refactor(agent): consolidate skill definition into single SKILL.md with YAML frontmatter

Merge skill.yaml (metadata) and prompt.md (prompt template) into a
single SKILL.md file with YAML frontmatter, matching the agent-skill
convention used by opencode and Claude Code.

- Add frontmatter parser (_parse_skill_file) to SkillRegistry
- Remove skill.yaml, prompt.md, empty skills/__init__.py
- Remove obsolete load_handler method
- Update tests for new format and cleaned-up fields

.docs/batch-import-design.md

@@ -1,153 +0,0 @@
-# Recipe Batch Import Feature Design
-
-## Overview
-Enable users to import multiple images as recipes in a single operation, rather than processing them individually. This feature addresses the need for efficient bulk recipe creation from existing image collections.
-
-## Architecture
-
-```
-┌─────────────────────────────────────────────────────────────────┐
-│                        Frontend                                  │
-├─────────────────────────────────────────────────────────────────┤
-│  BatchImportManager.js                                          │
-│  ├── InputCollector (收集URL列表/目录路径)                        │
-│  ├── ConcurrencyController (自适应并发控制)                       │
-│  ├── ProgressTracker (进度追踪)                                   │
-│  └── ResultAggregator (结果汇总)                                  │
-├─────────────────────────────────────────────────────────────────┤
-│  batch_import_modal.html                                        │
-│  └── 批量导入UI组件                                              │
-├─────────────────────────────────────────────────────────────────┤
-│  batch_import_progress.css                                      │
-│  └── 进度显示样式                                                │
-└─────────────────────────────────────────────────────────────────┘
-                              │
-                              ▼
-┌─────────────────────────────────────────────────────────────────┐
-│                        Backend                                   │
-├─────────────────────────────────────────────────────────────────┤
-│  py/routes/handlers/recipe_handlers.py                          │
-│  ├── start_batch_import() - 启动批量导入                         │
-│  ├── get_batch_import_progress() - 查询进度                      │
-│  └── cancel_batch_import() - 取消导入                            │
-├─────────────────────────────────────────────────────────────────┤
-│  py/services/batch_import_service.py                            │
-│  ├── 自适应并发执行                                              │
-│  ├── 结果汇总                                                    │
-│  └── WebSocket进度广播                                           │
-└─────────────────────────────────────────────────────────────────┘
-```
-
-## API Endpoints
-
-| 端点 | 方法 | 说明 |
-|------|------|------|
-| `/api/lm/recipes/batch-import/start` | POST | 启动批量导入，返回 operation_id |
-| `/api/lm/recipes/batch-import/progress` | GET | 查询进度状态 |
-| `/api/lm/recipes/batch-import/cancel` | POST | 取消导入 |
-
-## Backend Implementation Details
-
-### BatchImportService
-
-Location: `py/services/batch_import_service.py`
-
-Key classes:
-- `BatchImportItem`: Dataclass for individual import item
-- `BatchImportProgress`: Dataclass for tracking progress
-- `BatchImportService`: Main service class
-
-Features:
-- Adaptive concurrency control (adjusts based on success/failure rate)
-- WebSocket progress broadcasting
-- Graceful error handling (individual failures don't stop the batch)
-- Result aggregation
-
-### WebSocket Message Format
-
-```json
-{
-    "type": "batch_import_progress",
-    "operation_id": "xxx",
-    "total": 50,
-    "completed": 23,
-    "success": 21,
-    "failed": 2,
-    "skipped": 0,
-    "current_item": "image_024.png",
-    "status": "running"
-}
-```
-
-### Input Types
-
-1. **URL List**: Array of URLs (http/https)
-2. **Local Paths**: Array of local file paths
-3. **Directory**: Path to directory with optional recursive flag
-
-### Error Handling
-
-- Invalid URLs/paths: Skip and record error
-- Download failures: Record error, continue
-- Metadata extraction failures: Mark as "no metadata"
-- Duplicate detection: Option to skip duplicates
-
-## Frontend Implementation Details (TODO)
-
-### UI Components
-
-1. **BatchImportModal**: Main modal with tabs for URLs/Directory input
-2. **ProgressDisplay**: Real-time progress bar and status
-3. **ResultsSummary**: Final results with success/failure breakdown
-
-### Adaptive Concurrency Controller
-
-```javascript
-class AdaptiveConcurrencyController {
-    constructor(options = {}) {
-        this.minConcurrency = options.minConcurrency || 1;
-        this.maxConcurrency = options.maxConcurrency || 5;
-        this.currentConcurrency = options.initialConcurrency || 3;
-    }
-
-    adjustConcurrency(taskDuration, success) {
-        if (success && taskDuration < 1000 && this.currentConcurrency < this.maxConcurrency) {
-            this.currentConcurrency = Math.min(this.currentConcurrency + 1, this.maxConcurrency);
-        }
-        if (!success || taskDuration > 10000) {
-            this.currentConcurrency = Math.max(this.currentConcurrency - 1, this.minConcurrency);
-        }
-        return this.currentConcurrency;
-    }
-}
-```
-
-## File Structure
-
-```
-Backend (implemented):
-├── py/services/batch_import_service.py            # 后端服务
-├── py/routes/handlers/batch_import_handler.py     # API处理器 (added to recipe_handlers.py)
-├── tests/services/test_batch_import_service.py    # 单元测试
-└── tests/routes/test_batch_import_routes.py       # API集成测试
-
-Frontend (TODO):
-├── static/js/managers/BatchImportManager.js       # 主管理器
-├── static/js/managers/batch/                       # 子模块
-│   ├── ConcurrencyController.js                    # 并发控制
-│   ├── ProgressTracker.js                          # 进度追踪
-│   └── ResultAggregator.js                         # 结果汇总
-├── static/css/components/batch-import-modal.css   # 样式
-└── templates/components/batch_import_modal.html    # Modal模板
-```
-
-## Implementation Status
-
-- [x] Backend BatchImportService
-- [x] Backend API handlers
-- [x] WebSocket progress broadcasting
-- [x] Unit tests
-- [x] Integration tests
-- [ ] Frontend BatchImportManager
-- [ ] Frontend UI components
-- [ ] E2E tests

.gitignore

@@ -7,6 +7,10 @@ py/run_test.py
 .vscode/
 cache/
 civitai/
+stats/
+wildcards/
+backups/
+logs/
 node_modules/
 coverage/
 .coverage
@@ -19,6 +23,7 @@ model_cache/
 .codex
 .omo
 reasonix.toml
+.reasonix/
 .codegraph/
 
 # Vue widgets development cache (but keep build output)
@@ -28,3 +33,6 @@ vue-widgets/dist/
 
 # Hypothesis test cache
 .hypothesis/
+
+# Working/research notes (not committed)
+.docs/

data/supporters.json

@@ -11,42 +11,62 @@
     "Insomnia Art Designs",
     "2018cfh",
     "Arlecchino Shion",
+    "Charles Blakemore",
     "Rob Williams",
     "W+K+White",
     "$MetaSamsara",
     "wackop",
     "Phil",
     "Carl G.",
-    "Charles Blakemore",
     "stone9k",
+    "Rosenthal",
+    "Francisco Tatis",
+    "JongWon Han",
+    "FreelancerZ",
+    "Polymorphic Indeterminate",
+    "Skalabananen",
+    "Marc Whiffen",
+    "Birdy",
     "itismyelement",
     "Mozzel",
     "Gingko Biloba",
     "Kiba",
+    "Reno Lam",
     "onesecondinosaur",
+    "sig",
     "Christian Byrne",
     "DM",
     "Sen314",
     "Estragon",
-    "Rosenthal",
+    "J\\B/ 8r0wns0n",
     "ClockDaemon",
-    "Francisco Tatis",
+    "KD",
+    "Omnidex",
+    "Tyler Trebuchon",
+    "Release Cabrakan",
     "Tobi_Swagg",
     "SG",
+    "James Dooley",
+    "zenbound",
+    "Buzzard",
     "jmack",
     "Andrew Wilson",
     "Greybush",
+    "Mark Corneglio",
     "Ricky Carter",
-    "JongWon Han",
+    "James Todd",
+    "Steven Pfeiffer",
     "VantAI",
     "レプサイ",
+    "Lisster",
     "Michael Wong",
     "runte3221",
     "Illrigger",
     "Tom Corrigan",
     "JackieWang",
-    "FreelancerZ",
     "fnkylove",
+    "Yushio",
+    "Vik71it",
     "Echo",
     "Lilleman",
     "Robert Stacey",
@@ -54,174 +74,161 @@
     "Edgar Tejeda",
     "Fraser Cross",
     "Liam MacDougal",
-    "Polymorphic Indeterminate",
     "Sterilized",
+    "BadassArabianMofo",
     "JORGE+LUIZ+HUSSNI+MESSIAS",
-    "Marc Whiffen",
-    "Skalabananen",
-    "Birdy",
     "quarz",
-    "Reno Lam",
+    "Greg",
+    "jean jahren",
     "JSST",
-    "sig",
-    "J\\B/ 8r0wns0n",
     "Snaggwort",
+    "lmsupporter",
     "Takkan",
+    "wfpearl",
     "Matt+J",
     "Baekdoosixt",
     "Jonathan Ross",
-    "KD",
-    "Omnidex",
+    "Jack B Nimble",
     "Nazono_hito",
     "Melville Parrish",
     "daniel dove",
     "Lustre",
-    "Tyler Trebuchon",
-    "Release Cabrakan",
     "JW Sin",
     "Alex",
+    "bh",
     "carozzz",
     "Marlon Daniels",
-    "James Dooley",
-    "zenbound",
-    "Buzzard",
+    "Starkselle",
+    "Aaron Bleuer",
+    "LacesOut!",
+    "greebles",
     "Adam Shaw",
-    "Mark Corneglio",
     "SarcasticHashtag",
     "Anthony Rizzo",
     "iamresist",
+    "M Postkasse",
     "RedrockVP",
     "Wolffen",
-    "James Todd",
     "Wicked Choices by ASLPro3D",
+    "Jacob Hoehler",
     "FinalyFree",
-    "Steven Pfeiffer",
+    "Weasyl",
     "Timmy",
     "Johnny",
+    "Cory Paza",
     "Tak",
-    "Lisster",
     "Big Red",
     "whudunit",
     "Luc Job",
     "dl0901dm",
+    "Philip Hempel",
     "corde",
-    "Nick Walker",
-    "Yushio",
-    "Vik71it",
+    "nwalker94",
     "Bishoujoker",
+    "aai",
     "Todd Keck",
     "Briton Heilbrun",
     "Tori",
     "wildnut",
     "Aleksander Wujczyk",
     "AM Kuro",
-    "BadassArabianMofo",
     "Pascal Dahle",
-    "Greg",
+    "Sangheili460",
+    "MagnaInsomnia",
     "Akira_HentAI",
-    "lmsupporter",
+    "Karl P.",
     "andrew.tappan",
+    "N/A",
+    "The Spawn",
+    "graysock",
     "Greenmoustache",
     "zounic",
-    "wfpearl",
-    "Eldithor",
-    "Jack B Nimble",
+    "fancypants",
+    "Digital",
     "JaxMax",
     "contrite831",
-    "bh",
     "Jwk0205",
-    "Starkselle",
+    "Bro Xie",
+    "batblue",
+    "carey6409",
     "Olive",
-    "Aaron Bleuer",
-    "LacesOut!",
-    "greebles",
+    "太郎 ゲーム",
     "Some Guy Named Barry",
-    "M Postkasse",
+    "AELOX",
     "Gooohokrbe",
+    "Nicfit23",
+    "wamekukyouzin",
     "OldBones",
-    "Jacob Hoehler",
+    "drum matthieu",
+    "Dogmaster",
     "Matt Wenzel",
-    "Weasyl",
     "Lex Song",
-    "Cory Paza",
+    "Christopher Michel",
     "Gonzalo Andre Allendes Lopez",
     "Zach Gonser",
     "Serge Bekenkamp",
     "Jimmy Ledbetter",
-    "Philip Hempel",
+    "LeoZero",
+    "Antonio Pontes",
+    "nahinahi9",
+    "Dustin Chen",
     "dan",
-    "aai",
+    "Mouthlessman",
     "otaku fra",
-    "jean jahren",
     "MiraiKuriyamaSy",
     "Ran C",
     "ViperC",
     "Penfore",
-    "Sangheili460",
-    "MagnaInsomnia",
-    "Karl P.",
     "Gordon Cole",
     "Adam Taylor",
     "AbstractAss",
     "Weird_With_A_Beard",
-    "N/A",
-    "The Spawn",
-    "graysock",
     "Pozadine1",
     "Qarob",
     "AIGooner",
     "Luc",
     "ProtonPrince",
     "DiffDuck",
-    "Jackthemind",
-    "fancypants",
-    "Joboshy",
-    "Digital",
-    "takyamtom",
-    "Bohemian Corporal",
-    "Dan",
-    "Bro Xie",
-    "yer fey",
-    "batblue",
-    "carey6409",
-    "太郎 ゲーム",
-    "Roslynd",
-    "jinxedx",
-    "Cosmosis",
-    "AELOX",
-    "Dankin",
-    "Nicfit23",
-    "FloPro4Sho",
-    "Cristian Vazquez",
-    "wamekukyouzin",
-    "drum matthieu",
-    "Dogmaster",
-    "Frank Nitty",
-    "Magic Noob",
-    "Christopher Michel",
-    "DougPeterson",
-    "LeoZero",
-    "Antonio Pontes",
-    "ApathyJones",
-    "Bruce",
-    "Julian V",
-    "Steven Owens",
-    "nahinahi9",
-    "Kevin John Duck",
-    "Dustin Chen",
-    "Blackfish95",
-    "Mouthlessman",
-    "Paul Kroll",
-    "Bas Imagineer",
-    "John Statham",
-    "yuxz69",
-    "esthe",
-    "decoy",
     "elu3199",
     "Hasturkun",
     "Jon Sandman",
     "Ubivis",
     "CloudValley",
+    "linnfrey",
+    "Jackthemind",
+    "griffin+dahlberg",
+    "Joboshy",
+    "takyamtom",
+    "Bohemian Corporal",
+    "Dan",
+    "yer fey",
+    "Error_Rule34_Not_found",
+    "Roslynd",
+    "jinxedx",
+    "Neco28",
+    "Cosmosis",
+    "David Ortega",
+    "Dankin",
+    "FloPro4Sho",
+    "Cristian Vazquez",
+    "Frank Nitty",
+    "Magic Noob",
+    "DougPeterson",
+    "ApathyJones",
+    "Jeff",
+    "Bruce",
+    "Steven Owens",
+    "Kevin John Duck",
+    "Kevin Christopher",
+    "Blackfish95",
+    "dd",
+    "Paul Kroll",
+    "Bas Imagineer",
+    "John Statham",
+    "yuxz69",
+    "esthe",
+    "AlexDuKaNa",
+    "decoy",
     "thesoftwaredruid",
     "wundershark",
     "mr_dinosaur",
@@ -229,53 +236,11 @@
     "Ray Wing",
     "Ranzitho",
     "Gus",
+    "地獄の禄",
     "MJG",
     "David LaVallee",
-    "linnfrey",
-    "IamAyam",
-    "skaterb949",
-    "Josef Lanzl",
-    "Nerezza",
-    "sanborondon",
-    "confiscated Zyra",
-    "Error_Rule34_Not_found",
-    "Taylor Funk",
-    "aezin",
-    "jcay015",
-    "Gerald Welly",
-    "Erik Lopez",
-    "Mateo Curić",
-    "Tee Gee",
-    "Geolog",
-    "tarek helmi",
-    "Neco28",
-    "Eris3D",
-    "Max Marklund",
-    "David Ortega",
-    "Pronredn",
-    "a _",
-    "Jeff",
-    "lh qwe",
-    "James Coleman",
-    "conner",
-    "Kevin Christopher",
-    "Chad Idk",
-    "dd",
-    "Princess Bright Eyes",
-    "Dušan Ryban",
-    "Felipe dos Santos",
-    "Sam",
-    "sjon kreutz",
-    "Douglas Gaspar",
-    "Metryman55",
-    "AlexDuKaNa",
-    "George",
-    "dw",
-    "地獄の禄",
     "ae",
     "Tr4shP4nda",
-    "Gamalonia",
-    "WRL_SPR",
     "capn",
     "Joseph",
     "Mirko Katzula",
@@ -283,64 +248,64 @@
     "Piccio08",
     "kumakichi",
     "cppbel",
+    "IamAyam",
+    "jeaness",
+    "Brian M",
+    "Josef Lanzl",
+    "Nerezza",
+    "sanborondon",
+    "confiscated Zyra",
+    "Taylor Funk",
+    "aezin",
+    "Thought2Form",
+    "jcay015",
+    "Gerald Welly",
+    "Kevin Picco",
+    "Erik Lopez",
+    "Mateo Curić",
+    "Tee Gee",
+    "Geolog",
+    "tarek helmi",
+    "Eris3D",
+    "Max Marklund",
+    "m",
+    "Pierce McBride",
+    "Pronredn",
+    "Mikko Hemilä",
+    "Jamie Ogletree",
+    "a _",
+    "lh qwe",
+    "James Coleman",
+    "Martial",
+    "conner",
+    "Ouro Boros",
+    "Chad Idk",
+    "Princess Bright Eyes",
+    "Yuji Kaneko",
+    "Dušan Ryban",
+    "Felipe dos Santos",
+    "Rops Alot",
+    "Sam",
+    "sjon kreutz",
+    "Ace Ventura",
+    "Douglas Gaspar",
+    "Metryman55",
+    "George",
+    "dw",
+    "Gamalonia",
+    "WRL_SPR",
+    "momokai",
     "Moon Knight",
     "몽타주",
     "Kland",
     "Hailshem",
     "kudari",
     "Naomi Hale Danchi",
+    "ken",
     "epicgamer0020690",
     "Joshua Porrata",
-    "SuBu",
-    "Richard",
-    "奚明 刘",
-    "Andrew",
-    "Brian M",
-    "Robert Wegemund",
-    "Littlehuggy",
-    "준희 김",
-    "Brian Buie",
-    "Thought2Form",
-    "Kevin Picco",
-    "Sadlip",
-    "Tomohiro Baba",
-    "m",
-    "Noora",
-    "Pierce McBride",
-    "Joshua Gray",
-    "Mattssn",
-    "Mikko Hemilä",
-    "Jacob McDaniel",
-    "Jamie Ogletree",
-    "Temikus",
-    "Artokun",
-    "Michael Taylor",
-    "Martial",
-    "Michael Anthony Scott",
-    "Emil Andersson",
-    "Ouro Boros",
-    "Atilla Berke Pekduyar",
-    "Steam Steam",
-    "CryptoTraderJK",
-    "Decx _",
-    "Yuji Kaneko",
-    "Davaitamin",
-    "Rops Alot",
-    "tedcor",
-    "Fotek Design",
-    "Ace Ventura",
-    "四糸凜音",
-    "Nihongasuki",
-    "LarsesFPC",
-    "MadSpin",
-    "inbijiburu",
-    "Nick “Loadstone” D",
-    "momokai",
-    "starbugx",
-    "dc7431",
-    "ken",
-    "Crocket",
     "keemun",
+    "SuBu",
     "RedPIXel",
     "Wind",
     "Nexus",
@@ -353,6 +318,51 @@
     "JohnDoe42054",
     "BillyHill",
     "emyth",
+    "gzmzmvp",
+    "Richard",
+    "奚明 刘",
+    "Andrew",
+    "Robert Wegemund",
+    "Littlehuggy",
+    "Gregory Kozhemiak",
+    "준희 김",
+    "Brian Buie",
+    "Sadlip",
+    "Eric Whitney",
+    "Joey Callahan",
+    "Ivan Tadic",
+    "Tomohiro Baba",
+    "Mike Simone",
+    "Noora",
+    "Joshua Gray",
+    "Mattssn",
+    "Morgandel",
+    "Jacob McDaniel",
+    "X",
+    "Sloan Steddy",
+    "Temikus",
+    "Artokun",
+    "Michael Taylor",
+    "Derek Baker",
+    "Michael Anthony Scott",
+    "Emil Andersson",
+    "Atilla Berke Pekduyar",
+    "Steam Steam",
+    "CryptoTraderJK",
+    "Decx _",
+    "Davaitamin",
+    "tedcor",
+    "Fotek Design",
+    "四糸凜音",
+    "Nihongasuki",
+    "LarsesFPC",
+    "MadSpin",
+    "FrxzenSnxw",
+    "inbijiburu",
+    "Nick “Loadstone” D",
+    "starbugx",
+    "dc7431",
+    "Crocket",
     "chriphost",
     "KitKatM",
     "socrasteeze",
@@ -370,59 +380,66 @@
     "Vir",
     "Skyfire83",
     "Adam Rinehart",
-    "gzmzmvp",
-    "Gregory Kozhemiak",
+    "Pitpe11",
+    "TheD1rtyD03",
+    "moonpetal",
+    "g9p0o",
+    "TheHolySheep",
+    "Monte Won",
+    "SpringBootisTrash",
+    "carsten",
+    "ikok",
+    "Wolfe7D1",
     "Draven T",
-    "mrjuan",
-    "Eric Whitney",
-    "Joey Callahan",
+    "elleshar666",
+    "ACTUALLY_the_Real_Willem_Dafoe",
     "Aquatic Coffee",
-    "Ivan Tadic",
-    "Mike Simone",
+    "Kauffy",
     "John J Linehan",
     "ethanfel",
     "Elliot E",
-    "Morgandel",
     "Theerat Jiramate",
     "Focuschannel",
+    "Edward Kennedy",
     "Noah",
-    "X",
-    "Sloan Steddy",
+    "Vane Holzer",
+    "psytrax",
     "hexxish",
-    "Derek Baker",
     "Anthony Faxlandez",
     "battu",
+    "notedfakes",
     "Nathan",
     "NICHOLAS BAXLEY",
+    "Michael Scott",
     "Pat Hen",
     "Xeeosat",
+    "Saya",
     "Ed Wang",
     "Jordan Shaw",
+    "Wes Sims",
     "g unit",
     "Srdb",
+    "Filippo Ferrari",
     "JC",
     "Prompt Pirate",
     "uwutismxd",
-    "FrxzenSnxw",
     "zenobeus",
     "ryoma",
+    "Whitepinetrader",
     "Stryker",
     "Ginnie",
     "Raku",
     "smart.edge5178",
     "Menard",
-    "Pitpe11",
-    "TheD1rtyD03",
-    "moonpetal",
     "SomeDude",
-    "g9p0o",
     "Pkrsky",
-    "TheHolySheep",
+    "nanana",
     "raf8osz",
-    "Monte Won",
-    "SpringBootisTrash",
-    "carsten",
-    "ikok",
+    "FeralOpticsAI",
+    "Pavlaki",
+    "Doug+Rintoul",
+    "Noor",
+    "Yorunai",
     "quantenmecha",
     "Jason+Nash",
     "DarkRoast",
@@ -436,38 +453,35 @@
     "cocona",
     "ElitaSSJ4",
     "David Schenck",
-    "Wolfe7D1",
     "blikkies",
     "Chris",
     "Time Valentine",
-    "elleshar666",
     "Shock Shockor",
-    "ACTUALLY_the_Real_Willem_Dafoe",
     "Михал Михалыч",
+    "Matt",
     "Goldwaters",
-    "Kauffy",
     "Zude",
+    "Frogmilk",
+    "SPJ",
     "Kyler",
-    "Edward Kennedy",
+    "Bryan Rutkowski",
     "Justin Blaylock",
     "aRtFuL_DodGeR",
     "Nick Kage",
-    "Vane Holzer",
-    "psytrax",
     "Cyrus Fett",
     "Xenon Xue",
-    "notedfakes",
+    "Edward Ten Eyck",
     "Billy Gladky",
-    "Michael Scott",
     "Probis",
     "Solixer",
-    "Wes Sims",
     "ItsGeneralButtNaked",
     "Donor4115",
+    "jinksta187",
     "Distortik",
-    "Filippo Ferrari",
+    "Manu Thetug",
+    "Karlanx",
+    "operationancut",
     "Youguang",
-    "Saya",
     "andrewzpong",
     "BossGame",
     "lrdchs",
@@ -476,9 +490,19 @@
     "AIVORY3D",
     "Kevinj",
     "Mitchell Robson",
-    "Whitepinetrader",
     "POPPIN",
-    "nanana",
+    "YassineKhaled",
+    "Y",
+    "MatteKey",
+    "Flob",
+    "ShiroSenpai",
+    "Inkognito",
+    "G",
+    "Tan+Huynh",
+    "Bob+Barker",
+    "D",
+    "Dark_Pest",
+    "Eldithor",
     "Alex",
     "Karru",
     "ChaChanoKo",
@@ -491,38 +515,36 @@
     "g",
     "J",
     "Alan+Cano",
-    "FeralOpticsAI",
-    "Pavlaki",
-    "Doug+Rintoul",
-    "Noor",
-    "Yorunai",
     "BillyBoy84",
     "Buecyb99",
     "Welkor",
     "John Martin",
-    "Ink Temptation",
     "JBsuede",
     "moranqianlong",
     "Kalli Core",
+    "Ronan Delevacq",
     "Christian Schäfer",
     "りん あめ",
-    "Matt",
+    "Dave Abraham",
+    "Joaquin Hierrezuelo",
     "Locrospiel",
-    "Frogmilk",
-    "SPJ",
+    "Sean voets",
+    "Jarrid Lee",
     "Kor",
     "Joseph Hanson",
+    "John Rednoulf",
     "Kyron Mahan",
-    "Bryan Rutkowski",
+    "Boba Smith",
     "TBitz33",
     "Anonym dkjglfleeoeldldldlkf",
     "Ezokewn",
     "SendingRavens",
+    "Sauv",
+    "Steven",
     "JackJohnnyJim",
     "TenaciousD",
     "Dmitry Ryzhov",
     "Khánh Đặng",
-    "Edward Ten Eyck",
     "Michael Docherty",
     "Jimmy Borup",
     "Paul Hartsuyker",
@@ -530,16 +552,12 @@
     "Pete Pain",
     "Jacob Winter",
     "Ryan Presley Ng",
-    "jinksta187",
     "RHopkirk",
     "Andrew Wilkinson",
-    "Manu Thetug",
-    "Karlanx",
     "Lyavph",
     "Maxim",
     "David",
     "Meilo",
-    "operationancut",
     "shinonomeiro",
     "Snille",
     "MaartenAlbers",
@@ -558,6 +576,20 @@
     "Scott",
     "Muratoraccio",
     "D",
+    "Akkas+Haque",
+    "Kachac",
+    "SAVEagleBasement",
+    "Kevin+Isom",
+    "Rune+Osnes",
+    "you+halo9",
+    "cloudghost",
+    "Yongkwan+Lee",
+    "PoorStudent",
+    "lucites",
+    "Alex+Zaw",
+    "Mobius2020",
+    "ExLightSaber",
+    "YaboiRay",
     "nickname",
     "Sildoren",
     "Darv",
@@ -575,61 +607,51 @@
     "Lev+Lanevskiy",
     "low9",
     "Winged",
-    "YassineKhaled",
-    "Y",
-    "MatteKey",
-    "Flob",
-    "ShiroSenpai",
-    "Inkognito",
-    "G",
-    "Tan+Huynh",
-    "D",
-    "Dark_Pest",
     "Jacky+Ho",
     "generic404",
     "abattoirblues",
     "zounik",
     "4IXplr0r3r",
     "hayden",
+    "Obsidian.Studios",
     "ahoystan",
-    "Bob Barker",
+    "Zomba Mann",
     "edk",
     "Tú Nguyễn Lý Hoàng",
     "shira1011",
+    "Neko Desco",
     "Ben D",
     "G",
-    "Ronan Delevacq",
+    "Vinarus",
     "ja s",
     "Leslie Andrew Ridings",
     "Doug Mason",
-    "Jeremy Townsend",
-    "Dave Abraham",
-    "Joaquin Hierrezuelo",
-    "Sean voets",
+    "scoreswazey",
     "Owen Gwosdz",
-    "Jarrid Lee",
     "Poophead27 Blyat",
-    "John Rednoulf",
-    "Spire",
+    "Mythspire",
     "AZ Party Oasis",
-    "Boba Smith",
     "Devil Lude",
     "David Murcko",
+    "TheFusion",
     "MR.Bear",
     "Jack Dole",
     "matt",
     "somethingtosay8",
+    "3zS4QNQ4",
+    "Terminuz",
     "ivistorm",
     "max blo",
-    "Sauv",
-    "Steven",
+    "Ivan Imes",
     "CptNeo",
+    "Jack Lawfield",
+    "Borte",
     "Maso",
     "Ted Cart",
     "Sage Himeros",
     "Eric Ketchum",
     "Kevin Wallace",
-    "David Spearing",
+    "Zeeble",
     "ChicRic",
     "Tigon",
     "BastardSama",
@@ -637,11 +659,13 @@
     "SkibidiRizzler",
     "Tania Nayelli Fernandez",
     "Draconach",
+    "Kalle Björk",
     "Yavizu3d",
     "Yves Poezevara",
     "Teriak47",
     "Just me",
     "Raf Stahelin",
+    "Nacho Ferrando",
     "Вячеслав Маринин",
     "Marcos Tortosa Carmona",
     "Dkommander22",
@@ -688,6 +712,15 @@
     "SelfishMedic",
     "adderleighn",
     "EnragedAntelope",
+    "Somebody",
+    "Jasper",
+    "megameganck",
+    "thomasand01",
+    "Shiba+Sama",
+    "miduzza",
+    "KB",
+    "shw",
+    "Celestial+Kitten",
     "bakeliteboy",
     "TequiTequi",
     "Homero+Banda",
@@ -706,26 +739,12 @@
     "matter",
     "SRCRCOSS",
     "imer",
-    "Akkas+Haque",
-    "Kachac",
-    "tyrant2811",
-    "Kevin",
-    "Rune+Osnes",
     "jcx29",
-    "cloudghost",
-    "Yongkwan+Lee",
-    "PoorStudent",
-    "lucites",
-    "Alex+Zaw",
-    "Mobius2020",
-    "ExLightSaber",
-    "YaboiRay",
     "Drizzly",
     "Nebuleux",
     "Join+Chun",
     "GDS+DEV",
     "4rt+r3d",
-    "you+halo9",
     "Somebody",
     "Somebody",
     "Crescent~San",
@@ -738,34 +757,37 @@
     "Bula",
     "KUJYAKU",
     "Coeur+de+cochon",
-    "Obsidian.Studios",
     "han b",
-    "Zomba Mann",
     "Aquaneo",
     "Nico",
     "Maximilian Krischan",
     "Banana Joe",
+    "proto merp",
     "_ G3n",
+    "Brandon Thomas",
     "Donovan Jenkins",
     "Hans Meier",
     "sicarius",
     "Michael Eid",
     "Wolf and Fox Legends",
     "beersandbacon",
-    "Neko Desco",
     "Bob barker",
     "Ninja Tom",
     "karim ben brik",
-    "Vinarus",
+    "Elemnt",
     "Josh Snyder",
     "Michael Zhu",
     "Nemisu",
     "Seraphy",
     "雨の心 落",
     "AllTimeNoobie",
+    "swra",
+    "JollRodrigo",
     "jumpd",
     "John C",
     "Rim",
+    "Oliverfish",
+    "yfx507",
     "Room Light",
     "Jairus Knudsen",
     "Xan Dionysus",
@@ -776,30 +798,29 @@
     "Forbidden Atelier",
     "Thomas Sankowski",
     "DrB",
+    "Nimhloth",
     "Adictedtohumping",
     "Snorklebort",
     "vinter",
     "Towelie",
-    "TheFusion",
     "Jean-françois SEMA",
-    "3zS4QNQ4",
-    "Terminuz",
     "Kurt",
+    "Andrew Ly",
     "Matt M.",
-    "Ivan Imes",
     "J M",
+    "Slacks",
     "Bouya shaka",
+    "john Greene",
     "Faburizu",
-    "Jack Lawfield",
     "jimyjomson",
-    "Borte",
     "JaeHyun Jang",
     "Homero Banda",
     "Chase Kwon",
+    "Bob Ling",
     "yyuvuvu",
     "Inyoshu",
     "Chad Barnes",
-    "Person Y",
+    "Adam Gardner",
     "Nomki",
     "inusanorthcape",
     "James Ming",
@@ -821,5 +842,5 @@
     "Somebody",
     "CK"
   ],
-  "totalCount": 818
+  "totalCount": 839
 }

