// Checkpoint/LoRA/VAE are three fully independent keyframe timelines — same as
+// Prompt/KSampler — each with its own +/-, its own atIteration sequence, its own
+// forward-fill resolution (_resolveKeyframe). The Model column header's C/L/V toggle
+// only picks which ONE of the three renders into the (single, shared) cell list and
+// which one +/- and the cell-click modal operate on; it never bundles them together.
+const COLUMN_KEYS = ["checkpoint", "lora", "vae", "prompt", "ksampler"];
 const MAX_RESULTS = 9;
 const RESULTS_GRID_COLS = 3;
 const LAB_PLAN_PREFIX = "ws_labplan_";
 const LAB_PLAN_PNG_KEY = "wfm_lab_plan";
 
 function _defaultValueFor(col) {
+    if (col === "lora") return { name: "", strengthModel: 1.0, strengthClip: 1.0, nodeBypass: false, applyToPrompt: false };
     if (col === "prompt") return { positive: "", negative: "", styleApplied: false, styleName: "" };
     if (col === "ksampler") return { steps: null, cfg: null, sampler_name: "", scheduler: "", denoise: null, seed: null };
-    return "";
+    return ""; // checkpoint, vae
+}
+
+// Fills in defaults for a possibly-partial LoRA value — both for legacy plan data
+// migrated by _migrateLabColumns and defensively for any hand-edited Plan JSON.
+function _normalizeLoraValue(v) {
+    return {
+        name: v?.name || "",
+        strengthModel: v?.strengthModel ?? 1.0,
+        strengthClip: v?.strengthClip ?? 1.0,
+        nodeBypass: !!v?.nodeBypass,
+        applyToPrompt: !!v?.applyToPrompt,
+    };
 }
 
 function _emptyColumns() {
     return {
-        checkpoint: [{ atIteration: 1, value: "", revertToBase: false, bypassed: false }],
-        vae: [{ atIteration: 1, value: "", revertToBase: false, bypassed: false }],
+        checkpoint: [{ atIteration: 1, value: _defaultValueFor("checkpoint"), revertToBase: false, bypassed: false }],
+        lora: [{ atIteration: 1, value: _defaultValueFor("lora"), revertToBase: false, bypassed: false }],
+        vae: [{ atIteration: 1, value: _defaultValueFor("vae"), revertToBase: false, bypassed: false }],
         prompt: [{ atIteration: 1, value: _defaultValueFor("prompt"), revertToBase: false, bypassed: false }],
         ksampler: [{ atIteration: 1, value: _defaultValueFor("ksampler"), revertToBase: false, bypassed: false }],
     };
 }
 
+// Two prior on-disk Plan JSON shapes need upgrading to today's independent
+// checkpoint/lora/vae arrays:
+// - Pre-v0.4.2: separate top-level `checkpoint`/`vae` columns, no `lora` at all.
+// - v0.4.2's brief "merged Model row" design: `columns.model` was a single array of
+//   {atIteration, value:{checkpoint,lora,vae}, ...} rows bundling all three together.
+//   That bundling caused its own bugs (+/- and the cell modal affected all three
+//   fields at once) and was replaced the same release cycle, but a plan saved during
+//   that window must still load. Values are moved into their own independent arrays;
+//   iteration #1 is always kept (baseline row) even if a field was blank there.
+function _migrateLabColumns(columns) {
+    if (!columns) return _emptyColumns();
+    if (!columns.model && Array.isArray(columns.lora)) return columns; // already current shape (independent lora array is the marker)
+
+    const defaults = _emptyColumns();
+
+    if (Array.isArray(columns.model)) {
+        const checkpoint = [], lora = [], vae = [];
+        for (const kf of columns.model) {
+            const v = kf.value || {};
+            const base = { atIteration: kf.atIteration, revertToBase: !!kf.revertToBase, bypassed: !!kf.bypassed };
+            if (kf.atIteration === 1 || v.checkpoint) checkpoint.push({ ...base, value: v.checkpoint || "" });
+            if (kf.atIteration === 1 || v.lora?.name) lora.push({ ...base, value: _normalizeLoraValue(v.lora) });
+            if (kf.atIteration === 1 || v.vae) vae.push({ ...base, value: v.vae || "" });
+        }
+        const { model, ...rest } = columns;
+        return {
+            ...rest,
+            checkpoint: checkpoint.length ? checkpoint : defaults.checkpoint,
+            lora: lora.length ? lora : defaults.lora,
+            vae: vae.length ? vae : defaults.vae,
+        };
+    }
+
+    if (columns.checkpoint || columns.vae) {
+        return {
+            ...columns,
+            checkpoint: columns.checkpoint?.length ? columns.checkpoint : defaults.checkpoint,
+            lora: defaults.lora,
+            vae: columns.vae?.length ? columns.vae : defaults.vae,
+        };
+    }
+
+    return columns;
+}
+
 function _emptyLabState() {
     return {
         planFilename: null,
@@ -59,6 +126,18 @@ let _aborted = false;
 let _resumeResolve = null;
 let _lastGeneratedImageRef = null; // ComfyUI "name [type]" annotated ref of the previous iteration's first output
 
+// Which single field the Model column's cells currently display (C/L/V toggle in its
+// header) — purely a display preference, not part of the Plan data, so it's not saved
+// with the plan and simply resets to "checkpoint" on page load.
+let _modelViewMode = "checkpoint";
+
+// Single-click-selected keyframe index per real column key (checkpoint/lora/vae/prompt/
+// ksampler) — used by that column's up/down reorder buttons. A single click selects
+// (this object); a double click still opens the edit modal directly, bypassing
+// selection. Not part of the Plan data — resets whenever columns are replaced wholesale
+// (plan load/clear, Prompt Group apply) so a stale index never points past the new array.
+let _selectedIdx = {};
+
 // ============================================
 // Init
 // ============================================
@@ -68,6 +147,9 @@ export function initLabTab() {
     _initPlanDropZone();
     _initSubtabToggle();
     _initColumnButtons();
+    _initReorderButtons();
+    _initPromptGroupButton();
+    _initModelViewToggle();
     _initRunControls();
     _initPlanButtons();
     _initPlanJsonTab();
@@ -292,10 +374,22 @@ function _initPlanButtons() {
 // Column keyframes: render / add / remove / edit
 // ============================================
 
+// A click on +/- always targets whichever of checkpoint/lora/vae the Model column
+// header's C/L/V toggle currently has selected — "model" is a sentinel dataset.col
+// value on those two shared buttons (there's only one +/- pair in the DOM for the
+// merged-looking Model column), never a real key into _lab.columns. Resolving it here
+// means adding/removing a keyframe only ever touches the ONE field being viewed, never
+// the other two — Prompt/KSampler's own +/- buttons carry their real col directly and
+// pass through unchanged.
+function _resolveColumnKey(col) {
+    return col === "model" ? _modelViewMode : col;
+}
+
 function _initColumnButtons() {
     document.querySelectorAll(".wfm-lab-col-add").forEach((btn) => {
         btn.addEventListener("click", () => {
-            const col = btn.dataset.col;
+            const col = _resolveColumnKey(btn.dataset.col);
+            if (!_hasNodeForColumn(col)) return; // no matching node to apply an override to
             const kfs = _lab.columns[col];
             const lastIter = kfs[kfs.length - 1].atIteration;
             if (lastIter >= _lab.batchCount) {
@@ -303,38 +397,249 @@ function _initColumnButtons() {
                 return;
             }
             kfs.push({ atIteration: lastIter + 1, value: _defaultValueFor(col), revertToBase: false, bypassed: false });
-            _renderColumn(col);
+            _renderColumnForKey(col);
             _openCellModal(col, kfs.length - 1);
         });
     });
     document.querySelectorAll(".wfm-lab-col-remove").forEach((btn) => {
         btn.addEventListener("click", () => {
-            const col = btn.dataset.col;
+            const col = _resolveColumnKey(btn.dataset.col);
             const kfs = _lab.columns[col];
             if (kfs.length <= 1) return; // Row 1 (baseline) cannot be removed
             kfs.pop();
-            _renderColumn(col);
+            _renderColumnForKey(col);
+        });
+    });
+}
+
+// Single click on a cell selects it (for the up/down reorder buttons below) instead of
+// opening the edit modal — opening now requires a double click (_renderColumn wires
+// both listeners on the same cell).
+function _selectCell(col, idx) {
+    _selectedIdx[col] = idx;
+    _renderColumnForKey(col);
+}
+
+// Up/down buttons: swap the selected keyframe with its immediate array neighbor. Row 1
+// (idx 0, the baseline) can never move and nothing can be swapped into its slot — it has
+// no atIteration input of its own and drives the "live GenerateUI value" display, so
+// letting another keyframe take its place would silently change that keyframe's
+// behavior. Swapping the two keyframes' atIteration values (rather than reordering the
+// array directly) keeps each object's own value permanently paired with its own
+// atIteration field; re-deriving the array order from those swapped values below is
+// then just "swap array position to match", not a re-sort of the whole column.
+function _initReorderButtons() {
+    document.querySelectorAll(".wfm-lab-col-up").forEach((btn) => {
+        btn.addEventListener("click", () => _moveSelectedKeyframe(_resolveColumnKey(btn.dataset.col), -1));
+    });
+    document.querySelectorAll(".wfm-lab-col-down").forEach((btn) => {
+        btn.addEventListener("click", () => _moveSelectedKeyframe(_resolveColumnKey(btn.dataset.col), 1));
+    });
+}
+
+function _moveSelectedKeyframe(col, direction) {
+    const kfs = _lab.columns[col];
+    const idx = _selectedIdx[col];
+    if (idx == null || idx >= kfs.length) { // unselected, or stale after a bulk replace/remove
+        showToast(t("labSelectKeyframeFirst"), "error");
+        return;
+    }
+    const targetIdx = idx + direction;
+    if (idx <= 0 || targetIdx <= 0 || targetIdx >= kfs.length) return; // baseline immovable; can't swap past either end
+
+    const tmpIter = kfs[idx].atIteration;
+    kfs[idx].atIteration = kfs[targetIdx].atIteration;
+    kfs[targetIdx].atIteration = tmpIter;
+    [kfs[idx], kfs[targetIdx]] = [kfs[targetIdx], kfs[idx]];
+
+    _selectedIdx[col] = targetIdx;
+    _renderColumnForKey(col);
+}
+
+// "PG" button on the Prompt column header: lets the user pick one of the Prompt tab's
+// preset groups (pmGroups, defined in prompt-presets.js) and replace the ENTIRE Prompt
+// column with one keyframe per preset in the group, in group order, starting at
+// iteration #1 (which is overwritten, not appended to).
+function _initPromptGroupButton() {
+    const btn = document.getElementById("wfm-lab-prompt-group-btn");
+    if (btn) btn.title = t("labPromptGroupBtnTitle");
+    btn?.addEventListener("click", () => _openPromptGroupModal());
+}
+
+function _openPromptGroupModal() {
+    const groupNames = Object.keys(pmGroups).sort();
+    const listHtml = groupNames.length === 0
+        ? `<div class="wfm-pm-empty">${t("labPromptGroupEmpty")}</div>`
+        : groupNames.map((g) => {
+            const count = getPresetsInGroup(g).length;
+            return `<div class="wfm-pm-item wfm-lab-pg-item" data-group="${escapeHtml(g)}">
+                <div class="wfm-pm-item-body">
+                    <div class="wfm-pm-item-name">${escapeHtml(g)}</div>
+                    <div class="wfm-pm-item-sub">${t("labPromptGroupCount", count)}</div>
+                </div>
+            </div>`;
+        }).join("");
+
+    const html = `
+        <div id="wfm-lab-pg-list">${listHtml}</div>
+        <div style="display:flex;gap:8px;margin-top:12px;justify-content:flex-end;">
+            <button id="wfm-lab-pg-apply" class="wfm-btn wfm-btn-primary" disabled>${t("labApply")}</button>
+        </div>`;
+    openModal(t("labPromptGroupModalTitle"), html);
+
+    let selectedGroup = null;
+    const applyBtn = document.getElementById("wfm-lab-pg-apply");
+    document.querySelectorAll(".wfm-lab-pg-item").forEach((el) => {
+        el.addEventListener("click", () => {
+            document.querySelectorAll(".wfm-lab-pg-item").forEach((o) => o.classList.remove("active"));
+            el.classList.add("active");
+            selectedGroup = el.dataset.group;
+            if (applyBtn) applyBtn.disabled = false;
+        });
+    });
+    applyBtn?.addEventListener("click", () => {
+        if (!selectedGroup) { showToast(t("labPromptGroupSelectFirst"), "error"); return; }
+        _applyPromptGroupToLab(selectedGroup);
+    });
+}
+
+// Full replacement of _lab.columns.prompt: one keyframe per preset in the group, in
+// group order, atIteration 1..N — #1 is overwritten (never appended after), matching
+// how every other "bulk load" action on Lab (e.g. plan load) replaces rather than merges.
+function _applyPromptGroupToLab(groupName) {
+    const presets = getPresetsInGroup(groupName);
+    if (presets.length === 0) { showToast(t("labPromptGroupNoPresets"), "error"); return; }
+
+    // Batch input caps at 50 (see templates/index.html); raise batchCount to fit the
+    // group up to that cap, same as the existing keyframe-beyond-batch warning handles
+    // anything past it (see _warnIfKeyframesExceedBatch below) rather than silently
+    // dropping presets past #50.
+    if (presets.length > _lab.batchCount) {
+        _lab.batchCount = Math.min(presets.length, 50);
+        const batchEl = document.getElementById("wfm-lab-batch-count");
+        if (batchEl) batchEl.value = _lab.batchCount;
+        showToast(t("labPromptGroupBatchRaised", _lab.batchCount), "info");
+    }
+
+    _lab.columns.prompt = presets.map((p, idx) => ({
+        atIteration: idx + 1,
+        value: {
+            positive: p.text || p.posText || "",
+            negative: p.negText || "",
+            styleApplied: false,
+            styleName: "",
+        },
+        revertToBase: false,
+        bypassed: false,
+    }));
+    delete _selectedIdx.prompt; // stale selection would point past the freshly replaced array
+    _renderColumnForKey("prompt");
+    _warnIfKeyframesExceedBatch();
+    closeModal();
+    showToast(t("labPromptGroupApplied", groupName, presets.length), "success");
+}
+
+// Wires the Model column header's C/L/V buttons — switches which independent field
+// (checkpoint/lora/vae) the shared cell list, +/-, and cell-click modal all operate on.
+// Purely a display/routing selector: none of the three keyframe arrays are touched by
+// switching it.
+function _initModelViewToggle() {
+    document.querySelectorAll(".wfm-lab-model-view-btn").forEach((btn) => {
+        btn.addEventListener("click", () => {
+            _modelViewMode = btn.dataset.view;
+            document.querySelectorAll(".wfm-lab-model-view-btn").forEach((b) => b.classList.toggle("active", b === btn));
+            _renderModelColumn();
         });
     });
 }
 
 function _renderAllColumns() {
-    COLUMN_KEYS.forEach(_renderColumn);
+    _renderModelColumn();
+    _renderColumn("prompt");
+    _renderColumn("ksampler");
 }
 
-function _renderColumn(col) {
-    const container = document.getElementById(`wfm-lab-col-cells-${col}`);
+// Renders the Model column's shared cell list from whichever of checkpoint/lora/vae is
+// currently toggled (_modelViewMode) — the other two arrays exist in _lab.columns but
+// have no DOM of their own to render into; they're only ever shown when toggled to.
+function _renderModelColumn() {
+    _updateModelViewHighlight();
+    _renderColumn(_modelViewMode, "wfm-lab-col-cells-model");
+}
+
+// Routes a keyframe-array key to whichever container renders it: checkpoint/lora/vae
+// all share the Model column's single cell list, everything else has its own.
+function _renderColumnForKey(col) {
+    if (col === "checkpoint" || col === "lora" || col === "vae") _renderModelColumn();
+    else _renderColumn(col);
+}
+
+// True when the loaded workflow actually has a node the given Model column (checkpoint/
+// lora/vae) could apply an override to. VAE is special-cased: even with no standalone
+// VAELoader node, a checkpoint node means _buildWorkflowForIteration can still inject one
+// to override the checkpoint's baked-in VAE — so VAE only counts as "absent" when there's
+// no checkpoint either. Non-model columns (prompt/ksampler) always have somewhere to
+// apply, so they're not gated by this.
+function _hasNodeForColumn(col) {
+    const analysis = comfyUI.currentAnalysis;
+    if (col === "checkpoint") return (analysis?.checkpoint_nodes?.length || 0) > 0;
+    if (col === "lora") return (analysis?.lora_nodes?.length || 0) > 0;
+    if (col === "vae") {
+        return (analysis?.vae_nodes?.length || 0) > 0 || (analysis?.checkpoint_nodes?.length || 0) > 0;
+    }
+    // Video all-in-one nodes (e.g. MiniMaxH3ImageToVideo) DO wire through a
+    // SamplerCustomAdvanced internally, so sampler_nodes.length alone isn't enough to
+    // detect "no user-adjustable sampler here" — that internal sampler is a fixed part
+    // of the model's own preset, not something meant to be edited per keyframe (unlike a
+    // real Advanced Sampling workflow's SamplerCustomAdvanced, e.g. Flux/SD3.5). Treat
+    // any video workflow as having no editable Ksampler column, same as the Model
+    // sub-columns above hide when their node is absent.
+    if (col === "ksampler") {
+        if (comfyWorkflow.isVideoWorkflow(analysis)) return false;
+        return (analysis?.sampler_nodes?.length || 0) > 0;
+    }
+    return true;
+}
+
+function _renderColumn(col, containerId = `wfm-lab-col-cells-${col}`) {
+    const container = document.getElementById(containerId);
     if (!container) return;
+    // No matching node in the loaded workflow for this Model sub-column: showing a
+    // baseline "Setting 1" keyframe here would be a no-op at Run time (_buildWorkflowForIteration
+    // silently skips writes when the target node id doesn't exist), so skip rendering it
+    // entirely rather than presenting an override control that can never do anything.
+    if (!_hasNodeForColumn(col)) {
+        container.innerHTML = `<div class="wfm-lab-cell-empty">${t("labNoNodeForColumn")}</div>`;
+        return;
+    }
     const keyframes = _lab.columns[col];
     container.innerHTML = keyframes.map((kf, idx) => _cellHtml(col, kf, idx)).join("");
+    // Single click selects (for the up/down reorder buttons); double click still opens
+    // the edit modal directly, independent of whatever is currently selected.
     container.querySelectorAll(".wfm-lab-cell").forEach((el) => {
-        el.addEventListener("click", () => _openCellModal(col, parseInt(el.dataset.idx, 10)));
+        const idx = parseInt(el.dataset.idx, 10);
+        el.addEventListener("click", () => _selectCell(col, idx));
+        el.addEventListener("dblclick", () => _openCellModal(col, idx));
     });
 }
 