docs/agent_skills.md

@@ -0,0 +1,208 @@
+# Agent Skills System
+
+The LoRA Manager agent skills system enables LLM-powered metadata enrichment and other AI-driven tasks. Users configure their own LLM provider (BYOK), and skills are executed through right-click context menu actions.
+
+## Architecture
+
+```
+┌──────────────────────────────────────────────┐
+│              LoRA Manager Backend             │
+│                                               │
+│  ┌──────────────┐    ┌────────────────┐       │
+│  │ LLMService    │───▶│ LLM Provider   │       │
+│  │ (BYOK config, │◀───│ (OpenAI/Ollama │       │
+│  │  API calls)   │    │ /custom)       │       │
+│  └───────┬───────┘    └────────────────┘       │
+│          │                                     │
+│  ┌───────▼───────────────────────┐             │
+│  │     AgentService              │             │
+│  │  (orchestration: validate     │             │
+│  │   → LLM call → post-process   │             │
+│  │   → WebSocket broadcast)      │             │
+│  └───────┬───────────────────────┘             │
+│          │                                     │
+│  ┌───────▼───────────────────────┐             │
+│  │     SkillRegistry             │             │
+│  │  ┌─────────────────────────┐  │             │
+│  │  │ enrich_hf_metadata:     │  │             │
+│  │  │  - skill.yaml           │  │             │
+│  │  │  - prompt.md            │  │             │
+│  │  │  - handler.py           │  │             │
+│  │  └─────────────────────────┘  │             │
+│  └───────────────────────────────┘             │
+└──────────────────────────────────────────────┘
+```
+
+### Key Design Principle
+
+**Skills define *what* to do (prompt + post-processing). The AgentService handles *how* (LLM calls, validation, progress).**
+
+Skills never call the LLM directly. This keeps BYOK configuration centralized and provider-agnostic.
+
+## BYOK Configuration
+
+Users configure their LLM provider in **Settings → AI Provider**:
+
+| Setting | Description | Example |
+|---|---|---|
+| `llm_provider` | Provider type | `openai`, `ollama`, or `custom` |
+| `llm_api_key` | API key (not needed for local Ollama) | `sk-...` |
+| `llm_api_base` | Custom API base URL (empty = provider default) | `https://api.openai.com/v1` |
+| `llm_model` | Model name | `gpt-4o-mini` |
+
+Environment variable overrides: `LLM_API_KEY`, `LLM_MODEL`, `LLM_API_BASE`, `LLM_PROVIDER`.
+
+### Supported Providers
+
+- **OpenAI**: Uses `https://api.openai.com/v1` by default
+- **Ollama** (local): Uses `http://localhost:11434/v1`, no API key required
+- **Custom**: Any OpenAI-compatible endpoint (vLLM, LM Studio, etc.) — set `llm_api_base` explicitly
+
+## Available Skills
+
+### enrich_hf_metadata
+
+Enriches HuggingFace-downloaded models with metadata extracted by an LLM from the HF model card.
+
+**Entry point**: Right-click context menu → "Enrich Metadata (Agent)"
+
+**What it does**:
+1. Reads the model's `.metadata.json` to get the `hf_url`
+2. Fetches the README.md from the HuggingFace repository
+3. Sends the README + local metadata to the LLM for structured extraction
+4. Writes extracted fields to `.metadata.json`:
+   - `base_model` — only if current value is empty
+   - `trainedWords` — trigger words (LoRA only, if none exist)
+   - `modelDescription` — concise summary (if none exists)
+   - `tags` — merged with existing tags, deduplicated
+   - `metadata_source` — audit trail: `agent:enrich_hf_metadata`
+   - `llm_enriched_at` — ISO timestamp
+5. Downloads and optimizes preview image (if LLM found one in the README)
+6. Updates the scanner cache
+7. Broadcasts WebSocket progress events
+
+**Model types**: LoRA, Checkpoint, Embedding
+
+## Adding a New Skill
+
+### 1. Create the skill directory
+
+```
+py/services/agent/skills/<skill_name>/
+├── skill.yaml      # Skill metadata and schemas
+├── prompt.md       # LLM prompt template
+└── handler.py      # Pre-processing and post-processing
+```
+
+### 2. Write skill.yaml
+
+```yaml
+name: my_skill
+title: "My Skill"
+description: "What this skill does"
+llm_required: true
+model_type_filter: ["lora"]  # or null for all types
+input_schema:
+  type: object
+  properties:
+    model_paths:
+      type: array
+      items:
+        type: string
+  required:
+    - model_paths
+output_schema:
+  type: object
+  properties:
+    # ... JSON schema for LLM output
+permissions:
+  write_metadata: true
+  write_previews: false
+  network_domains:
+    - "example.com"
+```
+
+### 3. Write prompt.md
+
+Use `{{variable}}` placeholders that will be replaced with data from the `prepare` function:
+
+```markdown
+You are an expert assistant...
+
+Model URL: {{hf_url}}
+README content:
+{{readme_content}}
+
+Current metadata:
+{{current_metadata}}
+```
+
+### 4. Write handler.py
+
+```python
+async def prepare(model_path: str, input_data: dict) -> dict:
+    """Gather context for the LLM prompt. Returns variables for template rendering."""
+    return {
+        "model_path": model_path,
+        # ... other variables used in prompt.md
+    }
+
+async def post_process(context) -> dict:
+    """Apply the LLM-extracted data to the model."""
+    llm_response = context.llm_response
+    # ... write metadata, download previews, update cache
+    return {
+        "success": True,
+        "updated_fields": ["base_model", "tags"],
+        "errors": [],
+    }
+```
+
+**Important**: Use absolute imports (`from py.utils.metadata_manager import MetadataManager`) because skills are loaded via `importlib.util.spec_from_file_location`, which doesn't support relative imports.
+
+### 5. Test
+
+The skill is automatically discovered by `SkillRegistry` on startup. Test with:
+
+```python
+pytest tests/services/test_agent_service.py
+```
+
+## API Endpoints
+
+| Method | Path | Description |
+|---|---|---|
+| GET | `/api/lm/agent/skills` | List available skills |
+| POST | `/api/lm/agent/execute/{skill_name}` | Execute a skill (body: `{"model_paths": [...]}`) |
+| POST | `/api/lm/agent/cancel` | Cancel running skill (stub) |
+
+## WebSocket Events
+
+| Type | When | Key fields |
+|---|---|---|
+| `agent_progress` | Skill started/processing | `skill`, `status`, `total`, `processed`, `success`, `current_path` |
+| `agent_progress` | Skill completed | `skill`, `status`, `updated_models`, `errors`, `summary` |
+| `agent_progress` | Skill error | `skill`, `status`, `error` |
+
+## Security Model
+
+Skills declare permissions in `skill.yaml`:
+- `write_metadata` — can write `.metadata.json` files
+- `write_previews` — can download/replace preview images
+- `network_domains` — allowed domains for HTTP requests
+
+These are declarative constraints checked by `AgentService`. They are defense-in-depth, not a sandbox — the Python process can technically do anything, but the contract is clear and auditable.
+
+## File Locations
+
+| Component | Path |
+|---|---|
+| LLMService | `py/services/llm_service.py` |
+| AgentService | `py/services/agent/agent_service.py` |
+| SkillRegistry | `py/services/agent/skill_registry.py` |
+| SkillDefinition | `py/services/agent/skill_definition.py` |
+| Skills directory | `py/services/agent/skills/` |
+| Route handlers | `py/routes/handlers/agent_handlers.py` |
+| Frontend manager | `static/js/managers/AgentManager.js` |
+| Settings UI | `templates/components/modals/settings_modal.html` |
+| Context menu | `templates/components/context_menu.html` |

locales/de.json

@@ -105,6 +105,7 @@
             "removeFromFavorites": "Aus Favoriten entfernen",
             "viewOnCivitai": "Auf Civitai anzeigen",
             "notAvailableFromCivitai": "Nicht auf Civitai verfügbar",
+            "viewOnHuggingFace": "Auf Hugging Face ansehen",
             "sendToWorkflow": "An ComfyUI senden (Klick: Anhängen, Shift+Klick: Ersetzen)",
             "copyLoRASyntax": "LoRA-Syntax kopieren",
             "checkpointNameCopied": "Checkpoint-Name kopiert",
@@ -145,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "Verwendungsanzahl"
+        },
+        "footer": {
+            "versionCount": "{count} Versionen",
+            "viewAllVersions": "Alle lokalen Versionen anzeigen"
         }
     },
     "globalContextMenu": {
@@ -183,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "Ausgeschlossene Modelle verwalten"
+        },
+        "groupByModel": {
+            "label": "Nach Modell gruppieren"
         }
     },
     "header": {
@@ -195,13 +203,7 @@
             "statistics": "Statistiken"
         },
         "search": {
-            "placeholder": "Suchen...",
-            "placeholders": {
-                "loras": "LoRAs suchen...",
-                "recipes": "Rezepte suchen...",
-                "checkpoints": "Checkpoints suchen...",
-                "embeddings": "Embeddings suchen..."
-            },
+            "placeholder": "Suchen",
             "options": "Suchoptionen",
             "searchIn": "Suchen in:",
             "notAvailable": "Suche auf Statistikseite nicht verfügbar",
@@ -251,7 +253,18 @@
             "toggle": "Theme wechseln",
             "switchToLight": "Zu hellem Theme wechseln",
             "switchToDark": "Zu dunklem Theme wechseln",
-            "switchToAuto": "Zu automatischem Theme wechseln"
+            "switchToAuto": "Zu automatischem Theme wechseln",
+            "presets": "Theme-Voreinstellungen",
+            "default": "Standard",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "Modus",
+            "light": "Hell",
+            "dark": "Dunkel",
+            "auto": "Auto"
         },
         "actions": {
             "checkUpdates": "Updates prüfen",
@@ -263,6 +276,9 @@
         "civitaiApiKey": "Civitai API Key",
         "civitaiApiKeyPlaceholder": "Geben Sie Ihren Civitai API Key ein",
         "civitaiApiKeyHelp": "Wird für die Authentifizierung beim Herunterladen von Modellen von Civitai verwendet",
+        "civitaiApiKeyConfigured": "Konfiguriert",
+        "civitaiApiKeyNotConfigured": "Nicht konfiguriert",
+        "civitaiApiKeySet": "Einrichten",
         "civitaiHost": {
             "label": "Civitai-Host",
             "help": "Wählen Sie aus, welche Civitai-Seite geöffnet wird, wenn Sie „View on Civitai“-Links verwenden.",
@@ -303,6 +319,7 @@
             "downloads": "Downloads",
             "videoSettings": "Video-Einstellungen",
             "layoutSettings": "Layout-Einstellungen",
+            "licenseIcons": "Lizenzsymbole",
             "misc": "Verschiedenes",
             "backup": "Backups",
             "folderSettings": "Standard-Roots",
@@ -310,7 +327,7 @@
             "extraFolderPaths": "Zusätzliche Ordnerpfade",
             "downloadPathTemplates": "Download-Pfad-Vorlagen",
             "priorityTags": "Prioritäts-Tags",
-            "updateFlags": "Update-Markierungen",
+            "versionScope": "Update-Markierungen",
             "exampleImages": "Beispielbilder",
             "autoOrganize": "Auto-Organisierung",
             "metadata": "Metadaten",
@@ -415,6 +432,8 @@
             "help": "Wenn aktiviert, überspringt LoRA Manager den Download einer Modellversion, wenn der Download-Verlaufsdienst diese spezifische Version als bereits heruntergeladen erfasst hat. Gilt für alle Download-Abläufe."
         },
         "layoutSettings": {
+            "groupByModel": "Nach Modell gruppieren",
+            "groupByModelHelp": "Wenn aktiviert, wird nur die neueste Version jedes Civitai-Modells als einzelne Karte angezeigt. Ältere Versionen werden ausgeblendet.",
             "displayDensity": "Anzeige-Dichte",
             "displayDensityOptions": {
                 "default": "Standard",
@@ -571,7 +590,7 @@
             "download": "Herunterladen",
             "restartRequired": "Neustart erforderlich"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "Strategie für Update-Markierungen",
             "help": "Entscheide, ob Update-Badges nur dann erscheinen, wenn eine neue Version dasselbe Basismodell wie deine lokalen Dateien verwendet, oder sobald es irgendein neueres Release für dieses Modell gibt.",
             "options": {
@@ -583,6 +602,10 @@
             "label": "Früher Zugriff Updates ausblenden",
             "help": "Nur Early-Access-Updates"
         },
+        "licenseIcons": {
+            "useNewStyle": "Aktualisierte Lizenzsymbole verwenden",
+            "useNewStyleHelp": "Lizenzberechtigungen mit farbigen Indikatoren (neuer Stil) oder nur Einschränkungssymbolen (klassischer Stil) anzeigen. Orientiert sich am aktuellen CivitAI-Design."
+        },
         "misc": {
             "includeTriggerWords": "Trigger Words in LoRA-Syntax einschließen",
             "includeTriggerWordsHelp": "Trainierte Trigger Words beim Kopieren der LoRA-Syntax in die Zwischenablage einschließen",
@@ -634,6 +657,23 @@
             "proxyPassword": "Passwort (optional)",
             "proxyPasswordPlaceholder": "passwort",
             "proxyPasswordHelp": "Passwort für die Proxy-Authentifizierung (falls erforderlich)"
+        },
+        "aiProvider": {
+            "title": "KI-Anbieter",
+            "provider": "Anbieter",
+            "providerHelp": "Wählen Sie Ihren LLM-Anbieter. OpenAI und Ollama verwenden voreingestellte API-Endpunkte. Mit \"Benutzerdefiniert\" können Sie jeden OpenAI-kompatiblen Endpunkt angeben.",
+            "custom": "Benutzerdefiniert (OpenAI-kompatibel)",
+            "apiBase": "API-Basis-URL",
+            "apiBaseHelp": "Die Basis-URL für die LLM-API (z.B. https://api.openai.com/v1). Leer lassen, um die Anbietervoreinstellung zu verwenden.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API-Schlüssel",
+            "apiKeyHelp": "Ihr LLM-API-Schlüssel. Wird lokal gespeichert und niemals an einen anderen Server außer Ihrem gewählten LLM-Anbieter gesendet.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "Nicht festgelegt",
+            "apiKeyConfigured": "Konfiguriert",
+            "apiKeySet": "Einrichten",
+            "model": "Modell",
+            "modelHelp": "Der zu verwendende Modellname (z.B. deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). Prüfen Sie Ihren Anbieter auf verfügbare Modelle."
         }
     },
     "loras": {
@@ -651,7 +691,11 @@
                 "sizeAsc": "Kleinste",
                 "usage": "Anzahl Nutzung",
                 "usageDesc": "Meiste",
-                "usageAsc": "Wenigste"
+                "usageAsc": "Wenigste",
+                "versionsCount": "Lokale Versionen",
+                "versionsCountDesc": "Meiste Versionen zuerst",
+                "versionsCountAsc": "Wenigste Versionen zuerst",
+                "versionIdDesc": "Neueste Version zuerst"
             },
             "refresh": {
                 "title": "Modelliste aktualisieren",
@@ -727,7 +771,8 @@
                 "completed": "Abgeschlossen: {success} verschoben, {skipped} übersprungen, {failures} fehlgeschlagen",
                 "complete": "Automatische Organisation abgeschlossen",
                 "error": "Fehler: {error}"
-            }
+            },
+            "enrichHfAgent": "Metadaten mit KI anreichern"
         },
         "contextMenu": {
             "refreshMetadata": "Civitai-Daten aktualisieren",
@@ -751,7 +796,8 @@
             "shareRecipe": "Rezept teilen",
             "viewAllLoras": "Alle LoRAs anzeigen",
             "downloadMissingLoras": "Fehlende LoRAs herunterladen",
-            "deleteRecipe": "Rezept löschen"
+            "deleteRecipe": "Rezept löschen",
+            "enrichHfAgent": "Metadaten mit KI anreichern"
         }
     },
     "recipes": {
@@ -997,6 +1043,18 @@
             "storage": "Speicher",
             "insights": "Erkenntnisse"
         },
+        "metrics": {
+            "totalModels": "Modelle gesamt",
+            "totalStorage": "Speicher gesamt",
+            "totalGenerations": "Generationen gesamt",
+            "usageRate": "Nutzungsrate",
+            "loras": "LoRAs",
+            "checkpoints": "Checkpoints",
+            "embeddings": "Embeddings",
+            "uniqueTags": "Einzigartige Tags",
+            "unusedModels": "Ungenutzte Modelle",
+            "avgUsesPerModel": "Ø Nutzungen/Modell"
+        },
         "usage": {
             "mostUsedLoras": "Meistgenutzte LoRAs",
             "mostUsedCheckpoints": "Meistgenutzte Checkpoints",
@@ -1014,13 +1072,77 @@
         },
         "insights": {
             "smartInsights": "Intelligente Erkenntnisse",
-            "recommendations": "Empfehlungen"
+            "recommendations": "Empfehlungen",
+            "noInsights": "Keine Erkenntnisse verfügbar",
+            "unusedLoras": {
+                "high": {
+                    "title": "Hohe Anzahl ungenutzter LoRAs",
+                    "description": "{percent}% Ihrer LoRAs ({count}/{total}) wurden noch nie verwendet.",
+                    "suggestion": "Erwägen Sie, ungenutzte Modelle zu organisieren oder zu archivieren, um Speicherplatz freizugeben."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "Ungenutzte Checkpoints erkannt",
+                    "description": "{percent}% Ihrer Checkpoints ({count}/{total}) wurden noch nie verwendet.",
+                    "suggestion": "Überprüfen Sie nicht mehr benötigte Checkpoints und erwägen Sie deren Entfernung."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "Hohe Anzahl ungenutzter Embeddings",
+                    "description": "{percent}% Ihrer Embeddings ({count}/{total}) wurden noch nie verwendet.",
+                    "suggestion": "Organisieren oder archivieren Sie ungenutzte Embeddings, um Ihre Sammlung zu optimieren."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "Große Sammlung erkannt",
+                    "description": "Ihre Modellsammlung verwendet {size} Speicher.",
+                    "suggestion": "Erwägen Sie externe Speicher- oder Cloud-Lösungen für eine bessere Organisation."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "Aktiver Benutzer",
+                    "description": "Sie haben {count} Generationen abgeschlossen!",
+                    "suggestion": "Entdecken und erstellen Sie weiterhin großartige Inhalte mit Ihren Modellen."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "Sammlungsübersicht",
             "baseModelDistribution": "Basis-Modell-Verteilung",
             "usageTrends": "Nutzungstrends (Letzte 30 Tage)",
-            "usageDistribution": "Nutzungsverteilung"
+            "usageDistribution": "Nutzungsverteilung",
+            "date": "Datum",
+            "usageCount": "Nutzungsanzahl",
+            "fileSizeBytes": "Dateigröße (Bytes)",
+            "models": "Modelle",
+            "loraUsage": "LoRA-Nutzung",
+            "checkpointUsage": "Checkpoint-Nutzung",
+            "embeddingUsage": "Embedding-Nutzung"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "Diffusionsmodell",
+            "embedding": "Embeddings"
+        },
+        "placeholders": {
+            "loading": "Lädt...",
+            "noModels": "Keine Modelle gefunden",
+            "errorLoading": "Fehler beim Laden der Daten",
+            "noStorageData": "Keine Speicherdaten verfügbar",
+            "rootFolder": "Root",
+            "chartLibraryMissing": "Diagramm benötigt Chart.js-Bibliothek"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} Modelle",
+            "chartUsage": "{name}: {size}, {count} Nutzungen",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1032,7 +1154,10 @@
             "titleWithType": "{type} von URL herunterladen",
             "civitaiUrl": "Civitai URL:",
             "placeholder": "https://civitai.com/models/...",
-            "urlHint": "Geben Sie eine CivitAI- oder CivArchive-URL pro Zeile ein. Unterstützt mehrere URLs für den Batch-Download.",
+            "urlHint": "Geben Sie eine CivitAI-, CivArchive- oder Hugging Face-URL pro Zeile ein. Unterstützt mehrere URLs für den Batch-Download.",
+            "selectHfFiles": "Datei(en) zum Herunterladen aus diesem Repository auswählen:",
+            "selectAll": "Alle auswählen",
+            "fetchingRepoFiles": "Repository-Dateien werden abgerufen...",
             "locationPreview": "Download-Speicherort Vorschau",
             "useDefaultPath": "Standardpfad verwenden",
             "useDefaultPathTooltip": "Wenn aktiviert, werden Dateien automatisch mit konfigurierten Pfadvorlagen organisiert",
@@ -1061,7 +1186,9 @@
             },
             "errors": {
                 "invalidUrl": "Ungültiges Civitai URL-Format",
-                "noVersions": "Keine Versionen für dieses Modell verfügbar"
+                "noVersions": "Keine Versionen für dieses Modell verfügbar",
+                "mixedSources": "CivitAI- und Hugging Face-URLs können nicht in derselben Charge gemischt werden.",
+                "noModelFiles": "In diesem Repository wurden keine Modelldateien gefunden."
             },
             "status": {
                 "preparing": "Download wird vorbereitet...",
@@ -1212,6 +1339,8 @@
                 "editVersionName": "Versionsname bearbeiten",
                 "viewOnCivitai": "Auf Civitai anzeigen",
                 "viewOnCivitaiText": "Auf Civitai anzeigen",
+                "viewOnHuggingFace": "Auf Hugging Face ansehen",
+                "viewOnHuggingFaceText": "Auf Hugging Face ansehen",
                 "viewCreatorProfile": "Ersteller-Profil anzeigen",
                 "openFileLocation": "Dateispeicherort öffnen",
                 "sendToWorkflow": "An ComfyUI senden",
@@ -1237,7 +1366,10 @@
                 "additionalNotes": "Zusätzliche Notizen",
                 "notesHint": "Enter zum Speichern, Shift+Enter für neue Zeile",
                 "addNotesPlaceholder": "Fügen Sie hier Ihre Notizen hinzu...",
-                "aboutThisVersion": "Über diese Version"
+                "aboutThisVersion": "Über diese Version",
+                "baseModelSearchPlaceholder": "Basismodell suchen…",
+                "baseModelSuggested": "Vorschlag",
+                "baseModelNoMatch": "Keine passenden Basismodelle"
             },
             "notes": {
                 "saved": "Notizen erfolgreich gespeichert",
@@ -1424,15 +1556,6 @@
             "duplicate": "Dieser Tag existiert bereits"
         }
     },
-    "keyboard": {
-        "navigation": "Tastatur-Navigation:",
-        "shortcuts": {
-            "pageUp": "Eine Seite nach oben scrollen",
-            "pageDown": "Eine Seite nach unten scrollen",
-            "home": "Zum Anfang springen",
-            "end": "Zum Ende springen"
-        }
-    },
     "initialization": {
         "title": "Initialisierung",
         "message": "Ihr Arbeitsbereich wird vorbereitet...",
@@ -1522,12 +1645,15 @@
             "modelUpdated": "Modell im Workflow aktualisiert",
             "modelFailed": "Fehler beim Aktualisieren des Modellknotens",
             "embeddingAdded": "Embedding zum Workflow hinzugefügt",
-            "embeddingFailed": "Fehler beim Hinzufügen des Embeddings"
+            "embeddingFailed": "Fehler beim Hinzufügen des Embeddings",
+            "promptSent": "Prompt an Workflow gesendet",
+            "promptFailed": "Fehler beim Senden des Prompts"
         },
         "nodeSelector": {
             "recipe": "Rezept",
             "lora": "LoRA",
             "embedding": "Embedding",
+            "prompt": "Prompt",
             "replace": "Ersetzen",
             "append": "Anhängen",
             "selectTargetNode": "Zielknoten auswählen",
@@ -1714,6 +1840,7 @@
             "enterLoraName": "Bitte geben Sie einen LoRA-Namen oder Syntax ein",
             "reconnectedSuccessfully": "LoRA erfolgreich neu verbunden",
             "reconnectFailed": "Fehler beim Neuverbinden des LoRA: {message}",
+            "noPromptToSend": "Kein zu sendender Prompt",
             "cannotSend": "Kann Rezept nicht senden: Fehlende Rezept-ID",
             "sendFailed": "Fehler beim Senden des Rezepts an Workflow",
             "sendError": "Fehler beim Senden des Rezepts an Workflow",
@@ -1973,6 +2100,12 @@
             "moveFailed": "Failed to move item: {message}",
             "copiedToClipboard": "In die Zwischenablage kopiert",
             "downloadStarted": "Download gestartet"
+        },
+        "agent": {
+            "llmNotConfigured": "KI-Anbieter nicht konfiguriert. Aktivieren Sie ihn unter Einstellungen → KI-Anbieter.",
+            "enrichStarted": "Metadaten werden mit KI angereichert...",
+            "enrichComplete": "Metadatenanreicherung abgeschlossen: {{summary}}",
+            "enrichFailed": "Metadatenanreicherung fehlgeschlagen: {{error}}"
         }
     },
     "doctor": {

locales/es.json

@@ -105,6 +105,7 @@
             "removeFromFavorites": "Eliminar de favoritos",
             "viewOnCivitai": "Ver en Civitai",
             "notAvailableFromCivitai": "No disponible en Civitai",
+            "viewOnHuggingFace": "Ver en Hugging Face",
             "sendToWorkflow": "Enviar a ComfyUI (Clic: Añadir, Shift+Clic: Reemplazar)",
             "copyLoRASyntax": "Copiar sintaxis de LoRA",
             "checkpointNameCopied": "Nombre del checkpoint copiado",
@@ -145,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "Veces usado"
+        },
+        "footer": {
+            "versionCount": "{count} versiones",
+            "viewAllVersions": "Ver todas las versiones locales"
         }
     },
     "globalContextMenu": {
@@ -183,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "Gestionar modelos excluidos"
+        },
+        "groupByModel": {
+            "label": "Agrupar por modelo"
         }
     },
     "header": {
@@ -195,13 +203,7 @@
             "statistics": "Estadísticas"
         },
         "search": {
-            "placeholder": "Buscar...",
-            "placeholders": {
-                "loras": "Buscar LoRAs...",
-                "recipes": "Buscar recetas...",
-                "checkpoints": "Buscar checkpoints...",
-                "embeddings": "Buscar embeddings..."
-            },
+            "placeholder": "Buscar",
             "options": "Opciones de búsqueda",
             "searchIn": "Buscar en:",
             "notAvailable": "Búsqueda no disponible en la página de estadísticas",
@@ -251,7 +253,18 @@
             "toggle": "Cambiar tema",
             "switchToLight": "Cambiar a tema claro",
             "switchToDark": "Cambiar a tema oscuro",
-            "switchToAuto": "Cambiar a tema automático"
+            "switchToAuto": "Cambiar a tema automático",
+            "presets": "Preajustes de tema",
+            "default": "Predeterminado",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "Modo",
+            "light": "Claro",
+            "dark": "Oscuro",
+            "auto": "Auto"
         },
         "actions": {
             "checkUpdates": "Comprobar actualizaciones",
@@ -263,6 +276,9 @@
         "civitaiApiKey": "Clave API de Civitai",
         "civitaiApiKeyPlaceholder": "Introduce tu clave API de Civitai",
         "civitaiApiKeyHelp": "Utilizada para autenticación al descargar modelos de Civitai",
+        "civitaiApiKeyConfigured": "Configurado",
+        "civitaiApiKeyNotConfigured": "No configurado",
+        "civitaiApiKeySet": "Configurar",
         "civitaiHost": {
             "label": "Host de Civitai",
             "help": "Elige qué sitio de Civitai se abre al usar los enlaces de \"View on Civitai\".",
@@ -303,6 +319,7 @@
             "downloads": "Descargas",
             "videoSettings": "Configuración de video",
             "layoutSettings": "Configuración de diseño",
+            "licenseIcons": "Iconos de licencia",
             "misc": "Varios",
             "backup": "Copias de seguridad",
             "folderSettings": "Raíces predeterminadas",
@@ -310,7 +327,7 @@
             "extraFolderPaths": "Rutas de carpetas adicionales",
             "downloadPathTemplates": "Plantillas de rutas de descarga",
             "priorityTags": "Etiquetas prioritarias",
-            "updateFlags": "Indicadores de actualización",
+            "versionScope": "Indicadores de actualización",
             "exampleImages": "Imágenes de ejemplo",
             "autoOrganize": "Organización automática",
             "metadata": "Metadatos",
@@ -415,6 +432,8 @@
             "help": "Cuando está habilitado, LoRA Manager omitirá la descarga de una versión de modelo si el servicio de historial de descargas registra esa versión exacta como ya descargada. Aplica a todos los flujos de descarga."
         },
         "layoutSettings": {
+            "groupByModel": "Agrupar por modelo",
+            "groupByModelHelp": "Cuando está activado, solo se muestra la versión más reciente de cada modelo de Civitai como una tarjeta única. Las versiones anteriores están ocultas.",
             "displayDensity": "Densidad de visualización",
             "displayDensityOptions": {
                 "default": "Predeterminado",
@@ -571,7 +590,7 @@
             "download": "Descargar",
             "restartRequired": "Requiere reinicio"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "Estrategia de indicadores de actualización",
             "help": "Decide si las insignias de actualización deben mostrarse solo cuando una nueva versión comparte el mismo modelo base que tus archivos locales o siempre que exista cualquier versión más reciente de ese modelo.",
             "options": {
@@ -583,6 +602,10 @@
             "label": "Ocultar actualizaciones de acceso temprano",
             "help": "Solo actualizaciones de acceso temprano"
         },
+        "licenseIcons": {
+            "useNewStyle": "Usar iconos de licencia actualizados",
+            "useNewStyleHelp": "Mostrar permisos de licencia con indicadores de color (nuevo estilo) o solo iconos de restricción (estilo clásico). Refleja el diseño actual de CivitAI."
+        },
         "misc": {
             "includeTriggerWords": "Incluir palabras clave en la sintaxis de LoRA",
             "includeTriggerWordsHelp": "Incluir palabras clave entrenadas al copiar la sintaxis de LoRA al portapapeles",
@@ -634,6 +657,23 @@
             "proxyPassword": "Contraseña (opcional)",
             "proxyPasswordPlaceholder": "contraseña",
             "proxyPasswordHelp": "Contraseña para autenticación de proxy (si es necesario)"
+        },
+        "aiProvider": {
+            "title": "Proveedor de IA",
+            "provider": "Proveedor",
+            "providerHelp": "Elija su proveedor de LLM. OpenAI y Ollama usan endpoints predefinidos. Personalizado le permite especificar cualquier endpoint compatible con OpenAI.",
+            "custom": "Personalizado (compatible con OpenAI)",
+            "apiBase": "URL base de la API",
+            "apiBaseHelp": "La URL base para la API LLM (p.ej. https://api.openai.com/v1). Déjelo vacío para usar el valor predeterminado del proveedor.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "Clave de API",
+            "apiKeyHelp": "Su clave de API del proveedor LLM. Se almacena localmente y nunca se envía a ningún servidor excepto a su proveedor LLM elegido.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "No configurada",
+            "apiKeyConfigured": "Configurada",
+            "apiKeySet": "Configurar",
+            "model": "Modelo",
+            "modelHelp": "El nombre del modelo a usar (p.ej. deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). Consulte a su proveedor para ver los modelos disponibles."
         }
     },
     "loras": {
@@ -651,7 +691,11 @@
                 "sizeAsc": "Menor",
                 "usage": "Número de usos",
                 "usageDesc": "Más",
-                "usageAsc": "Menos"
+                "usageAsc": "Menos",
+                "versionsCount": "Versiones locales",
+                "versionsCountDesc": "Más versiones primero",
+                "versionsCountAsc": "Menos versiones primero",
+                "versionIdDesc": "Versión más nueva primero"
             },
             "refresh": {
                 "title": "Actualizar lista de modelos",
@@ -727,7 +771,8 @@
                 "completed": "Completado: {success} movidos, {skipped} omitidos, {failures} fallidos",
                 "complete": "Auto-organización completada",
                 "error": "Error: {error}"
-            }
+            },
+            "enrichHfAgent": "Enriquecer metadatos (IA)"
         },
         "contextMenu": {
             "refreshMetadata": "Actualizar datos de Civitai",
@@ -751,7 +796,8 @@
             "shareRecipe": "Compartir receta",
             "viewAllLoras": "Ver todos los LoRAs",
             "downloadMissingLoras": "Descargar LoRAs faltantes",
-            "deleteRecipe": "Eliminar receta"
+            "deleteRecipe": "Eliminar receta",
+            "enrichHfAgent": "Enriquecer metadatos (IA)"
         }
     },
     "recipes": {
@@ -997,6 +1043,18 @@
             "storage": "Almacenamiento",
             "insights": "Perspectivas"
         },
+        "metrics": {
+            "totalModels": "Total de modelos",
+            "totalStorage": "Almacenamiento total",
+            "totalGenerations": "Generaciones totales",
+            "usageRate": "Tasa de uso",
+            "loras": "LoRAs",
+            "checkpoints": "Puntos de control",
+            "embeddings": "Embeddings",
+            "uniqueTags": "Etiquetas únicas",
+            "unusedModels": "Modelos no usados",
+            "avgUsesPerModel": "Prom. usos/modelo"
+        },
         "usage": {
             "mostUsedLoras": "LoRAs más utilizados",
             "mostUsedCheckpoints": "Checkpoints más utilizados",
@@ -1014,13 +1072,77 @@
         },
         "insights": {
             "smartInsights": "Perspectivas inteligentes",
-            "recommendations": "Recomendaciones"
+            "recommendations": "Recomendaciones",
+            "noInsights": "No hay información disponible",
+            "unusedLoras": {
+                "high": {
+                    "title": "Alta cantidad de LoRAs no utilizadas",
+                    "description": "El {percent}% de tus LoRAs ({count}/{total}) nunca se han utilizado.",
+                    "suggestion": "Considera organizar o archivar modelos no utilizados para liberar espacio."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "Puntos de control no utilizados detectados",
+                    "description": "El {percent}% de tus puntos de control ({count}/{total}) nunca se han utilizado.",
+                    "suggestion": "Revisa y considera eliminar los puntos de control que ya no necesites."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "Alta cantidad de Embeddings no utilizados",
+                    "description": "El {percent}% de tus embeddings ({count}/{total}) nunca se han utilizado.",
+                    "suggestion": "Considera organizar o archivar embeddings no utilizados para optimizar tu colección."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "Colección grande detectada",
+                    "description": "Tu colección de modelos está usando {size} de almacenamiento.",
+                    "suggestion": "Considera usar almacenamiento externo o soluciones en la nube para una mejor organización."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "Usuario activo",
+                    "description": "¡Has completado {count} generaciones hasta ahora!",
+                    "suggestion": "Sigue explorando y creando contenido increíble con tus modelos."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "Resumen de colección",
             "baseModelDistribution": "Distribución de modelo base",
             "usageTrends": "Tendencias de uso (Últimos 30 días)",
-            "usageDistribution": "Distribución de uso"
+            "usageDistribution": "Distribución de uso",
+            "date": "Fecha",
+            "usageCount": "Conteo de uso",
+            "fileSizeBytes": "Tamaño del archivo (bytes)",
+            "models": "Modelos",
+            "loraUsage": "Uso de LoRA",
+            "checkpointUsage": "Uso de Checkpoint",
+            "embeddingUsage": "Uso de Embedding"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Punto de control",
+            "diffusion_model": "Modelo de difusión",
+            "embedding": "Embeddings"
+        },
+        "placeholders": {
+            "loading": "Cargando...",
+            "noModels": "No se encontraron modelos",
+            "errorLoading": "Error al cargar datos",
+            "noStorageData": "No hay datos de almacenamiento disponibles",
+            "rootFolder": "Raíz",
+            "chartLibraryMissing": "El gráfico requiere la librería Chart.js"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} modelos",
+            "chartUsage": "{name}: {size}, {count} usos",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1032,7 +1154,10 @@
             "titleWithType": "Descargar {type} desde URL",
             "civitaiUrl": "URL de Civitai:",
             "placeholder": "https://civitai.com/models/...",
-            "urlHint": "Ingrese una URL de CivitAI o CivArchive por línea. Admite múltiples URLs para descarga por lotes.",
+            "urlHint": "Ingrese una URL de CivitAI, CivArchive o Hugging Face por línea. Admite múltiples URLs para descarga por lotes.",
+            "selectHfFiles": "Seleccione el/los archivo(s) para descargar de este repositorio:",
+            "selectAll": "Seleccionar todo",
+            "fetchingRepoFiles": "Obteniendo archivos del repositorio...",
             "locationPreview": "Vista previa de ubicación de descarga",
             "useDefaultPath": "Usar ruta predeterminada",
             "useDefaultPathTooltip": "Cuando está habilitado, los archivos se organizan automáticamente usando plantillas de rutas configuradas",
@@ -1061,7 +1186,9 @@
             },
             "errors": {
                 "invalidUrl": "Formato de URL de Civitai inválido",
-                "noVersions": "No hay versiones disponibles para este modelo"
+                "noVersions": "No hay versiones disponibles para este modelo",
+                "mixedSources": "No se pueden mezclar URL de CivitAI y Hugging Face en el mismo lote.",
+                "noModelFiles": "No se encontraron archivos de modelo en este repositorio."
             },
             "status": {
                 "preparing": "Preparando descarga...",
@@ -1212,6 +1339,8 @@
                 "editVersionName": "Editar nombre de versión",
                 "viewOnCivitai": "Ver en Civitai",
                 "viewOnCivitaiText": "Ver en Civitai",
+                "viewOnHuggingFace": "Ver en Hugging Face",
+                "viewOnHuggingFaceText": "Ver en Hugging Face",
                 "viewCreatorProfile": "Ver perfil del creador",
                 "openFileLocation": "Abrir ubicación del archivo",
                 "sendToWorkflow": "Enviar a ComfyUI",
@@ -1237,7 +1366,10 @@
                 "additionalNotes": "Notas adicionales",
                 "notesHint": "Presiona Enter para guardar, Shift+Enter para nueva línea",
                 "addNotesPlaceholder": "Añade tus notas aquí...",
-                "aboutThisVersion": "Acerca de esta versión"
+                "aboutThisVersion": "Acerca de esta versión",
+                "baseModelSearchPlaceholder": "Buscar modelo base…",
+                "baseModelSuggested": "Sugerido",
+                "baseModelNoMatch": "No hay modelos base que coincidan"
             },
             "notes": {
                 "saved": "Notas guardadas exitosamente",
@@ -1424,15 +1556,6 @@
             "duplicate": "Esta etiqueta ya existe"
         }
     },
-    "keyboard": {
-        "navigation": "Navegación por teclado:",
-        "shortcuts": {
-            "pageUp": "Desplazar hacia arriba una página",
-            "pageDown": "Desplazar hacia abajo una página",
-            "home": "Saltar al inicio",
-            "end": "Saltar al final"
-        }
-    },
     "initialization": {
         "title": "Inicializando",
         "message": "Preparando tu espacio de trabajo...",
@@ -1522,12 +1645,15 @@
             "modelUpdated": "Modelo actualizado en el flujo de trabajo",
             "modelFailed": "Error al actualizar nodo de modelo",
             "embeddingAdded": "Embedding añadido al flujo de trabajo",
-            "embeddingFailed": "Error al añadir el embedding"
+            "embeddingFailed": "Error al añadir el embedding",
+            "promptSent": "Prompt enviado al flujo de trabajo",
+            "promptFailed": "Error al enviar el prompt"
         },
         "nodeSelector": {
             "recipe": "Receta",
             "lora": "LoRA",
             "embedding": "Embedding",
+            "prompt": "Prompt",
             "replace": "Reemplazar",
             "append": "Añadir",
             "selectTargetNode": "Seleccionar nodo de destino",
@@ -1714,6 +1840,7 @@
             "enterLoraName": "Por favor introduce un nombre de LoRA o sintaxis",
             "reconnectedSuccessfully": "LoRA reconectado exitosamente",
             "reconnectFailed": "Error reconectando LoRA: {message}",
+            "noPromptToSend": "No hay prompt para enviar",
             "cannotSend": "No se puede enviar receta: Falta ID de receta",
             "sendFailed": "Error al enviar receta al flujo de trabajo",
             "sendError": "Error enviando receta al flujo de trabajo",
@@ -1973,6 +2100,12 @@
             "moveFailed": "Failed to move item: {message}",
             "copiedToClipboard": "Copiado al portapapeles",
             "downloadStarted": "Descarga iniciada"
+        },
+        "agent": {
+            "llmNotConfigured": "Proveedor de IA no configurado. Actívelo en Configuración → Proveedor de IA.",
+            "enrichStarted": "Enriqueciendo metadatos con IA...",
+            "enrichComplete": "Enriquecimiento de metadatos completado: {{summary}}",
+            "enrichFailed": "Enriquecimiento de metadatos fallido: {{error}}"
         }
     },
     "doctor": {

locales/fr.json

@@ -105,6 +105,7 @@
             "removeFromFavorites": "Retirer des favoris",
             "viewOnCivitai": "Voir sur Civitai",
             "notAvailableFromCivitai": "Non disponible sur Civitai",
+            "viewOnHuggingFace": "Voir sur Hugging Face",
             "sendToWorkflow": "Envoyer vers ComfyUI (Clic: Ajouter, Maj+Clic: Remplacer)",
             "copyLoRASyntax": "Copier la syntaxe LoRA",
             "checkpointNameCopied": "Nom du checkpoint copié",
@@ -145,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "Nombre d'utilisations"
+        },
+        "footer": {
+            "versionCount": "{count} versions",
+            "viewAllVersions": "Voir toutes les versions locales"
         }
     },
     "globalContextMenu": {
@@ -183,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "Gérer les modèles exclus"
+        },
+        "groupByModel": {
+            "label": "Grouper par modèle"
         }
     },
     "header": {
@@ -195,13 +203,7 @@
             "statistics": "Statistiques"
         },
         "search": {
-            "placeholder": "Rechercher...",
-            "placeholders": {
-                "loras": "Rechercher des LoRAs...",
-                "recipes": "Rechercher des recipes...",
-                "checkpoints": "Rechercher des checkpoints...",
-                "embeddings": "Rechercher des embeddings..."
-            },
+            "placeholder": "Rechercher",
             "options": "Options de recherche",
             "searchIn": "Rechercher dans :",
             "notAvailable": "Recherche non disponible sur la page de statistiques",
@@ -251,7 +253,18 @@
             "toggle": "Basculer le thème",
             "switchToLight": "Passer au thème clair",
             "switchToDark": "Passer au thème sombre",
-            "switchToAuto": "Passer au thème automatique"
+            "switchToAuto": "Passer au thème automatique",
+            "presets": "Préréglages de thème",
+            "default": "Par défaut",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "Mode",
+            "light": "Clair",
+            "dark": "Sombre",
+            "auto": "Auto"
         },
         "actions": {
             "checkUpdates": "Vérifier les mises à jour",
@@ -263,6 +276,9 @@
         "civitaiApiKey": "Clé API Civitai",
         "civitaiApiKeyPlaceholder": "Entrez votre clé API Civitai",
         "civitaiApiKeyHelp": "Utilisée pour l'authentification lors du téléchargement de modèles depuis Civitai",
+        "civitaiApiKeyConfigured": "Configuré",
+        "civitaiApiKeyNotConfigured": "Non configuré",
+        "civitaiApiKeySet": "Configurer",
         "civitaiHost": {
             "label": "Hôte Civitai",
             "help": "Choisissez quel site Civitai s'ouvre lorsque vous utilisez les liens « View on Civitai ».",
@@ -303,6 +319,7 @@
             "downloads": "Téléchargements",
             "videoSettings": "Paramètres vidéo",
             "layoutSettings": "Paramètres d'affichage",
+            "licenseIcons": "Icônes de licence",
             "misc": "Divers",
             "backup": "Sauvegardes",
             "folderSettings": "Racines par défaut",
@@ -310,7 +327,7 @@
             "extraFolderPaths": "Chemins de dossiers supplémentaires",
             "downloadPathTemplates": "Modèles de chemin de téléchargement",
             "priorityTags": "Étiquettes prioritaires",
-            "updateFlags": "Indicateurs de mise à jour",
+            "versionScope": "Indicateurs de mise à jour",
             "exampleImages": "Images d'exemple",
             "autoOrganize": "Organisation automatique",
             "metadata": "Métadonnées",
@@ -415,6 +432,8 @@
             "help": "Lorsque activé, LoRA Manager ignorera le téléchargement d'une version de modèle si le service d'historique des téléchargements enregistre cette version exacte comme déjà téléchargée. S'applique à tous les flux de téléchargement."
         },
         "layoutSettings": {
+            "groupByModel": "Grouper par modèle",
+            "groupByModelHelp": "Lorsque activé, seule la version la plus récente de chaque modèle Civitai s'affiche sous forme de carte unique. Les versions plus anciennes sont masquées.",
             "displayDensity": "Densité d'affichage",
             "displayDensityOptions": {
                 "default": "Par défaut",
@@ -571,7 +590,7 @@
             "download": "Télécharger",
             "restartRequired": "Redémarrage requis"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "Stratégie des indicateurs de mise à jour",
             "help": "Choisissez si les badges de mise à jour doivent apparaître uniquement lorsqu’une nouvelle version partage le même modèle de base que vos fichiers locaux, ou dès qu’il existe une version plus récente pour ce modèle.",
             "options": {
@@ -583,6 +602,10 @@
             "label": "Masquer les mises à jour en accès anticipé",
             "help": "Seulement les mises à jour en accès anticipé"
         },
+        "licenseIcons": {
+            "useNewStyle": "Utiliser les icônes de licence mises à jour",
+            "useNewStyleHelp": "Afficher les permissions de licence avec des indicateurs colorés (nouveau style) ou des icônes de restriction uniquement (style classique). Reprend le design actuel de CivitAI."
+        },
         "misc": {
             "includeTriggerWords": "Inclure les mots-clés dans la syntaxe LoRA",
             "includeTriggerWordsHelp": "Inclure les mots-clés d'entraînement lors de la copie de la syntaxe LoRA dans le presse-papiers",
@@ -634,6 +657,23 @@
             "proxyPassword": "Mot de passe (optionnel)",
             "proxyPasswordPlaceholder": "mot_de_passe",
             "proxyPasswordHelp": "Mot de passe pour l'authentification proxy (si nécessaire)"
+        },
+        "aiProvider": {
+            "title": "Fournisseur d'IA",
+            "provider": "Fournisseur",
+            "providerHelp": "Choisissez votre fournisseur LLM. OpenAI et Ollama utilisent des endpoints prédéfinis. Personnalisé vous permet de spécifier n'importe quel endpoint compatible OpenAI.",
+            "custom": "Personnalisé (compatible OpenAI)",
+            "apiBase": "URL de base de l'API",
+            "apiBaseHelp": "L'URL de base pour l'API LLM (ex. https://api.openai.com/v1). Laissez vide pour utiliser le fournisseur par défaut.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "Clé API",
+            "apiKeyHelp": "Votre clé API du fournisseur LLM. Stockée localement, jamais envoyée à un serveur autre que votre fournisseur LLM choisi.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "Non définie",
+            "apiKeyConfigured": "Configurée",
+            "apiKeySet": "Configurer",
+            "model": "Modèle",
+            "modelHelp": "Le nom du modèle à utiliser (ex. deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). Consultez votre fournisseur pour les modèles disponibles."
         }
     },
     "loras": {
@@ -651,7 +691,11 @@
                 "sizeAsc": "Plus petit",
                 "usage": "Nombre d'utilisations",
                 "usageDesc": "Plus",
-                "usageAsc": "Moins"
+                "usageAsc": "Moins",
+                "versionsCount": "Versions locales",
+                "versionsCountDesc": "Plus de versions d'abord",
+                "versionsCountAsc": "Moins de versions d'abord",
+                "versionIdDesc": "Version la plus récente d'abord"
             },
             "refresh": {
                 "title": "Actualiser la liste des modèles",
@@ -727,7 +771,8 @@
                 "completed": "Terminé : {success} déplacés, {skipped} ignorés, {failures} échecs",
                 "complete": "Auto-organisation terminée",
                 "error": "Erreur : {error}"
-            }
+            },
+            "enrichHfAgent": "Enrichir les métadonnées (IA)"
         },
         "contextMenu": {
             "refreshMetadata": "Actualiser les données Civitai",
@@ -751,7 +796,8 @@
             "shareRecipe": "Partager la recipe",
             "viewAllLoras": "Voir tous les LoRAs",
             "downloadMissingLoras": "Télécharger les LoRAs manquants",
-            "deleteRecipe": "Supprimer la recipe"
+            "deleteRecipe": "Supprimer la recipe",
+            "enrichHfAgent": "Enrichir les métadonnées (IA)"
         }
     },
     "recipes": {
@@ -997,6 +1043,18 @@
             "storage": "Stockage",
             "insights": "Aperçus"
         },
+        "metrics": {
+            "totalModels": "Total des modèles",
+            "totalStorage": "Stockage total",
+            "totalGenerations": "Générations totales",
+            "usageRate": "Taux d'utilisation",
+            "loras": "LoRAs",
+            "checkpoints": "Points de contrôle",
+            "embeddings": "Embeddings",
+            "uniqueTags": "Tags uniques",
+            "unusedModels": "Modèles inutilisés",
+            "avgUsesPerModel": "Moy. utilisations/modèle"
+        },
         "usage": {
             "mostUsedLoras": "LoRAs les plus utilisés",
             "mostUsedCheckpoints": "Checkpoints les plus utilisés",
@@ -1014,13 +1072,77 @@
         },
         "insights": {
             "smartInsights": "Aperçus intelligents",
-            "recommendations": "Recommandations"
+            "recommendations": "Recommandations",
+            "noInsights": "Aucun aperçu disponible",
+            "unusedLoras": {
+                "high": {
+                    "title": "Nombre élevé de LoRAs inutilisées",
+                    "description": "{percent}% de vos LoRAs ({count}/{total}) n'ont jamais été utilisées.",
+                    "suggestion": "Envisagez d'organiser ou d'archiver les modèles inutilisés pour libérer de l'espace."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "Points de contrôle inutilisés détectés",
+                    "description": "{percent}% de vos points de contrôle ({count}/{total}) n'ont jamais été utilisés.",
+                    "suggestion": "Examinez et envisagez de supprimer les points de contrôle dont vous n'avez plus besoin."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "Nombre élevé d'Embeddings inutilisées",
+                    "description": "{percent}% de vos embeddings ({count}/{total}) n'ont jamais été utilisées.",
+                    "suggestion": "Envisagez d'organiser ou d'archiver les embeddings inutilisées pour optimiser votre collection."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "Grande collection détectée",
+                    "description": "Votre collection de modèles utilise {size} de stockage.",
+                    "suggestion": "Envisagez d'utiliser un stockage externe ou des solutions cloud pour une meilleure organisation."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "Utilisateur actif",
+                    "description": "Vous avez effectué {count} générations jusqu'à présent !",
+                    "suggestion": "Continuez à explorer et à créer du contenu formidable avec vos modèles."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "Aperçu de la collection",
             "baseModelDistribution": "Distribution des modèles de base",
             "usageTrends": "Tendances d'utilisation (30 derniers jours)",
-            "usageDistribution": "Distribution de l'utilisation"
+            "usageDistribution": "Distribution de l'utilisation",
+            "date": "Date",
+            "usageCount": "Nombre d'utilisations",
+            "fileSizeBytes": "Taille du fichier (octets)",
+            "models": "Modèles",
+            "loraUsage": "Utilisation LoRA",
+            "checkpointUsage": "Utilisation Checkpoint",
+            "embeddingUsage": "Utilisation Embedding"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Point de contrôle",
+            "diffusion_model": "Modèle de diffusion",
+            "embedding": "Embeddings"
+        },
+        "placeholders": {
+            "loading": "Chargement...",
+            "noModels": "Aucun modèle trouvé",
+            "errorLoading": "Erreur de chargement des données",
+            "noStorageData": "Aucune donnée de stockage disponible",
+            "rootFolder": "Racine",
+            "chartLibraryMissing": "Le graphique nécessite la bibliothèque Chart.js"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} modèles",
+            "chartUsage": "{name}: {size}, {count} utilisations",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1032,7 +1154,10 @@
             "titleWithType": "Télécharger {type} depuis une URL",
             "civitaiUrl": "URL Civitai :",
             "placeholder": "https://civitai.com/models/...",
-            "urlHint": "Entrez une URL CivitAI ou CivArchive par ligne. Prend en charge plusieurs URLs pour le téléchargement par lot.",
+            "urlHint": "Entrez une URL CivitAI, CivArchive ou Hugging Face par ligne. Prend en charge plusieurs URL pour le téléchargement par lot.",
+            "selectHfFiles": "Sélectionnez le(s) fichier(s) à télécharger depuis ce dépôt :",
+            "selectAll": "Tout sélectionner",
+            "fetchingRepoFiles": "Récupération des fichiers du dépôt...",
             "locationPreview": "Aperçu de l'emplacement de téléchargement",
             "useDefaultPath": "Utiliser le chemin par défaut",
             "useDefaultPathTooltip": "Lorsque activé, les fichiers sont automatiquement organisés selon les modèles de chemin configurés",
@@ -1061,7 +1186,9 @@
             },
             "errors": {
                 "invalidUrl": "Format d'URL Civitai invalide",
-                "noVersions": "Aucune version disponible pour ce modèle"
+                "noVersions": "Aucune version disponible pour ce modèle",
+                "mixedSources": "Impossible de mélanger les URL CivitAI et Hugging Face dans le même lot.",
+                "noModelFiles": "Aucun fichier de modèle trouvé dans ce dépôt."
             },
             "status": {
                 "preparing": "Préparation du téléchargement...",
@@ -1212,6 +1339,8 @@
                 "editVersionName": "Modifier le nom de la version",
                 "viewOnCivitai": "Voir sur Civitai",
                 "viewOnCivitaiText": "Voir sur Civitai",
+                "viewOnHuggingFace": "Voir sur Hugging Face",
+                "viewOnHuggingFaceText": "Voir sur Hugging Face",
                 "viewCreatorProfile": "Voir le profil du créateur",
                 "openFileLocation": "Ouvrir l'emplacement du fichier",
                 "sendToWorkflow": "Envoyer vers ComfyUI",
@@ -1237,7 +1366,10 @@
                 "additionalNotes": "Notes supplémentaires",
                 "notesHint": "Appuyez sur Entrée pour sauvegarder, Maj+Entrée pour nouvelle ligne",
                 "addNotesPlaceholder": "Ajoutez vos notes ici...",
-                "aboutThisVersion": "À propos de cette version"
+                "aboutThisVersion": "À propos de cette version",
+                "baseModelSearchPlaceholder": "Rechercher un modèle de base…",
+                "baseModelSuggested": "Suggéré",
+                "baseModelNoMatch": "Aucun modèle de base correspondant"
             },
             "notes": {
                 "saved": "Notes sauvegardées avec succès",
@@ -1424,15 +1556,6 @@
             "duplicate": "Ce tag existe déjà"
         }
     },
-    "keyboard": {
-        "navigation": "Navigation au clavier :",
-        "shortcuts": {
-            "pageUp": "Défiler d'une page vers le haut",
-            "pageDown": "Défiler d'une page vers le bas",
-            "home": "Aller en haut",
-            "end": "Aller en bas"
-        }
-    },
     "initialization": {
         "title": "Initialisation",
         "message": "Préparation de votre espace de travail...",
@@ -1522,12 +1645,15 @@
             "modelUpdated": "Modèle mis à jour dans le workflow",
             "modelFailed": "Échec de la mise à jour du nœud modèle",
             "embeddingAdded": "Embedding ajouté au workflow",
-            "embeddingFailed": "Échec de l'ajout de l'embedding"
+            "embeddingFailed": "Échec de l'ajout de l'embedding",
+            "promptSent": "Prompt envoyé au workflow",
+            "promptFailed": "Échec de l'envoi du prompt"
         },
         "nodeSelector": {
             "recipe": "Recipe",
             "lora": "LoRA",
             "embedding": "Embedding",
+            "prompt": "Prompt",
             "replace": "Remplacer",
             "append": "Ajouter",
             "selectTargetNode": "Sélectionner le nœud cible",
@@ -1714,6 +1840,7 @@
             "enterLoraName": "Veuillez entrer un nom ou une syntaxe LoRA",
             "reconnectedSuccessfully": "LoRA reconnecté avec succès",
             "reconnectFailed": "Erreur lors de la reconnexion du LoRA : {message}",
+            "noPromptToSend": "Aucun prompt à envoyer",
             "cannotSend": "Impossible d'envoyer la recipe : ID de recipe manquant",
             "sendFailed": "Échec de l'envoi de la recipe vers le workflow",
             "sendError": "Erreur lors de l'envoi de la recipe vers le workflow",
@@ -1973,6 +2100,12 @@
             "moveFailed": "Failed to move item: {message}",
             "copiedToClipboard": "Copié dans le presse-papiers",
             "downloadStarted": "Téléchargement démarré"
+        },
+        "agent": {
+            "llmNotConfigured": "Fournisseur d'IA non configuré. Activez-le dans Paramètres → Fournisseur d'IA.",
+            "enrichStarted": "Enrichissement des métadonnées par IA...",
+            "enrichComplete": "Enrichissement des métadonnées terminé : {{summary}}",
+            "enrichFailed": "Échec de l'enrichissement des métadonnées : {{error}}"
         }
     },
     "doctor": {

locales/he.json

@@ -105,6 +105,7 @@
             "removeFromFavorites": "הסר מהמועדפים",
             "viewOnCivitai": "הצג ב-Civitai",
             "notAvailableFromCivitai": "לא זמין מ-Civitai",
+            "viewOnHuggingFace": "צפייה ב-Hugging Face",
             "sendToWorkflow": "שלח ל-ComfyUI (לחיצה: הוסף, Shift+לחיצה: החלף)",
             "copyLoRASyntax": "העתק תחביר LoRA",
             "checkpointNameCopied": "שם Checkpoint הועתק",
@@ -145,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "מספר שימושים"
+        },
+        "footer": {
+            "versionCount": "{count} גרסאות",
+            "viewAllVersions": "הצג את כל הגרסאות המקומיות"
         }
     },
     "globalContextMenu": {
@@ -183,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "ניהול מודלים מוחרגים"
+        },
+        "groupByModel": {
+            "label": "קיבוץ לפי דגם"
         }
     },
     "header": {
@@ -195,13 +203,7 @@
             "statistics": "סטטיסטיקה"
         },
         "search": {
-            "placeholder": "חפש...",
-            "placeholders": {
-                "loras": "חפש LoRAs...",
-                "recipes": "חפש מתכונים...",
-                "checkpoints": "חפש checkpoints...",
-                "embeddings": "חפש embeddings..."
-            },
+            "placeholder": "חיפוש",
             "options": "אפשרויות חיפוש",
             "searchIn": "חפש ב:",
             "notAvailable": "חיפוש לא זמין בדף הסטטיסטיקה",
@@ -251,7 +253,18 @@
             "toggle": "החלף ערכת נושא",
             "switchToLight": "עבור לערכת נושא בהירה",
             "switchToDark": "עבור לערכת נושא כהה",
-            "switchToAuto": "עבור לערכת נושא אוטומטית"
+            "switchToAuto": "עבור לערכת נושא אוטומטית",
+            "presets": "ערכות נושא מוגדרות",
+            "default": "ברירת מחדל",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "מצב",
+            "light": "בהיר",
+            "dark": "כהה",
+            "auto": "אוטומטי"
         },
         "actions": {
             "checkUpdates": "בדוק עדכונים",
@@ -263,6 +276,9 @@
         "civitaiApiKey": "מפתח API של Civitai",
         "civitaiApiKeyPlaceholder": "הזן את מפתח ה-API שלך מ-Civitai",
         "civitaiApiKeyHelp": "משמש לאימות בעת הורדת מודלים מ-Civitai",
+        "civitaiApiKeyConfigured": "מוגדר",
+        "civitaiApiKeyNotConfigured": "לא מוגדר",
+        "civitaiApiKeySet": "הגדר",
         "civitaiHost": {
             "label": "מארח Civitai",
             "help": "בחר איזה אתר של Civitai ייפתח בעת שימוש בקישורי \"View on Civitai\".",
@@ -303,6 +319,7 @@
             "downloads": "הורדות",
             "videoSettings": "הגדרות וידאו",
             "layoutSettings": "הגדרות פריסה",
+            "licenseIcons": "סמלי רישיון",
             "misc": "שונות",
             "backup": "גיבויים",
             "folderSettings": "תיקיות ברירת מחדל",
@@ -310,7 +327,7 @@
             "extraFolderPaths": "נתיבי תיקיות נוספים",
             "downloadPathTemplates": "תבניות נתיב הורדה",
             "priorityTags": "תגיות עדיפות",
-            "updateFlags": "תגי עדכון",
+            "versionScope": "תגי עדכון",
             "exampleImages": "תמונות דוגמה",
             "autoOrganize": "ארגון אוטומטי",
             "metadata": "מטא-נתונים",
@@ -415,6 +432,8 @@
             "help": "כאשר מופעל, LoRA Manager ידלג על הורדת גרסת מודל אם שירות היסטוריית ההורדות רושם את הגרסה המדויקת הזו ככבר שהורדה. חל על כל תהליכי ההורדה."
         },
         "layoutSettings": {
+            "groupByModel": "קיבוץ לפי דגם",
+            "groupByModelHelp": "כאשר מופעל, רק הגרסה העדכנית ביותר של כל דגם Civitai מוצגת ככרטיס בודד. גרסאות ישנות יותר מוסתרות.",
             "displayDensity": "צפיפות תצוגה",
             "displayDensityOptions": {
                 "default": "ברירת מחדל",
@@ -571,7 +590,7 @@
             "download": "הורד",
             "restartRequired": "דורש הפעלה מחדש"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "אסטרטגיית תגי עדכון",
             "help": "בחרו אם תוויות העדכון יוצגו רק כאשר גרסה חדשה חולקת את אותו דגם בסיס כמו הקבצים המקומיים שלכם או בכל מקרה שבו קיימת גרסה חדשה עבור אותו דגם.",
             "options": {
@@ -583,6 +602,10 @@
             "label": "הסתר עדכוני גישה מוקדמת",
             "help": "רק עדכוני גישה מוקדמת"
         },
+        "licenseIcons": {
+            "useNewStyle": "השתמש בסמלי רישיון מעודכנים",
+            "useNewStyleHelp": "הצג הרשאות רישיון עם מחוונים צבעוניים (סגנון חדש) או סמלי הגבלה בלבד (סגנון קלאסי). משקף את העיצוב העדכני של CivitAI."
+        },
         "misc": {
             "includeTriggerWords": "כלול מילות טריגר בתחביר LoRA",
             "includeTriggerWordsHelp": "כלול מילות טריגר מאומנות בעת העתקת תחביר LoRA ללוח",
@@ -634,6 +657,23 @@
             "proxyPassword": "סיסמה (אופציונלי)",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "סיסמה לאימות מול הפרוקסי (אם נדרש)"
+        },
+        "aiProvider": {
+            "title": "ספק AI",
+            "provider": "ספק",
+            "providerHelp": "בחר את ספק ה-LLM שלך. OpenAI ו-Ollama משתמשים בנקודות קצה מוגדרות מראש. מותאם אישית מאפשר לך לציין כל נקודת קצה תואמת OpenAI.",
+            "custom": "מותאם אישית (תואם OpenAI)",
+            "apiBase": "כתובת בסיס API",
+            "apiBaseHelp": "כתובת ה-URL הבסיסית ל-API של LLM (לדוגמה https://api.openai.com/v1). השאר ריק לשימוש בברירת המחדל של הספק.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "מפתח API",
+            "apiKeyHelp": "מפתח ה-API של ספק ה-LLM שלך. נשמר מקומית, לעולם לא נשלח לשרת כלשהו מלבד ספק ה-LLM שבחרת.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "לא הוגדר",
+            "apiKeyConfigured": "הוגדר",
+            "apiKeySet": "הגדר",
+            "model": "מודל",
+            "modelHelp": "שם המודל לשימוש (לדוגמה deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). בדוק אצל הספק שלך אילו מודלים זמינים."
         }
     },
     "loras": {
@@ -651,7 +691,11 @@
                 "sizeAsc": "הקטן ביותר",
                 "usage": "מספר שימושים",
                 "usageDesc": "הכי הרבה",
-                "usageAsc": "הכי פחות"
+                "usageAsc": "הכי פחות",
+                "versionsCount": "גרסאות מקומיות",
+                "versionsCountDesc": "הכי הרבה גרסאות ראשונות",
+                "versionsCountAsc": "הכי מעט גרסאות ראשונות",
+                "versionIdDesc": "גרסה חדשה ביותר ראשונה"
             },
             "refresh": {
                 "title": "רענן רשימת מודלים",
@@ -727,7 +771,8 @@
                 "completed": "הושלם: {success} הועברו, {skipped} דולגו, {failures} נכשלו",
                 "complete": "ארגון אוטומטי הושלם",
                 "error": "שגיאה: {error}"
-            }
+            },
+            "enrichHfAgent": "העשרת מטא-דאטה (AI)"
         },
         "contextMenu": {
             "refreshMetadata": "רענן נתוני Civitai",
@@ -751,7 +796,8 @@
             "shareRecipe": "שתף מתכון",
             "viewAllLoras": "הצג את כל ה-LoRAs",
             "downloadMissingLoras": "הורד LoRAs חסרים",
-            "deleteRecipe": "מחק מתכון"
+            "deleteRecipe": "מחק מתכון",
+            "enrichHfAgent": "העשרת מטא-דאטה (AI)"
         }
     },
     "recipes": {
@@ -997,6 +1043,18 @@
             "storage": "אחסון",
             "insights": "תובנות"
         },
+        "metrics": {
+            "totalModels": "סה\"כ דגמים",
+            "totalStorage": "סה\"כ אחסון",
+            "totalGenerations": "סה\"כ יצירות",
+            "usageRate": "שיעור שימוש",
+            "loras": "LoRA",
+            "checkpoints": "נקודות ביקורת",
+            "embeddings": "הטמעות",
+            "uniqueTags": "תגיות ייחודיות",
+            "unusedModels": "דגמים שאינם בשימוש",
+            "avgUsesPerModel": "ממוצע שימושים/דגם"
+        },
         "usage": {
             "mostUsedLoras": "LoRAs הנפוצים ביותר",
             "mostUsedCheckpoints": "Checkpoints הנפוצים ביותר",
@@ -1014,13 +1072,77 @@
         },
         "insights": {
             "smartInsights": "תובנות חכמות",
-            "recommendations": "המלצות"
+            "recommendations": "המלצות",
+            "noInsights": "אין תובנות זמינות",
+            "unusedLoras": {
+                "high": {
+                    "title": "כמות גבוהה של LoRAs שאינן בשימוש",
+                    "description": "{percent}% מה-LoRAs שלך ({count}/{total}) מעולם לא נעשה בהם שימוש.",
+                    "suggestion": "שקול לארגן או לאחסן בארכיון מודלים שאינם בשימוש כדי לפנות שטח אחסון."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "התגלו נקודות ביקורת שאינן בשימוש",
+                    "description": "{percent}% מנקודות הביקורת שלך ({count}/{total}) מעולם לא נעשה בהן שימוש.",
+                    "suggestion": "בדוק ושקול להסיר נקודות ביקורת שאינך צריך עוד."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "כמות גבוהה של Embeddings שאינם בשימוש",
+                    "description": "{percent}% מה-Embeddings שלך ({count}/{total}) מעולם לא נעשה בהם שימוש.",
+                    "suggestion": "שקול לארגן או לאחסן בארכיון Embeddings שאינם בשימוש כדי לייעל את האוסף."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "התגלה אוסף גדול",
+                    "description": "אוסף המודלים שלך משתמש ב-{size} של אחסון.",
+                    "suggestion": "שקול להשתמש באחסון חיצוני או בפתרונות ענן לארגון טוב יותר."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "משתמש פעיל",
+                    "description": "השלמת {count} יצירות עד כה!",
+                    "suggestion": "המשך לחקור וליצור תוכן מדהים עם המודלים שלך."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "סקירת אוסף",
             "baseModelDistribution": "התפלגות מודלי בסיס",
             "usageTrends": "מגמות שימוש (30 יום אחרונים)",
-            "usageDistribution": "התפלגות שימוש"
+            "usageDistribution": "התפלגות שימוש",
+            "date": "תאריך",
+            "usageCount": "מספר שימושים",
+            "fileSizeBytes": "גודל קובץ (בתים)",
+            "models": "דגמים",
+            "loraUsage": "שימוש ב-LoRA",
+            "checkpointUsage": "שימוש ב-Checkpoint",
+            "embeddingUsage": "שימוש ב-Embedding"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "נקודת ביקורת",
+            "diffusion_model": "מודל דיפוזיה",
+            "embedding": "הטמעות"
+        },
+        "placeholders": {
+            "loading": "טוען...",
+            "noModels": "לא נמצאו דגמים",
+            "errorLoading": "שגיאה בטעינת נתונים",
+            "noStorageData": "אין נתוני אחסון זמינים",
+            "rootFolder": "שורש",
+            "chartLibraryMissing": "הגרף דורש את ספריית Chart.js"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} דגמים",
+            "chartUsage": "{name}: {size}, {count} שימושים",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1032,7 +1154,10 @@
             "titleWithType": "הורד {type} מכתובת URL",
             "civitaiUrl": "כתובת URL של Civitai:",
             "placeholder": "https://civitai.com/models/...",
-            "urlHint": "יש להזין כתובת URL אחת של CivitAI או CivArchive בכל שורה. תומך במספר כתובות URL להורדה בבת אחת.",
+            "urlHint": "יש להזין כתובת URL אחת של CivitAI, CivArchive או Hugging Face בכל שורה. תומך במספר כתובות URL להורדה בקבוצה.",
+            "selectHfFiles": "בחר קבצים להורדה ממאגר זה:",
+            "selectAll": "בחר הכל",
+            "fetchingRepoFiles": "מביא קבצים מהמאגר...",
             "locationPreview": "תצוגה מקדימה של מיקום ההורדה",
             "useDefaultPath": "השתמש בנתיב ברירת מחדל",
             "useDefaultPathTooltip": "כאשר מופעל, קבצים מאורגנים אוטומטית באמצעות תבניות נתיב מוגדרות",
@@ -1061,7 +1186,9 @@
             },
             "errors": {
                 "invalidUrl": "פורמט URL של Civitai לא חוקי",
-                "noVersions": "אין גרסאות זמינות למודל זה"
+                "noVersions": "אין גרסאות זמינות למודל זה",
+                "mixedSources": "לא ניתן לערבב כתובות URL של CivitAI ו-Hugging Face באותה קבוצה.",
+                "noModelFiles": "לא נמצאו קבצי מודל במאגר זה."
             },
             "status": {
                 "preparing": "מכין הורדה...",
@@ -1212,6 +1339,8 @@
                 "editVersionName": "ערוך שם גרסה",
                 "viewOnCivitai": "הצג ב-Civitai",
                 "viewOnCivitaiText": "הצג ב-Civitai",
+                "viewOnHuggingFace": "צפייה ב-Hugging Face",
+                "viewOnHuggingFaceText": "צפייה ב-Hugging Face",
                 "viewCreatorProfile": "הצג פרופיל יוצר",
                 "openFileLocation": "פתח מיקום קובץ",
                 "sendToWorkflow": "שלח ל-ComfyUI",
@@ -1237,7 +1366,10 @@
                 "additionalNotes": "הערות נוספות",
                 "notesHint": "לחץ Enter לשמירה, Shift+Enter לשורה חדשה",
                 "addNotesPlaceholder": "הוסף את ההערות שלך כאן...",
-                "aboutThisVersion": "אודות גרסה זו"
+                "aboutThisVersion": "אודות גרסה זו",
+                "baseModelSearchPlaceholder": "חפש מודל בסיס…",
+                "baseModelSuggested": "מוצע",
+                "baseModelNoMatch": "אין מודלי בסיס תואמים"
             },
             "notes": {
                 "saved": "הערות נשמרו בהצלחה",
@@ -1424,15 +1556,6 @@
             "duplicate": "תגית זו כבר קיימת"
         }
     },
-    "keyboard": {
-        "navigation": "ניווט במקלדת:",
-        "shortcuts": {
-            "pageUp": "גלול עמוד אחד למעלה",
-            "pageDown": "גלול עמוד אחד למטה",
-            "home": "קפוץ להתחלה",
-            "end": "קפוץ לסוף"
-        }
-    },
     "initialization": {
         "title": "מאתחל",
         "message": "מכין את סביבת העבודה שלך...",
@@ -1522,12 +1645,15 @@
             "modelUpdated": "מודל עודכן ב-workflow",
             "modelFailed": "עדכון צומת המודל נכשל",
             "embeddingAdded": "Embedding נוסף ל-workflow",
-            "embeddingFailed": "הוספת Embedding נכשלה"
+            "embeddingFailed": "הוספת Embedding נכשלה",
+            "promptSent": "הנחיה נשלחה ל-workflow",
+            "promptFailed": "שליחת ההנחיה נכשלה"
         },
         "nodeSelector": {
             "recipe": "מתכון",
             "lora": "LoRA",
             "embedding": "Embedding",
+            "prompt": "הנחיה",
             "replace": "החלף",
             "append": "הוסף",
             "selectTargetNode": "בחר צומת יעד",
@@ -1714,6 +1840,7 @@
             "enterLoraName": "אנא הזן שם LoRA או תחביר",
             "reconnectedSuccessfully": "LoRA קושר מחדש בהצלחה",
             "reconnectFailed": "שגיאה בקישור מחדש של LoRA: {message}",
+            "noPromptToSend": "אין הנחיה לשליחה",
             "cannotSend": "לא ניתן לשלוח מתכון: חסר מזהה מתכון",
             "sendFailed": "שליחת המתכון ל-workflow נכשלה",
             "sendError": "שגיאה בשליחת המתכון ל-workflow",
@@ -1973,6 +2100,12 @@
             "moveFailed": "Failed to move item: {message}",
             "copiedToClipboard": "הועתק ללוח",
             "downloadStarted": "ההורדה החלה"
+        },
+        "agent": {
+            "llmNotConfigured": "ספק AI לא הוגדר. הפעל אותו בהגדרות → ספק AI.",
+            "enrichStarted": "מעשיר מטא-דאטה באמצעות AI...",
+            "enrichComplete": "העשרת מטא-דאטה הושלמה: {{summary}}",
+            "enrichFailed": "העשרת מטא-דאטה נכשלה: {{error}}"
         }
     },
     "doctor": {