+// Colors the header's C/L/V buttons the same "this node type actually exists in the
+// loaded workflow" highlight GenerateUI's Model tab uses (wfm-model-label-active,
+// colored via settings-tab.js applyModelTabActiveColor) — independent of which one is
+// currently selected/active. Re-run on every model column render so it stays in sync
+// with whatever's loaded (initial render, keyframe edits, toggle clicks, plan load/
+// apply, and refreshLabLiveDefaults() when switching into the Lab subtab).
+function _updateModelViewHighlight() {
+    document.querySelector('.wfm-lab-model-view-btn[data-view="checkpoint"]')?.classList.toggle("wfm-model-label-active", _hasNodeForColumn("checkpoint"));
+    document.querySelector('.wfm-lab-model-view-btn[data-view="lora"]')?.classList.toggle("wfm-model-label-active", _hasNodeForColumn("lora"));
+    document.querySelector('.wfm-lab-model-view-btn[data-view="vae"]')?.classList.toggle("wfm-model-label-active", _hasNodeForColumn("vae"));
+}
+
 function _cellHtml(col, kf, idx) {
     const revertClass = kf.revertToBase ? " wfm-lab-cell-revert" : "";
     const bypassClass = kf.bypassed ? " wfm-lab-cell-bypassed" : "";
+    const nodeBypassClass = (col === "lora" && kf.value?.nodeBypass) ? " wfm-lab-cell-node-bypassed" : "";
+    const selectedClass = _selectedIdx[col] === idx ? " wfm-lab-cell-selected" : "";
     if (idx === 0) {
         // Bypassed takes priority over the live-reflect display — an explicit "skip this
         // row" toggle shouldn't be masked by Setting 1's usual "show the workflow's
@@ -342,13 +647,13 @@ function _cellHtml(col, kf, idx) {
         const live = !kf.bypassed && _isLiveDisplay(col, idx);
         const liveClass = live ? " wfm-lab-cell-live" : "";
         const label = live ? _cellLabel(col, { value: _liveValueFor(col), revertToBase: false, bypassed: false }) : _cellLabel(col, kf);
-        return `<div class="wfm-lab-cell${revertClass}${bypassClass}${liveClass}" data-idx="${idx}">
+        return `<div class="wfm-lab-cell${revertClass}${bypassClass}${nodeBypassClass}${liveClass}${selectedClass}" data-idx="${idx}">
             <span class="wfm-lab-cell-index">${kf.atIteration}:</span>
             <span class="wfm-lab-cell-value">${label}</span>
         </div>`;
     }
     // Iteration #2 and beyond: value on its own line, applied-at iteration below it
-    return `<div class="wfm-lab-cell${revertClass}${bypassClass}" data-idx="${idx}">
+    return `<div class="wfm-lab-cell${revertClass}${bypassClass}${nodeBypassClass}${selectedClass}" data-idx="${idx}">
         <div class="wfm-lab-cell-value">${_cellLabel(col, kf)}</div>
         <div class="wfm-lab-cell-applied">${t("labAppliedAt", kf.atIteration)}</div>
     </div>`;
@@ -360,6 +665,13 @@ function _cellLabel(col, kf) {
     if (col === "checkpoint" || col === "vae") {
         return kf.value ? escapeHtml(kf.value) : t("labUseWorkflowDefault");
     }
+    if (col === "lora") {
+        const v = kf.value;
+        if (!v?.name) return t("labUseWorkflowDefault");
+        if (v.nodeBypass) return escapeHtml(`${v.name} ${t("labLoraNodeBypassed")}`);
+        const promptTag = v.applyToPrompt ? ` ${t("labLoraPromptAppliedTag")}` : "";
+        return escapeHtml(`${v.name} (${v.strengthModel}/${v.strengthClip})${promptTag}`);
+    }
     if (col === "prompt") {
         const p = kf.value?.positive || "";
         const n = kf.value?.negative || "";
@@ -380,6 +692,7 @@ function _cellLabel(col, kf) {
 
 function _isEmptyValue(col, value) {
     if (col === "checkpoint" || col === "vae") return !value;
+    if (col === "lora") return !value?.name;
     if (col === "prompt") return !value || (!value.positive && !value.negative);
     if (col === "ksampler") {
         return !value || (value.steps == null && value.cfg == null && value.denoise == null &&
@@ -397,12 +710,11 @@ function _isEmptyValue(col, value) {
 function _liveValueFor(col) {
     const analysis = comfyUI.currentAnalysis;
     if (!analysis) return null;
-    if (col === "checkpoint") {
-        return analysis.checkpoint_nodes?.[0]?.ckpt_name || null;
-    }
-    if (col === "vae") {
-        return analysis.vae_nodes?.[0]?.vae_name || null;
-    }
+    if (col === "checkpoint") return analysis.checkpoint_nodes?.[0]?.ckpt_name || null;
+    if (col === "vae") return analysis.vae_nodes?.[0]?.vae_name || null;
+    // LoRA is intentionally never live-reflected — there's no safe way to pick "the"
+    // active LoRA back out of a workflow node to prefill with.
+    if (col === "lora") return null;
     if (col === "prompt") {
         const posNode = analysis.prompt_nodes?.find((n) => n.role === "positive");
         const negNode = analysis.prompt_nodes?.find((n) => n.role === "negative");
@@ -447,6 +759,12 @@ function _openCellModal(col, idx) {
     const effectiveValue = live ? _liveValueFor(col) : kf.value;
     const hasPrevious = col === "prompt" && idx > 0;
 
+    // What this row would inherit if left blank (the latest earlier keyframe in this
+    // SAME field's own array, not touching the other two) — shown as the blank option's
+    // label so it's clear leaving it untouched still carries a value forward, rather
+    // than resetting to the workflow's own default. Only meaningful past #1.
+    const inherited = isFirst ? null : _resolveKeyframe(col, kf.atIteration - 1);
+
     let valueFieldsHtml = "";
     if (col === "checkpoint" || col === "vae") {
         const options = comfyEditor.models[col === "checkpoint" ? "checkpoints" : "vaes"] || [];
@@ -458,10 +776,39 @@ function _openCellModal(col, idx) {
                     <button type="button" class="wfm-search-clear-btn" id="wfm-lab-modal-value-filter-clear" title="Clear search">✕</button>
                 </div>
                 <select id="wfm-lab-modal-value" class="wfm-select">
-                    <option value="">${t("labUseWorkflowDefault")}</option>
+                    <option value="">${escapeHtml(inherited ? t("labInheritsFrom", inherited) : t("labUseWorkflowDefault"))}</option>
                     ${options.map((m) => `<option value="${escapeHtml(m)}" ${m === effectiveValue ? "selected" : ""}>${escapeHtml(m)}</option>`).join("")}
                 </select>
             </div>`;
+    } else if (col === "lora") {
+        const v = _normalizeLoraValue(effectiveValue);
+        const options = comfyEditor.models.loras || [];
+        valueFieldsHtml = `
+            <div class="wfm-lab-modal-row">
+                <label>LoRA</label>
+                <div class="wfm-search-wrap" style="width:100%;">
+                    <input type="text" id="wfm-lab-modal-lora-filter" class="wfm-input wfm-search-input" placeholder="Filter...">
+                    <button type="button" class="wfm-search-clear-btn" id="wfm-lab-modal-lora-filter-clear" title="Clear search">✕</button>
+                </div>
+                <select id="wfm-lab-modal-lora-select" class="wfm-select">
+                    <option value="">${escapeHtml(inherited?.name ? t("labInheritsFrom", inherited.name) : t("labUseWorkflowDefault"))}</option>
+                    ${options.map((m) => `<option value="${escapeHtml(m)}" ${m === v.name ? "selected" : ""}>${escapeHtml(m)}</option>`).join("")}
+                </select>
+                <div class="wfm-lora-strength-single" style="margin-top:6px;">
+                    <span>M</span>
+                    <input type="number" class="wfm-input" id="wfm-lab-modal-lora-strmodel" value="${v.strengthModel}" step="0.05" min="0" max="2" ${v.nodeBypass ? "disabled" : ""}>
+                    <span>C</span>
+                    <input type="number" class="wfm-input" id="wfm-lab-modal-lora-strclip" value="${v.strengthClip}" step="0.05" min="0" max="2" ${v.nodeBypass ? "disabled" : ""}>
+                </div>
+                <div class="wfm-lab-modal-checkbox" style="margin-top:6px;">
+                    <input type="checkbox" id="wfm-lab-modal-lora-nodebypass" ${v.nodeBypass ? "checked" : ""}>
+                    <label for="wfm-lab-modal-lora-nodebypass">${t("labLoraNodeBypassCheckboxLabel")}</label>
+                </div>
+                <div class="wfm-lab-modal-checkbox">
+                    <input type="checkbox" id="wfm-lab-modal-lora-prompttoggle" ${v.applyToPrompt ? "checked" : ""} ${v.nodeBypass ? "disabled" : ""}>
+                    <label for="wfm-lab-modal-lora-prompttoggle">${t("labLoraApplyToPromptCheckboxLabel")}</label>
+                </div>
+            </div>`;
     } else if (col === "prompt") {
         const v = effectiveValue || {};
         valueFieldsHtml = `
@@ -555,6 +902,7 @@ function _openCellModal(col, idx) {
     openModal(`${col} #${idx + 1}`, html);
     if (col === "prompt") _wireLabPromptModalExtras(idx);
     if (col === "checkpoint" || col === "vae") _wireLabValueFilter(col);
+    if (col === "lora") _wireLabLoraFilter();
 
     // Bypass disables everything else on the modal (value fields AND the Revert
     // checkbox) — the two are mutually exclusive since bypass already means "ignore
@@ -563,6 +911,14 @@ function _openCellModal(col, idx) {
     const bypassCb = document.getElementById("wfm-lab-modal-bypass");
     const fieldsWrap = document.getElementById("wfm-lab-modal-fields");
     const revertRowEl = revertCb?.closest(".wfm-lab-modal-checkbox");
+    // LoRA-only: the node-bypass checkbox (force strength to 0 at apply-time — see
+    // _applyLabLoraToWorkflow) disables just the strength fields, independent of the
+    // row-level bypass/revert above (which disable everything in fieldsWrap instead).
+    const loraNodeBypassCb = document.getElementById("wfm-lab-modal-lora-nodebypass");
+    const loraStrModelEl = document.getElementById("wfm-lab-modal-lora-strmodel");
+    const loraStrClipEl = document.getElementById("wfm-lab-modal-lora-strclip");
+    // Bypassing the LoRA node makes "apply its syntax/triggers to the prompt" moot too.
+    const loraPromptCb = document.getElementById("wfm-lab-modal-lora-prompttoggle");
     const syncDisabled = () => {
         const bypassed = !!bypassCb?.checked;
         const fieldsDisabled = bypassed || !!revertCb?.checked;
@@ -570,14 +926,20 @@ function _openCellModal(col, idx) {
         fieldsWrap?.querySelectorAll("input,select,textarea").forEach((el) => { el.disabled = fieldsDisabled; });
         if (revertCb) revertCb.disabled = bypassed;
         if (revertRowEl) revertRowEl.style.opacity = bypassed ? "0.4" : "1";
+        if (!fieldsDisabled && loraNodeBypassCb?.checked) {
+            if (loraStrModelEl) loraStrModelEl.disabled = true;
+            if (loraStrClipEl) loraStrClipEl.disabled = true;
+            if (loraPromptCb) loraPromptCb.disabled = true;
+        }
     };
     revertCb?.addEventListener("change", syncDisabled);
     bypassCb?.addEventListener("change", syncDisabled);
+    loraNodeBypassCb?.addEventListener("change", syncDisabled);
     syncDisabled();
 
     document.getElementById("wfm-lab-modal-delete")?.addEventListener("click", () => {
         _lab.columns[col].splice(idx, 1);
-        _renderColumn(col);
+        _renderColumnForKey(col);
         closeModal();
     });
 
@@ -594,6 +956,15 @@ function _openCellModal(col, idx) {
         if (!revert && !bypassed) {
             if (col === "checkpoint" || col === "vae") {
                 value = document.getElementById("wfm-lab-modal-value")?.value || "";
+            } else if (col === "lora") {
+                const loraNodeBypassed = !!document.getElementById("wfm-lab-modal-lora-nodebypass")?.checked;
+                value = {
+                    name: document.getElementById("wfm-lab-modal-lora-select")?.value || "",
+                    strengthModel: parseFloat(document.getElementById("wfm-lab-modal-lora-strmodel")?.value) || 1.0,
+                    strengthClip: parseFloat(document.getElementById("wfm-lab-modal-lora-strclip")?.value) || 1.0,
+                    nodeBypass: loraNodeBypassed,
+                    applyToPrompt: !loraNodeBypassed && !!document.getElementById("wfm-lab-modal-lora-prompttoggle")?.checked,
+                };
             } else if (col === "prompt") {
                 value = {
                     positive: document.getElementById("wfm-lab-modal-positive")?.value || "",
@@ -619,7 +990,7 @@ function _openCellModal(col, idx) {
 
         _lab.columns[col][idx] = { atIteration, value, revertToBase: revert, bypassed };
         _lab.columns[col].sort((a, b) => a.atIteration - b.atIteration);
-        _renderColumn(col);
+        _renderColumnForKey(col);
         closeModal();
     });
 }
@@ -662,6 +1033,26 @@ function _wireLabValueFilter(col) {
     setupSearchClearBtn("wfm-lab-modal-value-filter", "wfm-lab-modal-value-filter-clear", applyFilter);
 }
 
+// Same filter behavior as _wireLabValueFilter, for the LoRA modal's own select (it
+// has separate element ids since a LoRA keyframe row also carries Strength M/C
+// inputs alongside it, unlike the plain single-select Checkpoint/VAE modal).
+function _wireLabLoraFilter() {
+    const filterInput = document.getElementById("wfm-lab-modal-lora-filter");
+    const select = document.getElementById("wfm-lab-modal-lora-select");
+    if (!filterInput || !select) return;
+    const options = comfyEditor.models.loras || [];
+
+    const applyFilter = () => {
+        const filter = filterInput.value.toLowerCase();
+        select.innerHTML = `<option value="">${t("labUseWorkflowDefault")}</option>` +
+            options.filter((m) => m.toLowerCase().includes(filter))
+                .map((m) => `<option value="${escapeHtml(m)}">${escapeHtml(m)}</option>`).join("");
+    };
+
+    filterInput.addEventListener("input", applyFilter);
+    setupSearchClearBtn("wfm-lab-modal-lora-filter", "wfm-lab-modal-lora-filter-clear", applyFilter);
+}
+
 // Wires the Prompt-column modal's extra actions: fetch the live GenerateUI prompt,
 // extract a prompt from a dropped image/workflow file, copy the previous keyframe's
 // prompt, clear the fields, and apply/toggle a saved Style. idx is this keyframe's own
@@ -795,10 +1186,10 @@ function _resolveKeyframe(col, iteration) {
     return _isEmptyValue(col, applicable.value) ? null : applicable.value;
 }
 
-function _buildWorkflowForIteration(iteration, chainImageRef) {
+function _buildWorkflowForIteration(iteration, chainImageRef, prevLoraInjection) {
     const wf = JSON.parse(JSON.stringify(comfyUI.currentWorkflow || {}));
     const analysis = comfyUI.currentAnalysis;
-    if (!analysis) return { workflow: wf, seed: null };
+    if (!analysis) return { workflow: wf, seed: null, loraInjection: null };
 
     const write = (nodeId, key, val) => {
         if (nodeId != null && wf[nodeId]) wf[nodeId].inputs[key] = val;
@@ -825,14 +1216,35 @@ function _buildWorkflowForIteration(iteration, chainImageRef) {
         }
     }
 
+    const lora = _resolveKeyframe("lora", iteration);
+    if (lora?.name) {
+        _applyLabLoraToWorkflow(wf, analysis, lora);
+    }
+
     const prompt = _resolveKeyframe("prompt", iteration);
+    const posNode = analysis.prompt_nodes?.find((n) => n.role === "positive");
+    const negNode = analysis.prompt_nodes?.find((n) => n.role === "negative");
     if (prompt) {
-        const posNode = analysis.prompt_nodes?.find((n) => n.role === "positive");
-        const negNode = analysis.prompt_nodes?.find((n) => n.role === "negative");
         if (posNode && prompt.positive) write(posNode.id, posNode.textKey || "text", prompt.positive);
         if (negNode && prompt.negative) write(negNode.id, negNode.textKey || "text", prompt.negative);
     }
 
+    // LoRA syntax/trigger-word injection into the Positive prompt (opt-in per keyframe
+    // via lora.applyToPrompt). Always strip whatever the previous iteration injected
+    // first, whether or not this iteration re-injects — so switching to a different
+    // LoRA (or a keyframe with the option off) starts from a clean prompt instead of
+    // accumulating every LoRA the batch has used so far. See _stripLoraPromptInjection.
+    let loraInjection = null;
+    if (posNode && wf[posNode.id]) {
+        const textKey = posNode.textKey || "text";
+        let posText = _stripLoraPromptInjection(wf[posNode.id].inputs[textKey] || "", prevLoraInjection);
+        if (lora?.name && lora.applyToPrompt && !lora.nodeBypass) {
+            loraInjection = _buildLoraPromptInjection(lora);
+            posText = posText ? `${posText}, ${loraInjection}` : loraInjection;
+        }
+        write(posNode.id, textKey, posText);
+    }
+
     let seed = null;
     const ks = _resolveKeyframe("ksampler", iteration);
     const sampler = analysis.sampler_nodes?.[0];
@@ -850,7 +1262,108 @@ function _buildWorkflowForIteration(iteration, chainImageRef) {
         write(analysis.load_image_nodes[0].id, "image", imageRef);
     }
 