locales/ja.json

@@ -105,6 +105,7 @@
             "removeFromFavorites": "お気に入りから削除",
             "viewOnCivitai": "Civitaiで表示",
             "notAvailableFromCivitai": "Civitaiでは利用できません",
+            "viewOnHuggingFace": "Hugging Face で見る",
             "sendToWorkflow": "ComfyUIに送信（クリック：追加、Shift+クリック：置換）",
             "copyLoRASyntax": "LoRA構文をコピー",
             "checkpointNameCopied": "checkpointの名前をコピーしました",
@@ -145,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "使用回数"
+        },
+        "footer": {
+            "versionCount": "{count} バージョン",
+            "viewAllVersions": "ローカルの全バージョンを表示"
         }
     },
     "globalContextMenu": {
@@ -183,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "除外モデルを管理"
+        },
+        "groupByModel": {
+            "label": "モデルでグループ化"
         }
     },
     "header": {
@@ -195,13 +203,7 @@
             "statistics": "統計"
         },
         "search": {
-            "placeholder": "検索...",
-            "placeholders": {
-                "loras": "LoRAを検索...",
-                "recipes": "レシピを検索...",
-                "checkpoints": "checkpointを検索...",
-                "embeddings": "embeddingを検索..."
-            },
+            "placeholder": "検索",
             "options": "検索オプション",
             "searchIn": "検索対象：",
             "notAvailable": "統計ページでは検索は利用できません",
@@ -251,7 +253,18 @@
             "toggle": "テーマの切り替え",
             "switchToLight": "ライトテーマに切り替え",
             "switchToDark": "ダークテーマに切り替え",
-            "switchToAuto": "自動テーマに切り替え"
+            "switchToAuto": "自動テーマに切り替え",
+            "presets": "テーマプリセット",
+            "default": "デフォルト",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "モード",
+            "light": "ライト",
+            "dark": "ダーク",
+            "auto": "自動"
         },
         "actions": {
             "checkUpdates": "更新確認",
@@ -263,6 +276,9 @@
         "civitaiApiKey": "Civitai APIキー",
         "civitaiApiKeyPlaceholder": "Civitai APIキーを入力してください",
         "civitaiApiKeyHelp": "Civitaiからモデルをダウンロードするときの認証に使用されます",
+        "civitaiApiKeyConfigured": "設定済み",
+        "civitaiApiKeyNotConfigured": "未設定",
+        "civitaiApiKeySet": "設定",
         "civitaiHost": {
             "label": "Civitai ホスト",
             "help": "「View on Civitai」リンクを使うときに開く Civitai サイトを選択します。",
@@ -303,6 +319,7 @@
             "downloads": "ダウンロード",
             "videoSettings": "動画設定",
             "layoutSettings": "レイアウト設定",
+            "licenseIcons": "ライセンスアイコン",
             "misc": "その他",
             "backup": "バックアップ",
             "folderSettings": "デフォルトルート",
@@ -310,7 +327,7 @@
             "extraFolderPaths": "追加フォルダーパス",
             "downloadPathTemplates": "ダウンロードパステンプレート",
             "priorityTags": "優先タグ",
-            "updateFlags": "アップデートフラグ",
+            "versionScope": "アップデートフラグ",
             "exampleImages": "例画像",
             "autoOrganize": "自動整理",
             "metadata": "メタデータ",
@@ -415,6 +432,8 @@
             "help": "有効にすると、ダウンロード履歴サービスがそのバージョンが既にダウンロード済みと記録している場合、LoRA Managerはそのモデルバージョンのダウンロードをスキップします。すべてのダウンロードフローに適用されます。"
         },
         "layoutSettings": {
+            "groupByModel": "モデルでグループ化",
+            "groupByModelHelp": "有効にすると、各Civitaiモデルの最新バージョンのみが1枚のカードとして表示され、古いバージョンは非表示になります。",
             "displayDensity": "表示密度",
             "displayDensityOptions": {
                 "default": "デフォルト",
@@ -571,7 +590,7 @@
             "download": "ダウンロード",
             "restartRequired": "再起動が必要"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "アップデートフラグの表示戦略",
             "help": "新リリースがローカルファイルと同じベースモデルを共有する場合にのみ更新バッジを表示するか、そのモデルに新しいバージョンがあれば常に表示するかを決めます。",
             "options": {
@@ -583,6 +602,10 @@
             "label": "早期アクセス更新を非表示",
             "help": "早期アクセスのみの更新"
         },
+        "licenseIcons": {
+            "useNewStyle": "更新されたライセンスアイコンを使用",
+            "useNewStyleHelp": "カラーインジケーター付きでライセンス許可を表示（新スタイル）するか、制限のみのアイコンを表示（クラシックスタイル）します。現在のCivitAIデザインを反映しています。"
+        },
         "misc": {
             "includeTriggerWords": "LoRA構文にトリガーワードを含める",
             "includeTriggerWordsHelp": "LoRA構文をクリップボードにコピーする際、学習済みトリガーワードを含めます",
@@ -634,6 +657,23 @@
             "proxyPassword": "パスワード（任意）",
             "proxyPasswordPlaceholder": "パスワード",
             "proxyPasswordHelp": "プロキシ認証用のパスワード（必要な場合）"
+        },
+        "aiProvider": {
+            "title": "AIプロバイダー",
+            "provider": "プロバイダー",
+            "providerHelp": "LLMプロバイダーを選択してください。OpenAIとOllamaはプリセットのAPIエンドポイントを使用します。カスタムでは任意のOpenAI互換エンドポイントを指定できます。",
+            "custom": "カスタム（OpenAI互換）",
+            "apiBase": "APIベースURL",
+            "apiBaseHelp": "LLM APIのベースURL（例：https://api.openai.com/v1）。空の場合はプロバイダーのデフォルトが使用されます。",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "APIキー",
+            "apiKeyHelp": "LLMプロバイダーのAPIキー。ローカルに保存され、選択したLLMプロバイダー以外のサーバーに送信されることはありません。",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "未設定",
+            "apiKeyConfigured": "設定済み",
+            "apiKeySet": "設定",
+            "model": "モデル",
+            "modelHelp": "使用するモデル名（例：deepseek-v4-flash, gemini-2.5-flash, gemma4:12b）。プロバイダーで利用可能なモデルをご確認ください。"
         }
     },
     "loras": {
@@ -651,7 +691,11 @@
                 "sizeAsc": "小さい順",
                 "usage": "使用回数",
                 "usageDesc": "多い",
-                "usageAsc": "少ない"
+                "usageAsc": "少ない",
+                "versionsCount": "ローカルバージョン数",
+                "versionsCountDesc": "バージョン数の多い順",
+                "versionsCountAsc": "バージョン数の少ない順",
+                "versionIdDesc": "最新バージョン順"
             },
             "refresh": {
                 "title": "モデルリストを更新",
@@ -727,7 +771,8 @@
                 "completed": "完了：{success} 移動、{skipped} スキップ、{failures} 失敗",
                 "complete": "自動整理が完了しました",
                 "error": "エラー：{error}"
-            }
+            },
+            "enrichHfAgent": "メタデータをAIで補完"
         },
         "contextMenu": {
             "refreshMetadata": "Civitaiデータを更新",
@@ -751,7 +796,8 @@
             "shareRecipe": "レシピを共有",
             "viewAllLoras": "すべてのLoRAを表示",
             "downloadMissingLoras": "不足しているLoRAをダウンロード",
-            "deleteRecipe": "レシピを削除"
+            "deleteRecipe": "レシピを削除",
+            "enrichHfAgent": "メタデータをAIで補完"
         }
     },
     "recipes": {
@@ -997,6 +1043,18 @@
             "storage": "ストレージ",
             "insights": "インサイト"
         },
+        "metrics": {
+            "totalModels": "モデル総数",
+            "totalStorage": "ストレージ合計",
+            "totalGenerations": "生成回数合計",
+            "usageRate": "使用率",
+            "loras": "LoRA",
+            "checkpoints": "Checkpoint",
+            "embeddings": "Embedding",
+            "uniqueTags": "ユニークタグ",
+            "unusedModels": "未使用モデル",
+            "avgUsesPerModel": "平均使用回数/モデル"
+        },
         "usage": {
             "mostUsedLoras": "最も使用されているLoRA",
             "mostUsedCheckpoints": "最も使用されているCheckpoint",
@@ -1014,13 +1072,77 @@
         },
         "insights": {
             "smartInsights": "スマートインサイト",
-            "recommendations": "推奨事項"
+            "recommendations": "推奨事項",
+            "noInsights": "インサイトはありません",
+            "unusedLoras": {
+                "high": {
+                    "title": "未使用のLoRAが多数あります",
+                    "description": "LoRAの{percent}%（{count}/{total}）が一度も使用されていません。",
+                    "suggestion": "未使用のモデルを整理またはアーカイブしてストレージを解放してください。"
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "未使用のCheckpointを検出",
+                    "description": "Checkpointの{percent}%（{count}/{total}）が一度も使用されていません。",
+                    "suggestion": "不要なCheckpointを確認して削除を検討してください。"
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "未使用のEmbeddingが多数あります",
+                    "description": "Embeddingの{percent}%（{count}/{total}）が一度も使用されていません。",
+                    "suggestion": "未使用のEmbeddingを整理またはアーカイブしてコレクションを最適化してください。"
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "大規模コレクションを検出",
+                    "description": "モデルコレクションが{size}のストレージを使用しています。",
+                    "suggestion": "外部ストレージやクラウドソリューションの使用を検討してください。"
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "アクティブユーザー",
+                    "description": "これまでに{count}回の生成を完了しました！",
+                    "suggestion": "モデルを使って素晴らしいコンテンツを作り続けてください。"
+                }
+            }
         },
         "charts": {
             "collectionOverview": "コレクション概要",
             "baseModelDistribution": "ベースモデル分布",
             "usageTrends": "使用傾向（過去30日）",
-            "usageDistribution": "使用分布"
+            "usageDistribution": "使用分布",
+            "date": "日付",
+            "usageCount": "使用回数",
+            "fileSizeBytes": "ファイルサイズ（バイト）",
+            "models": "モデル",
+            "loraUsage": "LoRA 使用量",
+            "checkpointUsage": "Checkpoint 使用量",
+            "embeddingUsage": "Embedding 使用量"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "拡散モデル",
+            "embedding": "Embedding"
+        },
+        "placeholders": {
+            "loading": "読み込み中...",
+            "noModels": "モデルが見つかりません",
+            "errorLoading": "データ読み込みエラー",
+            "noStorageData": "ストレージデータがありません",
+            "rootFolder": "ルート",
+            "chartLibraryMissing": "Chart.js ライブラリが必要です"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} モデル",
+            "chartUsage": "{name}: {size}, {count} 回使用",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1032,7 +1154,10 @@
             "titleWithType": "URLから{type}をダウンロード",
             "civitaiUrl": "Civitai URL：",
             "placeholder": "https://civitai.com/models/...",
-            "urlHint": "1行に1つのCivitAIまたはCivArchive URLを入力してください。複数のURLを一括ダウンロードできます。",
+            "urlHint": "1行に1つのCivitAI、CivArchive、またはHugging Face URLを入力してください。複数のURLを一括ダウンロードできます。",
+            "selectHfFiles": "このリポジトリからダウンロードするファイルを選択してください：",
+            "selectAll": "すべて選択",
+            "fetchingRepoFiles": "リポジトリのファイルを取得中...",
             "locationPreview": "ダウンロード場所プレビュー",
             "useDefaultPath": "デフォルトパスを使用",
             "useDefaultPathTooltip": "有効にすると、設定されたパステンプレートを使用してファイルが自動的に整理されます",
@@ -1061,7 +1186,9 @@
             },
             "errors": {
                 "invalidUrl": "無効なCivitai URL形式",
-                "noVersions": "このモデルの利用可能なバージョンがありません"
+                "noVersions": "このモデルの利用可能なバージョンがありません",
+                "mixedSources": "同じバッチ内でCivitAIとHugging FaceのURLを混在させることはできません。",
+                "noModelFiles": "このリポジトリにモデルファイルが見つかりませんでした。"
             },
             "status": {
                 "preparing": "ダウンロードを準備中...",
@@ -1212,6 +1339,8 @@
                 "editVersionName": "バージョン名を編集",
                 "viewOnCivitai": "Civitaiで表示",
                 "viewOnCivitaiText": "Civitaiで表示",
+                "viewOnHuggingFace": "Hugging Face で見る",
+                "viewOnHuggingFaceText": "Hugging Face で見る",
                 "viewCreatorProfile": "作成者プロフィールを表示",
                 "openFileLocation": "ファイルの場所を開く",
                 "sendToWorkflow": "ComfyUI に送信",
@@ -1237,7 +1366,10 @@
                 "additionalNotes": "追加メモ",
                 "notesHint": "Enterで保存、Shift+Enterで改行",
                 "addNotesPlaceholder": "メモをここに追加...",
-                "aboutThisVersion": "このバージョンについて"
+                "aboutThisVersion": "このバージョンについて",
+                "baseModelSearchPlaceholder": "ベースモデルを検索…",
+                "baseModelSuggested": "おすすめ",
+                "baseModelNoMatch": "該当するベースモデルがありません"
             },
             "notes": {
                 "saved": "メモが正常に保存されました",
@@ -1424,15 +1556,6 @@
             "duplicate": "このタグは既に存在します"
         }
     },
-    "keyboard": {
-        "navigation": "キーボードナビゲーション：",
-        "shortcuts": {
-            "pageUp": "1ページ上にスクロール",
-            "pageDown": "1ページ下にスクロール",
-            "home": "トップにジャンプ",
-            "end": "ボトムにジャンプ"
-        }
-    },
     "initialization": {
         "title": "初期化中",
         "message": "ワークスペースを準備中...",
@@ -1522,12 +1645,15 @@
             "modelUpdated": "モデルがワークフローで更新されました",
             "modelFailed": "モデルノードの更新に失敗しました",
             "embeddingAdded": "Embeddingをワークフローに追加しました",
-            "embeddingFailed": "Embeddingの追加に失敗しました"
+            "embeddingFailed": "Embeddingの追加に失敗しました",
+            "promptSent": "プロンプトをワークフローに送信しました",
+            "promptFailed": "プロンプトの送信に失敗しました"
         },
         "nodeSelector": {
             "recipe": "レシピ",
             "lora": "LoRA",
             "embedding": "Embedding",
+            "prompt": "プロンプト",
             "replace": "置換",
             "append": "追加",
             "selectTargetNode": "ターゲットノードを選択",
@@ -1714,6 +1840,7 @@
             "enterLoraName": "LoRA名または構文を入力してください",
             "reconnectedSuccessfully": "LoRAが正常に再接続されました",
             "reconnectFailed": "LoRA再接続エラー：{message}",
+            "noPromptToSend": "送信するプロンプトがありません",
             "cannotSend": "レシピを送信できません：レシピIDがありません",
             "sendFailed": "レシピのワークフローへの送信に失敗しました",
             "sendError": "レシピのワークフロー送信エラー",
@@ -1973,6 +2100,12 @@
             "moveFailed": "Failed to move item: {message}",
             "copiedToClipboard": "クリップボードにコピーしました",
             "downloadStarted": "ダウンロードを開始しました"
+        },
+        "agent": {
+            "llmNotConfigured": "AIプロバイダーが設定されていません。設定 → AIプロバイダーで有効にしてください。",
+            "enrichStarted": "AIでメタデータを補完中...",
+            "enrichComplete": "メタデータの補完が完了しました：{{summary}}",
+            "enrichFailed": "メタデータの補完に失敗しました：{{error}}"
         }
     },
     "doctor": {

locales/ko.json

@@ -105,6 +105,7 @@
             "removeFromFavorites": "즐겨찾기에서 제거",
             "viewOnCivitai": "Civitai에서 보기",
             "notAvailableFromCivitai": "Civitai에서 사용할 수 없음",
+            "viewOnHuggingFace": "Hugging Face에서 보기",
             "sendToWorkflow": "ComfyUI로 전송 (클릭: 추가, Shift+클릭: 교체)",
             "copyLoRASyntax": "LoRA 문법 복사",
             "checkpointNameCopied": "Checkpoint 이름 복사됨",
@@ -145,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "사용 횟수"
+        },
+        "footer": {
+            "versionCount": "{count}개 버전",
+            "viewAllVersions": "모든 로컬 버전 보기"
         }
     },
     "globalContextMenu": {
@@ -183,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "제외된 모델 관리"
+        },
+        "groupByModel": {
+            "label": "모델별 그룹화"
         }
     },
     "header": {
@@ -195,13 +203,7 @@
             "statistics": "통계"
         },
         "search": {
-            "placeholder": "검색...",
-            "placeholders": {
-                "loras": "LoRA 검색...",
-                "recipes": "레시피 검색...",
-                "checkpoints": "Checkpoint 검색...",
-                "embeddings": "Embedding 검색..."
-            },
+            "placeholder": "검색",
             "options": "검색 옵션",
             "searchIn": "검색 범위:",
             "notAvailable": "통계 페이지에서는 검색을 사용할 수 없습니다",
@@ -251,7 +253,18 @@
             "toggle": "테마 토글",
             "switchToLight": "라이트 테마로 전환",
             "switchToDark": "다크 테마로 전환",
-            "switchToAuto": "자동 테마로 전환"
+            "switchToAuto": "자동 테마로 전환",
+            "presets": "테마 프리셋",
+            "default": "기본",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "모드",
+            "light": "라이트",
+            "dark": "다크",
+            "auto": "자동"
         },
         "actions": {
             "checkUpdates": "업데이트 확인",
@@ -263,6 +276,9 @@
         "civitaiApiKey": "Civitai API 키",
         "civitaiApiKeyPlaceholder": "Civitai API 키를 입력하세요",
         "civitaiApiKeyHelp": "Civitai에서 모델을 다운로드할 때 인증에 사용됩니다",
+        "civitaiApiKeyConfigured": "설정됨",
+        "civitaiApiKeyNotConfigured": "설정되지 않음",
+        "civitaiApiKeySet": "설정",
         "civitaiHost": {
             "label": "Civitai 호스트",
             "help": "\"View on Civitai\" 링크를 사용할 때 어떤 Civitai 사이트를 열지 선택합니다.",
@@ -303,6 +319,7 @@
             "downloads": "다운로드",
             "videoSettings": "비디오 설정",
             "layoutSettings": "레이아웃 설정",
+            "licenseIcons": "라이선스 아이콘",
             "misc": "기타",
             "backup": "백업",
             "folderSettings": "기본 루트",
@@ -310,7 +327,7 @@
             "extraFolderPaths": "추가 폴다 경로",
             "downloadPathTemplates": "다운로드 경로 템플릿",
             "priorityTags": "우선순위 태그",
-            "updateFlags": "업데이트 표시",
+            "versionScope": "업데이트 표시",
             "exampleImages": "예시 이미지",
             "autoOrganize": "자동 정리",
             "metadata": "메타데이터",
@@ -415,6 +432,8 @@
             "help": "활성화하면 다운로드 기록 서비스가 해당 버전이 이미 다운로드되었음을 기록한 경우 LoRA Manager는 해당 모델 버전 다운로드를 건너뜁니다. 모든 다운로드 플로우에 적용됩니다."
         },
         "layoutSettings": {
+            "groupByModel": "모델별 그룹화",
+            "groupByModelHelp": "활성화하면 각 Civitai 모델의 최신 버전만 단일 카드로 표시되며, 이전 버전은 숨겨집니다.",
             "displayDensity": "표시 밀도",
             "displayDensityOptions": {
                 "default": "기본",
@@ -571,7 +590,7 @@
             "download": "다운로드",
             "restartRequired": "재시작 필요"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "업데이트 표시 전략",
             "help": "새 릴리스가 로컬 파일과 동일한 베이스 모델을 공유할 때만 업데이트 배지를 표시할지, 또는 해당 모델에 사용 가능한 새 버전이 있으면 항상 표시할지 결정합니다.",
             "options": {
@@ -583,6 +602,10 @@
             "label": "얼리 액세스 업데이트 숨기기",
             "help": "얼리 액세스 업데이트만"
         },
+        "licenseIcons": {
+            "useNewStyle": "업데이트된 라이선스 아이콘 사용",
+            "useNewStyleHelp": "색상 표시기가 있는 라이선스 권한(새 스타일) 또는 제한 전용 아이콘(클래식 스타일)을 표시합니다. 현재 CivitAI 디자인을 반영합니다."
+        },
         "misc": {
             "includeTriggerWords": "LoRA 문법에 트리거 단어 포함",
             "includeTriggerWordsHelp": "LoRA 문법을 클립보드에 복사할 때 학습된 트리거 단어를 포함합니다",
@@ -634,6 +657,23 @@
             "proxyPassword": "비밀번호 (선택사항)",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "프록시 인증에 필요한 비밀번호 (필요한 경우)"
+        },
+        "aiProvider": {
+            "title": "AI 제공자",
+            "provider": "제공자",
+            "providerHelp": "LLM 제공자를 선택하세요. OpenAI와 Ollama는 사전 설정된 API 엔드포인트를 사용합니다. 사용자 정의를 선택하면 모든 OpenAI 호환 엔드포인트를 지정할 수 있습니다.",
+            "custom": "사용자 정의 (OpenAI 호환)",
+            "apiBase": "API 기본 URL",
+            "apiBaseHelp": "LLM API의 기본 URL입니다 (예: https://api.openai.com/v1). 비워두면 제공자 기본값이 사용됩니다.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API 키",
+            "apiKeyHelp": "LLM 제공자의 API 키입니다. 로컬에 저장되며 선택한 LLM 제공자 외의 서버로 전송되지 않습니다.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "설정되지 않음",
+            "apiKeyConfigured": "설정됨",
+            "apiKeySet": "설정",
+            "model": "모델",
+            "modelHelp": "사용할 모델 이름 (예: deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). 제공자에서 사용 가능한 모델을 확인하세요."
         }
     },
     "loras": {
@@ -651,7 +691,11 @@
                 "sizeAsc": "작은 순서",
                 "usage": "사용 횟수",
                 "usageDesc": "많은 순",
-                "usageAsc": "적은 순"
+                "usageAsc": "적은 순",
+                "versionsCount": "로컬 버전 수",
+                "versionsCountDesc": "버전 수 많은 순",
+                "versionsCountAsc": "버전 수 적은 순",
+                "versionIdDesc": "최신 버전순"
             },
             "refresh": {
                 "title": "모델 목록 새로고침",
@@ -727,7 +771,8 @@
                 "completed": "완료: {success}개 이동, {skipped}개 건너뜀, {failures}개 실패",
                 "complete": "자동 정리 완료",
                 "error": "오류: {error}"
-            }
+            },
+            "enrichHfAgent": "AI로 메타데이터 보강"
         },
         "contextMenu": {
             "refreshMetadata": "Civitai 데이터 새로고침",
@@ -751,7 +796,8 @@
             "shareRecipe": "레시피 공유",
             "viewAllLoras": "모든 LoRA 보기",
             "downloadMissingLoras": "누락된 LoRA 다운로드",
-            "deleteRecipe": "레시피 삭제"
+            "deleteRecipe": "레시피 삭제",
+            "enrichHfAgent": "AI로 메타데이터 보강"
         }
     },
     "recipes": {
@@ -997,6 +1043,18 @@
             "storage": "저장소",
             "insights": "인사이트"
         },
+        "metrics": {
+            "totalModels": "모델 총계",
+            "totalStorage": "총 저장 공간",
+            "totalGenerations": "총 생성 횟수",
+            "usageRate": "사용률",
+            "loras": "LoRA",
+            "checkpoints": "Checkpoint",
+            "embeddings": "Embedding",
+            "uniqueTags": "고유 태그",
+            "unusedModels": "미사용 모델",
+            "avgUsesPerModel": "모델당 평균 사용"
+        },
         "usage": {
             "mostUsedLoras": "가장 많이 사용된 LoRA",
             "mostUsedCheckpoints": "가장 많이 사용된 Checkpoint",
@@ -1014,13 +1072,77 @@
         },
         "insights": {
             "smartInsights": "스마트 인사이트",
-            "recommendations": "추천"
+            "recommendations": "추천",
+            "noInsights": "인사이트 없음",
+            "unusedLoras": {
+                "high": {
+                    "title": "사용하지 않은 LoRA가 많음",
+                    "description": "LoRA의 {percent}%({count}/{total})가 한 번도 사용되지 않았습니다.",
+                    "suggestion": "사용하지 않는 모델을 정리하거나 보관하여 저장 공간을 확보하세요."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "사용하지 않은 Checkpoint 감지",
+                    "description": "Checkpoint의 {percent}%({count}/{total})가 한 번도 사용되지 않았습니다.",
+                    "suggestion": "더 이상 필요하지 않은 Checkpoint를 검토하고 제거하세요."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "사용하지 않은 Embedding이 많음",
+                    "description": "Embedding의 {percent}%({count}/{total})가 한 번도 사용되지 않았습니다.",
+                    "suggestion": "사용하지 않는 Embedding을 정리하여 컬렉션을 최적화하세요."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "대규모 컬렉션 감지",
+                    "description": "모델 컬렉션이 {size}의 저장 공간을 사용 중입니다.",
+                    "suggestion": "더 나은 관리를 위해 외부 저장소나 클라우드 솔루션을 고려하세요."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "활성 사용자",
+                    "description": "지금까지 {count}번의 생성을 완료했습니다!",
+                    "suggestion": "모델로 계속해서 멋진 콘텐츠를 탐색하고 만들어보세요."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "컬렉션 개요",
             "baseModelDistribution": "베이스 모델 분포",
             "usageTrends": "사용량 트렌드 (최근 30일)",
-            "usageDistribution": "사용량 분포"
+            "usageDistribution": "사용량 분포",
+            "date": "날짜",
+            "usageCount": "사용 횟수",
+            "fileSizeBytes": "파일 크기(바이트)",
+            "models": "모델",
+            "loraUsage": "LoRA 사용량",
+            "checkpointUsage": "Checkpoint 사용량",
+            "embeddingUsage": "Embedding 사용량"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "확산 모델",
+            "embedding": "Embedding"
+        },
+        "placeholders": {
+            "loading": "로딩 중...",
+            "noModels": "모델을 찾을 수 없음",
+            "errorLoading": "데이터 로딩 오류",
+            "noStorageData": "저장 데이터 없음",
+            "rootFolder": "루트",
+            "chartLibraryMissing": "Chart.js 라이브러리가 필요합니다"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count}개 모델",
+            "chartUsage": "{name}: {size}, {count}회 사용",
+            "chartPercentage": "{label}: {value}({pct}%)"
         }
     },
     "modals": {
@@ -1032,7 +1154,10 @@
             "titleWithType": "URL에서 {type} 다운로드",
             "civitaiUrl": "Civitai URL:",
             "placeholder": "https://civitai.com/models/...",
-            "urlHint": "한 줄에 하나의 CivitAI 또는 CivArchive URL을 입력하세요. 여러 URL을 일괄 다운로드할 수 있습니다.",
+            "urlHint": "한 줄에 하나의 CivitAI, CivArchive 또는 Hugging Face URL을 입력하세요. 여러 URL을 일괄 다운로드할 수 있습니다.",
+            "selectHfFiles": "이 저장소에서 다운로드할 파일을 선택하세요：",
+            "selectAll": "모두 선택",
+            "fetchingRepoFiles": "저장소 파일을 가져오는 중...",
             "locationPreview": "다운로드 위치 미리보기",
             "useDefaultPath": "기본 경로 사용",
             "useDefaultPathTooltip": "활성화하면 구성된 경로 템플릿을 사용하여 파일이 자동으로 정리됩니다",
@@ -1061,7 +1186,9 @@
             },
             "errors": {
                 "invalidUrl": "잘못된 Civitai URL 형식",
-                "noVersions": "이 모델에 사용 가능한 버전이 없습니다"
+                "noVersions": "이 모델에 사용 가능한 버전이 없습니다",
+                "mixedSources": "동일한 배치에서 CivitAI와 Hugging Face URL을 혼합할 수 없습니다.",
+                "noModelFiles": "이 저장소에서 모델 파일을 찾을 수 없습니다."
             },
             "status": {
                 "preparing": "다운로드 준비 중...",
@@ -1212,6 +1339,8 @@
                 "editVersionName": "버전명 편집",
                 "viewOnCivitai": "Civitai에서 보기",
                 "viewOnCivitaiText": "Civitai에서 보기",
+                "viewOnHuggingFace": "Hugging Face에서 보기",
+                "viewOnHuggingFaceText": "Hugging Face에서 보기",
                 "viewCreatorProfile": "제작자 프로필 보기",
                 "openFileLocation": "파일 위치 열기",
                 "sendToWorkflow": "ComfyUI로 보내기",
@@ -1237,7 +1366,10 @@
                 "additionalNotes": "추가 메모",
                 "notesHint": "Enter로 저장, Shift+Enter로 줄바꿈",
                 "addNotesPlaceholder": "메모를 여기에 추가하세요...",
-                "aboutThisVersion": "이 버전에 대해"
+                "aboutThisVersion": "이 버전에 대해",
+                "baseModelSearchPlaceholder": "베이스 모델 검색…",
+                "baseModelSuggested": "추천",
+                "baseModelNoMatch": "일치하는 베이스 모델 없음"
             },
             "notes": {
                 "saved": "메모가 성공적으로 저장됨",
@@ -1424,15 +1556,6 @@
             "duplicate": "이 태그는 이미 존재합니다"
         }
     },
-    "keyboard": {
-        "navigation": "키보드 내비게이션:",
-        "shortcuts": {
-            "pageUp": "한 페이지 위로 스크롤",
-            "pageDown": "한 페이지 아래로 스크롤",
-            "home": "맨 위로 이동",
-            "end": "맨 아래로 이동"
-        }
-    },
     "initialization": {
         "title": "초기화 중",
         "message": "작업공간을 준비하고 있습니다...",
@@ -1522,12 +1645,15 @@
             "modelUpdated": "모델이 워크플로에서 업데이트되었습니다",
             "modelFailed": "모델 노드 업데이트 실패",
             "embeddingAdded": "Embedding을 워크플로에 추가했습니다",
-            "embeddingFailed": "Embedding 추가 실패"
+            "embeddingFailed": "Embedding 추가 실패",
+            "promptSent": "프롬프트를 워크플로에 보냈습니다",
+            "promptFailed": "프롬프트 보내기 실패"
         },
         "nodeSelector": {
             "recipe": "레시피",
             "lora": "LoRA",
             "embedding": "임베딩",
+            "prompt": "프롬프트",
             "replace": "교체",
             "append": "추가",
             "selectTargetNode": "대상 노드 선택",
@@ -1714,6 +1840,7 @@
             "enterLoraName": "LoRA 이름 또는 문법을 입력해주세요",
             "reconnectedSuccessfully": "LoRA가 성공적으로 다시 연결되었습니다",
             "reconnectFailed": "LoRA 다시 연결 오류: {message}",
+            "noPromptToSend": "보낼 프롬프트가 없습니다",
             "cannotSend": "레시피를 전송할 수 없습니다: 레시피 ID 누락",
             "sendFailed": "레시피를 워크플로로 전송하는데 실패했습니다",
             "sendError": "레시피를 워크플로로 전송하는 중 오류",
@@ -1973,6 +2100,12 @@
             "moveFailed": "Failed to move item: {message}",
             "copiedToClipboard": "클립보드에 복사됨",
             "downloadStarted": "다운로드 시작됨"
+        },
+        "agent": {
+            "llmNotConfigured": "AI 제공자가 설정되지 않았습니다. 설정 → AI 제공자에서 활성화하세요.",
+            "enrichStarted": "AI로 메타데이터 보강 중...",
+            "enrichComplete": "메타데이터 보강 완료: {{summary}}",
+            "enrichFailed": "메타데이터 보강 실패: {{error}}"
         }
     },
     "doctor": {