-    return { workflow: wf, seed };
+    return { workflow: wf, seed, loraInjection };
+}
+
+// True once a "no LoRA node in this workflow" warning has already been shown during the
+// current Run — reset at the start of _runLabBatch so the toast fires at most once per
+// run instead of once per iteration.
+let _labLoraWarned = false;
+
+// metadata.json (sha256 lookup) + CivitAI cache (sha256 -> trainedWords), fetched once
+// per Run (see _runLabBatch) and used only to resolve trigger words for the Positive-
+// prompt LoRA injection below — same two endpoints comfyui-editor.js's renderLoraPane
+// uses for the GenerateUI Model tab's own LORA SYNTAX/TRIGGER WORDS display.
+let _labModelMetadata = {};
+let _labCivitaiCache = {};
+
+async function _fetchLabLoraMetadataCache() {
+    try {
+        const [metaRes, civRes] = await Promise.all([
+            fetch("/api/wfm/models/metadata"),
+            fetch("/api/wfm/models/civitai/cache"),
+        ]);
+        _labModelMetadata = metaRes.ok ? await metaRes.json() : {};
+        _labCivitaiCache = civRes.ok ? await civRes.json() : {};
+    } catch {
+        _labModelMetadata = {};
+        _labCivitaiCache = {};
+    }
+}
+
+function _labTriggerWordsFor(loraName) {
+    const sha = (_labModelMetadata[loraName] || {}).sha256;
+    const civInfo = sha && _labCivitaiCache[sha];
+    return civInfo?.trainedWords || [];
+}
+
+// Builds the exact "<lora:stem:m:c>[, trigger, words...]" string appended to the
+// Positive prompt for this LoRA keyframe — the same format comfyui-editor.js's Single
+// Apply writes. Returned as-is so _buildWorkflowForIteration can hand it back unchanged
+// to the next iteration for removal (see _stripLoraPromptInjection).
+function _buildLoraPromptInjection(lora) {
+    const stem = lora.name.replace(/\\/g, "/").split("/").pop().replace(/\.[^.]+$/, "");
+    const strModel = lora.strengthModel ?? 1.0;
+    const strClip = lora.strengthClip ?? 1.0;
+    const syntax = `<lora:${stem}:${strModel}:${strClip}>`;
+    const triggers = _labTriggerWordsFor(lora.name);
+    return triggers.length > 0 ? `${syntax}, ${triggers.join(", ")}` : syntax;
+}
+
+// Removes an exact previously-injected string (as returned by _buildLoraPromptInjection)
+// from a prompt, plus the comma that joined it to whatever came before — so switching
+// keyframes/LoRAs mid-batch never accumulates every LoRA the batch has used so far.
+// A plain string search (no regex) is safe here since `injection` is always something
+// _buildLoraPromptInjection generated, never arbitrary user text.
+function _stripLoraPromptInjection(text, injection) {
+    if (!injection || !text) return text || "";
+    let out = text.split(`, ${injection}`).join("");
+    out = out.split(injection).join("");
+    return out.replace(/,\s*,/g, ",").replace(/^\s*,\s*/, "").replace(/\s*,\s*$/, "").trim();
+}
+
+// Applies a Lab-configured LoRA (Single only — no Stack support here) to the given
+// per-iteration workflow clone. Mirrors models-tab.js's applyToGenUI LoRA branch: prefer
+// analysis.lora_nodes[0] (correctly picks up "Lora Loader (LoraManager)" nodes via
+// is_lora_manager), else fall back to any node with a lora_name input. If the workflow
+// has no LoRA node at all, this warns once and does nothing — unlike VAE above, Lab does
+// NOT auto-inject a LoraLoader node, since that would require rewiring model/clip links
+// between the checkpoint and every downstream consumer, not just overwriting one input.
+function _applyLabLoraToWorkflow(wf, analysis, lora) {
+    let nodeId = analysis.lora_nodes?.[0]?.id;
+    let isLoraManager = !!analysis.lora_nodes?.[0]?.is_lora_manager;
+    if (nodeId == null) {
+        nodeId = Object.keys(wf).find((id) => wf[id]?.inputs && "lora_name" in wf[id].inputs);
+        isLoraManager = false;
+    }
+
+    if (nodeId == null || !wf[nodeId]) {
+        if (!_labLoraWarned) {
+            _labLoraWarned = true;
+            showToast(t("labLoraNoNode"), "warning");
+        }
+        return;
+    }
+
+    // nodeBypass: user-requested "bypass the LoRA node" toggle (Lab keyframe-level,
+    // independent of the row's own bypassed flag above). There's no real node to set
+    // mode:4 on here — currentWorkflow is already-converted API format (see
+    // comfyui-workflow.js convertUiToApi) where mode has no effect — so this forces
+    // strength to 0 instead, which is behaviorally equivalent to bypassing the node.
+    const bypass = !!lora.nodeBypass;
+    const strModel = bypass ? 0 : (lora.strengthModel ?? 1.0);
+    const strClip = bypass ? 0 : (lora.strengthClip ?? 1.0);
+    if (isLoraManager) {
+        const stem = lora.name.replace(/\\/g, "/").split("/").pop().replace(/\.[^.]+$/, "");
+        wf[nodeId].inputs.loras = {
+            __value__: [{ name: stem, strength: strModel, active: !bypass, expanded: false, clipStrength: strClip, locked: false }],
+        };
+        wf[nodeId].inputs.text = bypass ? "" : `<lora:${stem}:${strModel}:${strClip}>`;
+    } else {
+        wf[nodeId].inputs.lora_name = comfyEditor.resolveLoraName(lora.name);
+        wf[nodeId].inputs.strength_model = strModel;
+        wf[nodeId].inputs.strength_clip = strClip;
+    }
 }
 
 // ComfyUI's LoadImage accepts "name [type]" (optionally "subfolder/name [type]") to
@@ -860,6 +1373,20 @@ function _annotatedImageRef(img) {
     return `${rel} [${img.type || "output"}]`;
 }
 
+// Extracts the last frame of a just-generated video output and uploads it as a plain
+// "input" image, returning the filename LoadImage's image widget expects (no [type]
+// suffix needed — uploadImage always lands in the input folder). Used only by the
+// chain-image path below; the Video tab will reuse extractLastFrameBlob() directly
+// once it grows its own batch/chaining feature.
+async function _extractAndUploadLastFrame(img) {
+    const url = _resultImageUrl(img);
+    const blob = await extractLastFrameBlob(url);
+    const file = new File([blob], `lab_lastframe_${Date.now()}.png`, { type: "image/png" });
+    const result = await comfyUI.uploadImage(file, file.name);
+    if (!result.name) throw new Error("Upload returned no filename");
+    return result.name;
+}
+
 // ============================================
 // Batch execution
 // ============================================
@@ -893,16 +1420,23 @@ async function _runLabBatch() {
 
     _paused = false;
     _aborted = false;
+    _labLoraWarned = false;
     _lastGeneratedImageRef = null;
     _lab.results.images = [];
     _renderResultsGrid();
     _setRunUiState(true);
+    await _fetchLabLoraMetadataCache();
 
     const progressBar = document.getElementById("wfm-lab-progress-bar");
     const progressText = document.getElementById("wfm-lab-progress-text");
     const eagleAutoSave = getEagleSettings().autoSave;
+    // Video workflows (e.g. MiniMax H3) run far longer than a still image — use a longer
+    // timeout for every iteration so a multi-minute generation doesn't get force-rejected
+    // mid-batch (see comfyWorkflow.isVideoWorkflow and generate-tab.js's own use of this).
+    const isVideoWf = comfyWorkflow.isVideoWorkflow(comfyUI.currentAnalysis);
 
     let completed = 0, failed = 0;
+    let prevLoraInjection = null;
     try {
         for (let i = 1; i <= _lab.batchCount; i++) {
             if (_aborted) break;
@@ -911,13 +1445,15 @@ async function _runLabBatch() {
 
             if (progressText) progressText.textContent = `[${i}/${_lab.batchCount}] ...`;
             const chainRef = (!_lab.t2iMode && _lab.chainImage && i > 1) ? _lastGeneratedImageRef : null;
-            const { workflow, seed } = _buildWorkflowForIteration(i, chainRef);
+            const { workflow, seed, loraInjection } = _buildWorkflowForIteration(i, chainRef, prevLoraInjection);
+            prevLoraInjection = loraInjection;
             const workflowExpanded = await _expandWildcardsInWorkflow(workflow);
 
             try {
                 const { images } = await comfyUI.generate(workflowExpanded, {
                     seedMode: seed != null ? "fixed" : "random",
                     seedValue: seed ?? -1,
+                    timeoutMs: isVideoWf ? 30 * 60 * 1000 : undefined,
                     onProgress: (pct) => {
                         if (progressBar) progressBar.style.width = `${(pct * 100).toFixed(1)}%`;
                         if (progressText) progressText.textContent = `[${i}/${_lab.batchCount}] ${(pct * 100).toFixed(0)}%`;
@@ -925,7 +1461,20 @@ async function _runLabBatch() {
                 });
                 const outputImages = images.filter((img) => img.type !== "temp");
                 if (outputImages.length > 0) {
-                    _lastGeneratedImageRef = _annotatedImageRef(outputImages[0]);
+                    const first = outputImages[0];
+                    if (isVideoFilename(first.filename)) {
+                        // LoadImage can't take an mp4 directly — extract its last frame
+                        // client-side and upload that as an "input" image, so the chain
+                        // (Use generated image for next) can feed it back in as first_frame.
+                        try {
+                            _lastGeneratedImageRef = await _extractAndUploadLastFrame(first);
+                        } catch (err) {
+                            showToast(t("labLastFrameExtractFailed", err.message), "error");
+                            _lastGeneratedImageRef = null;
+                        }
+                    } else {
+                        _lastGeneratedImageRef = _annotatedImageRef(first);
+                    }
                 }
                 for (const img of outputImages) {
                     if (_lab.results.images.length < MAX_RESULTS) {
@@ -975,14 +1524,23 @@ function _renderResultsGrid() {
         grid.innerHTML = `<p class="wfm-placeholder">${t("labNoResultsYet")}</p>`;
         return;
     }
-    grid.innerHTML = _lab.results.images.map((img, i) => `
+    grid.innerHTML = _lab.results.images.map((img, i) => {
+        const url = _resultImageUrl(img);
+        const media = isVideoFilename(img.filename)
+            ? `<video class="wfm-lab-result-thumb" data-idx="${i}" src="${url}" muted></video>`
+            : `<img class="wfm-lab-result-thumb" data-idx="${i}" src="${url}">`;
+        return `
         <div class="wfm-lab-result-item">
             <span class="wfm-lab-result-index">${img.iteration}</span>
-            <img class="wfm-lab-result-thumb" data-idx="${i}" src="${_resultImageUrl(img)}">
-        </div>`).join("");
+            ${media}
+        </div>`;
+    }).join("");
     grid.querySelectorAll(".wfm-lab-result-thumb").forEach((el) => {
         el.addEventListener("click", () => {
-            openModal(t("labResultPreview"), `<img src="${el.src}" style="max-width:100%;max-height:80vh;display:block;margin:0 auto;">`);
+            const content = el.tagName === "VIDEO"
+                ? `<video src="${el.src}" controls style="max-width:100%;max-height:80vh;display:block;margin:0 auto;"></video>`
+                : `<img src="${el.src}" style="max-width:100%;max-height:80vh;display:block;margin:0 auto;">`;
+            openModal(t("labResultPreview"), content);
         });
     });
 }
@@ -1029,6 +1587,23 @@ function _loadImageEl(src) {
     });
 }
 
+// A result item's own URL (e.g. .mp4) can't be loaded into an <img> — extract its
+// last frame first so the index/contact-sheet gets a real thumbnail instead of
+// silently failing onto the canvas's black background (see _buildIndexImageDataUrl).
+async function _loadIndexCellImage(img) {
+    const url = _resultImageUrl(img);
+    if (!isVideoFilename(img.filename)) return _loadImageEl(url);
+    try {
+        const blob = await extractLastFrameBlob(url);
+        const blobUrl = URL.createObjectURL(blob);
+        const im = await _loadImageEl(blobUrl);
+        URL.revokeObjectURL(blobUrl);
+        return im;
+    } catch {
+        return null;
+    }
+}
+
 // planData, when given, is embedded into the resulting PNG as an iTXt chunk
 // (LAB_PLAN_PNG_KEY) so the image alone — dropped onto the Plan drop zone, from
 // anywhere, not just lab_plan/ — can be loaded back without needing a same-named
@@ -1048,7 +1623,7 @@ async function _buildIndexImageDataUrl(planData) {
     ctx.fillRect(0, 0, canvas.width, canvas.height);
 
     for (let i = 0; i < imgs.length; i++) {
-        const im = await _loadImageEl(_resultImageUrl(imgs[i]));
+        const im = await _loadIndexCellImage(imgs[i]);
         if (!im) continue;
         const col = i % cols, row = Math.floor(i / cols);
         const scale = Math.max(cellSize / im.width, cellSize / im.height);
@@ -1153,8 +1728,9 @@ function _applyPlanData(filename, data) {
     _lab.saveIndexOnRun = !!data.save_index_on_run;
     _lab.sourceImageFilename = data.source_image || null;
     _lab.workflowFilename = data.workflow_filename || null;
-    _lab.columns = data.columns || _emptyColumns();
+    _lab.columns = _migrateLabColumns(data.columns);
     _lab.results = { images: (data.results?.images || []).slice(0, MAX_RESULTS) };
+    _selectedIdx = {}; // every column's array was just replaced wholesale
     _updatePlanWorkflowUI();
     _updateT2IModeUI();
 
@@ -1234,6 +1810,7 @@ async function _loadPlanFromServer(filename) {
 function _clearPlan() {
     if (!window.confirm(t("labConfirmClear"))) return;
     _lab = _emptyLabState();
+    _selectedIdx = {};
 
     const noteEl = document.getElementById("wfm-lab-note");
     const batchEl = document.getElementById("wfm-lab-batch-count");

static/js/metadata-tab.js

@@ -97,6 +97,81 @@ export 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 = [];
@@ -203,7 +278,7 @@ function resolveEditPlusText(mode, textEdit, text1, text2) {
     }
 }
 
-function extractPrompts(wf) {
+export function extractPrompts(wf) {
     if (!wf || typeof wf !== "object") return { positives: [], negatives: [] };
     return Array.isArray(wf.nodes) ? extractPromptsLiteGraph(wf) : extractPromptsAPI(wf);
 }
@@ -459,6 +534,13 @@ function extractPromptsAPI(wf) {
             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".
@@ -607,6 +689,7 @@ 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;
@@ -644,6 +727,17 @@ export 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;
@@ -693,7 +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;
@@ -723,7 +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);
 }
@@ -790,6 +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);

static/js/models/badges.js

@@ -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")}">&times;</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")}">&times;</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();
+        });
+    });
+}

static/js/models/detail-panel.js

@@ -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;">&#9881; ${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="${t("modelsRemoveFromGroup")}">&times;</button>
+                      </div>`).join("")}
+            </div>
+            <div style="margin-bottom:12px;">
+                <div style="font-weight:600;font-size:13px;margin-bottom:6px;">${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 = "";
+    }
+}

static/js/models/filters.js

@@ -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;
+}

static/js/models/grid-view.js

@@ -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 ? "&#9733;" : "&#9734;";
+            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("&#9733;", "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);
+}

static/js/models/helpers.js

@@ -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;
+}

static/js/models/selection-bulk.js

@@ -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");
+    }
+}

static/js/models/state.js

@@ -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" },
+};

static/js/prompt-ai-chat.js

@@ -0,0 +1,359 @@
+/**
+ * Prompt Tab - AI Assistant (Ollama / LM Studio / Lemonade / Unsloth chat, translate, apply to GenerateUI)
+ */
+
+import { showToast } from "./app.js";
+import { t } from "./i18n.js";
+import { unslothProxy } from "./util.js";
+import { openAiBackendSettingsModal, getAiBackendConfig } from "./ai-settings-modal.js";
+
+// ============================================
+// State
+// ============================================
+
+const ollamaState = {
+    chatHistory: [],
+    attachedImage: null,
+};
+
+const PROMPT_AI_KEY = "wfm_prompt_ai_settings";
+
+export function getPromptAiConfig() {
+    return getAiBackendConfig(PROMPT_AI_KEY);
+}
+
+// Ollama-style messages (with .images) → OpenAI互換 content 配列に変換
+function _toOpenAiMessages(messages) {
+    return 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,${b64}` } })),
+                ],
+            };
+        }
+        return { role: msg.role, content: msg.content };
+    });
+}
+
+function _stripThinkingTags(text) {
+    return (text || "")
+        .replace(/<think>[\s\S]*?<\/think>/gi, "")
+        .replace(/<thinking>[\s\S]*?<\/thinking>/gi, "")
+        .trim();
+}
+
+// Ollamaは `think` (bool) と `options.num_predict`、OpenAI互換は `max_tokens` で制御
+function _applyGenOptions(body, backend, cfg) {
+    if (backend === "ollama") {
+        body.think = !!cfg.thinkingMode;
+        if (cfg.maxTokens > 0) body.options = { ...(body.options || {}), num_predict: cfg.maxTokens };
+    } else if (cfg.maxTokens > 0) {
+        body.max_tokens = cfg.maxTokens;
+    }
+    return body;
+}
+
+async function chatWithAi(model, messages) {
+    const cfg = getPromptAiConfig();
+    const { backend, url } = cfg;
+    if (backend === "ollama") {
+        const res = await fetch(`${url}/api/chat`, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify(_applyGenOptions({ model, messages, stream: false }, backend, cfg)),
+        });
+        if (!res.ok) throw new Error(`HTTP ${res.status}`);
+        const text = (await res.json()).message?.content || "";
+        return cfg.thinkingMode ? text : _stripThinkingTags(text);
+    } else if (backend === "unsloth") {
+        const body = _applyGenOptions({ model, messages: _toOpenAiMessages(messages), stream: false }, backend, cfg);
+        const data = await unslothProxy(url, "/v1/chat/completions", "POST", body);
+        const msg = data.choices?.[0]?.message;
+        // Unslothはreasoningを別フィールドで返すため<think>タグに畳んで表示に混ぜる
+        const text = (msg?.reasoning_content ? `<think>${msg.reasoning_content}</think>` : "") + (msg?.content || "");
+        return cfg.thinkingMode ? text : _stripThinkingTags(text);
+    } else {
+        // LM Studio / Lemonade (OpenAI-compatible)
+        const res = await fetch(`${url}/v1/chat/completions`, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify(_applyGenOptions({ model, messages: _toOpenAiMessages(messages), stream: false }, backend, cfg)),
+        });
+        if (!res.ok) throw new Error(`HTTP ${res.status}`);
+        const text = (await res.json()).choices?.[0]?.message?.content || "";
+        return cfg.thinkingMode ? text : _stripThinkingTags(text);
+    }
+}
+
+// ============================================
+// 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);
+}
+
+// ============================================
+// Status label (設定モーダルで選択中のbackend/modelを表示)
+// ============================================
+
+function _updateStatusLabel() {
+    const status = document.getElementById("wfm-ollama-status");
+    if (!status) return;
+    const { backend, model } = getPromptAiConfig();
+    status.textContent = model ? `${backend}: ${model}` : `${backend}: ${t("selectModelFirst")}`;
+    status.className = model ? "wfm-ollama-status connected" : "wfm-ollama-status error";
+}
+
+// ============================================
+// Send Message
+// ============================================
+
+async function sendMessage() {
+    const input = document.getElementById("wfm-ollama-input");
+    if (!input) return;
+
+    const message = input.value.trim();
+    if (!message) return;
+
+    const { model } = getPromptAiConfig();
+    if (!model) {
+        showToast(t("selectModelFirst"), "error");
+        return;
+    }
+
+    addChatMessage("user", message);
+    input.value = "";
+
+    const userMsg = { role: "user", content: message };
+    if (ollamaState.attachedImage) {
+        userMsg.images = [ollamaState.attachedImage.dataUrl.split(",")[1]];
+    }
+    ollamaState.chatHistory.push(userMsg);
+
+    const sendBtn = document.getElementById("wfm-ollama-send-btn");
+    if (sendBtn) sendBtn.disabled = true;
+
+    try {
+        const reply = await chatWithAi(model, ollamaState.chatHistory);
+        addChatMessage("assistant", reply);
+        ollamaState.chatHistory.push({ role: "assistant", content: reply });
+        ollamaState.attachedImage = null;
+        updateAttachmentDisplay();
+    } catch (err) {
+        showToast(t("error") + ": " + err.message, "error");
+    } finally {
+        if (sendBtn) sendBtn.disabled = false;
+    }
+}
+
+// ============================================
+// Translation
+// ============================================
+
+async function sendTranslate(direction) {
+    const input = document.getElementById("wfm-ollama-input");
+    if (!input) return;
+
+    const text = input.value.trim();
+    if (!text) return;
+
+    const { model } = getPromptAiConfig();
+    if (!model) {
+        showToast(t("selectModelFirst"), "error");
+        return;
+    }
+
+    const labels = { ja2en: "JA→EN", en2ja: "EN→JA", zh2en: "ZH→EN", en2zh: "EN→ZH" };
+    addChatMessage("user", `[${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() {
+    _updateStatusLabel();
+
+    // Settings modal
+    document.getElementById("wfm-ollama-settings-btn")?.addEventListener("click", () => {
+        openAiBackendSettingsModal(PROMPT_AI_KEY, t("settings"), _updateStatusLabel);
+    });
+
+    // 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"));
+}

static/js/prompt-presets.js

@@ -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, "&amp;").replace(/</g, "&lt;").replace(/>/g, "&gt;") : "";
+}
+
+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">${preset.favorite ? '<span style="color:#ffd700;">&#9733;</span> ' : ""}${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">&#9733;</button>
+            <button class="wfm-pm-action-btn pm-del-btn" title="Delete" style="color:var(--wfm-danger);">&#10005;</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();
+    });
+}

static/js/prompt-styles.js

@@ -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">&#9998;</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();
+}

static/js/prompt-wildcards.js

@@ -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">&#9998;</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 = "&#128194; 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();
+}

static/js/settings-tab.js

@@ -6,7 +6,7 @@ import { showToast } from "./app.js";
 import { comfyUI } from "./comfyui-client.js";
 import { t, getLang, getSummaryLang, setLang, setSummaryLang, getLanguageOptions, getSummaryLanguageOptions } from "./i18n.js";
 