locales/ru.json

@@ -105,6 +105,7 @@
             "removeFromFavorites": "Удалить из избранного",
             "viewOnCivitai": "Посмотреть на Civitai",
             "notAvailableFromCivitai": "Недоступно на Civitai",
+            "viewOnHuggingFace": "Открыть Hugging Face",
             "sendToWorkflow": "Отправить в ComfyUI (Клик: Добавить, Shift+Клик: Заменить)",
             "copyLoRASyntax": "Копировать синтаксис LoRA",
             "checkpointNameCopied": "Имя checkpoint скопировано",
@@ -145,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "Количество использований"
+        },
+        "footer": {
+            "versionCount": "{count} версий",
+            "viewAllVersions": "Показать все локальные версии"
         }
     },
     "globalContextMenu": {
@@ -183,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "Управление исключёнными моделями"
+        },
+        "groupByModel": {
+            "label": "Группировать по модели"
         }
     },
     "header": {
@@ -195,13 +203,7 @@
             "statistics": "Статистика"
         },
         "search": {
-            "placeholder": "Поиск...",
-            "placeholders": {
-                "loras": "Поиск LoRAs...",
-                "recipes": "Поиск рецептов...",
-                "checkpoints": "Поиск checkpoints...",
-                "embeddings": "Поиск embeddings..."
-            },
+            "placeholder": "Поиск",
             "options": "Опции поиска",
             "searchIn": "Искать в:",
             "notAvailable": "Поиск недоступен на странице статистики",
@@ -251,7 +253,18 @@
             "toggle": "Переключить тему",
             "switchToLight": "Переключить на светлую тему",
             "switchToDark": "Переключить на тёмную тему",
-            "switchToAuto": "Переключить на автоматическую тему"
+            "switchToAuto": "Переключить на автоматическую тему",
+            "presets": "Предустановки тем",
+            "default": "По умолчанию",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "Режим",
+            "light": "Светлый",
+            "dark": "Тёмный",
+            "auto": "Авто"
         },
         "actions": {
             "checkUpdates": "Проверить обновления",
@@ -263,6 +276,9 @@
         "civitaiApiKey": "Ключ API Civitai",
         "civitaiApiKeyPlaceholder": "Введите ваш ключ API Civitai",
         "civitaiApiKeyHelp": "Используется для аутентификации при загрузке моделей с Civitai",
+        "civitaiApiKeyConfigured": "Настроен",
+        "civitaiApiKeyNotConfigured": "Не настроен",
+        "civitaiApiKeySet": "Настроить",
         "civitaiHost": {
             "label": "Хост Civitai",
             "help": "Выберите, какой сайт Civitai будет открываться при использовании ссылок «View on Civitai».",
@@ -303,6 +319,7 @@
             "downloads": "Загрузки",
             "videoSettings": "Настройки видео",
             "layoutSettings": "Настройки макета",
+            "licenseIcons": "Значки лицензии",
             "misc": "Разное",
             "backup": "Резервные копии",
             "folderSettings": "Корневые папки",
@@ -310,7 +327,7 @@
             "extraFolderPaths": "Дополнительные пути к папкам",
             "downloadPathTemplates": "Шаблоны путей загрузки",
             "priorityTags": "Приоритетные теги",
-            "updateFlags": "Метки обновлений",
+            "versionScope": "Метки обновлений",
             "exampleImages": "Примеры изображений",
             "autoOrganize": "Автоорганизация",
             "metadata": "Метаданные",
@@ -415,6 +432,8 @@
             "help": "Если включено, LoRA Manager будет пропускать загрузку версии модели, если сервис истории загрузок записал, что эта конкретная версия уже загружена. Применяется ко всем потокам загрузки."
         },
         "layoutSettings": {
+            "groupByModel": "Группировать по модели",
+            "groupByModelHelp": "При включении отображается только последняя версия каждой модели Civitai в виде одной карточки. Старые версии скрыты.",
             "displayDensity": "Плотность отображения",
             "displayDensityOptions": {
                 "default": "По умолчанию",
@@ -571,7 +590,7 @@
             "download": "Загрузить",
             "restartRequired": "Требует перезапуска"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "Стратегия меток обновлений",
             "help": "Выберите, отображать ли значки обновления только когда новая версия имеет тот же базовый модель, что и локальные файлы, или всегда при наличии любого нового релиза для этой модели.",
             "options": {
@@ -583,6 +602,10 @@
             "label": "Скрыть обновления раннего доступа",
             "help": "Только обновления раннего доступа"
         },
+        "licenseIcons": {
+            "useNewStyle": "Использовать обновлённые значки лицензии",
+            "useNewStyleHelp": "Отображать разрешения лицензии с цветными индикаторами (новый стиль) или только значки ограничений (классический стиль). Соответствует текущему дизайну CivitAI."
+        },
         "misc": {
             "includeTriggerWords": "Включать триггерные слова в синтаксис LoRA",
             "includeTriggerWordsHelp": "Включать обученные триггерные слова при копировании синтаксиса LoRA в буфер обмена",
@@ -634,6 +657,23 @@
             "proxyPassword": "Пароль (необязательно)",
             "proxyPasswordPlaceholder": "пароль",
             "proxyPasswordHelp": "Пароль для аутентификации на прокси (если требуется)"
+        },
+        "aiProvider": {
+            "title": "Поставщик ИИ",
+            "provider": "Поставщик",
+            "providerHelp": "Выберите поставщика LLM. OpenAI и Ollama используют предустановленные API-эндпоинты. Пользовательский позволяет указать любой совместимый с OpenAI эндпоинт.",
+            "custom": "Пользовательский (совместимый с OpenAI)",
+            "apiBase": "Базовый URL API",
+            "apiBaseHelp": "Базовый URL для LLM API (например, https://api.openai.com/v1). Оставьте пустым, чтобы использовать значение по умолчанию.",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API-ключ",
+            "apiKeyHelp": "Ваш API-ключ поставщика LLM. Хранится локально и никогда не отправляется на другие серверы, кроме выбранного поставщика LLM.",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "Не задан",
+            "apiKeyConfigured": "Настроен",
+            "apiKeySet": "Настроить",
+            "model": "Модель",
+            "modelHelp": "Имя модели для использования (например, deepseek-v4-flash, gemini-2.5-flash, gemma4:12b). Проверьте доступные модели у вашего поставщика."
         }
     },
     "loras": {
@@ -651,7 +691,11 @@
                 "sizeAsc": "Наименьшим",
                 "usage": "Число использований",
                 "usageDesc": "Больше",
-                "usageAsc": "Меньше"
+                "usageAsc": "Меньше",
+                "versionsCount": "Локальные версии",
+                "versionsCountDesc": "Сначала больше версий",
+                "versionsCountAsc": "Сначала меньше версий",
+                "versionIdDesc": "Сначала новые версии"
             },
             "refresh": {
                 "title": "Обновить список моделей",
@@ -727,7 +771,8 @@
                 "completed": "Завершено: {success} перемещено, {skipped} пропущено, {failures} не удалось",
                 "complete": "Автоматическая организация завершена",
                 "error": "Ошибка: {error}"
-            }
+            },
+            "enrichHfAgent": "Обогатить метаданные (ИИ)"
         },
         "contextMenu": {
             "refreshMetadata": "Обновить данные Civitai",
@@ -751,7 +796,8 @@
             "shareRecipe": "Поделиться рецептом",
             "viewAllLoras": "Посмотреть все LoRAs",
             "downloadMissingLoras": "Загрузить отсутствующие LoRAs",
-            "deleteRecipe": "Удалить рецепт"
+            "deleteRecipe": "Удалить рецепт",
+            "enrichHfAgent": "Обогатить метаданные (ИИ)"
         }
     },
     "recipes": {
@@ -997,6 +1043,18 @@
             "storage": "Хранение",
             "insights": "Аналитика"
         },
+        "metrics": {
+            "totalModels": "Всего моделей",
+            "totalStorage": "Всего хранилища",
+            "totalGenerations": "Всего генераций",
+            "usageRate": "Коэффициент использования",
+            "loras": "LoRA",
+            "checkpoints": "Контрольные точки",
+            "embeddings": "Эмбеддинги",
+            "uniqueTags": "Уникальные теги",
+            "unusedModels": "Неиспользуемые модели",
+            "avgUsesPerModel": "Сред. использований/модель"
+        },
         "usage": {
             "mostUsedLoras": "Наиболее используемые LoRAs",
             "mostUsedCheckpoints": "Наиболее используемые Checkpoints",
@@ -1014,13 +1072,77 @@
         },
         "insights": {
             "smartInsights": "Умная аналитика",
-            "recommendations": "Рекомендации"
+            "recommendations": "Рекомендации",
+            "noInsights": "Нет доступных данных",
+            "unusedLoras": {
+                "high": {
+                    "title": "Большое количество неиспользуемых LoRA",
+                    "description": "{percent}% ваших LoRA ({count}/{total}) никогда не использовались.",
+                    "suggestion": "Рассмотрите возможность организации или архивирования неиспользуемых моделей для освобождения места."
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "Обнаружены неиспользуемые контрольные точки",
+                    "description": "{percent}% ваших контрольных точек ({count}/{total}) никогда не использовались.",
+                    "suggestion": "Проверьте и удалите ненужные контрольные точки."
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "Большое количество неиспользуемых эмбеддингов",
+                    "description": "{percent}% ваших эмбеддингов ({count}/{total}) никогда не использовались.",
+                    "suggestion": "Организуйте или архивируйте неиспользуемые эмбеддинги для оптимизации коллекции."
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "Обнаружена большая коллекция",
+                    "description": "Ваша коллекция моделей использует {size} хранилища.",
+                    "suggestion": "Рассмотрите внешнее хранилище или облачные решения для лучшей организации."
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "Активный пользователь",
+                    "description": "Вы завершили {count} генераций!",
+                    "suggestion": "Продолжайте исследовать и создавать удивительный контент с вашими моделями."
+                }
+            }
         },
         "charts": {
             "collectionOverview": "Обзор коллекции",
             "baseModelDistribution": "Распределение базовых моделей",
             "usageTrends": "Тенденции использования (за последние 30 дней)",
-            "usageDistribution": "Распределение использования"
+            "usageDistribution": "Распределение использования",
+            "date": "Дата",
+            "usageCount": "Количество использований",
+            "fileSizeBytes": "Размер файла (байты)",
+            "models": "Модели",
+            "loraUsage": "Использование LoRA",
+            "checkpointUsage": "Использование Checkpoint",
+            "embeddingUsage": "Использование Embedding"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Контрольная точка",
+            "diffusion_model": "Диффузионная модель",
+            "embedding": "Эмбеддинги"
+        },
+        "placeholders": {
+            "loading": "Загрузка...",
+            "noModels": "Модели не найдены",
+            "errorLoading": "Ошибка загрузки данных",
+            "noStorageData": "Нет данных о хранилище",
+            "rootFolder": "Корень",
+            "chartLibraryMissing": "Для графика требуется библиотека Chart.js"
+        },
+        "tooltips": {
+            "tagCount": "{tag}: {count} моделей",
+            "chartUsage": "{name}: {size}, {count} использований",
+            "chartPercentage": "{label}: {value} ({pct}%)"
         }
     },
     "modals": {
@@ -1032,7 +1154,10 @@
             "titleWithType": "Скачать {type} по URL",
             "civitaiUrl": "Civitai URL:",
             "placeholder": "https://civitai.com/models/...",
-            "urlHint": "Введите один URL CivitAI или CivArchive в каждой строке. Поддерживается пакетная загрузка нескольких URL.",
+            "urlHint": "Введите один URL CivitAI, CivArchive или Hugging Face в каждой строке. Поддерживает несколько URL для пакетной загрузки.",
+            "selectHfFiles": "Выберите файл(ы) для загрузки из этого репозитория：",
+            "selectAll": "Выбрать все",
+            "fetchingRepoFiles": "Получение файлов репозитория...",
             "locationPreview": "Предпросмотр места загрузки",
             "useDefaultPath": "Использовать путь по умолчанию",
             "useDefaultPathTooltip": "При включении файлы автоматически организуются с использованием настроенных шаблонов путей",
@@ -1061,7 +1186,9 @@
             },
             "errors": {
                 "invalidUrl": "Неверный формат URL Civitai",
-                "noVersions": "Нет доступных версий для этой модели"
+                "noVersions": "Нет доступных версий для этой модели",
+                "mixedSources": "Нельзя смешивать URL-адреса CivitAI и Hugging Face в одном пакете.",
+                "noModelFiles": "В этом репозитории не найдено файлов моделей."
             },
             "status": {
                 "preparing": "Подготовка загрузки...",
@@ -1212,6 +1339,8 @@
                 "editVersionName": "Редактировать название версии",
                 "viewOnCivitai": "Посмотреть на Civitai",
                 "viewOnCivitaiText": "Посмотреть на Civitai",
+                "viewOnHuggingFace": "Открыть Hugging Face",
+                "viewOnHuggingFaceText": "Открыть Hugging Face",
                 "viewCreatorProfile": "Посмотреть профиль создателя",
                 "openFileLocation": "Открыть расположение файла",
                 "sendToWorkflow": "Отправить в ComfyUI",
@@ -1237,7 +1366,10 @@
                 "additionalNotes": "Дополнительные заметки",
                 "notesHint": "Нажмите Enter для сохранения, Shift+Enter для новой строки",
                 "addNotesPlaceholder": "Добавьте ваши заметки здесь...",
-                "aboutThisVersion": "Об этой версии"
+                "aboutThisVersion": "Об этой версии",
+                "baseModelSearchPlaceholder": "Поиск базовой модели…",
+                "baseModelSuggested": "Предполагаемые",
+                "baseModelNoMatch": "Нет подходящих базовых моделей"
             },
             "notes": {
                 "saved": "Заметки успешно сохранены",
@@ -1424,15 +1556,6 @@
             "duplicate": "Этот тег уже существует"
         }
     },
-    "keyboard": {
-        "navigation": "Навигация с клавиатуры:",
-        "shortcuts": {
-            "pageUp": "Прокрутить на страницу вверх",
-            "pageDown": "Прокрутить на страницу вниз",
-            "home": "Перейти к началу",
-            "end": "Перейти к концу"
-        }
-    },
     "initialization": {
         "title": "Инициализация",
         "message": "Подготовка вашего рабочего пространства...",
@@ -1522,12 +1645,15 @@
             "modelUpdated": "Модель обновлена в workflow",
             "modelFailed": "Не удалось обновить узел модели",
             "embeddingAdded": "Embedding добавлен в workflow",
-            "embeddingFailed": "Не удалось добавить embedding"
+            "embeddingFailed": "Не удалось добавить embedding",
+            "promptSent": "Запрос отправлен в workflow",
+            "promptFailed": "Не удалось отправить запрос"
         },
         "nodeSelector": {
             "recipe": "Рецепт",
             "lora": "LoRA",
             "embedding": "Эмбеддинг",
+            "prompt": "Запрос",
             "replace": "Заменить",
             "append": "Добавить",
             "selectTargetNode": "Выберите целевой узел",
@@ -1714,6 +1840,7 @@
             "enterLoraName": "Пожалуйста, введите название LoRA или синтаксис",
             "reconnectedSuccessfully": "LoRA успешно переподключена",
             "reconnectFailed": "Ошибка переподключения LoRA: {message}",
+            "noPromptToSend": "Нет запроса для отправки",
             "cannotSend": "Невозможно отправить рецепт: отсутствует ID рецепта",
             "sendFailed": "Не удалось отправить рецепт в workflow",
             "sendError": "Ошибка отправки рецепта в workflow",
@@ -1973,6 +2100,12 @@
             "moveFailed": "Failed to move item: {message}",
             "copiedToClipboard": "Скопировано в буфер обмена",
             "downloadStarted": "Загрузка начата"
+        },
+        "agent": {
+            "llmNotConfigured": "Поставщик ИИ не настроен. Включите его в Настройки → Поставщик ИИ.",
+            "enrichStarted": "Обогащение метаданных с помощью ИИ...",
+            "enrichComplete": "Обогащение метаданных завершено: {{summary}}",
+            "enrichFailed": "Ошибка обогащения метаданных: {{error}}"
         }
     },
     "doctor": {

locales/zh-CN.json

@@ -105,6 +105,7 @@
             "removeFromFavorites": "从收藏移除",
             "viewOnCivitai": "在 Civitai 查看",
             "notAvailableFromCivitai": "Civitai 上不可用",
+            "viewOnHuggingFace": "在 Hugging Face 查看",
             "sendToWorkflow": "发送到 ComfyUI（点击：追加，Shift+点击：替换）",
             "copyLoRASyntax": "复制 LoRA 语法",
             "checkpointNameCopied": "检查点名称已复制",
@@ -145,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "使用次数"
+        },
+        "footer": {
+            "versionCount": "{count} 个版本",
+            "viewAllVersions": "查看所有本地版本"
         }
     },
     "globalContextMenu": {
@@ -183,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "管理已排除的模型"
+        },
+        "groupByModel": {
+            "label": "按模型分组"
         }
     },
     "header": {
@@ -195,13 +203,7 @@
             "statistics": "统计"
         },
         "search": {
-            "placeholder": "搜索...",
-            "placeholders": {
-                "loras": "搜索 LoRA...",
-                "recipes": "搜索配方...",
-                "checkpoints": "搜索 Checkpoint...",
-                "embeddings": "搜索 Embedding..."
-            },
+            "placeholder": "搜索",
             "options": "搜索选项",
             "searchIn": "搜索范围：",
             "notAvailable": "统计页面不可用搜索",
@@ -251,7 +253,18 @@
             "toggle": "切换主题",
             "switchToLight": "切换到浅色主题",
             "switchToDark": "切换到深色主题",
-            "switchToAuto": "切换到自动主题"
+            "switchToAuto": "切换到自动主题",
+            "presets": "主题预设",
+            "default": "默认",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "模式",
+            "light": "浅色",
+            "dark": "深色",
+            "auto": "自动"
         },
         "actions": {
             "checkUpdates": "检查更新",
@@ -263,6 +276,9 @@
         "civitaiApiKey": "Civitai API 密钥",
         "civitaiApiKeyPlaceholder": "请输入你的 Civitai API 密钥",
         "civitaiApiKeyHelp": "用于从 Civitai 下载模型时的身份验证",
+        "civitaiApiKeyConfigured": "已配置",
+        "civitaiApiKeyNotConfigured": "未配置",
+        "civitaiApiKeySet": "设置",
         "civitaiHost": {
             "label": "Civitai 站点",
             "help": "选择使用“在 Civitai 中查看”时默认打开的 Civitai 站点。",
@@ -303,6 +319,7 @@
             "downloads": "下载",
             "videoSettings": "视频设置",
             "layoutSettings": "布局设置",
+            "licenseIcons": "许可协议图标",
             "misc": "其他",
             "backup": "备份",
             "folderSettings": "默认根目录",
@@ -310,7 +327,7 @@
             "extraFolderPaths": "额外文件夹路径",
             "downloadPathTemplates": "下载路径模板",
             "priorityTags": "优先标签",
-            "updateFlags": "更新标记",
+            "versionScope": "版本范围",
             "exampleImages": "示例图片",
             "autoOrganize": "自动整理",
             "metadata": "元数据",
@@ -415,6 +432,8 @@
             "help": "启用后，如果下载历史服务记录显示该版本已下载，LoRA Manager 将跳过下载该模型版本。适用于所有下载流程。"
         },
         "layoutSettings": {
+            "groupByModel": "按模型分组",
+            "groupByModelHelp": "开启后，每个 Civitai 模型仅显示最新版本的单张卡片，旧版本将被隐藏。",
             "displayDensity": "显示密度",
             "displayDensityOptions": {
                 "default": "默认",
@@ -571,18 +590,22 @@
             "download": "下载",
             "restartRequired": "需要重启"
         },
-        "updateFlagStrategy": {
-            "label": "更新标记策略",
-            "help": "决定更新徽章是否仅在新版本与本地文件共享相同基础模型时显示，或只要该模型有任何更新版本就显示。",
+        "versionGrouping": {
+            "label": "版本分组",
+            "help": "控制版本在 UI 中的分组方式：按基础模型分组或合并显示。同时影响更新徽章逻辑和版本列表的筛选行为。",
             "options": {
-                "sameBase": "按基础模型匹配更新",
-                "any": "显示任何可用更新"
+                "sameBase": "按基础模型分组",
+                "any": "显示所有版本"
             }
         },
         "hideEarlyAccessUpdates": {
             "label": "隐藏抢先体验更新",
             "help": "抢先体验更新"
         },
+        "licenseIcons": {
+            "useNewStyle": "使用新版许可协议图标",
+            "useNewStyleHelp": "以彩色指示器显示许可权限（新样式），或仅显示限制图标（经典样式）。与当前 CivitAI 设计保持一致。"
+        },
         "misc": {
             "includeTriggerWords": "复制 LoRA 语法时包含触发词",
             "includeTriggerWordsHelp": "复制 LoRA 语法到剪贴板时包含训练触发词",
@@ -634,6 +657,23 @@
             "proxyPassword": "密码 (可选)",
             "proxyPasswordPlaceholder": "密码",
             "proxyPasswordHelp": "代理认证的密码 (如果需要)"
+        },
+        "aiProvider": {
+            "title": "AI 提供商",
+            "provider": "提供商",
+            "providerHelp": "选择您的 LLM 提供商。OpenAI 和 Ollama 使用预设的 API 端点。自定义允许您指定任何兼容 OpenAI 的端点。",
+            "custom": "自定义（兼容 OpenAI）",
+            "apiBase": "API 基础地址",
+            "apiBaseHelp": "LLM API 的基础 URL（例如 https://api.openai.com/v1）。留空则使用提供商默认地址。",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API 密钥",
+            "apiKeyHelp": "您的 LLM 提供商 API 密钥。仅本地存储，不会发送到您选择的 LLM 提供商之外的任何服务器。",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "未设置",
+            "apiKeyConfigured": "已配置",
+            "apiKeySet": "设置",
+            "model": "模型",
+            "modelHelp": "要使用的模型名称（例如 deepseek-v4-flash, gemini-2.5-flash, gemma4:12b）。请查看您的提供商支持的可用模型列表。"
         }
     },
     "loras": {
@@ -651,7 +691,11 @@
                 "sizeAsc": "最小",
                 "usage": "使用次数",
                 "usageDesc": "最多",
-                "usageAsc": "最少"
+                "usageAsc": "最少",
+                "versionsCount": "本地版本数",
+                "versionsCountDesc": "版本数从多到少",
+                "versionsCountAsc": "版本数从少到多",
+                "versionIdDesc": "最新版本优先"
             },
             "refresh": {
                 "title": "刷新模型列表",
@@ -727,7 +771,8 @@
                 "completed": "完成：已移动 {success} 个，跳过 {skipped} 个，失败 {failures} 个",
                 "complete": "自动整理已完成",
                 "error": "错误：{error}"
-            }
+            },
+            "enrichHfAgent": "AI 元数据增强"
         },
         "contextMenu": {
             "refreshMetadata": "刷新 Civitai 数据",
@@ -751,7 +796,8 @@
             "shareRecipe": "分享配方",
             "viewAllLoras": "查看所有 LoRA",
             "downloadMissingLoras": "下载缺失的 LoRA",
-            "deleteRecipe": "删除配方"
+            "deleteRecipe": "删除配方",
+            "enrichHfAgent": "AI 元数据增强"
         }
     },
     "recipes": {
@@ -997,6 +1043,18 @@
             "storage": "存储",
             "insights": "洞察"
         },
+        "metrics": {
+            "totalModels": "模型总数",
+            "totalStorage": "总存储空间",
+            "totalGenerations": "总生成次数",
+            "usageRate": "使用率",
+            "loras": "LoRA",
+            "checkpoints": "Checkpoint",
+            "embeddings": "Embedding",
+            "uniqueTags": "唯一标签",
+            "unusedModels": "未使用模型",
+            "avgUsesPerModel": "平均使用次数/模型"
+        },
         "usage": {
             "mostUsedLoras": "最常用 LoRA",
             "mostUsedCheckpoints": "最常用 Checkpoint",
@@ -1014,13 +1072,77 @@
         },
         "insights": {
             "smartInsights": "智能洞察",
-            "recommendations": "推荐"
+            "recommendations": "推荐",
+            "noInsights": "暂无可用洞察",
+            "unusedLoras": {
+                "high": {
+                    "title": "大量未使用的 LoRA",
+                    "description": "你的 LoRA 中有 {percent}%（{count}/{total}）从未被使用过。",
+                    "suggestion": "考虑整理或归档未使用的模型以释放存储空间。"
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "检测到未使用的 Checkpoint",
+                    "description": "你的 Checkpoint 中有 {percent}%（{count}/{total}）从未被使用过。",
+                    "suggestion": "审查并考虑删除不再需要的 Checkpoint。"
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "大量未使用的 Embedding",
+                    "description": "你的 Embedding 中有 {percent}%（{count}/{total}）从未被使用过。",
+                    "suggestion": "考虑整理或归档未使用的 Embedding 以优化你的收藏。"
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "检测到大型收藏",
+                    "description": "你的模型收藏正在使用 {size} 的存储空间。",
+                    "suggestion": "考虑使用外部存储或云解决方案以获得更好的组织。"
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "活跃用户",
+                    "description": "你已经完成了 {count} 次生成！",
+                    "suggestion": "继续探索并用你的模型创作精彩内容。"
+                }
+            }
         },
         "charts": {
             "collectionOverview": "收藏概览",
             "baseModelDistribution": "基础模型分布",
             "usageTrends": "使用趋势（最近30天）",
-            "usageDistribution": "使用分布"
+            "usageDistribution": "使用分布",
+            "date": "日期",
+            "usageCount": "使用次数",
+            "fileSizeBytes": "文件大小（字节）",
+            "models": "模型",
+            "loraUsage": "LoRA 使用量",
+            "checkpointUsage": "Checkpoint 使用量",
+            "embeddingUsage": "Embedding 使用量"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "扩散模型",
+            "embedding": "Embedding"
+        },
+        "placeholders": {
+            "loading": "加载中...",
+            "noModels": "未找到模型",
+            "errorLoading": "数据加载失败",
+            "noStorageData": "暂无存储数据",
+            "rootFolder": "根目录",
+            "chartLibraryMissing": "需要 Chart.js 库来显示图表"
+        },
+        "tooltips": {
+            "tagCount": "{tag}：{count} 个模型",
+            "chartUsage": "{name}：{size}，{count} 次使用",
+            "chartPercentage": "{label}：{value}（{pct}%）"
         }
     },
     "modals": {
@@ -1032,7 +1154,10 @@
             "titleWithType": "从 URL 下载 {type}",
             "civitaiUrl": "Civitai URL:",
             "placeholder": "https://civitai.com/models/...",
-            "urlHint": "每行输入一个 CivitAI 或 CivArchive URL。支持批量下载多个 URL。",
+            "urlHint": "每行输入一个 CivitAI、CivArchive 或 Hugging Face URL。支持批量下载多个 URL。",
+            "selectHfFiles": "选择从此仓库下载的文件：",
+            "selectAll": "全选",
+            "fetchingRepoFiles": "正在获取仓库文件...",
             "locationPreview": "下载位置预览",
             "useDefaultPath": "使用默认路径",
             "useDefaultPathTooltip": "启用后，文件将自动按配置的路径模板进行整理",
@@ -1061,7 +1186,9 @@
             },
             "errors": {
                 "invalidUrl": "无效的 Civitai URL 格式",
-                "noVersions": "此模型没有可用版本"
+                "noVersions": "此模型没有可用版本",
+                "mixedSources": "无法在同一批次中混合使用 CivitAI 和 Hugging Face URL。",
+                "noModelFiles": "在此仓库中未找到模型文件。"
             },
             "status": {
                 "preparing": "正在准备下载...",
@@ -1212,6 +1339,8 @@
                 "editVersionName": "编辑版本名称",
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
+                "viewOnHuggingFace": "在 Hugging Face 查看",
+                "viewOnHuggingFaceText": "在 Hugging Face 查看",
                 "viewCreatorProfile": "查看创作者主页",
                 "openFileLocation": "打开文件位置",
                 "sendToWorkflow": "发送到 ComfyUI",
@@ -1237,7 +1366,10 @@
                 "additionalNotes": "附加备注",
                 "notesHint": "回车保存，Shift+回车换行",
                 "addNotesPlaceholder": "在此添加你的备注...",
-                "aboutThisVersion": "关于此版本"
+                "aboutThisVersion": "关于此版本",
+                "baseModelSearchPlaceholder": "搜索基础模型…",
+                "baseModelSuggested": "推荐",
+                "baseModelNoMatch": "没有匹配的基础模型"
             },
             "notes": {
                 "saved": "备注保存成功",
@@ -1424,15 +1556,6 @@
             "duplicate": "该标签已存在"
         }
     },
-    "keyboard": {
-        "navigation": "键盘导航：",
-        "shortcuts": {
-            "pageUp": "向上一页滚动",
-            "pageDown": "向下一页滚动",
-            "home": "跳到顶部",
-            "end": "跳到底部"
-        }
-    },
     "initialization": {
         "title": "初始化",
         "message": "正在准备你的工作空间...",
@@ -1522,12 +1645,15 @@
             "modelUpdated": "模型已更新到工作流",
             "modelFailed": "更新模型节点失败",
             "embeddingAdded": "Embedding 已追加到工作流",
-            "embeddingFailed": "添加 Embedding 失败"
+            "embeddingFailed": "添加 Embedding 失败",
+            "promptSent": "提示词已发送到工作流",
+            "promptFailed": "提示词发送失败"
         },
         "nodeSelector": {
             "recipe": "配方",
             "lora": "LoRA",
             "embedding": "Embedding",
+            "prompt": "提示词",
             "replace": "替换",
             "append": "追加",
             "selectTargetNode": "选择目标节点",
@@ -1714,6 +1840,7 @@
             "enterLoraName": "请输入 LoRA 名称或语法",
             "reconnectedSuccessfully": "LoRA 重新连接成功",
             "reconnectFailed": "LoRA 重新连接出错：{message}",
+            "noPromptToSend": "没有可发送的提示词",
             "cannotSend": "无法发送配方：缺少配方 ID",
             "sendFailed": "发送配方到工作流失败",
             "sendError": "发送配方到工作流出错",
@@ -1973,6 +2100,12 @@
             "moveFailed": "Failed to move item: {message}",
             "copiedToClipboard": "已复制到剪贴板",
             "downloadStarted": "下载已开始"
+        },
+        "agent": {
+            "llmNotConfigured": "AI 提供商未配置。请在 设置 → AI 提供商 中进行配置。",
+            "enrichStarted": "正在使用 AI 增强元数据...",
+            "enrichComplete": "元数据增强完成：{{summary}}",
+            "enrichFailed": "元数据增强失败：{{error}}"
         }
     },
     "doctor": {

locales/zh-TW.json

@@ -105,6 +105,7 @@
             "removeFromFavorites": "移除收藏",
             "viewOnCivitai": "在 Civitai 查看",
             "notAvailableFromCivitai": "Civitai 不提供",
+            "viewOnHuggingFace": "在 Hugging Face 查看",
             "sendToWorkflow": "傳送到 ComfyUI（點擊：附加，Shift+點擊：取代）",
             "copyLoRASyntax": "複製 LoRA 語法",
             "checkpointNameCopied": "Checkpoint 名稱已複製",
@@ -145,6 +146,10 @@
         },
         "usage": {
             "timesUsed": "使用次數"
+        },
+        "footer": {
+            "versionCount": "{count} 個版本",
+            "viewAllVersions": "檢視所有本地版本"
         }
     },
     "globalContextMenu": {
@@ -183,6 +188,9 @@
         },
         "manageExcludedModels": {
             "label": "管理已排除的模型"
+        },
+        "groupByModel": {
+            "label": "按模型分組"
         }
     },
     "header": {
@@ -195,13 +203,7 @@
             "statistics": "統計"
         },
         "search": {
-            "placeholder": "搜尋...",
-            "placeholders": {
-                "loras": "搜尋 LoRA...",
-                "recipes": "搜尋配方...",
-                "checkpoints": "搜尋 checkpoint...",
-                "embeddings": "搜尋 embedding..."
-            },
+            "placeholder": "搜尋",
             "options": "搜尋選項",
             "searchIn": "搜尋範圍：",
             "notAvailable": "統計頁面無法搜尋",
@@ -251,7 +253,18 @@
             "toggle": "切換主題",
             "switchToLight": "切換至淺色主題",
             "switchToDark": "切換至深色主題",
-            "switchToAuto": "自動主題"
+            "switchToAuto": "自動主題",
+            "presets": "主題預設",
+            "default": "預設",
+            "nord": "Nord",
+            "midnight": "Midnight",
+            "monokai": "Monokai",
+            "dracula": "Dracula",
+            "solarized": "Solarized",
+            "mode": "模式",
+            "light": "淺色",
+            "dark": "深色",
+            "auto": "自動"
         },
         "actions": {
             "checkUpdates": "檢查更新",
@@ -263,6 +276,9 @@
         "civitaiApiKey": "Civitai API 金鑰",
         "civitaiApiKeyPlaceholder": "請輸入您的 Civitai API 金鑰",
         "civitaiApiKeyHelp": "用於從 Civitai 下載模型時的身份驗證",
+        "civitaiApiKeyConfigured": "已設定",
+        "civitaiApiKeyNotConfigured": "未設定",
+        "civitaiApiKeySet": "設定",
         "civitaiHost": {
             "label": "Civitai 站點",
             "help": "選擇使用「在 Civitai 中查看」時預設開啟的 Civitai 站點。",
@@ -303,6 +319,7 @@
             "downloads": "下載",
             "videoSettings": "影片設定",
             "layoutSettings": "版面設定",
+            "licenseIcons": "許可協議圖標",
             "misc": "其他",
             "backup": "備份",
             "folderSettings": "預設根目錄",
@@ -310,7 +327,7 @@
             "extraFolderPaths": "額外資料夾路徑",
             "downloadPathTemplates": "下載路徑範本",
             "priorityTags": "優先標籤",
-            "updateFlags": "更新標記",
+            "versionScope": "版本範圍",
             "exampleImages": "範例圖片",
             "autoOrganize": "自動整理",
             "metadata": "中繼資料",
@@ -415,6 +432,8 @@
             "help": "啟用後，如果下載歷史服務記錄顯示該版本已下載，LoRA Manager 將跳過下載該模型版本。適用於所有下載流程。"
         },
         "layoutSettings": {
+            "groupByModel": "按模型分組",
+            "groupByModelHelp": "啟用後，每個 Civitai 模型僅顯示最新版本的單張卡片，舊版本將被隱藏。",
             "displayDensity": "顯示密度",
             "displayDensityOptions": {
                 "default": "預設",
@@ -571,7 +590,7 @@
             "download": "下載",
             "restartRequired": "需要重新啟動"
         },
-        "updateFlagStrategy": {
+        "versionGrouping": {
             "label": "更新標記策略",
             "help": "決定更新徽章是否僅在新版本與本地檔案共享相同基礎模型時顯示，或只要該模型有任何更新版本就顯示。",
             "options": {
@@ -583,6 +602,10 @@
             "label": "隱藏搶先體驗更新",
             "help": "搶先體驗更新"
         },
+        "licenseIcons": {
+            "useNewStyle": "使用新版許可協議圖標",
+            "useNewStyleHelp": "以彩色指示器顯示許可權限（新樣式），或僅顯示限制圖標（經典樣式）。與當前 CivitAI 設計保持一致。"
+        },
         "misc": {
             "includeTriggerWords": "在 LoRA 語法中包含觸發詞",
             "includeTriggerWordsHelp": "複製 LoRA 語法到剪貼簿時包含訓練觸發詞",
@@ -634,6 +657,23 @@
             "proxyPassword": "密碼（選填）",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "代理驗證所需的密碼（如有需要）"
+        },
+        "aiProvider": {
+            "title": "AI 提供者",
+            "provider": "提供者",
+            "providerHelp": "選擇您的 LLM 提供者。OpenAI 和 Ollama 使用預設 API 端點。自訂允許您指定任何相容 OpenAI 的端點。",
+            "custom": "自訂（相容 OpenAI）",
+            "apiBase": "API 基礎位址",
+            "apiBaseHelp": "LLM API 的基礎 URL（例如 https://api.openai.com/v1）。留空則使用提供者預設位址。",
+            "apiBasePlaceholder": "https://api.openai.com/v1",
+            "apiKey": "API 金鑰",
+            "apiKeyHelp": "您的 LLM 提供者 API 金鑰。僅儲存在本地，除了您選擇的 LLM 提供者外，不會發送到任何伺服器。",
+            "apiKeyPlaceholder": "sk-...",
+            "apiKeyNotSet": "未設定",
+            "apiKeyConfigured": "已設定",
+            "apiKeySet": "設定",
+            "model": "模型",
+            "modelHelp": "要使用的模型名稱（例如 deepseek-v4-flash, gemini-2.5-flash, gemma4:12b）。請查看您的提供者支援的可用模型列表。"
         }
     },
     "loras": {
@@ -651,7 +691,11 @@
                 "sizeAsc": "最小",
                 "usage": "使用次數",
                 "usageDesc": "最多",
-                "usageAsc": "最少"
+                "usageAsc": "最少",
+                "versionsCount": "本地版本數",
+                "versionsCountDesc": "版本數從多到少",
+                "versionsCountAsc": "版本數從少到多",
+                "versionIdDesc": "最新版本優先"
             },
             "refresh": {
                 "title": "重新整理模型列表",
@@ -727,7 +771,8 @@
                 "completed": "完成：已移動 {success}，已略過 {skipped}，失敗 {failures}",
                 "complete": "自動整理完成",
                 "error": "錯誤：{error}"
-            }
+            },
+            "enrichHfAgent": "AI 中繼資料增強"
         },
         "contextMenu": {
             "refreshMetadata": "刷新 Civitai 資料",
@@ -751,7 +796,8 @@
             "shareRecipe": "分享配方",
             "viewAllLoras": "檢視全部 LoRA",
             "downloadMissingLoras": "下載缺少的 LoRA",
-            "deleteRecipe": "刪除配方"
+            "deleteRecipe": "刪除配方",
+            "enrichHfAgent": "AI 中繼資料增強"
         }
     },
     "recipes": {
@@ -997,6 +1043,18 @@
             "storage": "儲存空間",
             "insights": "洞察"
         },
+        "metrics": {
+            "totalModels": "模型總數",
+            "totalStorage": "總儲存空間",
+            "totalGenerations": "總生成次數",
+            "usageRate": "使用率",
+            "loras": "LoRA",
+            "checkpoints": "Checkpoint",
+            "embeddings": "Embedding",
+            "uniqueTags": "唯一標籤",
+            "unusedModels": "未使用模型",
+            "avgUsesPerModel": "平均使用次數/模型"
+        },
         "usage": {
             "mostUsedLoras": "最常用的 LoRA",
             "mostUsedCheckpoints": "最常用的 Checkpoint",
@@ -1014,13 +1072,77 @@
         },
         "insights": {
             "smartInsights": "智慧洞察",
-            "recommendations": "推薦"
+            "recommendations": "推薦",
+            "noInsights": "暫無可用洞察",
+            "unusedLoras": {
+                "high": {
+                    "title": "大量未使用的 LoRA",
+                    "description": "你的 LoRA 中有 {percent}%（{count}/{total}）從未被使用過。",
+                    "suggestion": "考慮整理或封存未使用的模型以釋放儲存空間。"
+                }
+            },
+            "unusedCheckpoints": {
+                "detected": {
+                    "title": "檢測到未使用的 Checkpoint",
+                    "description": "你的 Checkpoint 中有 {percent}%（{count}/{total}）從未被使用過。",
+                    "suggestion": "審查並考慮刪除不再需要的 Checkpoint。"
+                }
+            },
+            "unusedEmbeddings": {
+                "high": {
+                    "title": "大量未使用的 Embedding",
+                    "description": "你的 Embedding 中有 {percent}%（{count}/{total}）從未被使用過。",
+                    "suggestion": "考慮整理或封存未使用的 Embedding 以優化你的收藏。"
+                }
+            },
+            "collection": {
+                "large": {
+                    "title": "檢測到大型收藏",
+                    "description": "你的模型收藏正在使用 {size} 的儲存空間。",
+                    "suggestion": "考慮使用外部儲存或雲端解決方案以獲得更好的組織。"
+                }
+            },
+            "activity": {
+                "active": {
+                    "title": "活躍用戶",
+                    "description": "你已經完成了 {count} 次生成！",
+                    "suggestion": "繼續探索並用你的模型創作精彩內容。"
+                }
+            }
         },
         "charts": {
             "collectionOverview": "收藏總覽",
             "baseModelDistribution": "基礎模型分布",
             "usageTrends": "使用趨勢（最近 30 天）",
-            "usageDistribution": "使用分布"
+            "usageDistribution": "使用分布",
+            "date": "日期",
+            "usageCount": "使用次數",
+            "fileSizeBytes": "檔案大小（位元組）",
+            "models": "模型",
+            "loraUsage": "LoRA 使用量",
+            "checkpointUsage": "Checkpoint 使用量",
+            "embeddingUsage": "Embedding 使用量"
+        },
+        "modelTypes": {
+            "lora": "LoRA",
+            "locon": "LyCORIS",
+            "dora": "DoRA",
+            "checkpoint": "Checkpoint",
+            "diffusion_model": "擴散模型",
+            "embedding": "Embedding"
+        },
+        "placeholders": {
+            "loading": "載入中...",
+            "noModels": "找不到模型",
+            "errorLoading": "資料載入失敗",
+            "noStorageData": "暫無儲存資料",
+            "rootFolder": "根目錄",
+            "chartLibraryMissing": "需要 Chart.js 函式庫來顯示圖表"
+        },
+        "tooltips": {
+            "tagCount": "{tag}：{count} 個模型",
+            "chartUsage": "{name}：{size}，{count} 次使用",
+            "chartPercentage": "{label}：{value}（{pct}%）"
         }
     },
     "modals": {
@@ -1032,7 +1154,10 @@
             "titleWithType": "從網址下載 {type}",
             "civitaiUrl": "Civitai 網址：",
             "placeholder": "https://civitai.com/models/...",
-            "urlHint": "每行輸入一個 CivitAI 或 CivArchive URL。支援批量下載多個 URL。",
+            "urlHint": "每行輸入一個 CivitAI、CivArchive 或 Hugging Face URL。支援批量下載多個 URL。",
+            "selectHfFiles": "選擇從此倉庫下載的檔案：",
+            "selectAll": "全選",
+            "fetchingRepoFiles": "正在獲取倉庫檔案...",
             "locationPreview": "下載位置預覽",
             "useDefaultPath": "使用預設路徑",
             "useDefaultPathTooltip": "啟用後，檔案將依照設定的路徑範本自動整理",
@@ -1061,7 +1186,9 @@
             },
             "errors": {
                 "invalidUrl": "Civitai 網址格式無效",
-                "noVersions": "此模型無可用版本"
+                "noVersions": "此模型無可用版本",
+                "mixedSources": "無法在同一批次中混合使用 CivitAI 和 Hugging Face URL。",
+                "noModelFiles": "在此倉庫中未找到模型檔案。"
             },
             "status": {
                 "preparing": "準備下載中...",
@@ -1212,6 +1339,8 @@
                 "editVersionName": "編輯版本名稱",
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
+                "viewOnHuggingFace": "在 Hugging Face 查看",
+                "viewOnHuggingFaceText": "在 Hugging Face 查看",
                 "viewCreatorProfile": "查看創作者個人檔案",
                 "openFileLocation": "開啟檔案位置",
                 "sendToWorkflow": "傳送到 ComfyUI",
@@ -1237,7 +1366,10 @@
                 "additionalNotes": "附加備註",
                 "notesHint": "按 Enter 儲存，Shift+Enter 換行",
                 "addNotesPlaceholder": "在此新增備註...",
-                "aboutThisVersion": "關於此版本"
+                "aboutThisVersion": "關於此版本",
+                "baseModelSearchPlaceholder": "搜尋基礎模型…",
+                "baseModelSuggested": "推薦",
+                "baseModelNoMatch": "沒有符合的基礎模型"
             },
             "notes": {
                 "saved": "備註已儲存",
@@ -1424,15 +1556,6 @@
             "duplicate": "此標籤已存在"
         }
     },
-    "keyboard": {
-        "navigation": "鍵盤導覽：",
-        "shortcuts": {
-            "pageUp": "向上捲動一頁",
-            "pageDown": "向下捲動一頁",
-            "home": "跳至頂部",
-            "end": "跳至底部"
-        }
-    },
     "initialization": {
         "title": "初始化",
         "message": "正在準備您的工作區...",
@@ -1522,12 +1645,15 @@
             "modelUpdated": "模型已更新到工作流",
             "modelFailed": "更新模型節點失敗",
             "embeddingAdded": "Embedding 已附加到工作流",
-            "embeddingFailed": "傳送 Embedding 到工作流失敗"
+            "embeddingFailed": "傳送 Embedding 到工作流失敗",
+            "promptSent": "提示詞已發送到工作流",
+            "promptFailed": "提示詞發送失敗"
         },
         "nodeSelector": {
             "recipe": "配方",
             "lora": "LoRA",
             "embedding": "Embedding",
+            "prompt": "提示詞",
             "replace": "取代",
             "append": "附加",
             "selectTargetNode": "選擇目標節點",
@@ -1714,6 +1840,7 @@
             "enterLoraName": "請輸入 LoRA 名稱或語法",
             "reconnectedSuccessfully": "LoRA 重新連結成功",
             "reconnectFailed": "LoRA 重新連結錯誤：{message}",
+            "noPromptToSend": "沒有可發送的提示詞",
             "cannotSend": "無法傳送配方：缺少配方 ID",
             "sendFailed": "傳送配方到工作流失敗",
             "sendError": "傳送配方到工作流錯誤",
@@ -1973,6 +2100,12 @@
             "moveFailed": "Failed to move item: {message}",
             "copiedToClipboard": "已複製到剪貼簿",
             "downloadStarted": "下載已開始"
+        },
+        "agent": {
+            "llmNotConfigured": "AI 提供者尚未設定。請在 設定 → AI 提供者 中進行設定。",
+            "enrichStarted": "正在使用 AI 增強中繼資料...",
+            "enrichComplete": "中繼資料增強完成：{{summary}}",
+            "enrichFailed": "中繼資料增強失敗：{{error}}"
         }
     },
     "doctor": {