-import { getSettings, readJsonStorage, getAiBackendDefaultUrl } from "./util.js";
+import { getSettings, readJsonStorage } from "./util.js";
 
 const SETTINGS_KEY = "wfm_settings";
 
@@ -336,6 +336,34 @@ export function applyTextareaFontSize(size) {
     el.textContent = `${TEXTAREA_FONT_SIZE_TARGETS} { font-size: ${px}px !important; }`;
 }
 
+// Video playback volume — shared by every <video> player in the app (Video tab preview,
+// Gallery detail/lightbox/compare). The browser default for a freshly created <video> element
+// is always 100%, and Gallery's players are recreated from scratch each time a different video
+// is opened, so without this the volume silently jumps back to max on every video switch.
+const VIDEO_VOLUME_DEFAULT = 1;
+
+export function getVideoVolume() {
+    const v = loadLocalSettings().videoVolume;
+    return typeof v === "number" && v >= 0 && v <= 1 ? v : VIDEO_VOLUME_DEFAULT;
+}
+
+function setVideoVolume(volume) {
+    const clamped = Math.max(0, Math.min(1, volume));
+    saveLocalSettings({ videoVolume: clamped });
+    return clamped;
+}
+
+// Applies the saved volume to a <video> element, and keeps it in sync with any further change
+// the user makes via that element's own native controls — so adjusting the volume on any video
+// player anywhere in the app is remembered for the next video opened anywhere else.
+export function applyStoredVideoVolume(videoEl) {
+    if (!videoEl) return;
+    videoEl.volume = getVideoVolume();
+    if (videoEl.dataset.wfmVolumeWired) return;
+    videoEl.dataset.wfmVolumeWired = "1";
+    videoEl.addEventListener("volumechange", () => setVideoVolume(videoEl.volume));
+}
+
 // JSON highlight color settings
 const JSON_COLOR_STYLE_ID = "wfm-json-color-style";
 
@@ -366,6 +394,21 @@ 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,\n.wfm-video-plan-content label.wfm-model-label-active { color: ${val}; }`;
+}
+
 export function applyTheme(themeId) {
     if (themeId) {
         document.documentElement.setAttribute("data-theme", themeId);
@@ -560,6 +603,24 @@ export async function initSettingsTab() {
             </div>
         </details>
 
+        <!-- Video Volume -->
+        <details class="wfm-settings-section" open>
+            <summary class="wfm-settings-summary">${t("videoVolumeLabel") || "Video Volume"}</summary>
+            <div class="wfm-form-group">
+                <label style="display:flex;align-items:center;justify-content:space-between;">
+                    <span>${t("videoVolumeLabel") || "Video Volume"}</span>
+                    <span id="wfm-video-volume-val" style="font-size:12px;color:var(--wfm-text-secondary);">${Math.round(getVideoVolume() * 100)}%</span>
+                </label>
+                <input type="range" class="wfm-range" id="wfm-video-volume"
+                    min="0" max="100" step="1"
+                    value="${Math.round(getVideoVolume() * 100)}"
+                    style="width:100%;margin-top:4px;">
+                <small style="color:var(--wfm-text-secondary);font-size:11px;display:block;margin-top:4px;">
+                    ${t("videoVolumeHint") || "Applies to: Video tab preview, Gallery detail/lightbox/compare video players"}
+                </small>
+            </div>
+        </details>
+
         <!-- RAW JSON Colors -->
         <details class="wfm-settings-section" open>
             <summary class="wfm-settings-summary">${t("jsonColorsSection") || "RAW JSON Colors"}</summary>
@@ -577,6 +638,21 @@ 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>
@@ -661,44 +737,6 @@ export async function initSettingsTab() {
             <button class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-settings-default-ckpt-save">${t("save")}</button>
         </details>
 
-        <!-- AI Assistant Settings (Prompt Tab) -->
-        <details class="wfm-settings-section">
-            <summary class="wfm-settings-summary">${t("ollamaSettings")}</summary>
-            <div class="wfm-form-group">
-                <label>${t("aiSettingsBackend")}</label>
-                <div class="wfm-ai-backend-row">
-                    <label class="wfm-ai-radio-label">
-                        <input type="radio" name="wfm-prompt-ai-backend" value="ollama"> Ollama
-                    </label>
-                    <label class="wfm-ai-radio-label">
-                        <input type="radio" name="wfm-prompt-ai-backend" value="lmstudio"> LM Studio
-                    </label>
-                    <label class="wfm-ai-radio-label">
-                        <input type="radio" name="wfm-prompt-ai-backend" value="lemonade"> Lemonade
-                    </label>
-                </div>
-            </div>
-            <div class="wfm-form-group">
-                <label>${t("ollamaUrl")}</label>
-                <input type="text" class="wfm-input" id="wfm-settings-prompt-ai-url"
-                    placeholder="http://localhost:11434">
-            </div>
-            <div class="wfm-form-group">
-                <label>${t("ollamaDefaultModel")}</label>
-                <div style="display:flex;gap:8px;">
-                    <select class="wfm-select" id="wfm-settings-prompt-ai-model" style="flex:1;">
-                        <option value="">${t("selectModel")}</option>
-                    </select>
-                    <button class="wfm-btn wfm-btn-sm" id="wfm-settings-prompt-ai-refresh">${t("refresh")}</button>
-                </div>
-            </div>
-            <div style="display:flex;gap:8px;margin-top:8px;">
-                <button class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-settings-prompt-ai-save">${t("saveOllama")}</button>
-                <button class="wfm-btn wfm-btn-sm" id="wfm-settings-prompt-ai-test">${t("testConnection")}</button>
-                <span id="wfm-settings-prompt-ai-status" style="font-size:12px;line-height:28px;"></span>
-            </div>
-        </details>
-
         <!-- CivitAI Settings -->
         <details class="wfm-settings-section">
             <summary class="wfm-settings-summary">${t("civitaiApiKeySetting")}</summary>
@@ -794,6 +832,25 @@ export async function initSettingsTab() {
                     <input type="file" id="wfm-settings-import-file" accept=".json" style="display:none;">
                 </label>
             </div>
+            <p style="font-size:12px;color:var(--wfm-text-secondary);margin:12px 0 8px 0;">${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>
 
@@ -1109,92 +1166,6 @@ export async function initSettingsTab() {
         }
     });
 
-    // --- AI Assistant (Prompt Tab) settings ---
-    const PROMPT_AI_KEY = "wfm_prompt_ai_settings";
-    const loadPromptAiCfg = () => {
-        try { return JSON.parse(localStorage.getItem(PROMPT_AI_KEY) || "{}"); } catch { return {}; }
-    };
-    const savePromptAiCfg = (patch) => {
-        const data = { ...loadPromptAiCfg(), ...patch };
-        localStorage.setItem(PROMPT_AI_KEY, JSON.stringify(data));
-    };
-
-    // Restore saved values into UI
-    const promptAiSaved = loadPromptAiCfg();
-    const promptAiBackendSaved = promptAiSaved.backend || "ollama";
-    document.querySelectorAll('input[name="wfm-prompt-ai-backend"]').forEach(r => {
-        r.checked = r.value === promptAiBackendSaved;
-    });
-    const promptAiUrlEl = document.getElementById("wfm-settings-prompt-ai-url");
-    if (promptAiUrlEl) {
-        promptAiUrlEl.value = promptAiSaved.backendUrl || "";
-        promptAiUrlEl.placeholder = getAiBackendDefaultUrl(promptAiBackendSaved);
-    }
-
-    // Backend change → switch URL to the new backend's default
-    document.querySelectorAll('input[name="wfm-prompt-ai-backend"]').forEach(r => {
-        r.addEventListener("change", () => {
-            const backend = document.querySelector('input[name="wfm-prompt-ai-backend"]:checked')?.value || "ollama";
-            if (promptAiUrlEl) promptAiUrlEl.value = getAiBackendDefaultUrl(backend);
-        });
-    });
-
-    // Load models directly from the selected backend
-    async function loadPromptAiModels() {
-        const select = document.getElementById("wfm-settings-prompt-ai-model");
-        if (!select) return;
-        const backend = document.querySelector('input[name="wfm-prompt-ai-backend"]:checked')?.value || "ollama";
-        const url = (promptAiUrlEl?.value.trim() || getAiBackendDefaultUrl(backend)).replace(/\/$/, "");
-        try {
-            let models = [];
-            if (backend === "ollama") {
-                const res = await fetch(`${url}/api/tags`);
-                models = (await res.json()).models?.map(m => m.name) || [];
-            } else {
-                const res = await fetch(`${url}/v1/models`);
-                models = ((await res.json()).data || []).map(m => m.id);
-            }
-            const saved = loadPromptAiCfg().model || "";
-            select.innerHTML = models.length
-                ? models.map(name => `<option value="${name}" ${name === saved ? "selected" : ""}>${name}</option>`).join("")
-                : `<option value="">${t("noModelsFound")}</option>`;
-        } catch {
-            select.innerHTML = `<option value="">${t("failedLoadModels")}</option>`;
-        }
-    }
-    await loadPromptAiModels();
-
-    document.getElementById("wfm-settings-prompt-ai-refresh")?.addEventListener("click", loadPromptAiModels);
-
-    document.getElementById("wfm-settings-prompt-ai-test")?.addEventListener("click", async () => {
-        const statusEl = document.getElementById("wfm-settings-prompt-ai-status");
-        const backend = document.querySelector('input[name="wfm-prompt-ai-backend"]:checked')?.value || "ollama";
-        const url = (promptAiUrlEl?.value.trim() || getAiBackendDefaultUrl(backend)).replace(/\/$/, "");
-        if (statusEl) { statusEl.textContent = t("aiStatusConnecting"); statusEl.style.color = "var(--wfm-text-secondary)"; }
-        try {
-            let count = 0;
-            if (backend === "ollama") {
-                const res = await fetch(`${url}/api/tags`);
-                count = ((await res.json()).models || []).length;
-            } else {
-                const res = await fetch(`${url}/v1/models`);
-                count = ((await res.json()).data || []).length;
-            }
-            if (statusEl) { statusEl.textContent = `${t("ollamaConnected")} (${count} models)`; statusEl.style.color = "var(--wfm-success)"; }
-            await loadPromptAiModels();
-        } catch (err) {
-            if (statusEl) { statusEl.textContent = `${t("ollamaFailed")}: ${err.message}`; statusEl.style.color = "var(--wfm-danger)"; }
-        }
-    });
-
-    document.getElementById("wfm-settings-prompt-ai-save")?.addEventListener("click", () => {
-        const backend = document.querySelector('input[name="wfm-prompt-ai-backend"]:checked')?.value || "ollama";
-        const url = promptAiUrlEl?.value.trim() || "";
-        const model = document.getElementById("wfm-settings-prompt-ai-model")?.value || "";
-        savePromptAiCfg({ backend, backendUrl: url, model });
-        showToast(t("ollamaSaved"), "success");
-    });
-
     // CivitAI host select
     document.getElementById("wfm-settings-civitai-host")?.addEventListener("change", async (e) => {
         const host = e.target.value;
@@ -1312,6 +1283,16 @@ export async function initSettingsTab() {
         applyTextareaFontSize(px);
     });
 
+    // --- Video volume slider (auto-saves immediately, no "Save" click needed — a dragged
+    // slider that silently didn't persist would defeat the whole point of this setting) ---
+    document.getElementById("wfm-video-volume")?.addEventListener("input", (e) => {
+        const pct = parseInt(e.target.value) || 0;
+        const valEl = document.getElementById("wfm-video-volume-val");
+        if (valEl) valEl.textContent = `${pct}%`;
+        const volume = setVideoVolume(pct / 100);
+        document.querySelectorAll("video").forEach((v) => { v.volume = volume; });
+    });
+
     // --- JSON highlight color pickers ---
     document.getElementById("wfm-json-color-grid")?.addEventListener("input", (e) => {
         if (e.target.type !== "color") return;
@@ -1333,6 +1314,22 @@ 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 +1483,53 @@ 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

static/js/tagger-tab.js

@@ -1,10 +1,16 @@
 /**
  * Tagger Tab
- * Handles WD Tagger / DeepDanbooru + Ollama VLM single/batch tagging.
+ * Handles WD Tagger / DeepDanbooru + VLM (Ollama/LM Studio/Lemonade/Unsloth) single/batch tagging.
  */
 
 import { t } from "./i18n.js";
 import { showToast } from "./app.js";
+import { openAiBackendSettingsModal, getAiBackendConfig } from "./ai-settings-modal.js";
+
+const TAGGER_AI_KEY = "wfm_tagger_ai_settings";
+function getTaggerAiConfig() {
+    return getAiBackendConfig(TAGGER_AI_KEY);
+}
 
 // ── 状態 ────────────────────────────────────────────────────────
 
@@ -32,7 +38,7 @@ export function initTaggerTab() {
     _setupBatchActions();
     _setupDbActions();
     _loadModels();
-    _loadSettings();
+    _updateVlmBackendIndicators();
 }
 
 /** GalleryタブからTaggerタブへ画像を渡す */
@@ -74,8 +80,6 @@ function _applyI18n() {
         "wfm-tagger-char-threshold-label": "taggerCharThreshold",
         "wfm-tagger-generate-btn": "taggerGenerateTags",
         "wfm-tagger-ollama-header-label": "taggerOllamaEnable",
-        "wfm-tagger-ollama-url-label": "taggerOllamaUrl",
-        "wfm-tagger-ollama-model-label": "taggerOllamaModel",
         "wfm-tagger-ollama-prompt-label": "taggerOllamaPrompt",
         "wfm-tagger-ollama-max-label": "taggerOllamaMaxTags",
         "wfm-tagger-result-header": "taggerResult",
@@ -87,8 +91,7 @@ function _applyI18n() {
         "wfm-tagger-batch-folder-header": "taggerBatchFolder",
         "wfm-tagger-batch-threshold-label": "taggerThreshold",
         "wfm-tagger-batch-char-threshold-label": "taggerCharThreshold",
-        "wfm-tagger-batch-url-label": "taggerOllamaUrl",
-        "wfm-tagger-batch-model-label": "taggerOllamaModel",
+        "wfm-tagger-batch-ollama-header-label": "taggerOllamaEnable",
         "wfm-tagger-batch-prompt-label": "taggerOllamaPrompt",
         "wfm-tagger-batch-output-header": "taggerBatchOutput",
         "wfm-tagger-batch-save-db-label": "taggerSaveToDB",
@@ -107,12 +110,18 @@ function _applyI18n() {
         "wfm-tagger-db-edit-vlm-label": "taggerDbVlm",
         "wfm-tagger-db-save-btn": "taggerDbSave",
         "wfm-tagger-db-delete-btn": "taggerDbDelete",
+        "wfm-tagger-db-delete-selected-btn": "taggerDbDelete"
     };
     for (const [id, key] of Object.entries(map)) {
         const el = document.getElementById(id);
         if (el) el.textContent = t(key);
     }
 
+    ["wfm-tagger-ollama-settings-btn", "wfm-tagger-batch-ollama-settings-btn"].forEach(id => {
+        const el = document.getElementById(id);
+        if (el) el.title = t("settings");
+    });
+
     // サブタブボタン
     const subtabKeys = ["taggerSingleTab", "taggerBatchTab", "taggerDbTab"];
     document.querySelectorAll(".wfm-tagger-subtab-btn").forEach((btn, i) => {
@@ -168,6 +177,24 @@ function _setupOllamaToggle() {
         });
         wrap.style.display = chk.checked ? "" : "none";
     }
+
+    document.getElementById("wfm-tagger-ollama-settings-btn")?.addEventListener("click", () => {
+        openAiBackendSettingsModal(TAGGER_AI_KEY, t("settings"), _updateVlmBackendIndicators);
+    });
+    document.getElementById("wfm-tagger-batch-ollama-settings-btn")?.addEventListener("click", () => {
+        openAiBackendSettingsModal(TAGGER_AI_KEY, t("settings"), _updateVlmBackendIndicators);
+    });
+}
+
+// ── VLMバックエンド表示 (Taggerタブ独自のAI設定を参照) ────────────
+
+function _updateVlmBackendIndicators() {
+    const { backend, model } = getTaggerAiConfig();
+    const label = `${backend}: ${model || t("taggerNoModel")}`;
+    const singleEl = document.getElementById("wfm-tagger-ollama-backend-value");
+    if (singleEl) singleEl.textContent = label;
+    const batchEl = document.getElementById("wfm-tagger-batch-backend-value");
+    if (batchEl) batchEl.textContent = label;
 }
 
 // ── モデル一覧ロード ─────────────────────────────────────────────
@@ -188,61 +215,6 @@ async function _loadModels() {
     }
 }
 
-// ── Ollamaモデル一覧ロード ───────────────────────────────────────
-
-async function _loadOllamaModels(urlId, selId) {
-    const urlEl = document.getElementById(urlId);
-    const selEl = document.getElementById(selId);
-    if (!urlEl || !selEl) return;
-    const api = urlEl.value || "http://127.0.0.1:11434";
-    try {
-        const res = await fetch(`/wfm/tagger/ollama/models?api_url=${encodeURIComponent(api)}`);
-        const data = await res.json();
-        const models = data.models || [];
-        if (models.length === 0) {
-            selEl.innerHTML = `<option value="">(${t("taggerNoModel")})</option>`;
-        } else {
-            selEl.innerHTML = models.map(m => `<option value="${m}">${m}</option>`).join("");
-        }
-    } catch {
-        selEl.innerHTML = `<option value="">(${t("taggerNoModel")})</option>`;
-    }
-}
-
-// ── 設定の読み込み/保存 ─────────────────────────────────────────
-
-async function _loadSettings() {
-    try {
-        const res = await fetch("/wfm/tagger/settings");
-        const data = await res.json();
-        if (data.ollama_url) {
-            const el = document.getElementById("wfm-tagger-ollama-url");
-            if (el) el.value = data.ollama_url;
-        }
-        if (data.batch_ollama_url) {
-            const el = document.getElementById("wfm-tagger-batch-ollama-url");
-            if (el) el.value = data.batch_ollama_url;
-        }
-    } catch {
-        // 初回は無視
-    }
-}
-
-async function _saveSettings() {
-    try {
-        await fetch("/wfm/tagger/settings", {
-            method: "POST",
-            headers: { "Content-Type": "application/json" },
-            body: JSON.stringify({
-                ollama_url: document.getElementById("wfm-tagger-ollama-url")?.value || "",
-                batch_ollama_url: document.getElementById("wfm-tagger-batch-ollama-url")?.value || "",
-            }),
-        });
-    } catch {
-        // サイレントに失敗
-    }
-}
-
 // ── Single サブタブ ─────────────────────────────────────────────
 
 function _setupSingleActions() {
@@ -279,11 +251,6 @@ function _setupSingleActions() {
     // タグ生成
     document.getElementById("wfm-tagger-generate-btn")?.addEventListener("click", _generateTags);
 
-    // Ollamaリフレッシュ
-    document.getElementById("wfm-tagger-ollama-refresh-btn")?.addEventListener("click", () => {
-        _loadOllamaModels("wfm-tagger-ollama-url", "wfm-tagger-ollama-model");
-    });
-
     // 結果アクション
     document.getElementById("wfm-tagger-send-genui-btn")?.addEventListener("click", _sendToGenUI);
     document.getElementById("wfm-tagger-send-prompt-btn")?.addEventListener("click", _sendToPrompt);
@@ -321,8 +288,7 @@ async function _generateTags() {
     const threshold = parseFloat(document.getElementById("wfm-tagger-threshold")?.value || "0.35");
     const charThreshold = parseFloat(document.getElementById("wfm-tagger-char-threshold")?.value || "0.85");
     const useOllama = document.getElementById("wfm-tagger-ollama-enable")?.checked || false;
-    const ollamaUrl = document.getElementById("wfm-tagger-ollama-url")?.value || "http://127.0.0.1:11434";
-    const ollamaModel = document.getElementById("wfm-tagger-ollama-model")?.value || "";
+    const { backend: vlmBackend, url: vlmUrl, model: ollamaModel, thinkingMode, maxTokens } = getTaggerAiConfig();
     const ollamaPrompt = document.getElementById("wfm-tagger-ollama-prompt")?.value || "";
     const maxTags = parseInt(document.getElementById("wfm-tagger-ollama-max-tags")?.value || "40");
 
@@ -344,12 +310,13 @@ async function _generateTags() {
         }
 
         if (useOllama && ollamaModel) {
-            const res2 = await fetch("/wfm/tagger/ollama/predict", {
+            const res2 = await fetch("/wfm/tagger/vlm/predict", {
                 method: "POST",
                 headers: { "Content-Type": "application/json" },
                 body: JSON.stringify({
-                    image_b64: state.imageB64, api_url: ollamaUrl,
+                    image_b64: state.imageB64, backend: vlmBackend, api_url: vlmUrl,
                     model: ollamaModel, prompt: ollamaPrompt, max_tags: maxTags,
+                    thinking_mode: thinkingMode, max_tokens: maxTokens,
                 }),
             });
             const data2 = await res2.json();
@@ -361,7 +328,6 @@ async function _generateTags() {
         if (!tags) { showToast(t("taggerNoTags"), "warning"); return; }
         document.getElementById("wfm-tagger-result").value = tags;
         showToast(t("taggerDone"), "success");
-        _saveSettings();
     } catch (e) {
         showToast(e.message, "error");
     } finally {
@@ -453,10 +419,6 @@ async function _saveToDb() {
 // ── Batch サブタブ ───────────────────────────────────────────────
 
 function _setupBatchActions() {
-    document.getElementById("wfm-tagger-batch-ollama-refresh-btn")?.addEventListener("click", () => {
-        _loadOllamaModels("wfm-tagger-batch-ollama-url", "wfm-tagger-batch-ollama-model");
-    });
-
     document.getElementById("wfm-tagger-batch-start-btn")?.addEventListener("click", _batchStart);
     document.getElementById("wfm-tagger-batch-stop-btn")?.addEventListener("click", _batchStop);
 }
@@ -465,15 +427,19 @@ async function _batchStart() {
     const folder = document.getElementById("wfm-tagger-batch-folder")?.value || "";
     if (!folder) { showToast(t("taggerBatchFolder"), "warning"); return; }
 
+    const { backend: vlmBackend, url: vlmUrl, model: vlmModel, thinkingMode, maxTokens } = getTaggerAiConfig();
     const body = {
         folder,
         model: document.getElementById("wfm-tagger-batch-model")?.value || "",
         threshold: parseFloat(document.getElementById("wfm-tagger-batch-threshold")?.value || "0.35"),
         char_threshold: parseFloat(document.getElementById("wfm-tagger-batch-char-threshold")?.value || "0.85"),
         use_ollama: document.getElementById("wfm-tagger-batch-ollama-enable")?.checked || false,
-        ollama_api: document.getElementById("wfm-tagger-batch-ollama-url")?.value || "http://127.0.0.1:11434",
-        ollama_model: document.getElementById("wfm-tagger-batch-ollama-model")?.value || "",
+        vlm_backend: vlmBackend,
+        vlm_api_url: vlmUrl,
+        ollama_model: vlmModel,
         ollama_prompt: document.getElementById("wfm-tagger-batch-ollama-prompt")?.value || "",
+        thinking_mode: thinkingMode,
+        max_tokens: maxTokens,
         save_db: document.getElementById("wfm-tagger-batch-save-db")?.checked ?? true,
         write_file: document.getElementById("wfm-tagger-batch-write-file")?.checked || false,
         write_txt: document.getElementById("wfm-tagger-batch-write-txt")?.checked || false,
@@ -549,6 +515,8 @@ function _setupDbActions() {
     });
     document.getElementById("wfm-tagger-db-save-btn")?.addEventListener("click", _dbSave);
     document.getElementById("wfm-tagger-db-delete-btn")?.addEventListener("click", _dbDelete);
+    document.getElementById("wfm-tagger-db-select").addEventListener("click", _dbToggleSelection);
+    document.getElementById("wfm-tagger-db-delete-selected-btn").addEventListener("click", _dbDeleteSelected);
 }
 
 async function _dbLoad() {
@@ -576,12 +544,20 @@ async function _dbSearch() {
 function _renderDbTable(rows) {
     const tbody = document.getElementById("wfm-tagger-db-tbody");
     if (!tbody) return;
+
     if (!rows.length) {
-        tbody.innerHTML = `<tr><td colspan="5" style="text-align:center;padding:20px;color:var(--wfm-text-secondary)">${t("taggerDbEmpty")}</td></tr>`;
+        tbody.innerHTML = `<tr><td colspan="6" style="text-align:center;padding:20px;color:var(--wfm-text-secondary)">${t("taggerDbEmpty")}</td></tr>`;
         return;
     }
+
     tbody.innerHTML = rows.map(row => `
         <tr data-id="${row.id}" title="${_esc(row.all_tags)}">
+            <td class="wfm-tagger-db-select">
+                <input
+                    type="checkbox"
+                    data-id="${row.id}"
+                >
+            </td>
             <td>${row.id}</td>
             <td>${_esc(row.filename)}</td>
             <td>${_esc(_truncate(row.interrogator_tags, 60))}</td>
@@ -591,9 +567,13 @@ function _renderDbTable(rows) {
     `).join("");
 
     tbody.querySelectorAll("tr").forEach(tr => {
-        tr.addEventListener("click", () => {
+        tr.addEventListener("click", e => {
+            // チェックボックスをクリックした場合は行選択を発生させない
+            if (e.target.closest(".wfm-tagger-db-select")) return;
+
             tbody.querySelectorAll("tr").forEach(r => r.classList.remove("selected"));
             tr.classList.add("selected");
+
             const row = rows.find(r => r.id === parseInt(tr.dataset.id));
             if (row) _openDbEdit(row);
         });
@@ -655,6 +635,55 @@ function _dbExport() {
     a.click();
 }
 
+async function _dbDeleteSelected() {
+    const ids = _getSelectedDbIds();
+
+    if (!ids.length) {
+        showToast(t("taggerDbNoSelection"), "warning");
+        return;
+    }
+
+    if (!confirm(t("taggerDbDeleteConfirm", ids.length))) {
+        return;
+    }
+
+    try {
+        const res = await fetch("/wfm/tagger/db", {
+            method: "DELETE",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ ids }),
+        });
+
+        const data = await res.json();
+
+        if (data.error) {
+            throw new Error(data.error);
+        }
+
+        showToast(t("taggerDbDeletedCount", data.count), "success");
+        _dbLoad();
+    } catch (e) {
+        showToast(e.message, "error");
+    }
+}
+
+function _getSelectedDbIds() {
+    return [...document.querySelectorAll(".wfm-tagger-db-select input:checked")]
+        .map(el => parseInt(el.dataset.id))
+        .filter(Number.isInteger);
+}
+
+function _dbToggleSelection(e) {
+    const checked = e.target.checked;
+    const checkboxes = document.querySelectorAll(
+        '#wfm-tagger-db-tbody input[type="checkbox"]'
+    );
+
+    checkboxes.forEach(checkbox => {
+        checkbox.checked = checked;
+    });
+}
+
 // ── ユーティリティ ───────────────────────────────────────────────
 
 function _esc(str) {

static/js/util.js

@@ -102,12 +102,59 @@ 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;
 }
 
+// Unslothはローカルでも常にAPIキーが必要なため、フロントから直接叩かず
+// サーバー側プロキシ(/api/wfm/unsloth/proxy)経由でキーを付与して中継する
+// (see py/routes/unsloth_routes.py)
+export 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 ${res.status}`);
+    return data;
+}
+
+// ローカルVLM/LLMバックエンドからモデルをVRAM/メモリからアンロードする。
+// 公式APIが存在しない場合(Unsloth)は "UNSUPPORTED" エラーを投げる。
+export async function unloadAiModel(url, backend, model) {
+    if (!model) throw new Error("No model selected");
+    if (backend === "ollama") {
+        // keep_alive:0 + 空プロンプトが公式に文書化されたアンロード方法
+        // (専用エンドポイントは存在しない) https://docs.ollama.com/api
+        const res = await fetch(`${url}/api/generate`, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ model, prompt: "", keep_alive: 0 }),
+        });
+        if (!res.ok) throw new Error(`HTTP ${res.status}`);
+    } else if (backend === "lmstudio") {
+        const res = await fetch(`${url}/api/v1/models/unload`, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ instance_id: model }),
+        });
+        if (!res.ok) throw new Error(`HTTP ${res.status}`);
+    } else if (backend === "lemonade") {
+        const res = await fetch(`${url}/api/v1/unload`, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ model_name: model }),
+        });
+        if (!res.ok) throw new Error(`HTTP ${res.status}`);
+    } else {
+        throw new Error("UNSUPPORTED");
+    }
+}
+
 // ============================================
 // Eagle Auto-Save
 // ============================================