py/agent_cli/__init__.py

@@ -0,0 +1,225 @@
+"""Agent CLI — thin in-process wrappers around LoRA Manager internal services.
+
+All functions are simple Python async functions that delegate to the
+appropriate internal service.  They use **relative imports** within the
+``py`` package, so ``sys.modules`` caching works normally and there is no
+risk of double import or circular dependencies.
+
+Usage (in-process, primary)::
+
+    from py.agent_cli import list_base_models, read_metadata
+
+    models = await list_base_models()
+    meta   = await read_metadata("/path/to/model.safetensors")
+
+Usage (subprocess, debugging / external)::
+
+    python -m py.agent_cli base-models list
+    python -m py.agent_cli metadata read /path/to/model.safetensors
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+async def _find_scanner_for_model(
+    model_path: str,
+) -> tuple[object, object] | tuple[None, None]:
+    """Find the (scanner, cache_entry) responsible for *model_path*.
+
+    Iterates all known scanner types and returns the first one whose cache
+    contains the given path.  Returns ``(None, None)`` when no scanner
+    claims the model.
+    """
+    from ..services.service_registry import ServiceRegistry
+
+    normalized = os.path.normpath(model_path)
+    for getter_name in (
+        "get_lora_scanner",
+        "get_checkpoint_scanner",
+        "get_embedding_scanner",
+    ):
+        getter = getattr(ServiceRegistry, getter_name, None)
+        if getter is None:
+            continue
+        try:
+            scanner = await getter()
+            if scanner is None:
+                continue
+            cache = await scanner.get_cached_data()
+            for entry in cache.raw_data:
+                if os.path.normpath(entry.get("file_path", "")) == normalized:
+                    return scanner, entry
+        except Exception as exc:
+            logger.debug(
+                "Scanner %s check failed for %s: %s",
+                getter_name,
+                model_path,
+                exc,
+            )
+    return None, None
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+async def list_base_models(limit: int = 0) -> List[str]:
+    """Return deduplicated base model names from all model caches.
+
+    The result is ordered by frequency (most common first).  Pass
+    *limit* = 0 (default) for all models.
+    """
+    from ..services.service_registry import ServiceRegistry
+
+    counts: Dict[str, int] = {}
+    for getter_name in (
+        "get_lora_scanner",
+        "get_checkpoint_scanner",
+        "get_embedding_scanner",
+    ):
+        getter = getattr(ServiceRegistry, getter_name, None)
+        if getter is None:
+            continue
+        try:
+            scanner = await getter()
+            if scanner is None:
+                continue
+            cache = await scanner.get_cached_data()
+            for entry in cache.raw_data:
+                bm = entry.get("base_model")
+                if bm:
+                    counts[bm] = counts.get(bm, 0) + 1
+        except Exception as exc:
+            logger.debug("list_base_models scanner %s error: %s", getter_name, exc)
+
+    sorted_names = [name for name, _ in sorted(counts.items(), key=lambda x: -x[1])]
+    if limit > 0:
+        return sorted_names[:limit]
+    return sorted_names
+
+
+async def read_metadata(model_path: str) -> Dict[str, Any]:
+    """Load the full metadata payload for *model_path* from disk.
+
+    Returns an empty dict when the metadata file does not exist or cannot
+    be parsed — never raises.
+    """
+    from ..utils.metadata_manager import MetadataManager
+
+    try:
+        return await MetadataManager.load_metadata_payload(model_path) or {}
+    except Exception as exc:
+        logger.warning("read_metadata failed for %s: %s", model_path, exc)
+        return {}
+
+
+async def apply_metadata_updates(
+    model_path: str,
+    updates: Dict[str, Any],
+) -> List[str]:
+    """Merge *updates* into the model's on-disk metadata and persist.
+
+    Returns the list of field names that actually changed.
+    """
+    from ..utils.metadata_manager import MetadataManager
+
+    metadata = await read_metadata(model_path)
+    updated_fields: List[str] = []
+    for key, value in updates.items():
+        old = metadata.get(key)
+        if old != value:
+            metadata[key] = value
+            updated_fields.append(key)
+    if updated_fields:
+        await MetadataManager.save_metadata(model_path, metadata)
+    return updated_fields
+
+
+async def download_preview(
+    model_path: str,
+    url: str,
+    *,
+    target_width: int = 480,
+    quality: int = 85,
+) -> bool:
+    """Download a preview image from *url*, optimise to .webp, and save it.
+
+    The output file is placed alongside the model file with a ``.webp``
+    extension.  Returns ``True`` on success.
+    """
+    from ..services.downloader import get_downloader
+    from ..utils.exif_utils import ExifUtils
+
+    if not url or not url.strip():
+        return False
+
+    base_name = os.path.splitext(os.path.basename(model_path))[0]
+    preview_dir = os.path.dirname(model_path)
+    output_path = os.path.join(preview_dir, base_name + ".webp")
+
+    downloader = await get_downloader()
+
+    # Try in-memory download + optimise first
+    success, content, _headers = await downloader.download_to_memory(
+        url, use_auth=False,
+    )
+    if success and content:
+        try:
+            optimized_data, _ = ExifUtils.optimize_image(
+                image_data=content,
+                target_width=target_width,
+                format="webp",
+                quality=quality,
+                preserve_metadata=False,
+            )
+            with open(output_path, "wb") as f:
+                f.write(optimized_data)
+            logger.info("Preview downloaded and optimised for %s", model_path)
+            return True
+        except Exception as exc:
+            logger.warning("Preview optimisation failed, saving raw: %s", exc)
+            # Fall through to raw save
+
+    # Fallback: download directly to file
+    try:
+        ok, _ = await downloader.download_file(url, output_path, use_auth=False)
+        if ok:
+            logger.info("Preview downloaded (fallback) for %s", model_path)
+            return True
+    except Exception as exc:
+        logger.warning("Preview fallback download failed for %s: %s", model_path, exc)
+
+    return False
+
+
+async def refresh_cache(model_path: str) -> bool:
+    """Invalidate and reload the scanner cache entry for *model_path*.
+
+    Returns ``True`` when the model was found and the cache was refreshed.
+    """
+    scanner, entry = await _find_scanner_for_model(model_path)
+    if scanner is None:
+        logger.warning("refresh_cache: no scanner found for %s", model_path)
+        return False
+    try:
+        metadata = await read_metadata(model_path)
+        if not metadata:
+            logger.warning("refresh_cache: no metadata for %s", model_path)
+            return False
+        await scanner.update_single_model_cache(model_path, model_path, metadata)
+        return True
+    except Exception as exc:
+        logger.warning("refresh_cache failed for %s: %s", model_path, exc)
+        return False

py/agent_cli/__main__.py

@@ -0,0 +1,118 @@
+"""Subprocess entry point for AgentCLI (debugging / external use).
+
+Usage::
+
+    python -m py.agent_cli base-models list [--limit N]
+    python -m py.agent_cli metadata read <path>
+    python -m py.agent_cli metadata update <path> --json '{...}'
+    python -m py.agent_cli preview download <path> --url <url>
+    python -m py.agent_cli cache refresh <path>
+
+NOTE: This is an **optional** convenience wrapper.  The primary consumer of
+AgentCLI is the :mod:`AgentService` (in-process).  This entry point exists
+for manual debugging and future integration with subprocess-based agent
+frameworks.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import sys
+from typing import Any, Dict, List
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(prog="lmcli", description="LoRA Manager Agent CLI")
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    # base-models list
+    base_models = sub.add_parser("base-models", aliases=["bm"])
+    base_models_cmds = base_models.add_subparsers(dest="subcommand", required=True)
+    base_models_list = base_models_cmds.add_parser("list")
+    base_models_list.add_argument(
+        "--limit", type=int, default=0, help="Max number of models (0 = all)"
+    )
+
+    # metadata read
+    meta = sub.add_parser("metadata", aliases=["md"])
+    meta_cmds = meta.add_subparsers(dest="subcommand", required=True)
+    meta_read = meta_cmds.add_parser("read")
+    meta_read.add_argument("path", type=str, help="Model file path")
+
+    # metadata update
+    meta_update = meta_cmds.add_parser("update")
+    meta_update.add_argument("path", type=str, help="Model file path")
+    meta_update.add_argument(
+        "--json",
+        type=str,
+        required=True,
+        help='JSON object of fields to update, e.g. \'{"base_model": "SDXL 1.0"}\'',
+    )
+
+    # preview download
+    prev = sub.add_parser("preview", aliases=["pv"])
+    prev_cmds = prev.add_subparsers(dest="subcommand", required=True)
+    prev_dl = prev_cmds.add_parser("download")
+    prev_dl.add_argument("path", type=str, help="Model file path")
+    prev_dl.add_argument("--url", type=str, required=True, help="Preview image URL")
+
+    # cache refresh
+    cache = sub.add_parser("cache")
+    cache_cmds = cache.add_subparsers(dest="subcommand", required=True)
+    cache_refresh = cache_cmds.add_parser("refresh")
+    cache_refresh.add_argument("path", type=str, help="Model file path")
+
+    return parser
+
+
+async def _run(args: argparse.Namespace) -> Any:
+    from . import (  # lazy import so startup is fast
+        list_base_models,
+        read_metadata,
+        apply_metadata_updates,
+        download_preview,
+        refresh_cache,
+    )
+
+    cmd = args.command
+    sub = args.subcommand
+
+    if cmd in ("base-models", "bm") and sub == "list":
+        return await list_base_models(limit=args.limit)
+
+    if cmd in ("metadata", "md") and sub == "read":
+        return await read_metadata(args.path)
+
+    if cmd in ("metadata", "md") and sub == "update":
+        updates: Dict[str, Any] = json.loads(args.json)
+        return await apply_metadata_updates(args.path, updates)
+
+    if cmd in ("preview", "pv") and sub == "download":
+        return await download_preview(args.path, args.url)
+
+    if cmd == "cache" and sub == "refresh":
+        return await refresh_cache(args.path)
+
+    raise ValueError(f"Unknown command: {cmd} {sub}")
+
+
+def main() -> None:
+    parser = _build_parser()
+    args = parser.parse_args()
+
+    result = asyncio.run(_run(args))
+    # Always print as JSON so callers can parse reliably
+    if isinstance(result, list):
+        for item in result:
+            print(item)
+    elif isinstance(result, dict):
+        json.dump(result, sys.stdout, ensure_ascii=False, indent=2)
+        print()
+    else:
+        print(json.dumps(result))
+
+
+if __name__ == "__main__":
+    main()

py/config.py

@@ -8,6 +8,8 @@ from typing import Any, Dict, Iterable, List, Mapping, Optional, Set, Tuple
 import logging
 import json
 import urllib.parse
+import sys as _sys
+import types as _types
 import time
 
 from .utils.cache_paths import CacheType, get_cache_file_path, get_legacy_cache_paths
@@ -1380,4 +1382,20 @@ class Config:
 
 
 # Global config instance
-config = Config()
+# NOTE: Guard against re-import.  When ServiceRegistry.get_lora_scanner() triggers
+# a fresh import of lora_scanner → config, we must NOT re-execute Config.__init__()
+# (which re-scans all roots, re-registers libraries, etc.).
+#
+# Strategy: store the config instance in a dedicated sentinel module
+# ('_lm_config_cache') that is NEVER removed from sys.modules (its key does
+# NOT start with 'py.'), so it survives re-imports of py.* modules.
+_CONFIG_SENTINEL = "_lm_config_cache"
+if _CONFIG_SENTINEL in _sys.modules:
+    # Re-import: reuse the existing singleton from the sentinel.
+    config: Config = _sys.modules[_CONFIG_SENTINEL].config  # type: ignore[valid-type]
+else:
+    config: Config = Config()
+    # Register the sentinel so re-imports of py.config find us.
+    _sentinel_mod = _types.ModuleType(_CONFIG_SENTINEL)
+    _sentinel_mod.config = config
+    _sys.modules[_CONFIG_SENTINEL] = _sentinel_mod

py/lora_manager.py

@@ -445,5 +445,12 @@ class LoraManager:
                     scanner.cancel_task()
                     logger.debug("LoRA Manager: Cancelled %s", name)
 
+            # Close shared aiohttp sessions to avoid "Unclosed client session" warnings
+            try:
+                from py.routes.handlers.hf_handlers import close_hf_api_session
+                await close_hf_api_session()
+            except Exception as exc:
+                logger.debug("Error closing HF API session: %s", exc)
+
         except Exception as e:
             logger.error(f"Error during cleanup: {e}", exc_info=True)

py/metadata_collector/node_extractors.py

@@ -901,6 +901,55 @@ class LoraLoaderManagerExtractor(NodeMetadataExtractor):
                 "node_id": node_id
             }
 
+class LoraTextLoaderManagerExtractor(NodeMetadataExtractor):
+    """Extract LoRA metadata from LoraTextLoaderLM (LoRA Text Loader).
+
+    The node accepts a `lora_syntax` STRING containing <lora:name:strength> tags
+    (same format as the ComfyUI prompt), plus an optional `lora_stack`.
+    This extractor parses the syntax string using the same regex as the node.
+    """
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        active_loras = []
+
+        # Process lora_stack if available (optional input)
+        if "lora_stack" in inputs:
+            lora_stack = inputs.get("lora_stack", [])
+            for item in lora_stack:
+                # lora_stack entries are (path, model_strength, clip_strength) tuples
+                if isinstance(item, (list, tuple)) and len(item) >= 2:
+                    lora_path = item[0]
+                    model_strength = item[1]
+                    lora_name = os.path.splitext(os.path.basename(lora_path))[0]
+                    active_loras.append({
+                        "name": lora_name,
+                        "strength": round(float(model_strength), 2)
+                    })
+
+        # Process lora_syntax string input
+        if "lora_syntax" in inputs:
+            lora_syntax = inputs.get("lora_syntax", "")
+            if lora_syntax and isinstance(lora_syntax, str):
+                pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
+                matches = re.findall(pattern, lora_syntax, re.IGNORECASE)
+                for match in matches:
+                    lora_name = match[0]
+                    model_strength = float(match[1])
+                    active_loras.append({
+                        "name": lora_name,
+                        "strength": round(model_strength, 2)
+                    })
+
+        if active_loras:
+            metadata[LORAS][node_id] = {
+                "lora_list": active_loras,
+                "node_id": node_id
+            }
+
+
 class FluxGuidanceExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -1146,6 +1195,7 @@ NODE_EXTRACTORS = {
     "UNETLoaderLM": UNETLoaderExtractor,  # LoRA Manager
     "LoraLoader": LoraLoaderExtractor,
     "LoraLoaderLM": LoraLoaderManagerExtractor,
+    "LoraTextLoaderLM": LoraTextLoaderManagerExtractor,
     "RgthreePowerLoraLoader": RgthreePowerLoraLoaderExtractor,
     "TensorRTLoader": TensorRTLoaderExtractor,
     # Conditioning

py/nodes/save_image.py

@@ -608,7 +608,7 @@ class SaveImageLM:
             img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
 
             # Generate filename with counter if needed
-            base_filename = filename
+            base_filename = filename.replace("%batch_num%", str(i))
             if add_counter_to_filename:
                 # Use counter + i to ensure unique filenames for all images in batch
                 current_counter = counter + i

py/recipes/parsers/automatic.py

@@ -123,24 +123,39 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         if model_hash_from_hashes:
                             metadata["model_hash"] = model_hash_from_hashes
                     
-                    # Extract Lora hashes in alternative format
+                    # Extract Lora hashes in alternative format.
+                    # Run unconditionally (not just as fallback) so that
+                    # non-empty hashes from Lora hashes fill in the gaps left
+                    # by empty values in the Hashes JSON dict.  Some WebUI
+                    # builds write real hash values only to Lora hashes and
+                    # leave the Hashes JSON values empty.
                     lora_hashes_match = re.search(self.LORA_HASHES_REGEX, params_section)
-                    if not hashes_match and lora_hashes_match:
+                    if lora_hashes_match:
                         try:
                             lora_hashes_str = lora_hashes_match.group(1)
                             lora_hash_entries = lora_hashes_str.split(', ')
-                            
-                            # Initialize hashes dict if it doesn't exist
-                            if "hashes" not in metadata:
-                                metadata["hashes"] = {}
-                                
+
                             # Parse each lora hash entry (format: "name: hash")
                             for entry in lora_hash_entries:
                                 if ': ' in entry:
                                     lora_name, lora_hash = entry.split(': ', 1)
-                                    # Add as lora type in the same format as regular hashes
-                                    metadata["hashes"][f"lora:{lora_name}"] = lora_hash.strip()
-                            
+                                    lora_hash = lora_hash.strip()
+                                    if not lora_hash:
+                                        # Skip entries without a hash value
+                                        continue
+                                    # Initialize hashes dict if it doesn't exist
+                                    if "hashes" not in metadata:
+                                        metadata["hashes"] = {}
+                                    # Add as lora type in the same format as
+                                    # regular hashes.  Only override an
+                                    # existing entry if its value is empty
+                                    # (Lora hashes is the more reliable
+                                    # source when Hashes JSON has blanks).
+                                    key = f"lora:{lora_name}"
+                                    existing = metadata["hashes"].get(key, "")
+                                    if not existing:
+                                        metadata["hashes"][key] = lora_hash
+
                             # Remove lora hashes from params section
                             params_section = params_section.replace(lora_hashes_match.group(0), '')
                         except Exception as e:
@@ -362,6 +377,12 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         # Only process lora or hypernet types
                         if not hash_key.startswith(("lora:", "hypernet:")):
                             continue
+                        
+                        # Skip entries without a hash value — they can't be
+                        # resolved via CivitAI and would only produce a
+                        # useless "Deleted" entry in the recipe.
+                        if not lora_hash:
+                            continue
                             
                         lora_type, lora_name = hash_key.split(':', 1)
                         
@@ -387,11 +408,7 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         # Try to get info from Civitai
                         if metadata_provider:
                             try:
-                                if lora_hash:
-                                    # If we have hash, use it for lookup
-                                    civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
-                                else:
-                                    civitai_info = None
+                                civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
                                 
                                 populated_entry = await self.populate_lora_from_civitai(
                                     lora_entry, 

py/recipes/parsers/civitai_image.py

@@ -514,11 +514,21 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                         result["loras"].append(lora_entry)
 
-            # Process modelVersionIds from Civitai image API
-            # These are model version IDs returned at root level when meta doesn't contain resources
-            if "modelVersionIds" in metadata and isinstance(
-                metadata["modelVersionIds"], list
+            # Process modelVersionIds from Civitai image API.
+            # These are version IDs returned at root level of the API response.
+            # When resources or civitaiResources are already present in metadata
+            # (which they are when ?withMeta=true is passed), those sections have
+            # complete hash/type information — modelVersionIds is a fallback for
+            # when meta is null and only the flat ID list is available. Skipping
+            # it here avoids duplicates: the same file hash often resolves to
+            # different version IDs via hash lookup (resources) vs the original
+            # version ID in modelVersionIds, and both paths would create entries.
+            if (
+                "modelVersionIds" in metadata
+                and isinstance(metadata["modelVersionIds"], list)
+                and not result.get("loras")
             ):
+
                 for version_id in metadata["modelVersionIds"]:
                     version_id_str = str(version_id)
 
@@ -526,6 +536,13 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     if version_id_str in added_loras:
                         continue
 
+                    # Skip if this version ID is already the recipe's checkpoint
+                    # (resolved earlier from embedded resources/Model hash,
+                    # avoiding a duplicate CivitAI API call).
+                    existing_model = result.get("model")
+                    if existing_model and str(existing_model.get("id")) == version_id_str:
+                        continue
+
                     # Initialize lora entry with version ID
                     lora_entry = {
                         "id": version_id,
@@ -559,9 +576,40 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                             )
 
                             if populated_entry is None:
-                                continue  # Skip invalid LoRA types
+                                # Not a LoRA — try as checkpoint (only if we
+                                # don't already have one).  Reuses the same
+                                # civitai_info from the API call above so no
+                                # extra query is made.
+                                if result["model"] is None:
+                                    checkpoint_entry = {
+                                        "id": version_id,
+                                        "modelId": 0,
+                                        "name": "Unknown Model",
+                                        "version": "",
+                                        "type": "checkpoint",
+                                        "existsLocally": False,
+                                        "localPath": None,
+                                        "file_name": "",
+                                        "hash": "",
+                                        "thumbnailUrl": (
+                                            "/loras_static/images/no-preview.png"
+                                        ),
+                                        "baseModel": "",
+                                        "size": 0,
+                                        "downloadUrl": "",
+                                        "isDeleted": False,
+                                    }
+                                    cp_populated = await (
+                                        self.populate_checkpoint_from_civitai(
+                                            checkpoint_entry, civitai_info
+                                        )
+                                    )
+                                    if cp_populated.get("modelId"):
+                                        result["model"] = cp_populated
+                                continue  # Not a LoRA, don't add to loras
 
                             lora_entry = populated_entry
+
                         except Exception as e:
                             logger.error(
                                 f"Error fetching Civitai info for model version {version_id}: {e}"

py/routes/handlers/agent_handlers.py

@@ -0,0 +1,167 @@
+"""HTTP route handlers for agent skill endpoints.
+
+These handlers expose the :class:`AgentService` via HTTP, allowing the
+frontend to list available skills and execute them on selected models.
+Progress is reported via WebSocket broadcast.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any, Dict
+
+from aiohttp import web
+
+from ...services.agent import AgentService, AgentProgressReporter
+from ...services.llm_service import LLMNotConfiguredError
+
+logger = logging.getLogger(__name__)
+
+
+class AgentHandler:
+    """HTTP handler for agent skill operations."""
+
+    def __init__(self, agent_service: AgentService | None = None) -> None:
+        self._agent_service = agent_service
+
+    async def _ensure_service(self) -> AgentService:
+        if self._agent_service is None:
+            self._agent_service = await AgentService.get_instance()
+        return self._agent_service
+
+    # ------------------------------------------------------------------
+    # GET /api/lm/agent/skills
+    # ------------------------------------------------------------------
+
+    async def get_agent_skills(self, request: web.Request) -> web.Response:
+        """Return a list of available agent skills."""
+
+        service = await self._ensure_service()
+        skills = await service.list_skills()
+        return web.json_response({"skills": skills})
+
+    # ------------------------------------------------------------------
+    # POST /api/lm/agent/execute/{skill_name}
+    # ------------------------------------------------------------------
+
+    async def execute_agent_skill(self, request: web.Request) -> web.Response:
+        """Execute an agent skill on the provided model paths.
+
+        Request body::
+
+            {"model_paths": ["/path/to/model1.safetensors", ...], "options": {}}
+
+        Returns immediately with a task ID.  Execution runs in the
+        background; progress and completion are pushed via WebSocket
+        events of type ``agent_progress``.
+        """
+
+        skill_name = request.match_info.get("skill_name", "")
+        if not skill_name:
+            return web.json_response(
+                {"error": "Skill name is required"}, status_code=400
+            )
+
+        try:
+            body = await request.json()
+        except Exception:
+            return web.json_response(
+                {"error": "Invalid JSON body"}, status_code=400
+            )
+
+        model_paths = body.get("model_paths", [])
+        if not model_paths or not isinstance(model_paths, list):
+            return web.json_response(
+                {"error": "model_paths must be a non-empty array"},
+                status_code=400,
+            )
+
+        service = await self._ensure_service()
+
+        # Validate LLM configuration early for skills that need it
+        # (fail fast rather than after starting background work)
+        try:
+            from ...services.llm_service import LLMService
+
+            llm = await LLMService.get_instance()
+            if not llm.is_configured():
+                return web.json_response(
+                    {
+                        "error": "LLM provider is not configured. "
+                        "Enable it in Settings → AI Provider.",
+                    },
+                    status=400,
+                )
+        except Exception as exc:
+            logger.error("Failed to check LLM configuration: %s", exc)
+
+        # Launch execution in the background
+        progress_reporter = AgentProgressReporter()
+        logger.info(
+            "Agent skill '%s' starting for %d model(s) in background task",
+            skill_name, len(model_paths),
+        )
+
+        async def _run() -> None:
+            logger.info("_run background task started for skill '%s'", skill_name)
+            try:
+                result = await service.execute_skill(
+                    skill_name=skill_name,
+                    input_data={"model_paths": model_paths},
+                    progress_callback=progress_reporter,
+                )
+                logger.info(
+                    "Agent skill '%s' finished: success=%s, summary='%s', errors=%s",
+                    skill_name, result.success, result.summary, result.errors,
+                )
+            except LLMNotConfiguredError as exc:
+                logger.warning("Agent skill '%s' not configured: %s", skill_name, exc)
+                await progress_reporter.on_progress(
+                    {
+                        "type": "agent_progress",
+                        "skill": skill_name,
+                        "status": "error",
+                        "error": str(exc),
+                    }
+                )
+            except Exception as exc:
+                logger.error("Agent skill '%s' failed: %s", skill_name, exc, exc_info=True)
+                await progress_reporter.on_progress(
+                    {
+                        "type": "agent_progress",
+                        "skill": skill_name,
+                        "status": "error",
+                        "error": str(exc),
+                    }
+                )
+
+        # Fire and forget — progress comes via WebSocket
+        task = asyncio.create_task(_run())
+        logger.info("Agent skill '%s' background task created (id=%s)", skill_name, task)
+
+        return web.json_response(
+            {
+                "status": "started",
+                "skill": skill_name,
+                "model_count": len(model_paths),
+            }
+        )
+
+    # ------------------------------------------------------------------
+    # POST /api/lm/agent/cancel
+    # ------------------------------------------------------------------
+
+    async def cancel_agent_skill(self, request: web.Request) -> web.Response:
+        """Cancel a running agent skill.
+
+        NOTE: Cancellation is a stub for now — the AgentService processes
+        models sequentially and does not yet support mid-execution
+        cancellation.  This endpoint exists for API completeness.
+        """
+
+        # TODO: implement cooperative cancellation in AgentService
+        return web.json_response(
+            {"status": "acknowledged", "note": "Cancellation not yet implemented"},
+            status_code=200,
+        )