static/js/video-asset-tab.js

@@ -0,0 +1,415 @@
+/**
+ * Video Tab - Asset subtab: browses the videos a Plan run has generated.
+ *
+ * Deliberately thin — it reuses Gallery's existing backend (groups, tags/memo
+ * sidecar, thumbnailing) exactly like feeder-tab.js's own Gallery mode does,
+ * rather than re-implementing folder browsing, bulk selection, etc. Anything
+ * beyond basic browsing/tagging (move, export, delete, multi-select) is left
+ * to the real Gallery tab via the "Open in Gallery" button.
+ */
+
+import { VIDEO_GROUP, VTEMP_GROUP, ensureVideoGroup, isVideoFile } from "./gallery-tab.js";
+import { setSourcePreview } from "./video-preview.js";
+import { showToast } from "./app.js";
+import { t } from "./i18n.js";
+import { addClipFromFile } from "./video-edit-tab.js";
+import { setBlockImageFromFile } from "./video-plan-tab.js";
+
+// Sentinel for the "All Video Assets" option — not a real backend group, since
+// Gallery's group filter only ever matches one group at a time. Selecting it
+// fetches VIDEO_GROUP and VTEMP_GROUP separately and merges them (see _loadImages).
+const ALL_GROUPS_VALUE = "__video_asset_all__";
+
+// Display names are now identical to the underlying group names (minus the
+// "__...__" wrapping) — no more per-plan "VideoPlan:<name>" sub-groups, so the
+// dropdown is always exactly these three fixed entries.
+const _GROUPS = [ALL_GROUPS_VALUE, VIDEO_GROUP, VTEMP_GROUP];
+
+const _s = {
+    outputDir: "",
+    group: ALL_GROUPS_VALUE,
+    images: [],
+    selectedPath: null,
+    loaded: false, // becomes true once the Asset subtab has been shown at least once
+    searchQuery: "",
+    viewMode: "grid", // "grid" | "table"
+    kindFilter: "all", // "all" | "video" | "image"
+};
+
+function _formatDate(mtime) {
+    return new Date(mtime * 1000).toLocaleString();
+}
+
+function _filteredImages() {
+    const q = _s.searchQuery.trim().toLowerCase();
+    let list = _s.images;
+    if (_s.kindFilter === "video") list = list.filter((img) => isVideoFile(img));
+    else if (_s.kindFilter === "image") list = list.filter((img) => !isVideoFile(img));
+    if (q) list = list.filter((img) => img.filename.toLowerCase().includes(q));
+    return list;
+}
+
+async function _fetchOutputDir() {
+    if (_s.outputDir) return;
+    try {
+        const res = await fetch("/api/wfm/settings/output-dir");
+        if (res.ok) {
+            const data = await res.json();
+            _s.outputDir = (data.current || "").replace(/\\/g, "/").replace(/\/$/, "");
+        }
+    } catch { /* non-critical */ }
+}
+
+function _groupLabel(g) {
+    if (g === ALL_GROUPS_VALUE) return "All Video Assets";
+    if (g === VIDEO_GROUP) return "Video Assets";
+    if (g === VTEMP_GROUP) return "Video Temp";
+    return g;
+}
+
+function _renderGroupSelect() {
+    const sel = document.getElementById("wfm-video-asset-group");
+    if (!sel) return;
+    sel.innerHTML = _GROUPS
+        .map((g) => `<option value="${g}"${g === _s.group ? " selected" : ""}>${_groupLabel(g)}</option>`)
+        .join("");
+}
+
+async function _fetchGroupImages(group) {
+    const params = new URLSearchParams({ folder: _s.outputDir, group, recursive: "true", sort: "date_desc" });
+    const res = await fetch(`/wfm/gallery/images?${params}`);
+    if (!res.ok) throw new Error(String(res.status));
+    const data = await res.json();
+    return data.images || [];
+}
+
+async function _loadImages() {
+    const grid = document.getElementById("wfm-video-asset-grid");
+    const statusEl = document.getElementById("wfm-video-asset-status");
+    if (!grid) return;
+    await _fetchOutputDir();
+    if (!_s.outputDir) {
+        grid.innerHTML = `<div class="wfm-placeholder">No output folder configured</div>`;
+        return;
+    }
+    grid.innerHTML = `<div class="wfm-placeholder">Loading...</div>`;
+    try {
+        let images;
+        if (_s.group === ALL_GROUPS_VALUE) {
+            // "All Video Assets" = the union of the curated __Video Assets__
+            // group and __Video Temp__ (auto-populated by every plan run,
+            // organized into __Video Assets__ by the user on their own
+            // schedule) — not a single backend group filter, so fetch both
+            // and merge.
+            const [curated, temp] = await Promise.all([
+                _fetchGroupImages(VIDEO_GROUP),
+                _fetchGroupImages(VTEMP_GROUP),
+            ]);
+            const byPath = new Map();
+            for (const img of [...curated, ...temp]) byPath.set(img.path, img);
+            images = Array.from(byPath.values()).sort((a, b) => b.mtime - a.mtime);
+        } else {
+            images = await _fetchGroupImages(_s.group);
+        }
+        _s.images = images;
+        _renderList();
+        if (statusEl) statusEl.textContent = `${_s.images.length} video(s)`;
+    } catch (err) {
+        _s.images = [];
+        grid.innerHTML = `<div class="wfm-placeholder" style="color:var(--wfm-danger)">Load failed: ${err.message}</div>`;
+        if (statusEl) statusEl.textContent = "";
+    }
+}
+
+// Renders whichever of grid/table is active for the current viewMode — called
+// on load, on search input, and after switching view modes.
+function _renderList() {
+    if (_s.viewMode === "table") _renderTable();
+    else _renderGrid();
+}
+
+function _renderGrid() {
+    const grid = document.getElementById("wfm-video-asset-grid");
+    if (!grid) return;
+    const images = _filteredImages();
+    if (images.length === 0) {
+        grid.innerHTML = `<div class="wfm-placeholder">No videos yet</div>`;
+        return;
+    }
+    grid.innerHTML = "";
+    for (const img of images) grid.appendChild(_makeCard(img));
+}
+
+function _renderTable() {
+    const body = document.getElementById("wfm-video-asset-table-body");
+    if (!body) return;
+    const images = _filteredImages();
+    if (images.length === 0) {
+        body.innerHTML = `<tr><td colspan="3" class="wfm-placeholder">No videos yet</td></tr>`;
+        return;
+    }
+    body.innerHTML = "";
+    for (const img of images) body.appendChild(_makeRow(img));
+}
+
+function _makeRow(img) {
+    const row = document.createElement("tr");
+    row.className = "wfm-video-asset-row" + (img.path === _s.selectedPath ? " selected" : "");
+    row.dataset.path = img.path;
+
+    const nameCell = document.createElement("td");
+    nameCell.textContent = img.filename;
+    nameCell.title = img.filename;
+
+    const dateCell = document.createElement("td");
+    dateCell.textContent = _formatDate(img.mtime);
+
+    const tagsCell = document.createElement("td");
+    tagsCell.textContent = (img.tags || []).join(", ");
+
+    row.append(nameCell, dateCell, tagsCell);
+    row.addEventListener("click", () => _selectImage(img));
+    return row;
+}
+
+function _makeCard(img) {
+    const card = document.createElement("div");
+    card.className = "wfm-video-asset-card" + (img.path === _s.selectedPath ? " selected" : "");
+    card.dataset.path = img.path;
+
+    const thumb = document.createElement("img");
+    thumb.className = "wfm-video-asset-card-img";
+    thumb.loading = "lazy";
+    thumb.src = `/wfm/gallery/image/thumb?path=${encodeURIComponent(img.path)}&w=200`;
+    thumb.onerror = () => { thumb.style.display = "none"; };
+
+    const name = document.createElement("div");
+    name.className = "wfm-video-asset-card-name";
+    name.textContent = img.filename;
+    name.title = img.filename;
+
+    card.append(thumb, name);
+    card.addEventListener("click", () => _selectImage(img));
+    return card;
+}
+
+function _selectImage(img) {
+    _s.selectedPath = img.path;
+    document.querySelectorAll(".wfm-video-asset-card, .wfm-video-asset-row").forEach((c) => {
+        c.classList.toggle("selected", c.dataset.path === img.path);
+    });
+    _renderDetail(img);
+    _loadIntoSourcePreview(img);
+}
+
+// Feeds the selected asset's media into the center panel's Asset/Source preview
+// pane (see video-preview.js) so it's visible without switching tabs, and so the
+// Frame/GIF property tools can operate on it just like a Video Source drop.
+// Fetched as a Blob and wrapped as a "local" source rather than trying to map
+// Gallery's arbitrary absolute path onto ComfyUI's filename/subfolder/type
+// triple — that keeps the GIF tool's existing upload-on-first-use path working
+// unchanged for both cases. Still/animated images (this group isn't exclusively
+// videos — anything tagged into it shows up here) preview via the pane's <img>
+// companion element instead of trying to play them as a <video>.
+async function _loadIntoSourcePreview(img) {
+    try {
+        const res = await fetch(`/wfm/gallery/image/serve?path=${encodeURIComponent(img.path)}`);
+        if (!res.ok) throw new Error(String(res.status));
+        const blob = await res.blob();
+        const isVideo = isVideoFile(img);
+        const file = new File([blob], img.filename, { type: blob.type || (isVideo ? "video/mp4" : "image/png") });
+        setSourcePreview(URL.createObjectURL(file), { kind: "local", file }, isVideo ? "video" : "image");
+    } catch (err) {
+        console.warn("[VideoAsset] failed to load preview:", err);
+    }
+}
+
+function _renderDetail(img) {
+    const panel = document.getElementById("wfm-video-asset-detail");
+    if (!panel) return;
+    // Filename/memo are untrusted (filesystem names, user-entered text) — the
+    // markup skeleton is static, and those two values are assigned afterward
+    // via textContent/.value, never interpolated into the HTML string itself.
+    panel.innerHTML = `
+        <div class="wfm-video-asset-name" id="wfm-video-asset-name"></div>
+        <label>Tags</label>
+        <div class="wfm-video-asset-tags" id="wfm-video-asset-tags"></div>
+        <div style="display:flex;gap:6px;">
+            <input type="text" id="wfm-video-asset-tag-input" class="wfm-input" placeholder="Add tag" style="flex:1;">
+            <button type="button" class="wfm-btn wfm-btn-sm" id="wfm-video-asset-tag-add">Add</button>
+        </div>
+        <label style="margin-top:10px;">Memo</label>
+        <textarea id="wfm-video-asset-memo" class="wfm-textarea" rows="3"></textarea>
+        <button type="button" class="wfm-btn wfm-btn-sm" id="wfm-video-asset-memo-save" style="margin-top:6px;">Save Memo</button>
+        <button type="button" class="wfm-btn wfm-btn-sm" id="wfm-video-asset-send-to-edit" style="width:100%;margin-top:12px;">${t("videoEditSendToEdit")}</button>
+        <div id="wfm-video-asset-set-plan-image-row" style="display:${isVideoFile(img) ? "none" : "flex"};gap:6px;margin-top:6px;">
+            <button type="button" class="wfm-btn wfm-btn-sm" id="wfm-video-asset-set-first" style="flex:1;">${t("videoAssetSetAsFirst")}</button>
+            <button type="button" class="wfm-btn wfm-btn-sm" id="wfm-video-asset-set-last" style="flex:1;">${t("videoAssetSetAsLast")}</button>
+        </div>
+        <button type="button" class="wfm-btn wfm-btn-primary wfm-btn-sm" id="wfm-video-asset-open-gallery" style="width:100%;margin-top:6px;">Open in Gallery</button>
+    `;
+
+    const nameEl = document.getElementById("wfm-video-asset-name");
+    if (nameEl) { nameEl.textContent = img.filename; nameEl.title = img.filename; }
+    const memoEl = document.getElementById("wfm-video-asset-memo");
+    if (memoEl) memoEl.value = img.memo || "";
+
+    _renderTags(img);
+
+    panel.querySelector("#wfm-video-asset-tag-add")?.addEventListener("click", async () => {
+        const input = panel.querySelector("#wfm-video-asset-tag-input");
+        const tag = input?.value.trim();
+        if (!tag) return;
+        const tags = Array.from(new Set([...(img.tags || []), tag]));
+        await _saveMeta(img.path, { tags });
+        img.tags = tags;
+        input.value = "";
+        _renderTags(img);
+    });
+
+    panel.querySelector("#wfm-video-asset-memo-save")?.addEventListener("click", async () => {
+        const memo = panel.querySelector("#wfm-video-asset-memo")?.value || "";
+        await _saveMeta(img.path, { memo });
+        img.memo = memo;
+    });
+
+    panel.querySelector("#wfm-video-asset-send-to-edit")?.addEventListener("click", async () => {
+        try {
+            const res = await fetch(`/wfm/gallery/image/serve?path=${encodeURIComponent(img.path)}`);
+            if (!res.ok) throw new Error(String(res.status));
+            const blob = await res.blob();
+            const file = new File([blob], img.filename, { type: blob.type || (isVideoFile(img) ? "video/mp4" : "image/png") });
+            addClipFromFile(file, img.filename);
+            document.querySelector('.wfm-video-center-panel .wfm-video-subtab-btn[data-video-subtab="edit"]')?.click();
+            showToast(t("videoEditClipSent", img.filename), "success");
+        } catch (err) {
+            showToast(t("errorWithMsg", err.message), "error");
+        }
+    });
+
+    const setAsPlanImage = async (which) => {
+        try {
+            const res = await fetch(`/wfm/gallery/image/serve?path=${encodeURIComponent(img.path)}`);
+            if (!res.ok) throw new Error(String(res.status));
+            const blob = await res.blob();
+            const file = new File([blob], img.filename, { type: blob.type || "image/png" });
+            await setBlockImageFromFile(which, file);
+            document.querySelector('.wfm-video-center-panel .wfm-video-subtab-btn[data-video-subtab="plan"]')?.click();
+            showToast(t(which === "first" ? "videoAssetSetAsFirstDone" : "videoAssetSetAsLastDone", img.filename), "success");
+        } catch (err) {
+            showToast(t("errorWithMsg", err.message), "error");
+        }
+    };
+    panel.querySelector("#wfm-video-asset-set-first")?.addEventListener("click", () => setAsPlanImage("first"));
+    panel.querySelector("#wfm-video-asset-set-last")?.addEventListener("click", () => setAsPlanImage("last"));
+
+    panel.querySelector("#wfm-video-asset-open-gallery")?.addEventListener("click", () => {
+        document.querySelector('.wfm-tab[data-tab="gallery"]')?.click();
+        const filterSel = document.getElementById("wfm-gallery-group-filter");
+        if (filterSel) {
+            // ALL_GROUPS_VALUE is a virtual union with no Gallery-side equivalent
+            // (Gallery's own filter only ever matches one real group) — fall back
+            // to the curated group, the closer of the two to "everything worth keeping".
+            filterSel.value = _s.group === ALL_GROUPS_VALUE ? VIDEO_GROUP : _s.group;
+            filterSel.dispatchEvent(new Event("change"));
+        }
+    });
+}
+
+function _renderTags(img) {
+    const container = document.getElementById("wfm-video-asset-tags");
+    if (!container) return;
+    const tags = img.tags || [];
+    container.innerHTML = "";
+    if (tags.length === 0) {
+        const placeholder = document.createElement("span");
+        placeholder.className = "wfm-placeholder";
+        placeholder.textContent = "No tags";
+        container.appendChild(placeholder);
+        return;
+    }
+    // Built via DOM APIs (not innerHTML) since tag text is user-entered —
+    // textContent keeps it inert regardless of what characters it contains.
+    for (const tag of tags) {
+        const pill = document.createElement("span");
+        pill.className = "wfm-video-asset-tag";
+        pill.append(document.createTextNode(`${tag} `));
+
+        const removeBtn = document.createElement("button");
+        removeBtn.type = "button";
+        removeBtn.textContent = "×";
+        removeBtn.addEventListener("click", async () => {
+            const newTags = tags.filter((t2) => t2 !== tag);
+            await _saveMeta(img.path, { tags: newTags });
+            img.tags = newTags;
+            _renderTags(img);
+        });
+
+        pill.appendChild(removeBtn);
+        container.appendChild(pill);
+    }
+}
+
+async function _saveMeta(path, data) {
+    try {
+        await fetch("/wfm/gallery/image/meta", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ path, ...data }),
+        });
+    } catch (err) {
+        console.warn("[VideoAsset] saveMeta failed:", err);
+    }
+}
+
+// Called whenever the Asset subtab becomes active (see video-tab.js's subtab
+// toggle) — always refetches rather than caching, so a Plan run's newly
+// generated videos show up as soon as the user switches over to look.
+export async function refreshVideoAssetTab() {
+    if (!_s.loaded) {
+        _renderGroupSelect();
+        _s.loaded = true;
+    }
+    await _loadImages();
+}
+
+function _setViewMode(mode) {
+    _s.viewMode = mode;
+    document.getElementById("wfm-video-asset-view-grid")?.classList.toggle("active", mode === "grid");
+    document.getElementById("wfm-video-asset-view-table")?.classList.toggle("active", mode === "table");
+    const grid = document.getElementById("wfm-video-asset-grid");
+    const tableWrap = document.getElementById("wfm-video-asset-table-wrap");
+    if (grid) grid.style.display = mode === "grid" ? "" : "none";
+    if (tableWrap) tableWrap.style.display = mode === "table" ? "" : "none";
+    _renderList();
+}
+
+export function initVideoAssetTab() {
+    document.getElementById("wfm-video-asset-group")?.addEventListener("change", (e) => {
+        _s.group = e.target.value;
+        _s.selectedPath = null;
+        const panel = document.getElementById("wfm-video-asset-detail");
+        if (panel) panel.innerHTML = `<span class="wfm-placeholder">Select a video</span>`;
+        _loadImages();
+    });
+    document.getElementById("wfm-video-asset-refresh")?.addEventListener("click", () => _loadImages());
+    document.getElementById("wfm-video-asset-kind")?.addEventListener("change", (e) => {
+        _s.kindFilter = e.target.value;
+        _renderList();
+    });
+    document.getElementById("wfm-video-asset-search")?.addEventListener("input", (e) => {
+        _s.searchQuery = e.target.value;
+        _renderList();
+    });
+    document.getElementById("wfm-video-asset-view-grid")?.addEventListener("click", () => _setViewMode("grid"));
+    document.getElementById("wfm-video-asset-view-table")?.addEventListener("click", () => _setViewMode("table"));
+
+    // Neither reserved group is ever auto-created by a batch run itself (see
+    // video-plan-tab.js's _ensureVideoAssetGroups, which only ensures
+    // __Video Temp__ right before a run starts), so a fresh install or a group
+    // deleted via Gallery's Manage Groups would otherwise never come back on
+    // its own — add_to_group() happily records membership on individual images
+    // without ever re-registering the group itself. ensureVideoGroup() ensures
+    // both __Video Assets__ and __Video Temp__ every time the Asset subtab inits.
+    ensureVideoGroup();
+}

static/js/video-preview.js

@@ -0,0 +1,113 @@
+/**
+ * Video Tab - shared state for the two center-panel preview panes:
+ * "source" (#wfm-video-source-preview-video — an Asset selection or a
+ * locally-dropped file) and "result" (#wfm-video-preview-video — the latest
+ * generated clip). video-asset-tab.js (Asset selection), video-plan-tab.js
+ * (batch run results) and video-tab.js (the Video Source drop zone, Frame/GIF
+ * tools) all need to read or write these. Pulling this into its own module
+ * lets all three depend on it without needing to import each other —
+ * video-tab.js already imports both of the others for init/refresh, so a
+ * reverse import from either back into video-tab.js would create a cycle.
+ *
+ * Each pane also has a companion <img> element (wfm-video-*-preview-img) so a
+ * still/animated image (from Asset or a Video Source drop) can be previewed
+ * without forcing it through a <video> tag, which can't render image formats
+ * at all. mediaType controls which element is shown; Frame/GIF's
+ * getActivePreviewVideoElement() returns null while a pane is showing an
+ * image, since those tools only make sense against real video.
+ */
+
+const _PANES = {
+    source: { video: "wfm-video-source-preview-video", img: "wfm-video-source-preview-img", placeholder: "wfm-video-source-preview-placeholder" },
+    result: { video: "wfm-video-preview-video", img: "wfm-video-preview-img", placeholder: "wfm-video-preview-placeholder" },
+};
+
+const _objectUrls = { source: null, result: null };
+
+// Whichever pane most recently received media is what the Frame/GIF property
+// tabs act on (see getActivePreviewSource/getActivePreviewVideoElement below) —
+// an Asset selection or Video Source drop activates "source", a batch run
+// finishing a block (or the Edit tab's export/preview) activates "result".
+let _activePane = null; // "source" | "result" | null
+let _activeSource = null;
+let _activeMediaType = null; // "video" | "image" | null — mirrors _activePane's current content
+
+function _setPreview(pane, url, source, mediaType = "video") {
+    const ids = _PANES[pane];
+    const video = document.getElementById(ids.video);
+    const img = document.getElementById(ids.img);
+    const placeholder = document.getElementById(ids.placeholder);
+    if (!video || !placeholder) return;
+
+    if (_objectUrls[pane]) {
+        URL.revokeObjectURL(_objectUrls[pane]);
+        _objectUrls[pane] = null;
+    }
+    if (url && url.startsWith("blob:")) _objectUrls[pane] = url;
+
+    if (url) {
+        if (mediaType === "image") {
+            video.pause();
+            video.removeAttribute("src");
+            video.load();
+            video.style.display = "none";
+            if (img) { img.src = url; img.style.display = "block"; }
+        } else {
+            if (img) { img.removeAttribute("src"); img.style.display = "none"; }
+            video.src = url;
+            video.style.display = "block";
+        }
+        placeholder.style.display = "none";
+        _activePane = pane;
+        _activeSource = source;
+        _activeMediaType = mediaType;
+    } else {
+        video.pause();
+        video.removeAttribute("src");
+        video.load();
+        video.style.display = "none";
+        if (img) { img.removeAttribute("src"); img.style.display = "none"; }
+        placeholder.style.display = "";
+        if (_activePane === pane) { _activePane = null; _activeSource = null; _activeMediaType = null; }
+    }
+}
+
+// Sets (or clears, with url=null) the Asset-selection/Video-Source-drop pane.
+// mediaType: "video" (default) or "image".
+export function setSourcePreview(url, source, mediaType = "video") { _setPreview("source", url, source, mediaType); }
+
+// Sets (or clears) the generated-result pane — called once per block as a
+// batch run's video output comes in, and by the Edit tab's timeline preview
+// (which may show either a video clip or a held still image).
+export function setResultPreview(url, source, mediaType = "video") { _setPreview("result", url, source, mediaType); }
+
+// What the Frame/GIF property tabs operate on: { kind: "output"|"input", filename,
+// subfolder, type } for anything already on the server, or { kind: "local", file }
+// for a picked/dropped file not yet uploaded. null if neither pane has media.
+export function getActivePreviewSource() { return _activeSource; }
+
+// The GIF tab uploads a still-local source on first use and needs to remember
+// the resulting server-side reference so a repeat conversion doesn't re-upload —
+// this rewrites the active source in place without touching either <video> element.
+export function updateActivePreviewSourceRef(source) { _activeSource = source; }
+
+// The <video> element the Frame/GIF tools should read/capture from, or null if
+// neither pane currently has a video loaded (including when the active pane is
+// showing a still image instead).
+export function getActivePreviewVideoElement() {
+    if (!_activePane || _activeMediaType === "image") return null;
+    return document.getElementById(_PANES[_activePane].video);
+}
+
+// Both panes' <video> elements share the same persistent volume setting.
+export function getAllPreviewVideoElements() {
+    return Object.values(_PANES).map((ids) => document.getElementById(ids.video)).filter(Boolean);
+}
+
+// Direct access to the "result" pane's <video> element — used by
+// video-edit-tab.js's timeline preview, which needs to drive playback
+// (seek to a clip's trim-in, listen for the trim-out point, swap to the
+// next clip) rather than just set a src via setResultPreview().
+export function getResultPreviewVideoElement() {
+    return document.getElementById(_PANES.result.video);
+}