py/routes/handlers/hf_handlers.py

@@ -0,0 +1,417 @@
+"""Handlers for Hugging Face model listing and download.
+
+Minimal MVP implementation — uses direct HTTP to the HF API for file
+listing and the project's existing aiohttp-based Downloader for
+downloading.  No huggingface_hub dependency required.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+from typing import Any
+
+import aiohttp
+from aiohttp import web
+
+from ...config import config
+from ...services.downloader import (
+    DownloadProgress,
+    get_downloader,
+)
+from ...services.aria2_downloader import Aria2Downloader
+from ...services.settings_manager import get_settings_manager
+from ...services.service_registry import ServiceRegistry
+from ...services.websocket_manager import ws_manager
+from ...utils.constants import MODEL_FILE_EXTENSIONS
+from ...utils.metadata_manager import MetadataManager
+from ...utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_MODEL_CLASS = LoraMetadata
+_DEFAULT_SCANNER_GETTER = "get_lora_scanner"
+
+# Shared aiohttp session for HF API calls (created on first use)
+_hf_api_session: aiohttp.ClientSession | None = None
+
+
+async def _get_hf_api_session() -> aiohttp.ClientSession:
+    """Get or create the shared aiohttp session for HF API calls."""
+    global _hf_api_session  # needed because we reassign the module-level name
+    if _hf_api_session is None or _hf_api_session.closed:
+        _hf_api_session = aiohttp.ClientSession(
+            headers={"User-Agent": "ComfyUI-LoRA-Manager/1.0"},
+            timeout=aiohttp.ClientTimeout(total=30),
+        )
+    return _hf_api_session
+
+
+async def close_hf_api_session() -> None:
+    """Close the shared HF API session, if it was ever created."""
+    global _hf_api_session
+    if _hf_api_session is not None and not _hf_api_session.closed:
+        await _hf_api_session.close()
+        _hf_api_session = None
+
+
+def _infer_model_type(model_root: str) -> tuple[Any, str]:
+    """Determine model class and scanner by matching ``model_root`` against the
+    configured root paths for each model type (from ``Config``).
+
+    The ``model_root`` value comes from the frontend's model-root dropdown,
+    which is populated from the current page's scanner roots.  By checking
+    which scanner's root list it belongs to, we avoid fragile heuristics
+    like substring-matching path names.
+    """
+    norm = os.path.normpath(model_root).replace(os.sep, "/")
+
+    # LoRA roots
+    for p in (config.loras_roots or []) + (config.extra_loras_roots or []):
+        if os.path.normpath(p).replace(os.sep, "/") == norm:
+            return LoraMetadata, "get_lora_scanner"
+
+    # Checkpoint / UNet roots
+    for p in (
+        (config.checkpoints_roots or [])
+        + (config.extra_checkpoints_roots or [])
+        + (config.unet_roots or [])
+        + (config.extra_unet_roots or [])
+    ):
+        if os.path.normpath(p).replace(os.sep, "/") == norm:
+            return CheckpointMetadata, "get_checkpoint_scanner"
+
+    # Embedding roots
+    for p in (config.embeddings_roots or []) + (config.extra_embeddings_roots or []):
+        if os.path.normpath(p).replace(os.sep, "/") == norm:
+            return EmbeddingMetadata, "get_embedding_scanner"
+
+    # Fallback — should not happen in normal use
+    logger.warning(
+        "Could not determine model type for root '%s'; defaulting to LoRA",
+        model_root,
+    )
+    return _DEFAULT_MODEL_CLASS, _DEFAULT_SCANNER_GETTER
+
+
+async def _save_hf_metadata(dest_path: str, repo: str, model_root: str) -> None:
+    """Create a proper .metadata.json and add the model to the scanner cache.
+
+    Uses ``MetadataManager.create_default_metadata()`` which computes the
+    SHA256 hash, extracts safetensors header metadata (base_model), and
+    produces a fully-populated ``LoraMetadata`` (or ``CheckpointMetadata`` /
+    ``EmbeddingMetadata``) object.  We then overlay HF-specific fields and
+    register the model in the in-memory scanner cache so it appears
+    immediately without a full filesystem walk.
+    """
+    try:
+        hf_url = f"https://huggingface.co/{repo}"
+        model_class, scanner_getter_name = _infer_model_type(model_root)
+
+        # 1. Create proper metadata (computes SHA256, reads safetensors headers)
+        metadata = await MetadataManager.create_default_metadata(
+            dest_path, model_class=model_class
+        )
+        if metadata is None:
+            logger.warning("create_default_metadata returned None for %s", dest_path)
+            return
+
+        # 2. Overlay HF-specific fields
+        metadata._unknown_fields["hf_url"] = hf_url
+        metadata.from_civitai = False  # HF models are not from CivitAI
+
+        # 3. Save metadata atomically
+        await MetadataManager.save_metadata(dest_path, metadata)
+        logger.info("Saved HF metadata (with hf_url) for %s", dest_path)
+
+        # 4. Determine relative folder path for cache
+        #    model_root is an absolute path; dest_path is under it
+        folder = ""
+        if os.path.isabs(model_root) and dest_path.startswith(model_root):
+            rel = os.path.relpath(os.path.dirname(dest_path), model_root)
+            folder = rel.replace(os.sep, "/") if rel != "." else ""
+
+        # 5. Add to scanner cache (same as CivitAI's _execute_download does)
+        scanner_getter = getattr(ServiceRegistry, scanner_getter_name, None)
+        if scanner_getter is not None:
+            scanner = await scanner_getter()
+            if scanner is not None:
+                metadata_dict = metadata.to_dict()
+                metadata_dict["hf_url"] = hf_url
+                await scanner.add_model_to_cache(metadata_dict, folder)
+                logger.info("Added %s to scanner cache (folder=%s)", dest_path, folder)
+
+    except Exception as exc:
+        logger.warning("Failed to save HF metadata for %s: %s", dest_path, exc)
+
+
+class HfHandler:
+    """Handle Hugging Face model browsing and download."""
+
+    async def get_hf_repo_files(self, request: web.Request) -> web.Response:
+        """List model-weight files from a HF repo with real file sizes.
+
+        Uses the HF tree API endpoint which returns accurate file sizes
+        (including LFS-tracked files), unlike the model info endpoint.
+        """
+        repo = request.query.get("repo", "").strip()
+        if not repo or "/" not in repo:
+            return web.json_response(
+                {"error": "Missing or invalid 'repo' parameter (expected user/repo)"},
+                status=400,
+            )
+
+        url = f"https://huggingface.co/api/models/{repo}/tree/main"
+
+        try:
+            session = await _get_hf_api_session()
+            async with session.get(url) as resp:
+                if resp.status == 404:
+                    return web.json_response(
+                        {"error": f"Repo '{repo}' not found"}, status=404
+                    )
+                if resp.status != 200:
+                    text = await resp.text()
+                    return web.json_response(
+                        {"error": f"HF API error {resp.status}: {text[:200]}"},
+                        status=resp.status,
+                    )
+                tree: list[dict[str, Any]] = await resp.json()
+        except Exception as exc:
+            logger.error("Failed to fetch HF repo files: %s", exc)
+            return web.json_response({"error": str(exc)}, status=502)
+
+        files: list[dict[str, Any]] = []
+        for entry in tree:
+            path: str = entry.get("path", "")
+            ext = os.path.splitext(path)[1].lower()
+            if ext not in MODEL_FILE_EXTENSIONS:
+                continue
+            size = entry.get("size", 0) or 0
+            if size == 0 and "lfs" in entry:
+                size = entry["lfs"].get("size", 0) or 0
+            files.append({
+                "filename": path,
+                "size": size,
+            })
+
+        files.sort(key=lambda f: f["size"], reverse=True)
+        return web.json_response(files)
+
+    async def download_hf_model(self, request: web.Request) -> web.Response:
+        """Download a single file from Hugging Face into the model directory.
+
+        POST JSON body::
+
+            {
+              "repo": "dx8152/Flux2-Klein-9B-Consistency",
+              "filename": "Flux2-Klein-9B-consistency-V2.safetensors",
+              "revision": "main",
+              "model_root": "loras",
+              "relative_path": "",
+              "use_default_paths": false,
+              "download_id": "optional-batch-id"
+            }
+
+        If ``download_id`` is provided, real-time progress (bytes, speed,
+        percentage) is broadcast via the WebSocket progress system, matching
+        the CivitAI download experience.
+
+        Respects the ``download_backend`` setting (``aria2`` or ``default``).
+        """
+        try:
+            payload: dict[str, Any] = await request.json()
+        except json.JSONDecodeError:
+            return web.json_response({"error": "Invalid JSON"}, status=400)
+
+        repo = (payload.get("repo") or "").strip()
+        filename = (payload.get("filename") or "").strip()
+        revision = (payload.get("revision") or "main").strip()
+        model_root = (payload.get("model_root") or "").strip()
+        relative_path = (payload.get("relative_path") or "").strip()
+        use_default_paths = bool(payload.get("use_default_paths", False))
+        download_id: str | None = payload.get("download_id")
+
+        logger.info(
+            "download_hf_model: repo=%s file=%s root=%s download_id=%s",
+            repo, filename, model_root, download_id,
+        )
+
+        if not repo or not filename:
+            return web.json_response(
+                {"error": "Missing required fields: 'repo' and 'filename'"}, status=400
+            )
+
+        # Validate repo format — must be user/repo_name
+        if repo.count("/") != 1 or not re.match(r"^[a-zA-Z0-9_.-]+/[a-zA-Z0-9_.-]+$", repo):
+            return web.json_response({"error": f"Invalid repo format: {repo}"}, status=400)
+        author, repo_name = repo.split("/", 1)
+        if ".." in (author, repo_name) or "." in (author, repo_name):
+            return web.json_response({"error": f"Invalid repo format: {repo}"}, status=400)
+
+        # Validate filename — must not contain path separators or ..
+        if "/" in filename or "\\" in filename or ".." in filename:
+            return web.json_response({"error": "Invalid filename"}, status=400)
+
+        # Validate relative_path — must not be absolute or escape base directory
+        if relative_path:
+            if os.path.isabs(relative_path):
+                return web.json_response({"error": "relative_path must not be absolute"}, status=400)
+            if ".." in relative_path.split("/") or "\\" in relative_path:
+                return web.json_response({"error": "Invalid relative_path"}, status=400)
+
+        # Validate model_root — must not contain path traversal
+        if not os.path.isabs(model_root):
+            # For relative model_root, check it doesn't escape
+            resolved_model_root = os.path.realpath(
+                os.path.join(os.getcwd(), "models", model_root)
+            )
+        else:
+            resolved_model_root = os.path.realpath(model_root)
+
+        # Verify model_root is within a configured scanner root
+        allowed_roots = set()
+        for root_list in (
+            config.loras_roots or [],
+            config.extra_loras_roots or [],
+            config.checkpoints_roots or [],
+            config.extra_checkpoints_roots or [],
+            config.unet_roots or [],
+            config.extra_unet_roots or [],
+            config.embeddings_roots or [],
+            config.extra_embeddings_roots or [],
+        ):
+            for r in root_list:
+                allowed_roots.add(os.path.realpath(r))
+
+        if not any(resolved_model_root == root or resolved_model_root.startswith(root + os.sep) for root in allowed_roots):
+            logger.warning("Invalid model_root rejected: %s", model_root)
+            return web.json_response({"error": f"Invalid model_root: {model_root}"}, status=400)
+
+        base_dir = resolved_model_root
+
+        if use_default_paths:
+            target_dir = os.path.join(base_dir, "huggingface", author, repo_name)
+        elif relative_path:
+            target_dir = os.path.join(base_dir, relative_path)
+        else:
+            target_dir = base_dir
+
+        os.makedirs(target_dir, exist_ok=True)
+        dest_path = os.path.join(target_dir, filename)
+
+        # Resolve symlinks and check for path traversal escape
+        real_dest = os.path.realpath(dest_path)
+        real_base = os.path.realpath(target_dir)
+        if not real_dest.startswith(real_base + os.sep):
+            logger.warning("Path traversal blocked: %s -> %s", dest_path, real_dest)
+            return web.json_response({"error": "Path traversal detected"}, status=400)
+
+        # Check if already exists (simple skip)
+        if os.path.exists(dest_path) and os.path.getsize(dest_path) > 0:
+            logger.info("download_hf_model: file already exists, skipping — %s", dest_path)
+            return web.json_response({
+                "success": True,
+                "message": f"File already exists: {dest_path}",
+                "path": dest_path,
+            })
+
+        # Build HF resolve URL
+        resolve_url = (
+            f"https://huggingface.co/{repo}/resolve/{revision}/{filename}"
+        )
+
+        # Set up progress callback if download_id is provided
+        progress_callback = None
+        if download_id:
+
+            async def _progress_callback(
+                progress: float | DownloadProgress,
+                snapshot: DownloadProgress | None = None,
+            ) -> None:
+                percent = 0.0
+                metrics = snapshot if isinstance(snapshot, DownloadProgress) else None
+
+                if isinstance(progress, DownloadProgress):
+                    percent = progress.percent_complete
+                    metrics = progress
+                elif isinstance(snapshot, DownloadProgress):
+                    percent = snapshot.percent_complete
+                else:
+                    percent = float(progress)
+
+                broadcast: dict[str, Any] = {
+                    "status": "progress",
+                    "progress": round(percent),
+                }
+                if metrics:
+                    broadcast["bytes_downloaded"] = metrics.bytes_downloaded
+                    broadcast["total_bytes"] = metrics.total_bytes
+                    broadcast["bytes_per_second"] = metrics.bytes_per_second
+
+                await ws_manager.broadcast_download_progress(download_id, broadcast)
+
+            progress_callback = _progress_callback
+
+        # Respect download backend setting (aria2 vs default)
+        download_backend = (
+            get_settings_manager().get("download_backend", "default")
+        )
+
+        if download_backend == "aria2":
+            aria2 = await Aria2Downloader.get_instance()
+            aid = download_id or f"hf_{repo}_{filename}"
+            try:
+                hf_success, hf_result = await aria2.download_file(
+                    url=resolve_url,
+                    save_path=dest_path,
+                    download_id=aid,
+                    progress_callback=progress_callback,
+                )
+                if hf_success:
+                    await _save_hf_metadata(dest_path, repo, model_root)
+                    return web.json_response({
+                        "success": True,
+                        "message": f"Downloaded to {dest_path}",
+                        "path": dest_path,
+                    })
+                else:
+                    return web.json_response(
+                        {"success": False, "error": hf_result or "aria2 download failed"},
+                        status=500,
+                    )
+            except Exception as exc:
+                logger.error("HF download (aria2) failed: %s", exc)
+                return web.json_response(
+                    {"success": False, "error": str(exc)}, status=500
+                )
+
+        # Default: use built-in aiohttp Downloader
+        downloader = await get_downloader()
+        try:
+            success, result = await downloader.download_file(
+                url=resolve_url,
+                save_path=dest_path,
+                use_auth=False,
+                allow_resume=True,
+                progress_callback=progress_callback,
+            )
+            if success:
+                await _save_hf_metadata(dest_path, repo, model_root)
+                return web.json_response({
+                    "success": True,
+                    "message": f"Downloaded to {result}",
+                    "path": result,
+                })
+            else:
+                return web.json_response(
+                    {"success": False, "error": result or "Download failed"},
+                    status=500,
+                )
+        except Exception as exc:
+            logger.error("HF download failed: %s", exc)
+            return web.json_response(
+                {"success": False, "error": str(exc)}, status=500
+            )

py/routes/handlers/misc_handlers.py

@@ -48,8 +48,13 @@ from ...utils.constants import (
     SUPPORTED_MEDIA_EXTENSIONS,
     VALID_LORA_TYPES,
 )
+from .hf_handlers import HfHandler
+from .agent_handlers import AgentHandler
 from ...utils.civitai_utils import rewrite_preview_url
-from ...utils.example_images_paths import is_valid_example_images_root
+from ...utils.example_images_paths import (
+    find_non_compliant_items_in_example_images_root,
+    is_valid_example_images_root,
+)
 from ...utils.lora_metadata import extract_trained_words
 from ...utils.session_logging import get_standalone_session_log_snapshot
 from ...utils.usage_stats import UsageStats
@@ -411,9 +416,10 @@ class PromptServerProtocol(Protocol):
     """Subset of PromptServer used by the handlers."""
 
     instance: "PromptServerProtocol"
+    sockets: dict  # maps clientId (sid) → WebSocketResponse
 
     def send_sync(
-        self, event: str, payload: dict
+        self, event: str, payload: dict | None = None, sid: str | None = None
     ) -> None:  # pragma: no cover - protocol
         ...
 
@@ -468,89 +474,154 @@ class BackupServiceProtocol(Protocol):
 
 
 class NodeRegistry:
-    """Thread-safe registry for tracking LoRA nodes in active workflows."""
+    """Thread-safe registry for tracking LoRA nodes across ComfyUI tabs.
+
+    Each connected ComfyUI browser tab (identified by its ``sid`` / ``clientId``)
+    registers its own set of workflow nodes.  Queries merge all known tabs into
+    a single result so that the calling LM panel always sees *every* available
+    target node, regardless of which tab responded fastest.
+    """
 
     def __init__(self) -> None:
         self._lock = asyncio.Lock()
-        self._nodes: Dict[str, dict] = {}
-        self._registry_updated = asyncio.Event()
+        # sid → {unique_id → node_info}
+        self._tab_nodes: Dict[str, Dict[str, dict]] = {}
+        self._ready = asyncio.Event()
+        self._waiting_clients: set[str] = set()
+
+    @property
+    def pending_client_count(self) -> int:
+        """Number of clients that have not yet responded in the current refresh cycle."""
+        return len(self._waiting_clients)
+
+    # ------------------------------------------------------------------
+    # Helpers to build one node dict (extracted so it's reused for each tab)
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _build_node_dict(node: dict) -> dict:
+        node_id = node["node_id"]
+        graph_id = str(node["graph_id"])
+        unique_id = f"{graph_id}:{node_id}"
+        node_type = node.get("type", "")
+        type_id = NODE_TYPES.get(node_type, 0)
+        bgcolor = node.get("bgcolor") or DEFAULT_NODE_COLOR
+
+        raw_capabilities = node.get("capabilities")
+        capabilities: dict = {}
+        if isinstance(raw_capabilities, dict):
+            capabilities = dict(raw_capabilities)
+
+        raw_widget_names: list | None = node.get("widget_names")
+        if not isinstance(raw_widget_names, list):
+            capability_widget_names = capabilities.get("widget_names")
+            raw_widget_names = (
+                capability_widget_names
+                if isinstance(capability_widget_names, list)
+                else None
+            )
+
+        widget_names: list[str] = []
+        if isinstance(raw_widget_names, list):
+            widget_names = [
+                str(widget_name)
+                for widget_name in raw_widget_names
+                if isinstance(widget_name, str) and widget_name
+            ]
+
+        if widget_names:
+            capabilities["widget_names"] = widget_names
+        else:
+            capabilities.pop("widget_names", None)
+
+        if "supports_lora" in capabilities:
+            capabilities["supports_lora"] = bool(capabilities["supports_lora"])
+
+        comfy_class = node.get("comfy_class")
+        if not isinstance(comfy_class, str) or not comfy_class:
+            comfy_class = node_type if isinstance(node_type, str) else None
+
+        return {
+            "id": node_id,
+            "graph_id": graph_id,
+            "graph_name": node.get("graph_name"),
+            "unique_id": unique_id,
+            "bgcolor": bgcolor,
+            "title": node.get("title"),
+            "type": type_id,
+            "type_name": node_type,
+            "comfy_class": comfy_class,
+            "capabilities": capabilities,
+            "widget_names": widget_names,
+            "mode": node.get("mode"),
+            "marker_role": node.get("marker_role"),
+        }
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+    async def register_nodes(self, sid: str, nodes: list[dict]) -> None:
+        """Register/replace the node list for a single ComfyUI tab (identified by *sid*)."""
+        tab_nodes: dict[str, dict] = {}
+        for node in nodes:
+            nd = self._build_node_dict(node)
+            tab_nodes[nd["unique_id"]] = nd
 
-    async def register_nodes(self, nodes: list[dict]) -> None:
         async with self._lock:
-            self._nodes.clear()
-            for node in nodes:
-                node_id = node["node_id"]
-                graph_id = str(node["graph_id"])
-                unique_id = f"{graph_id}:{node_id}"
-                node_type = node.get("type", "")
-                type_id = NODE_TYPES.get(node_type, 0)
-                bgcolor = node.get("bgcolor") or DEFAULT_NODE_COLOR
-                raw_capabilities = node.get("capabilities")
-                capabilities: dict = {}
-                if isinstance(raw_capabilities, dict):
-                    capabilities = dict(raw_capabilities)
+            self._tab_nodes[sid] = tab_nodes
+            self._waiting_clients.discard(sid)
+            if not self._waiting_clients:
+                self._ready.set()
 
-                raw_widget_names: list | None = node.get("widget_names")
-                if not isinstance(raw_widget_names, list):
-                    capability_widget_names = capabilities.get("widget_names")
-                    raw_widget_names = (
-                        capability_widget_names
-                        if isinstance(capability_widget_names, list)
-                        else None
-                    )
+        logger.debug("Registered %s nodes from client %s", len(nodes), sid)
 
-                widget_names: list[str] = []
-                if isinstance(raw_widget_names, list):
-                    widget_names = [
-                        str(widget_name)
-                        for widget_name in raw_widget_names
-                        if isinstance(widget_name, str) and widget_name
-                    ]
+    def prepare_for_refresh(self, active_sids: list[str]) -> None:
+        """Set the list of client IDs we expect to hear from during the next refresh cycle."""
+        self._ready.clear()
+        self._waiting_clients = set(active_sids)
 
-                if widget_names:
-                    capabilities["widget_names"] = widget_names
-                else:
-                    capabilities.pop("widget_names", None)
-
-                if "supports_lora" in capabilities:
-                    capabilities["supports_lora"] = bool(capabilities["supports_lora"])
-
-                comfy_class = node.get("comfy_class")
-                if not isinstance(comfy_class, str) or not comfy_class:
-                    comfy_class = node_type if isinstance(node_type, str) else None
-
-                self._nodes[unique_id] = {
-                    "id": node_id,
-                    "graph_id": graph_id,
-                    "graph_name": node.get("graph_name"),
-                    "unique_id": unique_id,
-                    "bgcolor": bgcolor,
-                    "title": node.get("title"),
-                    "type": type_id,
-                    "type_name": node_type,
-                    "comfy_class": comfy_class,
-                    "capabilities": capabilities,
-                    "widget_names": widget_names,
-                    "mode": node.get("mode"),
-                }
-            logger.debug("Registered %s nodes in registry", len(nodes))
-            self._registry_updated.set()
-
-    async def get_registry(self) -> dict:
-        async with self._lock:
-            return {
-                "nodes": dict(self._nodes),
-                "node_count": len(self._nodes),
-            }
-
-    async def wait_for_update(self, timeout: float = 1.0) -> bool:
-        self._registry_updated.clear()
+    async def wait_for_all(self, timeout: float = 2.0) -> bool:
+        """Block until every client in the current waiting set has responded
+        (or *timeout* seconds elapse).  Returns ``True`` if all responded."""
+        if not self._waiting_clients:
+            return True
         try:
-            await asyncio.wait_for(self._registry_updated.wait(), timeout=timeout)
+            await asyncio.wait_for(self._ready.wait(), timeout=timeout)
             return True
         except asyncio.TimeoutError:
             return False
 
+    async def get_merged_registry(self, active_sids: set[str] | None = None) -> dict:
+        """Return the union of all known tab nodes, pruning any tab that is no
+        longer connected."""
+        async with self._lock:
+            # Garbage-collect stale entries (disconnected tabs)
+            if active_sids is not None:
+                for sid in list(self._tab_nodes):
+                    if sid not in active_sids:
+                        del self._tab_nodes[sid]
+
+            merged: dict[str, dict] = {}
+            tab_info: dict[str, dict] = {}
+            for sid, nodes in self._tab_nodes.items():
+                tab_info[sid] = {
+                    "node_count": len(nodes),
+                    "graph_names": list(
+                        {
+                            n.get("graph_name")
+                            for n in nodes.values()
+                            if n.get("graph_name")
+                        }
+                    ),
+                }
+                merged.update(nodes)
+
+            return {
+                "nodes": merged,
+                "node_count": len(merged),
+                "tab_count": len(self._tab_nodes),
+                "tabs": tab_info,
+            }
+
 
 class HealthCheckHandler:
     async def health_check(self, request: web.Request) -> web.Response:
@@ -1328,6 +1399,9 @@ class SettingsHandler:
             "folder_paths",
             "libraries",
             "active_library",
+            # Sensitive — never expose the actual value to the frontend;
+            # frontend receives a boolean instead (civitai_api_key_set).
+            "civitai_api_key",
         }
     )
 
@@ -1382,6 +1456,9 @@ class SettingsHandler:
                     value = self._settings.get(key)
                     if value is not None:
                         response_data[key] = value
+            # Sensitive fields: only expose a boolean indicating whether set
+            raw_key = self._settings.get("civitai_api_key")
+            response_data["civitai_api_key_set"] = bool(raw_key)
             settings_file = getattr(self._settings, "settings_file", None)
             if settings_file:
                 response_data["settings_file"] = settings_file
@@ -1492,6 +1569,16 @@ class SettingsHandler:
         if not os.path.isdir(folder_path):
             return "Please set a dedicated folder for example images."
         if not self._is_dedicated_example_images_folder(folder_path):
+            offending = find_non_compliant_items_in_example_images_root(folder_path)
+            if offending:
+                items_str = ", ".join(repr(item) for item in offending[:5])
+                if len(offending) > 5:
+                    items_str += f" … and {len(offending) - 5} more"
+                return (
+                    f"The folder contains items that are not valid example image "
+                    f"folders: {items_str}. Please use a dedicated, empty folder "
+                    f"for example images to prevent accidental data loss."
+                )
             return "Please set a dedicated folder for example images."
         return None
 
@@ -2975,10 +3062,21 @@ class NodeRegistryHandler:
         try:
             data = await request.json()
             nodes = data.get("nodes", [])
+            client_id = data.get("client_id")
             if not isinstance(nodes, list):
                 return web.json_response(
                     {"success": False, "error": "nodes must be a list"}, status=400
                 )
+
+            if not isinstance(client_id, str) or not client_id:
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "Missing client_id parameter",
+                    },
+                    status=400,
+                )
+
             for index, node in enumerate(nodes):
                 if not isinstance(node, dict):
                     return web.json_response(
@@ -3022,7 +3120,7 @@ class NodeRegistryHandler:
                 else:
                     node["graph_name"] = str(graph_name)
 
-            await self._node_registry.register_nodes(nodes)
+            await self._node_registry.register_nodes(client_id, nodes)
             return web.json_response(
                 {
                     "success": True,
@@ -3046,9 +3144,15 @@ class NodeRegistryHandler:
                     status=503,
                 )
 
+            # Snapshot of currently-connected ComfyUI tabs
+            active_sids = list(self._prompt_server.instance.sockets.keys())
+            self._node_registry.prepare_for_refresh(active_sids)
+
             try:
                 self._prompt_server.instance.send_sync("lora_registry_refresh", {})
-                logger.debug("Sent registry refresh request to frontend")
+                logger.debug(
+                    "Sent registry refresh request (expecting %s clients)", len(active_sids)
+                )
             except Exception as exc:
                 logger.error("Failed to send registry refresh message: %s", exc)
                 return web.json_response(
@@ -3060,19 +3164,31 @@ class NodeRegistryHandler:
                     status=500,
                 )
 
-            registry_updated = await self._node_registry.wait_for_update(timeout=1.0)
-            if not registry_updated:
-                logger.warning("Registry refresh timeout after 1 second")
+            if not await self._node_registry.wait_for_all(timeout=2.0):
+                logger.warning(
+                    "Registry refresh timeout after 2s (%s/%s clients responded)",
+                    len(active_sids) - self._node_registry.pending_client_count,
+                    len(active_sids),
+                )
+
+            # Re-read current sockets after the wait: a tab may have connected
+            # while we were waiting, and we don't want to garbage-collect it.
+            current_sids = set(self._prompt_server.instance.sockets.keys())
+            registry_info = await self._node_registry.get_merged_registry(
+                active_sids=current_sids
+            )
+
+            if registry_info["node_count"] == 0:
+                logger.warning("No nodes registered after refresh")
                 return web.json_response(
                     {
                         "success": False,
-                        "error": "Timeout Error",
-                        "message": "Registry refresh timeout - ComfyUI frontend may not be responsive",
+                        "error": "Empty Registry",
+                        "message": "No workflow nodes found — ensure ComfyUI is open and the extension is loaded.",
                     },
                     status=408,
                 )
 
-            registry_info = await self._node_registry.get_registry()
             return web.json_response({"success": True, "data": registry_info})
         except Exception as exc:  # pragma: no cover - defensive logging
             logger.error("Failed to get registry: %s", exc, exc_info=True)
@@ -3085,13 +3201,17 @@ class NodeRegistryHandler:
         try:
             data = await request.json()
             widget_name = data.get("widget_name")
+            action = data.get("action")
             value = data.get("value")
             mode = data.get("mode", "replace")
             node_ids = data.get("node_ids")
 
-            if not isinstance(widget_name, str) or not widget_name:
+            if not action and (not isinstance(widget_name, str) or not widget_name):
                 return web.json_response(
-                    {"success": False, "error": "Missing widget_name parameter"},
+                    {
+                        "success": False,
+                        "error": "Missing parameter: provide either 'action' or 'widget_name'",
+                    },
                     status=400,
                 )
 
@@ -3130,12 +3250,15 @@ class NodeRegistryHandler:
                 except (TypeError, ValueError):
                     parsed_node_id = node_identifier
 
-                payload = {
+                payload: dict = {
                     "id": parsed_node_id,
-                    "widget_name": widget_name,
                     "value": value,
                     "mode": mode,
                 }
+                if action:
+                    payload["action"] = action
+                if widget_name:
+                    payload["widget_name"] = widget_name
 
                 if graph_identifier is not None:
                     payload["graph_id"] = str(graph_identifier)
@@ -3194,6 +3317,8 @@ class MiscHandlerSet:
         doctor: DoctorHandler,
         example_workflows: ExampleWorkflowsHandler,
         base_model: BaseModelHandlerSet,
+        hf_handler: HfHandler | None = None,
+        agent_handler: AgentHandler | None = None,
     ) -> None:
         self.health = health
         self.settings = settings
@@ -3212,6 +3337,8 @@ class MiscHandlerSet:
         self.doctor = doctor
         self.example_workflows = example_workflows
         self.base_model = base_model
+        self.hf_handler = hf_handler
+        self.agent_handler = agent_handler
 
     def to_route_mapping(
         self,
@@ -3257,6 +3384,13 @@ class MiscHandlerSet:
             "get_supporters": self.supporters.get_supporters,
             "get_example_workflows": self.example_workflows.get_example_workflows,
             "get_example_workflow": self.example_workflows.get_example_workflow,
+            # Hugging Face handlers
+            "get_hf_repo_files": self.hf_handler.get_hf_repo_files,
+            "download_hf_model": self.hf_handler.download_hf_model,
+            # Agent skill handlers
+            "get_agent_skills": self.agent_handler.get_agent_skills,
+            "execute_agent_skill": self.agent_handler.execute_agent_skill,
+            "cancel_agent_skill": self.agent_handler.cancel_agent_skill,
             # Base model handlers
             "get_base_models": self.base_model.get_base_models,
             "refresh_base_models": self.base_model.refresh_base_models,

py/routes/handlers/model_handlers.py

@@ -203,11 +203,17 @@ class ModelListingHandler:
             result = await self._service.get_paginated_data(**params)
 
             format_start = time.perf_counter()
+            formatted_raw = [
+                await self._service.format_response(entry)
+                for entry in result["items"]
+            ]
+            # Filter out None entries returned for corrupted cache rows (issue #730).
+            # Note: "total" intentionally remains the pre-filter count to reflect
+            # the true number of models in the cache; corrupted entries are rare
+            # and adjusting total would cause pagination drift on every page.
+            formatted_items = [item for item in formatted_raw if item is not None]
             formatted_result = {
-                "items": [
-                    await self._service.format_response(item)
-                    for item in result["items"]
-                ],
+                "items": formatted_items,
                 "total": result["total"],
                 "page": result["page"],
                 "page_size": result["page_size"],
@@ -233,14 +239,20 @@ class ModelListingHandler:
         start_time = time.perf_counter()
         try:
             params = self._parse_common_params(request)
+            # group_by_model is meaningless for excluded view; strip it
+            params.pop("group_by_model", None)
             result = await self._service.get_excluded_paginated_data(**params)
 
             format_start = time.perf_counter()
+            formatted_raw = [
+                await self._service.format_response(entry)
+                for entry in result["items"]
+            ]
+            # Filter out None entries returned for corrupted cache rows (issue #730).
+            # "total" stays at the pre-filter count; see get_models for rationale.
+            formatted_items = [item for item in formatted_raw if item is not None]
             formatted_result = {
-                "items": [
-                    await self._service.format_response(item)
-                    for item in result["items"]
-                ],
+                "items": formatted_items,
                 "total": result["total"],
                 "page": result["page"],
                 "page_size": result["page_size"],
@@ -366,6 +378,19 @@ class ModelListingHandler:
             request.query.get("name_pattern_use_regex", "false").lower() == "true"
         )
 
+        # Group-by-model flag: deduplicate versions sharing the same civitai modelId
+        group_by_model = (
+            request.query.get("group_by_model", "false").lower() == "true"
+        )
+
+        # View-local-versions filter: show all local versions of a specific model
+        civitai_model_id = request.query.get("civitai_model_id")
+        if civitai_model_id is not None:
+            try:
+                civitai_model_id = int(civitai_model_id)
+            except (TypeError, ValueError):
+                civitai_model_id = None
+
         return {
             "page": page,
             "page_size": page_size,
@@ -389,6 +414,8 @@ class ModelListingHandler:
             "name_pattern_include": name_pattern_include,
             "name_pattern_exclude": name_pattern_exclude,
             "name_pattern_use_regex": name_pattern_use_regex,
+            "group_by_model": group_by_model,
+            "civitai_model_id": civitai_model_id,
             **self._parse_specific_params(request),
         }
 
@@ -516,8 +543,13 @@ class ModelManagementHandler:
             if not success:
                 return web.json_response({"success": False, "error": error})
 
-            formatted_metadata = await self._service.format_response(model_data)
-            return web.json_response({"success": True, "metadata": formatted_metadata})
+            formatted = await self._service.format_response(model_data)
+            if formatted is None:
+                return web.json_response(
+                    {"success": False, "error": "Model entry is corrupted (missing file_path)"},
+                    status=500,
+                )
+            return web.json_response({"success": True, "metadata": formatted})
         except Exception as exc:
             if is_expected_offline_error(str(exc)):
                 return web.json_response(
@@ -1074,10 +1106,12 @@ class ModelQueryHandler:
                 # Sort: originals first, copies last
                 sorted_models = self._sort_duplicate_group(filtered)
 
-                # Format response
+                # Format response, filtering out corrupted entries (issue #730)
                 group = {"hash": sha256, "models": []}
                 for model in sorted_models:
-                    group["models"].append(await self._service.format_response(model))
+                    formatted = await self._service.format_response(model)
+                    if formatted is not None:
+                        group["models"].append(formatted)
 
                 # Only include groups with 2+ models after filtering
                 if len(group["models"]) > 1:
@@ -1194,9 +1228,9 @@ class ModelQueryHandler:
                         (m for m in cache.raw_data if m["file_path"] == path), None
                     )
                     if model:
-                        group["models"].append(
-                            await self._service.format_response(model)
-                        )
+                        formatted = await self._service.format_response(model)
+                        if formatted is not None:
+                            group["models"].append(formatted)
                 hash_val = self._service.scanner.get_hash_by_filename(filename)
                 if hash_val:
                     main_path = self._service.get_path_by_hash(hash_val)
@@ -1206,9 +1240,9 @@ class ModelQueryHandler:
                             None,
                         )
                         if main_model:
-                            group["models"].insert(
-                                0, await self._service.format_response(main_model)
-                            )
+                            formatted = await self._service.format_response(main_model)
+                            if formatted is not None:
+                                group["models"].insert(0, formatted)
                 if group["models"]:
                     result.append(group)
             return web.json_response(
@@ -1272,6 +1306,14 @@ class ModelQueryHandler:
                     license_flags = (model_data or {}).get("license_flags")
                     if license_flags is not None:
                         response_payload["license_flags"] = int(license_flags)
+                    # Include the user's license icon style preference so the
+                    # ComfyUI tooltip can pick the right set without a separate
+                    # API call.
+                    try:
+                        settings = get_settings_manager()
+                        response_payload["use_new_license_icons"] = settings.get("use_new_license_icons", True)
+                    except Exception:
+                        pass
                 return web.json_response(response_payload)
             return web.json_response(
                 {
@@ -1820,6 +1862,39 @@ class ModelDownloadHandler:
             )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def update_download_queue_status(self, request: web.Request) -> web.Response:
+        """Update the status of a queue item (non-terminal transitions).
+
+        Supported transitions include ``queued → downloading``,
+        ``downloading → paused``, ``paused → downloading``, etc.
+        Terminal transitions (``completed``, ``failed``, ``canceled``)
+        should use ``complete_download_in_queue`` instead.
+        """
+        try:
+            download_id = request.query.get("download_id")
+            status = request.query.get("status")
+            if not download_id or not status:
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "download_id and status are required",
+                    },
+                    status=400,
+                )
+            service = await DownloadQueueService.get_instance()
+            updated = await service.update_status(download_id, status)
+            if not updated:
+                return web.json_response(
+                    {"success": False, "error": "Download not found in queue"},
+                    status=404,
+                )
+            return web.json_response({"success": True})
+        except Exception as exc:
+            self._logger.error(
+                "Error updating download queue status: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
 
 class ModelCivitaiHandler:
     """CivitAI integration endpoints."""
@@ -2864,6 +2939,7 @@ class ModelHandlerSet:
             "retry_all_failed_downloads": self.download.retry_all_failed_downloads,
             "complete_download_in_queue": self.download.complete_download_in_queue,
             "get_download_stats": self.download.get_download_stats,
+            "update_download_queue_status": self.download.update_download_queue_status,
             "get_civitai_versions": self.civitai.get_civitai_versions,
             "get_civitai_model_by_version": self.civitai.get_civitai_model_by_version,
             "get_civitai_model_by_hash": self.civitai.get_civitai_model_by_hash,

py/routes/handlers/recipe_handlers.py

@@ -32,6 +32,7 @@ from ...utils.civitai_utils import (
     extract_civitai_image_id_from_cdn_url,
     rewrite_preview_url,
 )
+from ...utils.constants import NSFW_LEVELS
 from ...utils.exif_utils import ExifUtils
 from ...recipes.merger import GenParamsMerger
 from ...recipes.enrichment import RecipeEnricher
@@ -1120,6 +1121,13 @@ class RecipeManagementHandler:
             if parsed_embedded.get("base_model") and not metadata.get("base_model"):
                 metadata["base_model"] = parsed_embedded["base_model"]
 
+        # Extract preview_nsfw_level from the CivitAI API response
+        # (injected into civitai_meta_raw by _download_remote_media).
+        if isinstance(civitai_meta_raw, dict):
+            bl = civitai_meta_raw.get("browsingLevel")
+            if isinstance(bl, int) and bl > 0:
+                metadata["preview_nsfw_level"] = bl
+
         civitai_client = self._civitai_client_getter()
         await RecipeEnricher.enrich_recipe(
             recipe=metadata,
@@ -1515,8 +1523,31 @@ class RecipeManagementHandler:
                     # CivitAI API returns modelVersionIds at the root level of
                     # the image response, NOT inside the meta object.
                     mvids = image_info.get("modelVersionIds")
-                    if mvids and isinstance(civitai_meta_raw, dict):
-                        civitai_meta_raw["modelVersionIds"] = mvids
+                    if mvids:
+                        if isinstance(civitai_meta_raw, dict):
+                            civitai_meta_raw["modelVersionIds"] = mvids
+                        else:
+                            # meta is null but modelVersionIds exists — create a
+                            # minimal dict so downstream parsers can discover
+                            # LoRAs and checkpoints from the API response.
+                            civitai_meta_raw = {"modelVersionIds": mvids}
+
+                    # Inject browsingLevel (canonical integer) so the recipe's
+                    # preview_nsfw_level can be set, enabling proper NSFW blur
+                    # of the preview image.  Fall back to nsfwLevel (string)
+                    # when browsingLevel is absent.
+                    if isinstance(civitai_meta_raw, dict):
+                        browsing_level = image_info.get("browsingLevel")
+                        nsfw_level_str = image_info.get("nsfwLevel")
+                        if isinstance(browsing_level, int) and browsing_level > 0:
+                            civitai_meta_raw["browsingLevel"] = browsing_level
+                        elif (
+                            isinstance(nsfw_level_str, str)
+                            and nsfw_level_str in NSFW_LEVELS
+                        ):
+                            civitai_meta_raw["browsingLevel"] = NSFW_LEVELS[
+                                nsfw_level_str
+                            ]
 
                 original_url = (
                     image_info.get("url") if civitai_image_id and image_info else None
@@ -1796,6 +1827,13 @@ class RecipeManagementHandler:
             "source_path": image_url,
         }
 
+        # Extract preview_nsfw_level from the CivitAI API response
+        # (injected into civitai_meta_raw by _download_remote_media).
+        if isinstance(civitai_meta_raw, dict):
+            bl = civitai_meta_raw.get("browsingLevel")
+            if isinstance(bl, int) and bl > 0:
+                metadata["preview_nsfw_level"] = bl
+
         if civitai_parsed:
             civitai_loras = civitai_parsed.get("loras", [])
             if civitai_loras and not metadata.get("loras"):

py/routes/misc_route_registrar.py

@@ -94,6 +94,23 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition(
         "GET", "/api/lm/delete-model-version", "delete_model_version"
     ),
+    # Hugging Face model endpoints
+    RouteDefinition(
+        "GET", "/api/lm/hf-repo-files", "get_hf_repo_files"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/download-hf-model", "download_hf_model"
+    ),
+    # Agent skill endpoints
+    RouteDefinition(
+        "GET", "/api/lm/agent/skills", "get_agent_skills"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/agent/execute/{skill_name}", "execute_agent_skill"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/agent/cancel", "cancel_agent_skill"
+    ),
 )
 