static/js/video-project-tab.js

@@ -0,0 +1,175 @@
+/**
+ * Video Tab - Sidebar "Project" subtab (next to Asset): browses saved Video
+ * Plan files (ws_videoplan_*.json in video_plan/) OR saved Video Edit
+ * projects (ws_videoeditproj_*.json in video_edit_project/), depending on
+ * which center subtab (Plan/Edit) is currently active — video-tab.js passes
+ * that mode in on every refresh (Asset stays on whichever of the two was
+ * last active, since it has no saved-list concept of its own).
+ *
+ * Deliberately thin, mirroring video-asset-tab.js's own scope — clicking an
+ * item hands off to video-plan-tab.js's openSavedVideoPlan() or
+ * video-edit-tab.js's openSavedVideoEditProject() rather than
+ * re-implementing plan/project loading here.
+ */
+
+import { showToast } from "./app.js";
+import { t } from "./i18n.js";
+import { escapeHtml } from "./util.js";
+import { openSavedVideoPlan } from "./video-plan-tab.js";
+import { openSavedVideoEditProject } from "./video-edit-tab.js";
+
+// Per-mode config: which endpoints to hit, which loader/toast strings to use,
+// and how to read each item's count field — keeps _renderList/_makeItem/etc.
+// mode-agnostic instead of branching throughout.
+const _MODES = {
+    plan: {
+        listUrl: "/api/wfm/video/plans",
+        deleteUrl: "/api/wfm/video/plans/delete",
+        open: (filename) => openSavedVideoPlan(filename),
+        countOf: (item) => item.block_count ?? 0,
+        countLabel: (n) => t("videoProjectBlockCount", n),
+        hintKey: "videoProjectHint",
+        emptyKey: "videoProjectEmpty",
+        deleteConfirmKey: "videoProjectDeleteConfirm",
+        deletedKey: "videoProjectDeleted",
+        deleteFailedKey: "videoProjectDeleteFailed",
+        loadListFailedKey: "videoProjectLoadListFailed",
+    },
+    edit: {
+        listUrl: "/api/wfm/video/edit/projects",
+        deleteUrl: "/api/wfm/video/edit/projects/delete",
+        open: (filename) => openSavedVideoEditProject(filename),
+        countOf: (item) => item.clip_count ?? 0,
+        countLabel: (n) => t("videoEditProjectClipCount", n),
+        hintKey: "videoEditProjectHint",
+        emptyKey: "videoEditProjectEmpty",
+        deleteConfirmKey: "videoEditProjectDeleteConfirm",
+        deletedKey: "videoEditProjectDeleted",
+        deleteFailedKey: "videoEditProjectDeleteFailed",
+        loadListFailedKey: "videoEditProjectLoadListFailed",
+    },
+};
+
+const _s = {
+    mode: "plan",
+    plans: [],
+    activeFilename: null,
+    loaded: false, // becomes true once the Project subtab has been shown at least once
+};
+
+function _formatUpdatedAt(iso) {
+    if (!iso) return "";
+    const d = new Date(iso);
+    if (isNaN(d.getTime())) return "";
+    return d.toLocaleString();
+}
+
+function _renderList() {
+    const list = document.getElementById("wfm-video-project-list");
+    if (!list) return;
+    const cfg = _MODES[_s.mode];
+    if (_s.plans.length === 0) {
+        list.innerHTML = `<span class="wfm-placeholder">${t(cfg.emptyKey)}</span>`;
+        return;
+    }
+    list.innerHTML = "";
+    for (const plan of _s.plans) list.appendChild(_makeItem(plan));
+}
+
+function _makeItem(plan) {
+    const cfg = _MODES[_s.mode];
+    const item = document.createElement("div");
+    item.className = "wfm-video-project-item" + (plan.filename === _s.activeFilename ? " active" : "");
+    item.dataset.filename = plan.filename;
+
+    const thumb = document.createElement("img");
+    thumb.className = "wfm-video-project-item-thumb";
+    thumb.loading = "lazy";
+    if (plan.thumbnail) thumb.src = plan.thumbnail;
+    thumb.onerror = () => { thumb.style.visibility = "hidden"; };
+
+    const info = document.createElement("div");
+    info.className = "wfm-video-project-item-info";
+
+    const name = document.createElement("div");
+    name.className = "wfm-video-project-item-name";
+    name.textContent = plan.name || plan.filename;
+    name.title = plan.filename;
+
+    const meta = document.createElement("div");
+    meta.className = "wfm-video-project-item-meta";
+    const countLabel = cfg.countLabel(cfg.countOf(plan));
+    const updated = _formatUpdatedAt(plan.updated_at);
+    meta.textContent = updated ? `${countLabel} · ${updated}` : countLabel;
+    meta.title = plan.note || "";
+
+    info.append(name, meta);
+
+    const deleteBtn = document.createElement("button");
+    deleteBtn.type = "button";
+    deleteBtn.className = "wfm-video-project-item-delete";
+    deleteBtn.textContent = "×";
+    deleteBtn.title = t("delete");
+    deleteBtn.addEventListener("click", (e) => {
+        e.stopPropagation();
+        _deletePlan(plan);
+    });
+
+    item.append(thumb, info, deleteBtn);
+    item.addEventListener("click", () => _openPlan(plan));
+    return item;
+}
+
+async function _openPlan(plan) {
+    _s.activeFilename = plan.filename;
+    _renderList();
+    await _MODES[_s.mode].open(plan.filename);
+}
+
+async function _deletePlan(plan) {
+    const cfg = _MODES[_s.mode];
+    if (!window.confirm(t(cfg.deleteConfirmKey, plan.name || plan.filename))) return;
+    try {
+        const res = await fetch(cfg.deleteUrl, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ filename: plan.filename }),
+        });
+        const json = await res.json();
+        if (!res.ok) throw new Error(json.error || `HTTP ${res.status}`);
+        if (_s.activeFilename === plan.filename) _s.activeFilename = null;
+        showToast(t(cfg.deletedKey), "success");
+        await refreshVideoProjectTab(_s.mode);
+    } catch (err) {
+        showToast(`${t(cfg.deleteFailedKey)}: ${err.message}`, "error");
+    }
+}
+
+// Called whenever the Project subtab becomes active (see video-tab.js's prop-tab
+// toggle) — always refetches rather than caching, so a plan/project saved
+// elsewhere (Plan pane's Save/Save As, Edit's Save/Save As) shows up as soon
+// as the user switches over to look. `mode` ("plan"|"edit") is video-tab.js's
+// record of which center subtab is currently active; defaults to "plan" so
+// direct calls (e.g. from initVideoProjectTab) keep the original behavior.
+export async function refreshVideoProjectTab(mode = "plan") {
+    const list = document.getElementById("wfm-video-project-list");
+    if (!list) return;
+    _s.mode = mode === "edit" ? "edit" : "plan";
+    _s.activeFilename = null;
+    const cfg = _MODES[_s.mode];
+    const hintEl = document.getElementById("wfm-video-project-hint");
+    if (hintEl) hintEl.textContent = t(cfg.hintKey);
+    try {
+        const res = await fetch(cfg.listUrl);
+        if (!res.ok) throw new Error(String(res.status));
+        _s.plans = await res.json();
+        _renderList();
+    } catch (err) {
+        list.innerHTML = `<span class="wfm-placeholder" style="color:var(--wfm-danger)">${escapeHtml(t(cfg.loadListFailedKey))}: ${escapeHtml(err.message)}</span>`;
+    }
+    _s.loaded = true;
+}
+
+export function initVideoProjectTab() {
+    document.getElementById("wfm-video-project-refresh")?.addEventListener("click", () => refreshVideoProjectTab());
+}

static/js/video-tab.js

@@ -0,0 +1,434 @@
+/**
+ * Video Tab - top-level orchestration for the Plan / Asset / Edit subtabs, plus
+ * the Frame-capture / GIF-conversion / Video-Source tools (kept exactly as they
+ * were — they don't depend on which subtab is active and stay visible across
+ * all three; see video-tab.css's persistent-panel layout).
+ *
+ * Batch generation (the former single-shot form) now lives in
+ * video-plan-tab.js, and per-block workflow manipulation in video-workflow.js.
+ * This file only re-exports loadWorkflowIntoVideoEditor so workflow-tab.js's
+ * "Load in Video" button needs no changes.
+ */
+
+import { showToast } from "./app.js";
+import { t } from "./i18n.js";
+import { comfyUI } from "./comfyui-client.js";
+import { applyStoredVideoVolume } from "./settings-tab.js";
+import { initVideoPlanTab, loadWorkflowIntoVideoEditor } from "./video-plan-tab.js";
+import { initVideoAssetTab, refreshVideoAssetTab } from "./video-asset-tab.js";
+import { initVideoProjectTab, refreshVideoProjectTab } from "./video-project-tab.js";
+import { initVideoEditTab, addClipFromFile } from "./video-edit-tab.js";
+import { VIDEO_GROUP, ensureVideoGroup } from "./gallery-tab.js";
+import {
+    setSourcePreview, getActivePreviewSource, updateActivePreviewSourceRef,
+    getActivePreviewVideoElement, getAllPreviewVideoElements,
+} from "./video-preview.js";
+
+export { loadWorkflowIntoVideoEditor };
+
+// ============================================
+// Subtab switching — two INDEPENDENT groups sharing the same .wfm-video-subtab-*
+// look, kept deliberately uncoupled: the sidebar's Project button collapses its
+// panel entirely when re-clicked (nothing needs to always be visible there —
+// Video Source covers the "load something to work with" case), while the center
+// Plan/Edit/Asset trio is a "always exactly one visible" tab group. Querying each
+// scoped to its own container (rather than one global querySelectorAll) is
+// what keeps clicking one from affecting the other.
+// ============================================
+
+// Which of the center Plan/Edit subtabs was last active — the sidebar
+// Project panel uses this to decide whether to list saved Video Plans or
+// saved Video Edit projects (Asset has no saved-list concept, so selecting
+// it leaves this unchanged rather than resetting to "plan").
+let _centerSubtabMode = "plan";
+
+function _isProjectPanelOpen() {
+    const panel = document.getElementById("wfm-video-subtab-project");
+    return !!panel && panel.style.display !== "none";
+}
+
+function _initCenterSubtabToggle() {
+    const scope = document.querySelector(".wfm-video-center-panel");
+    if (!scope) return;
+    scope.querySelectorAll(".wfm-video-subtab-btn").forEach((btn) => {
+        btn.addEventListener("click", () => {
+            const target = btn.dataset.videoSubtab;
+            scope.querySelectorAll(".wfm-video-subtab-btn").forEach((b) => b.classList.remove("active"));
+            btn.classList.add("active");
+            scope.querySelectorAll(".wfm-video-subtab-panel").forEach((p) => {
+                p.style.display = p.dataset.videoSubtabPanel === target ? "" : "none";
+            });
+            if (target === "asset") refreshVideoAssetTab();
+            if (target === "plan" || target === "edit") {
+                _centerSubtabMode = target;
+                // Project panel may already be open (e.g. switching Plan<->Edit
+                // while it's pinned) — keep its list in sync with the new mode.
+                if (_isProjectPanelOpen()) refreshVideoProjectTab(_centerSubtabMode);
+            }
+        });
+    });
+}
+
+// Sidebar Project button: clicking it shows its panel; clicking it again while
+// active collapses it, letting the video preview reclaim the sidebar's width
+// when it isn't needed.
+function _initSidebarSubtabToggle() {
+    const sidebar = document.querySelector(".wfm-video-form-panel");
+    if (!sidebar) return;
+    const buttons = sidebar.querySelectorAll(".wfm-video-subtab-btn");
+    buttons.forEach((btn) => {
+        btn.addEventListener("click", () => {
+            const target = btn.dataset.videoSubtab;
+            const panel = document.getElementById(`wfm-video-subtab-${target}`);
+            const willShow = !!panel && panel.style.display === "none";
+            buttons.forEach((b) => b.classList.remove("active"));
+            sidebar.querySelectorAll(".wfm-video-subtab-panel").forEach((p) => { p.style.display = "none"; });
+            if (willShow) {
+                btn.classList.add("active");
+                panel.style.display = "";
+                if (target === "project") refreshVideoProjectTab(_centerSubtabMode);
+            }
+        });
+    });
+}
+
+// ============================================
+// first_frame / last_frame アップロード（Video Sourceパネル・Frame/GIFタブと共有する
+// 汎用ドロップゾーン配線ヘルパー。video-plan-tab.jsのブロック画像アップロードとは別物）
+// ============================================
+
+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]);
+    });
+}
+
+// ============================================
+// 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);
+    };
+    const setTitle = (id, key) => {
+        const el = document.getElementById(id);
+        if (el) el.title = t(key);
+    };
+    setText("wfm-video-edit-export-btn", "videoEditExportBtn");
+    setTitle("wfm-video-edit-move-left-btn", "videoEditMoveLeft");
+    setTitle("wfm-video-edit-move-right-btn", "videoEditMoveRight");
+    setText("wfm-video-edit-duplicate-btn", "videoEditDuplicate");
+    setText("wfm-video-edit-delete-btn", "videoEditDelete");
+    setText("wfm-video-edit-clear-btn", "videoEditClearBtn");
+    setText("wfm-video-source-add-to-edit", "videoSourceAddToEdit");
+    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-source-preview-placeholder", "videoSourcePreviewPlaceholder");
+    setText("wfm-video-preview-placeholder", "videoResultPreviewPlaceholder");
+    setText("wfm-video-prop-tab-frame", "videoPropFrame");
+    setText("wfm-video-prop-tab-gif", "videoPropGif");
+    setText("wfm-video-subtab-btn-project", "videoPropProject");
+    setText("wfm-video-project-hint", "videoProjectHint");
+    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 the loaded video workflow first.
+// ============================================
+
+function _wireVideoSourcePanel() {
+    _wireDropZone("wfm-video-source-drop", "wfm-video-source-file", (file) => {
+        const isVideo = file.type.startsWith("video/");
+        const isImage = file.type.startsWith("image/");
+        if (!isVideo && !isImage) return;
+        const statusEl = document.getElementById("wfm-video-source-status");
+        setSourcePreview(URL.createObjectURL(file), { kind: "local", file }, isVideo ? "video" : "image");
+        if (statusEl) { statusEl.textContent = file.name; statusEl.style.color = ""; }
+    });
+
+    // "Add to Edit" — explicit, user-triggered handoff from whatever is loaded
+    // in the Source pane (a plain drop, an Asset selection, a Frame/GIF-tool
+    // source) into the Edit subtab's clip timeline. Deliberately NOT automatic
+    // on every Source drop — Frame/GIF is a single-video tool the user reaches
+    // for far more often than multi-clip editing, so auto-adding would clutter
+    // the Edit timeline with videos the user never meant to combine.
+    document.getElementById("wfm-video-source-add-to-edit")?.addEventListener("click", async () => {
+        const ref = getActivePreviewSource();
+        if (!ref) { showToast(t("videoNoSourceLoaded"), "error"); return; }
+        try {
+            let file;
+            if (ref.kind === "local") {
+                file = ref.file;
+            } else {
+                const blob = await comfyUI.getImageBlob(ref);
+                const isVideoExt = /\.(mp4|webm|mkv|mov)$/i.test(ref.filename || "");
+                file = new File([blob], ref.filename, { type: blob.type || (isVideoExt ? "video/mp4" : "image/png") });
+            }
+            addClipFromFile(file);
+            document.querySelector('.wfm-video-center-panel .wfm-video-subtab-btn[data-video-subtab="edit"]')?.click();
+            showToast(t("videoEditClipSent", file.name), "success");
+        } catch (err) {
+            showToast(t("errorWithMsg", err.message), "error");
+        }
+    });
+
+    document.getElementById("wfm-video-source-clear")?.addEventListener("click", () => {
+        setSourcePreview(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-${target}` ? "" : "none";
+            });
+        });
+    });
+}
+
+// ============================================
+// Frame tab — captures the preview video's current playback position as a PNG,
+// client-side (canvas), and saves it into ComfyUI's own output folder.
+// ============================================
+
+let _capturedFrameBlob = null;
+let _frameOutputDir = "";
+
+function _blobToDataUrl(blob) {
+    return new Promise((resolve, reject) => {
+        const reader = new FileReader();
+        reader.onload = () => resolve(reader.result);
+        reader.onerror = () => reject(new Error("Failed to read blob"));
+        reader.readAsDataURL(blob);
+    });
+}
+
+// Same small output-dir lookup each of video-asset-tab.js/video-plan-tab.js/
+// video-edit-tab.js keeps its own copy of — needed to build the absolute path
+// Gallery's group-tagging API expects.
+async function _fetchFrameOutputDir() {
+    if (_frameOutputDir) return;
+    try {
+        const res = await fetch("/api/wfm/settings/output-dir");
+        if (res.ok) {
+            const data = await res.json();
+            _frameOutputDir = (data.current || "").replace(/\\/g, "/").replace(/\/$/, "");
+        }
+    } catch { /* non-critical */ }
+}
+
+// Opt-in tagging of a saved frame into Gallery's curated __Video Assets__
+// group (see the "Add to Video Assets group" checkbox) — mirrors
+// video-edit-tab.js's _addOutputToVideoTemp, but targets the curated group
+// directly since a manually-captured frame is a deliberate keep, not the
+// every-run scratch output __Video Temp__ exists for.
+async function _addFrameToVideoAssets(filename, subfolder) {
+    await _fetchFrameOutputDir();
+    if (!_frameOutputDir) return;
+    const parts = [_frameOutputDir];
+    if (subfolder) parts.push(subfolder);
+    parts.push(filename);
+    const path = parts.join("/");
+    try {
+        await ensureVideoGroup();
+        await fetch(`/wfm/gallery/groups/${encodeURIComponent(VIDEO_GROUP)}/add`, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ path }),
+        });
+    } catch (err) {
+        console.warn("[VideoTab] failed to tag frame into video group:", err);
+    }
+}
+
+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 = getActivePreviewVideoElement();
+        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");
+            if (document.getElementById("wfm-video-frame-add-to-assets")?.checked) {
+                await _addFrameToVideoAssets(json.filename, json.subfolder);
+            }
+        } 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 (!getActivePreviewSource()) {
+            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 = getActivePreviewSource();
+            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.
+                updateActivePreviewSourceRef(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();
+    _initCenterSubtabToggle();
+    _initSidebarSubtabToggle();
+    initVideoPlanTab();
+    initVideoAssetTab();
+    initVideoProjectTab();
+    initVideoEditTab();
+
+    _wireVideoSourcePanel();
+    _initPropTabs();
+    _initFrameTab();
+    _initGifTab();
+
+    // 保存済みの音量を適用し、以降ユーザーがネイティブコントロールで変更した音量も
+    // 自動保存する（要素自体は永続的なので初期化時に一度呼べば十分）。両方のプレビュー
+    // ペイン(Asset/Source用・生成結果用)の<video>要素に適用する。
+    getAllPreviewVideoElements().forEach((video) => applyStoredVideoVolume(video));
+}

static/js/video-utils.js

@@ -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;
+    });
+}

static/js/video-workflow.js

@@ -0,0 +1,276 @@
+/**
+ * Video workflow helpers — model-family detection and node manipulation for
+ * MiniMax H3 / LTX-2.5 (Text・Image・First&Last-Frame to Video) / Wan2.2 (Text
+ * to Video) workflows. Pure functions operating on UI-format workflow JSON,
+ * with no DOM/state dependency, so both the Plan tab's per-block batch loop
+ * and (previously) the single-shot form can share the exact same logic.
+ */
+
+// ============================================
+// 動画モデルノードの動的特定（ノードIDをハードコードしない）
+// サブグラフの内部ノード型（Wan2.2のみサブグラフ名）で対象ワークフローの種類を判定し、
+// 以降はサブグラフが公開する入力のlabel（無ければname）で意味的にスロットを特定する。
+// これによりLTX-2.5の3バリアント間で公開入力のname（text/value/value_1...）が揺れて
+// いても、label（prompt/duration/width...）は共通しているため同じロジックで処理できる。
+// ============================================
+
+// 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、指定した入力定義の
+// 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 _widgetIndexOf(node, inputDef) {
+    if (!inputDef) return -1;
+    const widgetInputs = (node?.inputs || []).filter((inp) => inp.widget);
+    return widgetInputs.indexOf(inputDef);
+}
+
+// 上と同じ並び順ルールで、名前から直接インデックスを引く版（ResolutionSelectorや
+// LoadImageなど、モデルファミリー間で入力名が揺れないノードに使う）。
+export function widgetIndex(node, name) {
+    const widgetInputs = (node?.inputs || []).filter((inp) => inp.widget);
+    return widgetInputs.findIndex((inp) => inp.name === name);
+}
+
+// サブグラフが公開する入力は、labelがあればlabel、無ければnameが意味的な役割を表す
+// （例: MiniMax H3の"width"はlabel無しでname自体が"width"、LTX-2.5の"value_2"はlabelが
+// "width"）。
+function _findInputByLabel(node, label) {
+    return (node?.inputs || []).find((inp) => (inp.label || inp.name) === label) || null;
+}
+
+// noise_seedはLTX-2.5のText to Videoバリアントだけlabel="seed"を持つ(他は無label)ため、
+// 意味的ラベルではなくname自体（全バリアントで"noise_seed"に統一）で検索する。
+function _findInputByName(node, name) {
+    return (node?.inputs || []).find((inp) => inp.name === name) || null;
+}
+
+function _detectVideoSubgraph(workflow) {
+    if (!Array.isArray(workflow?.nodes) || !Array.isArray(workflow?.links)) return null;
+    const subgraphs = workflow.definitions?.subgraphs || [];
+
+    const minimax = subgraphs.find((sg) => (sg.nodes || []).some((n) => n.type === "MiniMaxH3ImageToVideo"));
+    if (minimax) return { family: "minimax", sgDef: minimax };
+
+    // LTX-2.5のText/Image/First&Last-Frame to Videoはいずれも内部にLTXV*系ノードを持つ
+    // サブグラフとして配布される。バリアントごとにサブグラフの公開入力nameが異なるため、
+    // 内部ノード型のプレフィックスで検出する。
+    const ltx = subgraphs.find((sg) => (sg.nodes || []).some((n) => typeof n.type === "string" && n.type.startsWith("LTXV")));
+    if (ltx) return { family: "ltx25", sgDef: ltx };
+
+    // Wan2.2はUNETLoader/KSamplerAdvanced/EmptyHunyuanLatentVideoなど汎用ノードのみで
+    // 構成されており、MiniMax H3/LTX-2.5のような固有ノード型による検出ができない。
+    // そのためサブグラフ自体の名前（公式テンプレート由来の "...(Wan2.2)" 表記）で検出する。
+    const wan22 = subgraphs.find((sg) => /wan\s*2\.2/i.test(sg.name || ""));
+    if (wan22) return { family: "wan22", sgDef: wan22 };
+
+    return null;
+}
+
+export function locateVideoModelNodes(workflow) {
+    const detected = _detectVideoSubgraph(workflow);
+    if (!detected) return null;
+    const { family, sgDef } = detected;
+
+    const instanceNode = workflow.nodes.find((n) => n.type === sgDef.id);
+    if (!instanceNode || !Array.isArray(instanceNode.widgets_values)) return null;
+
+    // Wan2.2の公開入力はプロンプトが label 無しの name="text" のまま(MiniMax H3の"prompt"や
+    // LTX-2.5の label="prompt" と異なる)ため、"prompt" が見つからない場合は "text" にフォール
+    // バックする。
+    const promptInput = _findInputByLabel(instanceNode, "prompt") || _findInputByLabel(instanceNode, "text");
+    const durationInput = _findInputByLabel(instanceNode, "duration");
+    const widthInput = _findInputByLabel(instanceNode, "width");
+    if (!promptInput || !durationInput || !widthInput) return null;
+
+    const promptIdx = _widgetIndexOf(instanceNode, promptInput);
+    const durationIdx = _widgetIndexOf(instanceNode, durationInput);
+    if (promptIdx === -1 || durationIdx === -1) return null;
+
+    const widthSlot = instanceNode.inputs.indexOf(widthInput);
+
+    // ワークフローにより有無・意味が異なる任意項目。見つからなければ全て-1を返し、
+    // 呼び出し側(UI)はその項目を無効化・非ハイライトにする。
+    const heightInput = _findInputByLabel(instanceNode, "height");
+    const noiseSeedInput = _findInputByName(instanceNode, "noise_seed");
+    // MiniMax H3は"turbo_mode"、Wan2.2は"enable_turbo_mode" — 同じ意味のトグルをUI上は
+    // 1つのフィールドとして兼用するため、どちらか見つかった方を使う。
+    const turboModeInput = _findInputByLabel(instanceNode, "turbo_mode") || _findInputByLabel(instanceNode, "enable_turbo_mode");
+    const turboStrengthInput = _findInputByLabel(instanceNode, "turbo_model_strength");
+    const turboStepsInput = _findInputByLabel(instanceNode, "turbo_steps");
+    const promptEnhanceInput = _findInputByLabel(instanceNode, "prompt_enhance");
+    const noiseSeedIdx = _widgetIndexOf(instanceNode, noiseSeedInput);
+    const turboModeIdx = _widgetIndexOf(instanceNode, turboModeInput);
+    const turboStrengthIdx = _widgetIndexOf(instanceNode, turboStrengthInput);
+    const turboStepsIdx = _widgetIndexOf(instanceNode, turboStepsInput);
+    const promptEnhanceIdx = _widgetIndexOf(instanceNode, promptEnhanceInput);
+
+    const findLinkedNode = (targetSlot) => {
+        if (targetSlot === -1) return null;
+        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;
+    };
+
+    // 一部のLTX-2.5バリアント(First & Last Frame to Video等)はResolutionSelectorを介さず、
+    // width/heightをサブグラフの直接ウィジェット値として持つ。その場合はresolutionNode: null
+    // を返し、以降の処理ではaspect_ratio等の操作をスキップする(テンプレートのwidth/height値が
+    // そのまま使われる)。
+    const rawResolutionNode = findLinkedNode(widthSlot);
+    const resolutionNode = (rawResolutionNode && rawResolutionNode.type === "ResolutionSelector") ? rawResolutionNode : null;
+
+    // first_frame/last_frameはoptional入力のため、対応する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nullのまま返し、必要ならapplyBlockToWorkflow側で動的にノードを注入する。
+    const firstFrameInput = _findInputByLabel(instanceNode, "first_frame");
+    const lastFrameInput = _findInputByLabel(instanceNode, "last_frame");
+    const firstFrameSlot = firstFrameInput ? instanceNode.inputs.indexOf(firstFrameInput) : -1;
+    const lastFrameSlot = lastFrameInput ? instanceNode.inputs.indexOf(lastFrameInput) : -1;
+
+    const rawLoadImageNode = findLinkedNode(firstFrameSlot);
+    const loadImageNode = (rawLoadImageNode && rawLoadImageNode.type === "LoadImage") ? rawLoadImageNode : null;
+    const rawLastLoadImageNode = findLinkedNode(lastFrameSlot);
+    const lastLoadImageNode = (rawLastLoadImageNode && rawLastLoadImageNode.type === "LoadImage") ? rawLastLoadImageNode : null;
+
+    // width/heightはResolutionSelectorがあればその出力に接続されており(widthSlotの
+    // link経由)、その場合は直接ウィジェット値を書き換えても実行時にリンク元の出力で
+    // 上書きされ意味がない。resolutionNodeがnullのワークフロー(LTX-2.5 First&Last-Frame,
+    // Wan2.2全種)でのみ、instanceNode自身のwidth/heightウィジェットを直接編集できる。
+    const widthIdx = resolutionNode ? -1 : _widgetIndexOf(instanceNode, widthInput);
+    const heightIdx = resolutionNode ? -1 : _widgetIndexOf(instanceNode, heightInput);
+
+    return {
+        family, sgDef, instanceNode, loadImageNode, lastLoadImageNode, resolutionNode,
+        firstFrameSlot, lastFrameSlot, promptIdx, durationIdx,
+        widthIdx, heightIdx, noiseSeedIdx, turboModeIdx, turboStrengthIdx, turboStepsIdx, promptEnhanceIdx,
+    };
+}
+
+// Reads the current values a freshly-loaded template workflow carries, so a UI
+// (the Plan tab's first block, previously the single-shot form) can seed its
+// fields from whatever the workflow file already had.
+export function readTemplateDefaults(nodes) {
+    const {
+        instanceNode, resolutionNode, loadImageNode, lastLoadImageNode, promptIdx, durationIdx,
+        widthIdx, heightIdx, noiseSeedIdx, turboModeIdx, turboStrengthIdx, turboStepsIdx, promptEnhanceIdx,
+    } = nodes;
+    const arIdx = widgetIndex(resolutionNode, "aspect_ratio");
+    const mpIdx = widgetIndex(resolutionNode, "megapixels");
+    const multIdx = widgetIndex(resolutionNode, "multiple");
+    const fnIdx = widgetIndex(loadImageNode, "image");
+    const lastFnIdx = widgetIndex(lastLoadImageNode, "image");
+    return {
+        prompt: instanceNode.widgets_values[promptIdx] ?? "",
+        duration: instanceNode.widgets_values[durationIdx] ?? 2,
+        hasResolution: !!resolutionNode,
+        aspectRatio: (resolutionNode && arIdx !== -1) ? resolutionNode.widgets_values[arIdx] : null,
+        megapixels: (resolutionNode && mpIdx !== -1) ? resolutionNode.widgets_values[mpIdx] : null,
+        multiple: (resolutionNode && multIdx !== -1) ? resolutionNode.widgets_values[multIdx] : null,
+        firstImageFilename: (loadImageNode && fnIdx !== -1) ? loadImageNode.widgets_values[fnIdx] : null,
+        lastImageFilename: (lastLoadImageNode && lastFnIdx !== -1) ? lastLoadImageNode.widgets_values[lastFnIdx] : null,
+        // width/heightは resolutionNode がある場合は widthIdx/heightIdx が -1 になる
+        // (locateVideoModelNodes参照) ため、常にnullが返り直接入力欄は無効化される。
+        hasDirectResolution: widthIdx !== -1 && heightIdx !== -1,
+        width: widthIdx !== -1 ? instanceNode.widgets_values[widthIdx] : null,
+        height: heightIdx !== -1 ? instanceNode.widgets_values[heightIdx] : null,
+        hasNoiseSeed: noiseSeedIdx !== -1,
+        noiseSeed: noiseSeedIdx !== -1 ? instanceNode.widgets_values[noiseSeedIdx] : null,
+        hasTurboMode: turboModeIdx !== -1,
+        turboMode: turboModeIdx !== -1 ? instanceNode.widgets_values[turboModeIdx] : null,
+        hasTurboStrength: turboStrengthIdx !== -1,
+        turboStrength: turboStrengthIdx !== -1 ? instanceNode.widgets_values[turboStrengthIdx] : null,
+        hasTurboSteps: turboStepsIdx !== -1,
+        turboSteps: turboStepsIdx !== -1 ? instanceNode.widgets_values[turboStepsIdx] : null,
+        hasPromptEnhance: promptEnhanceIdx !== -1,
+        promptEnhance: promptEnhanceIdx !== -1 ? instanceNode.widgets_values[promptEnhanceIdx] : null,
+    };
+}
+
+// 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)。
+export 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;
+}
+
+// Writes one block's (or the old single-shot form's) values onto a workflow
+// clone already located via locateVideoModelNodes(clone) — `nodes`' node
+// references point into `clone` itself. First/last frame images are only
+// touched when a filename is actually given: an existing LoadImage node gets
+// its widget overwritten, a missing one gets injected on demand, and leaving
+// both unset keeps the slot disconnected (bypassed) exactly like the
+// template's own clean state.
+export function applyBlockToWorkflow(clone, nodes, params) {
+    const {
+        instanceNode, resolutionNode, loadImageNode, lastLoadImageNode, promptIdx, durationIdx, firstFrameSlot, lastFrameSlot,
+        widthIdx, heightIdx, turboModeIdx, turboStrengthIdx, turboStepsIdx, promptEnhanceIdx,
+    } = nodes;
+
+    instanceNode.widgets_values[promptIdx] = params.prompt || "";
+    instanceNode.widgets_values[durationIdx] = params.duration;
+
+    const arIdx = widgetIndex(resolutionNode, "aspect_ratio");
+    const mpIdx = widgetIndex(resolutionNode, "megapixels");
+    const multIdx = widgetIndex(resolutionNode, "multiple");
+    if (arIdx !== -1) resolutionNode.widgets_values[arIdx] = params.aspectRatio;
+    if (mpIdx !== -1) resolutionNode.widgets_values[mpIdx] = params.megapixels;
+    if (multIdx !== -1) resolutionNode.widgets_values[multIdx] = params.multiple;
+
+    // widthIdx/heightIdxはresolutionNodeがあるワークフローでは-1固定(locateVideoModelNodes
+    // 参照)なので、そちらはaspect_ratio/megapixels/multiple経由の計算値のまま変更しない。
+    if (widthIdx !== -1 && params.width != null) instanceNode.widgets_values[widthIdx] = params.width;
+    if (heightIdx !== -1 && params.height != null) instanceNode.widgets_values[heightIdx] = params.height;
+    // noise_seedはここでは書かない — comfyUI.generate()がAPI変換後のワークフロー全体から
+    // "noise_seed"/"seed"キーを持つ全ノードを一括で上書きする(applySeedToWorkflow)ため、
+    // ここで書いてもその後必ず上書きされる。Plan全体のシード管理は引き続きそちら任せでよい。
+    if (turboModeIdx !== -1 && params.turboMode != null) instanceNode.widgets_values[turboModeIdx] = params.turboMode;
+    if (turboStrengthIdx !== -1 && params.turboStrength != null) instanceNode.widgets_values[turboStrengthIdx] = params.turboStrength;
+    if (turboStepsIdx !== -1 && params.turboSteps != null) instanceNode.widgets_values[turboStepsIdx] = params.turboSteps;
+    if (promptEnhanceIdx !== -1 && params.promptEnhance != null) instanceNode.widgets_values[promptEnhanceIdx] = params.promptEnhance;
+
+    if (params.firstImageFilename) {
+        if (loadImageNode) {
+            const fnIdx = widgetIndex(loadImageNode, "image");
+            if (fnIdx !== -1) loadImageNode.widgets_values[fnIdx] = params.firstImageFilename;
+        } else if (firstFrameSlot !== -1) {
+            injectFrameNode(clone, instanceNode, firstFrameSlot, params.firstImageFilename);
+        }
+    }
+    if (params.lastImageFilename) {
+        if (lastLoadImageNode) {
+            const fnIdx = widgetIndex(lastLoadImageNode, "image");
+            if (fnIdx !== -1) lastLoadImageNode.widgets_values[fnIdx] = params.lastImageFilename;
+        } else if (lastFrameSlot !== -1) {
+            injectFrameNode(clone, instanceNode, lastFrameSlot, params.lastImageFilename);
+        }
+    }
+}

static/js/workflow-tab.js

@@ -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";
@@ -30,6 +31,7 @@ const state = {
     groupFilter: "", // empty = all groups
     currentPage: 0,
     showBatchOnly: false,
+    sortBy: localStorage.getItem("wfm_workflow_sort") || "date_desc",
 };
 
 
@@ -46,7 +48,7 @@ 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 +56,13 @@ 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");
     }
@@ -449,6 +450,26 @@ function filterWorkflows() {
     });
 }
 
+function sortWorkflows(list) {
+    const sorted = [...list];
+    switch (state.sortBy) {
+        case "date_asc":
+            sorted.sort((a, b) => a.mtime - b.mtime);
+            break;
+        case "name_asc":
+            sorted.sort((a, b) => a.filename.localeCompare(b.filename));
+            break;
+        case "name_desc":
+            sorted.sort((a, b) => b.filename.localeCompare(a.filename));
+            break;
+        case "date_desc":
+        default:
+            sorted.sort((a, b) => b.mtime - a.mtime);
+            break;
+    }
+    return sorted;
+}
+
 // ============================================
 // Grid Rendering (3 view modes)
 // ============================================
@@ -459,7 +480,7 @@ function renderGrid() {
 
     grid.className = `wfm-grid wfm-view-${state.viewMode}`;
 
-    const filtered = filterWorkflows();
+    const filtered = sortWorkflows(filterWorkflows());
 
     if (filtered.length === 0) {
         grid.innerHTML =
@@ -616,6 +637,8 @@ 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 +684,8 @@ 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 +982,6 @@ 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 +1118,6 @@ 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 {
@@ -1386,6 +1386,16 @@ export function initWorkflowTab() {
         renderGrid();
     });
 
+    // Toolbar: sort dropdown
+    const sortSel = document.getElementById("wfm-sort");
+    if (sortSel) sortSel.value = state.sortBy;
+    sortSel?.addEventListener("change", (e) => {
+        state.sortBy = e.target.value;
+        localStorage.setItem("wfm_workflow_sort", state.sortBy);
+        state.currentPage = 0;
+        renderGrid();
+    });
+
     // Toolbar: Load in GenerateUI button
     document.getElementById("wfm-list-load-btn")?.addEventListener("click", async () => {
         if (!state.selectedWf) return;
@@ -1399,12 +1409,25 @@ export function initWorkflowTab() {
         }
     });
 
+    // Toolbar: Load in Video button (MiniMax H3 / LTX-2.5 / Wan2.2対応。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");
         }

tools/generate_doc_index.py

@@ -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()

web/comfyui/node_sets_menu.js

@@ -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、
@@ -3171,6 +3405,13 @@ function _extractPromptsAPI(wf) {
             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".
@@ -3298,6 +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;
@@ -3333,6 +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;
@@ -3442,7 +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 = [
@@ -3481,7 +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) {
@@ -3530,7 +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 = [
@@ -3595,8 +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>
@@ -3622,11 +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);
@@ -3645,22 +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);
     }
 }
 
@@ -3676,54 +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}`);
@@ -3875,6 +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">
@@ -3894,6 +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">
@@ -3947,6 +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;
@@ -3978,10 +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");
@@ -3992,10 +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";
@@ -4069,11 +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");
     });
 
@@ -4119,7 +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 = "";
@@ -4351,7 +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())
@@ -4376,7 +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";

web/comfyui/top_menu_extension.js

@@ -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;

workflows/ws_yaml_wildcard_sample.json

@@ -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
+}