 

py/routes/misc_routes.py

@@ -39,6 +39,8 @@ from .handlers.misc_handlers import (
     build_service_registry_adapter,
 )
 from .handlers.base_model_handlers import BaseModelHandlerSet
+from .handlers.hf_handlers import HfHandler
+from .handlers.agent_handlers import AgentHandler
 from .misc_route_registrar import MiscRouteRegistrar
 
 logger = logging.getLogger(__name__)
@@ -136,6 +138,8 @@ class MiscRoutes:
         doctor = DoctorHandler(settings_service=self._settings)
         example_workflows = ExampleWorkflowsHandler()
         base_model = BaseModelHandlerSet()
+        hf_handler = HfHandler()
+        agent_handler = AgentHandler()
 
         return self._handler_set_factory(
             health=health,
@@ -155,6 +159,8 @@ class MiscRoutes:
             doctor=doctor,
             example_workflows=example_workflows,
             base_model=base_model,
+            hf_handler=hf_handler,
+            agent_handler=agent_handler,
         )
 
 

py/routes/model_route_registrar.py

@@ -138,6 +138,9 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition(
         "GET", "/api/lm/downloads/queue/complete", "complete_download_in_queue"
     ),
+    RouteDefinition(
+        "GET", "/api/lm/downloads/queue/status", "update_download_queue_status"
+    ),
     RouteDefinition("POST", "/api/lm/{prefix}/cancel-task", "cancel_task"),
     RouteDefinition("GET", "/{prefix}", "handle_models_page"),
 )

py/routes/stats_routes.py

@@ -11,6 +11,8 @@ from ..config import config
 from ..services.settings_manager import get_settings_manager
 from ..services.server_i18n import server_i18n
 from ..services.service_registry import ServiceRegistry
+from ..services.model_query import normalize_sub_type, resolve_sub_type
+from ..utils.constants import VALID_LORA_SUB_TYPES, VALID_CHECKPOINT_SUB_TYPES
 from ..utils.usage_stats import UsageStats
 
 logger = logging.getLogger(__name__)
@@ -140,6 +142,21 @@ class StatsRoutes:
             # Get usage statistics
             usage_data = await self.usage_stats.get_stats()
             
+            # CivitAI model type distribution across all model types
+            # Use the same logic as the filter panel: normalize_sub_type(resolve_sub_type(entry))
+            # with sub-type validation per model type
+            model_types_counter: Counter[str] = Counter()
+            for entry in lora_cache.raw_data:
+                ntype = normalize_sub_type(resolve_sub_type(entry))
+                if ntype and ntype in VALID_LORA_SUB_TYPES:
+                    model_types_counter[ntype] += 1
+            for entry in checkpoint_cache.raw_data:
+                ntype = normalize_sub_type(resolve_sub_type(entry))
+                if ntype and ntype in VALID_CHECKPOINT_SUB_TYPES:
+                    model_types_counter[ntype] += 1
+            # Embeddings: always count as "embedding" regardless of CivitAI sub-type
+            model_types_counter['embedding'] = len(embedding_cache.raw_data)
+            
             return web.json_response({
                 'success': True,
                 'data': {
@@ -154,7 +171,8 @@ class StatsRoutes:
                     'total_generations': usage_data.get('total_executions', 0),
                     'unused_loras': self._count_unused_models(lora_cache.raw_data, usage_data.get('loras', {})),
                     'unused_checkpoints': self._count_unused_models(checkpoint_cache.raw_data, usage_data.get('checkpoints', {})),
-                    'unused_embeddings': self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {}))
+                    'unused_embeddings': self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {})),
+                    'model_types_distribution': dict(model_types_counter.most_common())
                 }
             })
             
@@ -459,9 +477,12 @@ class StatsRoutes:
                 if unused_lora_percent > 50:
                     insights.append({
                         'type': 'warning',
-                        'title': 'High Number of Unused LoRAs',
-                        'description': f'{unused_lora_percent:.1f}% of your LoRAs ({unused_loras}/{total_loras}) have never been used.',
-                        'suggestion': 'Consider organizing or archiving unused models to free up storage space.'
+                        'key': 'insights.unusedLoras.high',
+                        'params': {
+                            'percent': f'{unused_lora_percent:.1f}',
+                            'count': str(unused_loras),
+                            'total': str(total_loras)
+                        }
                     })
             
             if total_checkpoints > 0:
@@ -469,9 +490,12 @@ class StatsRoutes:
                 if unused_checkpoint_percent > 30:
                     insights.append({
                         'type': 'warning',
-                        'title': 'Unused Checkpoints Detected',
-                        'description': f'{unused_checkpoint_percent:.1f}% of your checkpoints ({unused_checkpoints}/{total_checkpoints}) have never been used.',
-                        'suggestion': 'Review and consider removing checkpoints you no longer need.'
+                        'key': 'insights.unusedCheckpoints.detected',
+                        'params': {
+                            'percent': f'{unused_checkpoint_percent:.1f}',
+                            'count': str(unused_checkpoints),
+                            'total': str(total_checkpoints)
+                        }
                     })
             
             if total_embeddings > 0:
@@ -479,9 +503,12 @@ class StatsRoutes:
                 if unused_embedding_percent > 50:
                     insights.append({
                         'type': 'warning',
-                        'title': 'High Number of Unused Embeddings',
-                        'description': f'{unused_embedding_percent:.1f}% of your embeddings ({unused_embeddings}/{total_embeddings}) have never been used.',
-                        'suggestion': 'Consider organizing or archiving unused embeddings to optimize your collection.'
+                        'key': 'insights.unusedEmbeddings.high',
+                        'params': {
+                            'percent': f'{unused_embedding_percent:.1f}',
+                            'count': str(unused_embeddings),
+                            'total': str(total_embeddings)
+                        }
                     })
             
             # Storage insights
@@ -492,18 +519,20 @@ class StatsRoutes:
             if total_size > 100 * 1024 * 1024 * 1024:  # 100GB
                 insights.append({
                     'type': 'info',
-                    'title': 'Large Collection Detected',
-                    'description': f'Your model collection is using {self._format_size(total_size)} of storage.',
-                    'suggestion': 'Consider using external storage or cloud solutions for better organization.'
+                    'key': 'insights.collection.large',
+                    'params': {
+                        'size': self._format_size(total_size)
+                    }
                 })
             
             # Recent activity insight
             if usage_data.get('total_executions', 0) > 100:
                 insights.append({
                     'type': 'success',
-                    'title': 'Active User',
-                    'description': f'You\'ve completed {usage_data["total_executions"]} generations so far!',
-                    'suggestion': 'Keep exploring and creating amazing content with your models.'
+                    'key': 'insights.activity.active',
+                    'params': {
+                        'count': str(usage_data['total_executions'])
+                    }
                 })
             
             return web.json_response({

py/routes/update_routes.py

@@ -16,6 +16,27 @@ logger = logging.getLogger(__name__)
 
 NETWORK_EXCEPTIONS = (ClientError, OSError, asyncio.TimeoutError)
 
+# User-managed directories that live inside the plugin folder (portable
+# mode) and must survive a Git-based update. ``git clean -fd`` would
+# otherwise delete them because they are untracked and, in released tags,
+# not listed in ``.gitignore``. ``-e`` excludes a path from cleaning
+# regardless of whether it is ignored.
+_PRESERVE_DIRS = ('settings.json', 'civitai', 'wildcards', 'backups', 'stats', 'logs', 'cache', 'model_cache')
+
+
+def _clean_excludes() -> List[str]:
+    """Build the ``-e`` arguments for ``git clean`` from :data:`_PRESERVE_DIRS`."""
+    excludes: List[str] = []
+    for name in _PRESERVE_DIRS:
+        excludes.append('-e')
+        excludes.append(name)
+        # For directories, also exclude nested matches explicitly
+        # (``-e dir`` alone matches the dir entry; ``-e dir/**`` guards
+        # contents under all git versions as defense-in-depth).
+        excludes.append('-e')
+        excludes.append(f'{name}/**')
+    return excludes
+
 
 class UpdateRoutes:
     """Routes for handling plugin update checks"""
@@ -365,6 +386,8 @@ class UpdateRoutes:
             )
             return False, ""
 
+        clean_excludes = _clean_excludes()
+
         try:
             # Open the Git repository
             repo = git.Repo(plugin_root)
@@ -376,8 +399,9 @@ class UpdateRoutes:
             if nightly:
                 # Reset to discard any local changes
                 repo.git.reset('--hard')
-                # Clean untracked files
-                repo.git.clean('-fd')
+                # Clean untracked files, but preserve user-managed directories
+                # (wildcards, backups, stats, civitai, caches, settings.json).
+                repo.git.clean('-fd', *clean_excludes)
                 
                 # Switch to main branch and pull latest
                 main_branch = 'main'
@@ -394,8 +418,9 @@ class UpdateRoutes:
             else:
                 # Reset to discard any local changes
                 repo.git.reset('--hard')
-                # Clean untracked files
-                repo.git.clean('-fd')
+                # Clean untracked files, but preserve user-managed directories
+                # (wildcards, backups, stats, civitai, caches, settings.json).
+                repo.git.clean('-fd', *clean_excludes)
                 
                 # Get latest release tag
                 tags = sorted(repo.tags, key=lambda t: t.commit.committed_datetime, reverse=True)

py/services/agent/__init__.py

@@ -0,0 +1,23 @@
+"""Agent-powered skill system for LoRA Manager.
+
+This package provides the orchestration layer for LLM/agent-powered features.
+Skills define *what* to do (prompt template).  The :class:`AgentService`
+handles *how* (LLM calls, context gathering, validation, progress).
+"""
+
+from __future__ import annotations
+
+from .skill_definition import SkillDefinition, SkillPermissions
+from .skill_registry import SkillRegistry
+from .agent_service import AgentService, AgentProgressReporter, SkillResult
+from .post_processor import PostProcessor
+
+__all__ = [
+    "AgentProgressReporter",
+    "AgentService",
+    "PostProcessor",
+    "SkillDefinition",
+    "SkillPermissions",
+    "SkillRegistry",
+    "SkillResult",
+]

py/services/agent/agent_service.py

@@ -0,0 +1,413 @@
+"""Agent orchestration service.
+
+The :class:`AgentService` coordinates skill execution:
+
+1. Look up the skill in :class:`SkillRegistry`
+2. Validate input against the skill's ``input_schema``
+3. Prepare context via :mod:`~py.agent_cli` (read metadata, list base models, fetch HF README)
+4. If ``llm_required``: call :class:`LLMService` with the rendered prompt
+5. Post-process via :class:`PostProcessor` (delegates I/O to :mod:`~py.agent_cli`)
+6. Broadcast progress and completion via :class:`WebSocketManager`
+
+Skills define *what* to do (prompt template).  The AgentService handles *how*
+(LLM calls, context gathering, validation, progress).
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import re
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+import aiohttp
+
+from ..llm_service import LLMService
+from ..websocket_manager import ws_manager
+from .post_processor import PostProcessor
+from .skill_registry import SkillRegistry
+
+logger = logging.getLogger(__name__)
+
+
+class AgentProgressReporter:
+    """Protocol-compatible progress reporter backed by WebSocket broadcast."""
+
+    async def on_progress(self, payload: Dict[str, Any]) -> None:
+        await ws_manager.broadcast(payload)
+
+
+@dataclass
+class SkillResult:
+    """Outcome of a skill execution."""
+
+    success: bool
+    updated_models: List[Dict[str, Any]] = field(default_factory=list)
+    errors: List[str] = field(default_factory=list)
+    summary: str = ""
+
+
+def _validate_schema(data: Any, schema: Dict[str, Any], path: str = "") -> List[str]:
+    """Minimal JSON schema validator.
+
+    Supports a subset of JSON Schema: ``type``, ``properties``, ``required``,
+    ``items``, ``enum``.  Returns a list of error messages (empty = valid).
+    """
+
+    errors: List[str] = []
+    if not schema:
+        return errors
+
+    expected_type = schema.get("type")
+    if expected_type:
+        type_map = {
+            "string": str,
+            "number": (int, float),
+            "integer": int,
+            "boolean": bool,
+            "array": list,
+            "object": dict,
+            "null": type(None),
+        }
+        expected_py = type_map.get(expected_type)
+        if expected_py is not None and not isinstance(data, expected_py):
+            errors.append(f"{path or 'root'}: expected {expected_type}, got {type(data).__name__}")
+            return errors
+
+    if expected_type == "object" and isinstance(data, dict):
+        properties = schema.get("properties", {})
+        required = schema.get("required", [])
+        for req_key in required:
+            if req_key not in data:
+                errors.append(f"{path or 'root'}: missing required property '{req_key}'")
+        for key, value in data.items():
+            if key in properties:
+                errors.extend(_validate_schema(value, properties[key], f"{path}.{key}"))
+
+    if expected_type == "array" and isinstance(data, list):
+        items_schema = schema.get("items")
+        if items_schema:
+            for i, item in enumerate(data):
+                errors.extend(_validate_schema(item, items_schema, f"{path}[{i}]"))
+
+    if "enum" in schema and data not in schema["enum"]:
+        errors.append(f"{path or 'root'}: value '{data}' not in enum {schema['enum']}")
+
+    return errors
+
+
+# ------------------------------------------------------------------
+# Prompt template rendering
+# ------------------------------------------------------------------
+
+
+def _render_prompt(template: str, variables: Dict[str, Any]) -> str:
+    """Render a prompt template with ``{{variable}}`` placeholders.
+
+    Uses simple regex substitution — no Jinja2 dependency needed.
+    """
+
+    def replace(match: re.Match) -> str:
+        key = match.group(1).strip()
+        value = variables.get(key, "")
+        if isinstance(value, (dict, list)):
+            return json.dumps(value, ensure_ascii=False, indent=2)
+        return str(value)
+
+    return re.sub(r"\{\{(\w+)\}\}", replace, template)
+
+
+class AgentService:
+    """Orchestrate agent skill execution.
+
+    Usage::
+
+        service = await AgentService.get_instance()
+        result = await service.execute_skill(
+            skill_name="enrich_hf_metadata",
+            input_data={"model_paths": ["/path/to/model.safetensors"]},
+            progress_callback=AgentProgressReporter(),
+        )
+    """
+
+    _instance: Optional["AgentService"] = None
+    _lock: asyncio.Lock = asyncio.Lock()
+
+    def __init__(
+        self,
+        *,
+        skill_registry: Optional[SkillRegistry] = None,
+        llm_service: Optional[LLMService] = None,
+    ) -> None:
+        self._registry = skill_registry
+        self._llm_service = llm_service
+
+    @classmethod
+    async def get_instance(cls) -> "AgentService":
+        """Return the lazily-initialised global ``AgentService``."""
+
+        if cls._instance is None:
+            async with cls._lock:
+                if cls._instance is None:
+                    cls._instance = cls(
+                        skill_registry=await SkillRegistry.get_instance(),
+                        llm_service=await LLMService.get_instance(),
+                    )
+        return cls._instance
+
+    @classmethod
+    def reset_instance(cls) -> None:
+        """Reset the cached singleton — primarily for tests."""
+
+        cls._instance = None
+
+    async def _ensure_registry(self) -> SkillRegistry:
+        if self._registry is None:
+            self._registry = await SkillRegistry.get_instance()
+        return self._registry
+
+    async def _ensure_llm(self) -> LLMService:
+        if self._llm_service is None:
+            self._llm_service = await LLMService.get_instance()
+        return self._llm_service
+
+    async def list_skills(self) -> List[Dict[str, Any]]:
+        """Return a JSON-serialisable list of available skills."""
+
+        registry = await self._ensure_registry()
+        return [
+            {
+                "name": s.name,
+                "title": s.title,
+                "description": s.description,
+                "llm_required": s.llm_required,
+                "model_type_filter": s.model_type_filter,
+            }
+            for s in registry.list_skills()
+        ]
+
+    async def execute_skill(
+        self,
+        *,
+        skill_name: str,
+        input_data: Dict[str, Any],
+        progress_callback: Optional[AgentProgressReporter] = None,
+    ) -> SkillResult:
+        """Execute an agent skill.
+
+        Args:
+            skill_name: Name of the skill to execute
+            input_data: Input validated against the skill's ``input_schema``
+            progress_callback: Optional WebSocket progress reporter
+
+        Returns:
+            :class:`SkillResult` with success status and updated model info
+        """
+
+        registry = await self._ensure_registry()
+        logger.info("execute_skill '%s': looking up skill", skill_name)
+        skill = registry.get_skill(skill_name)
+        if skill is None:
+            return SkillResult(
+                success=False,
+                errors=[f"Skill not found: {skill_name}"],
+                summary=f"Skill '{skill_name}' does not exist",
+            )
+
+        input_errors = _validate_schema(input_data, skill.input_schema)
+        if input_errors:
+            return SkillResult(
+                success=False,
+                errors=input_errors,
+                summary=f"Invalid input: {'; '.join(input_errors)}",
+            )
+
+        model_paths = input_data.get("model_paths", [])
+        if not model_paths:
+            return SkillResult(
+                success=False,
+                errors=["No model_paths provided"],
+                summary="No models to process",
+            )
+
+        total = len(model_paths)
+        processed = 0
+        success_count = 0
+        updated_models: List[Dict[str, Any]] = []
+        errors: List[str] = []
+        post_processor = PostProcessor()
+
+        logger.info("execute_skill '%s': starting with %d model(s)", skill_name, total)
+        await self._emit_progress(
+            progress_callback, skill_name, status="started",
+            total=total, processed=0, success=0,
+        )
+
+        llm = await self._ensure_llm()
+        llm_configured = llm.is_configured() if skill.llm_required else True
+
+        for model_path in model_paths:
+            logger.info(
+                "execute_skill '%s': processing model %d/%d: %s",
+                skill_name, processed + 1, total, model_path,
+            )
+            try:
+                from ...agent_cli import read_metadata
+                metadata = await read_metadata(model_path)
+
+                prompt_vars: Dict[str, Any] = {"model_path": model_path}
+                if skill.llm_required and llm_configured:
+                    prompt_vars = await self._build_prompt_context(
+                        skill_name, model_path, metadata, registry, llm,
+                    )
+
+                llm_response: Optional[Dict[str, Any]] = None
+                if skill.llm_required and llm_configured:
+                    prompt_template = registry.load_prompt(skill_name)
+                    rendered = _render_prompt(prompt_template, prompt_vars)
+                    logger.info(
+                        "execute_skill '%s': LLM call for %s (prompt=%d chars)",
+                        skill_name, model_path, len(rendered),
+                    )
+                    llm_response = await llm.chat_completion_json(
+                        system_prompt=prompt_vars.get(
+                            "system_prompt",
+                            "You are a helpful assistant that extracts structured metadata.",
+                        ),
+                        user_prompt=rendered,
+                    )
+
+                model_result = await post_processor.process(
+                    skill_name=skill_name,
+                    model_path=model_path,
+                    llm_output=llm_response or {},
+                    metadata=metadata,
+                )
+
+                if model_result.get("success", True):
+                    success_count += 1
+                    uf = model_result.get("updated_fields", [])
+                    if uf:
+                        updated_models.append({"path": model_path, "updated_fields": uf})
+                else:
+                    errors.extend(
+                        model_result.get("errors", [model_result.get("error", "Unknown error")])
+                    )
+
+            except Exception as exc:
+                logger.error("Skill %s failed for %s: %s", skill_name, model_path, exc)
+                errors.append(f"{model_path}: {exc}")
+
+            processed += 1
+            await self._emit_progress(
+                progress_callback, skill_name, status="processing",
+                total=total, processed=processed, success=success_count,
+                current_path=model_path,
+            )
+
+        result = SkillResult(
+            success=success_count > 0,
+            updated_models=updated_models,
+            errors=errors,
+            summary=f"Processed {processed}/{total} models, {success_count} succeeded",
+        )
+
+        logger.info("execute_skill '%s': done — %s", skill_name, result.summary)
+        await self._emit_progress(
+            progress_callback, skill_name, status="completed",
+            total=total, processed=processed, success=success_count,
+            updated_models=updated_models, errors=errors, summary=result.summary,
+        )
+
+        return result
+
+    async def _build_prompt_context(
+        self,
+        skill_name: str,
+        model_path: str,
+        metadata: Dict[str, Any],
+        registry: SkillRegistry,
+        llm: Any,
+    ) -> Dict[str, Any]:
+        """Gather variables for the skill's prompt template.
+
+        Reads metadata, fetches the HF README (if applicable), lists available
+        base models, and returns a dict that maps to ``{{variable}}``
+        placeholders in ``prompt.md``.
+        """
+        from ...agent_cli import list_base_models
+
+        context: Dict[str, Any] = {
+            "model_path": model_path,
+            "hf_url": "",
+            "repo": "",
+            "readme_content": "",
+            "current_metadata": {},
+            "base_models": [],
+        }
+
+        context["current_metadata"] = {
+            "file_name": metadata.get("file_name", ""),
+            "base_model": metadata.get("base_model", ""),
+            "tags": metadata.get("tags", []),
+            "modelDescription": metadata.get("modelDescription", ""),
+            "trainedWords": metadata.get("trainedWords", []),
+            "sha256": (metadata.get("sha256") or "")[:16] + "..." if metadata.get("sha256") else "",
+            "size": metadata.get("size", 0),
+        }
+
+        hf_url = metadata.get("hf_url", "")
+        context["hf_url"] = hf_url
+        repo = self._extract_repo_from_url(hf_url) if hf_url else ""
+        context["repo"] = repo or ""
+        if repo:
+            readme = await self._fetch_readme(repo)
+            context["readme_content"] = readme[:8000] if readme else "(README not available)"
+
+        try:
+            context["base_models"] = await list_base_models()
+        except Exception as exc:
+            logger.debug("Failed to list base models: %s", exc)
+
+        return context
+
+    @staticmethod
+    def _extract_repo_from_url(hf_url: str) -> Optional[str]:
+        """Extract ``user/repo`` from a HuggingFace URL."""
+        if not hf_url:
+            return None
+        m = re.match(r"https?://huggingface\.co/([^/]+/[^/]+)", hf_url)
+        return m.group(1) if m else None
+
+    @staticmethod
+    async def _fetch_readme(repo: str) -> str:
+        """Fetch README.md from HuggingFace (tries ``main``, then ``master``)."""
+        async with aiohttp.ClientSession(
+            headers={"User-Agent": "ComfyUI-LoRA-Manager/1.0"},
+            timeout=aiohttp.ClientTimeout(total=30),
+        ) as session:
+            for branch in ("main", "master"):
+                url = f"https://huggingface.co/{repo}/raw/{branch}/README.md"
+                try:
+                    async with session.get(url) as resp:
+                        if resp.status == 200:
+                            return await resp.text()
+                except Exception as exc:
+                    logger.debug("Failed to fetch README from %s: %s", url, exc)
+        return ""
+
+    async def _emit_progress(
+        self,
+        callback: Optional[AgentProgressReporter],
+        skill_name: str,
+        *,
+        status: str,
+        **extra: Any,
+    ) -> None:
+        """Send a progress update via WebSocket (if callback is set)."""
+        payload: Dict[str, Any] = {"type": "agent_progress", "skill": skill_name, "status": status}
+        payload.update(extra)
+        if callback is not None:
+            await callback.on_progress(payload)

py/services/agent/post_processor.py

@@ -0,0 +1,168 @@
+"""Post-processing engine for agent skill outputs.
+
+The :class:`PostProcessor` takes the LLM's structured JSON output and applies
+it to a model's on-disk metadata via the :mod:`~py.agent_cli` functions.
+
+It handles all the skill-specific business logic — conditions, transformations,
+and orchestration of multiple side-effects (write metadata, download preview,
+refresh cache).  All actual I/O is delegated to :mod:`~py.agent_cli`.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class PostProcessor:
+    """Deterministic post-processor for agent skill outputs.
+
+    Usage (called by :class:`~py.services.agent.agent_service.AgentService`)::
+
+        processor = PostProcessor()
+        result = await processor.process(
+            skill_name="enrich_hf_metadata",
+            model_path="/path/to/model.safetensors",
+            llm_output={...},
+            metadata={...},    # from agent_cli.read_metadata()
+        )
+    """
+
+    async def process(
+        self,
+        *,
+        skill_name: str,
+        model_path: str,
+        llm_output: Dict[str, Any],
+        metadata: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """Route *llm_output* to the correct skill post-processor.
+
+        Returns a dict with keys ``success`` (bool), ``updated_fields`` (list),
+        ``preview_downloaded`` (bool), and ``errors`` (list).
+        """
+        if skill_name == "enrich_hf_metadata":
+            return await self._process_enrich_hf_metadata(
+                model_path, llm_output, metadata,
+            )
+        return {
+            "success": False,
+            "updated_fields": [],
+            "errors": [f"No post-processor registered for skill: {skill_name}"],
+        }
+
+    # ------------------------------------------------------------------
+    # enrich_hf_metadata
+    # ------------------------------------------------------------------
+
+    async def _process_enrich_hf_metadata(
+        self,
+        model_path: str,
+        llm_output: Dict[str, Any],
+        metadata: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        from ...agent_cli import (
+            apply_metadata_updates,
+            download_preview,
+            refresh_cache,
+        )
+
+        updated_fields: List[str] = []
+        preview_downloaded = False
+
+        # -- Determine whether this is an HF-sourced model -----------------
+        is_hf_model = not metadata.get("from_civitai", True)
+
+        # -- Collect updates -----------------------------------------------
+        updates: Dict[str, Any] = {}
+
+        # base_model
+        new_base = (llm_output.get("base_model") or "").strip()
+        current_base = metadata.get("base_model", "") or ""
+        if new_base and self._should_overwrite(current_base, is_hf_model):
+            updates["base_model"] = new_base
+
+        # trainedWords / trigger words
+        new_triggers = llm_output.get("trigger_words", [])
+        if isinstance(new_triggers, list):
+            cleaned = [t.strip() for t in new_triggers if t.strip()]
+            if cleaned:
+                current_triggers = metadata.get("trainedWords") or []
+                if self._should_overwrite_list(current_triggers, is_hf_model):
+                    updates["trainedWords"] = cleaned
+
+        # modelDescription
+        new_desc = (llm_output.get("description") or "").strip()
+        if new_desc:
+            current_desc = metadata.get("modelDescription", "") or ""
+            if self._should_overwrite(current_desc, is_hf_model):
+                updates["modelDescription"] = new_desc
+
+        # tags — merge with existing, deduplicate (case-insensitive)
+        new_tags = llm_output.get("tags", [])
+        if isinstance(new_tags, list) and new_tags:
+            existing_tags = metadata.get("tags") or []
+            merged = self._merge_tags(existing_tags, new_tags)
+            if len(merged) > len(existing_tags) or is_hf_model:
+                updates["tags"] = merged
+
+        # metadata_source & llm_enriched_at (always set)
+        updates["metadata_source"] = "agent:enrich_hf_metadata"
+        updates["llm_enriched_at"] = datetime.now(timezone.utc).isoformat()
+
+        # -- Persist updates ------------------------------------------------
+        if updates:
+            updated_fields = await apply_metadata_updates(model_path, updates)
+
+        # -- Download preview -----------------------------------------------
+        preview_url = (llm_output.get("preview_url") or "").strip()
+        current_preview = metadata.get("preview_url") or ""
+        if preview_url and not (current_preview and os.path.exists(current_preview)):
+            preview_downloaded = await download_preview(model_path, preview_url)
+
+        # -- Refresh scanner cache ------------------------------------------
+        if updated_fields or preview_downloaded:
+            await refresh_cache(model_path)
+
+        return {
+            "success": True,
+            "updated_fields": updated_fields,
+            "preview_downloaded": preview_downloaded,
+            "errors": [],
+        }
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _should_overwrite(current_value: str, is_hf_model: bool) -> bool:
+        """Return ``True`` when a scalar field should be overwritten."""
+        return is_hf_model or not current_value or current_value.lower() in (
+            "", "unknown",
+        )
+
+    @staticmethod
+    def _should_overwrite_list(current_list: List[str], is_hf_model: bool) -> bool:
+        """Return ``True`` when a list field should be overwritten."""
+        return is_hf_model or not current_list
+
+    @staticmethod
+    def _merge_tags(existing: List[str], new: List[str]) -> List[str]:
+        """Merge *new* tags into *existing*, all lowercased.
+
+        This matches the behaviour of :class:`TagUpdateService` which
+        normalises every tag to lowercase for case-insensitive dedup.
+        """
+        merged: List[str] = []
+        seen: set = set()
+        for tag in list(existing) + list(new):
+            t = tag.strip().lower()
+            if t and t not in seen:
+                merged.append(t)
+                seen.add(t)
+        return merged

py/services/agent/skill_definition.py

@@ -0,0 +1,45 @@
+"""Skill definition data structures.
+
+Each skill is described by a :class:`SkillDefinition` that declares its
+input/output schemas, whether it needs an LLM call, and what permissions
+its post-processor has.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Tuple
+
+
+@dataclass(frozen=True)
+class SkillPermissions:
+    """Declarative permission scope for a skill's post-processor.
+
+    These are auditable constraints — the :class:`AgentService` checks them
+    before invoking the handler.  They are defense-in-depth, not a sandbox.
+    """
+
+    write_metadata: bool = True
+    write_previews: bool = True
+    network_domains: Tuple[str, ...] = ()
+
+
+@dataclass(frozen=True)
+class SkillDefinition:
+    """Immutable description of an agent skill."""
+
+    name: str
+    title: str
+    description: str
+    llm_required: bool
+    input_schema: Dict[str, Any] = field(default_factory=dict)
+    output_schema: Dict[str, Any] = field(default_factory=dict)
+    model_type_filter: Optional[List[str]] = None
+    permissions: SkillPermissions = field(default_factory=SkillPermissions)
+
+    def applies_to_model_type(self, model_type: str) -> bool:
+        """Return ``True`` if this skill can run on the given model type."""
+
+        if self.model_type_filter is None:
+            return True
+        return model_type in self.model_type_filter

py/services/agent/skill_registry.py

@@ -0,0 +1,184 @@
+"""Discovery and loading of agent skills.
+
+Skills live in ``py/services/agent/skills/<name>/`` directories.  Each
+directory must contain a ``SKILL.md`` file with YAML frontmatter::
+
+    ---
+    name: my_skill
+    title: "My Skill"
+    description: "What this skill does"
+    llm_required: true
+    ---
+
+    Prompt template with ``{{variable}}`` placeholders.
+
+The registry scans the skills directory on first access and caches results.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import re
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import yaml
+
+from .skill_definition import SkillDefinition, SkillPermissions
+
+logger = logging.getLogger(__name__)
+
+# Directory where built-in skills are stored
+_SKILLS_DIR = Path(__file__).parent / "skills"
+
+
+# ---------------------------------------------------------------------------
+# Frontmatter parser
+# ---------------------------------------------------------------------------
+
+_FRONTMATTER_RE = re.compile(
+    r"^---\s*\n(.*?\n)---\s*\n?(.*)", re.DOTALL
+)
+
+
+def _parse_skill_file(path: Path) -> tuple[dict, str]:
+    """Read a ``SKILL.md`` file and return (frontmatter_dict, body_text).
+
+    Raises ``ValueError`` if the file lacks valid YAML frontmatter.
+    """
+    text = path.read_text(encoding="utf-8")
+    m = _FRONTMATTER_RE.match(text)
+    if not m:
+        raise ValueError(f"Missing or invalid YAML frontmatter in {path}")
+    frontmatter = yaml.safe_load(m.group(1))
+    if not isinstance(frontmatter, dict):
+        raise ValueError(f"Frontmatter in {path} is not a mapping")
+    body = m.group(2).strip()
+    return frontmatter, body
+
+
+class SkillRegistry:
+    """Discover and load agent skills from the filesystem."""
+
+    _instance: Optional["SkillRegistry"] = None
+    _lock: asyncio.Lock = asyncio.Lock()
+
+    def __init__(self, skills_dir: Path = _SKILLS_DIR) -> None:
+        self._skills_dir = skills_dir
+        self._skills: Dict[str, SkillDefinition] = {}
+        self._loaded: bool = False
+
+    # ------------------------------------------------------------------
+    # Singleton access
+    # ------------------------------------------------------------------
+
+    @classmethod
+    async def get_instance(cls) -> "SkillRegistry":
+        """Return the lazily-initialised global ``SkillRegistry``."""
+
+        if cls._instance is None:
+            async with cls._lock:
+                if cls._instance is None:
+                    registry = cls()
+                    registry._discover()
+                    cls._instance = registry
+        return cls._instance
+
+    @classmethod
+    def reset_instance(cls) -> None:
+        """Reset the cached singleton — primarily for tests."""
+
+        cls._instance = None
+
+    # ------------------------------------------------------------------
+    # Discovery
+    # ------------------------------------------------------------------
+
+    def _discover(self) -> None:
+        """Scan the skills directory and load all valid skill definitions."""
+
+        self._skills.clear()
+        if not self._skills_dir.is_dir():
+            logger.warning("Skills directory does not exist: %s", self._skills_dir)
+            self._loaded = True
+            return
+
+        for entry in sorted(self._skills_dir.iterdir()):
+            if not entry.is_dir():
+                continue
+            skill_md = entry / "SKILL.md"
+            if not skill_md.exists():
+                continue
+            try:
+                definition = self._load_skill_definition(skill_md)
+                if definition is not None:
+                    self._skills[definition.name] = definition
+                    logger.debug("Loaded skill: %s", definition.name)
+            except Exception as exc:
+                logger.warning("Failed to load skill from %s: %s", skill_md, exc)
+
+        self._loaded = True
+        logger.info("Discovered %d agent skills", len(self._skills))
+
+    def _load_skill_definition(self, path: Path) -> Optional[SkillDefinition]:
+        """Parse a ``SKILL.md`` frontmatter into a :class:`SkillDefinition`."""
+
+        try:
+            data, _body = _parse_skill_file(path)
+        except (ValueError, yaml.YAMLError) as exc:
+            logger.warning("Failed to parse SKILL.md %s: %s", path, exc)
+            return None
+
+        if "name" not in data:
+            logger.warning("SKILL.md missing required 'name' field: %s", path)
+            return None
+
+        perm_data = data.get("permissions", {})
+        permissions = SkillPermissions(
+            write_metadata=perm_data.get("write_metadata", True),
+            write_previews=perm_data.get("write_previews", True),
+            network_domains=tuple(perm_data.get("network_domains", [])),
+        )
+
+        return SkillDefinition(
+            name=data["name"],
+            title=data.get("title", data["name"]),
+            description=data.get("description", ""),
+            llm_required=data.get("llm_required", False),
+            input_schema=data.get("input_schema", {}),
+            output_schema=data.get("output_schema", {}),
+            model_type_filter=data.get("model_type_filter"),
+            permissions=permissions,
+        )
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def list_skills(self) -> List[SkillDefinition]:
+        """Return all discovered skill definitions."""
+
+        if not self._loaded:
+            self._discover()
+        return list(self._skills.values())
+
+    def get_skill(self, name: str) -> Optional[SkillDefinition]:
+        """Return the skill definition for ``name``, or ``None`` if not found."""
+
+        if not self._loaded:
+            self._discover()
+        return self._skills.get(name)
+
+    def load_prompt(self, name: str) -> str:
+        """Load and return the prompt template body from a skill's ``SKILL.md``."""
+
+        skill_dir = self._skills_dir / name
+        skill_path = skill_dir / "SKILL.md"
+        if not skill_path.exists():
+            raise FileNotFoundError(f"SKILL.md not found: {skill_path}")
+        try:
+            _frontmatter, body = _parse_skill_file(skill_path)
+            return body
+        except (ValueError, yaml.YAMLError) as exc:
+            raise ValueError(f"Failed to parse prompt from {skill_path}: {exc}") from exc

py/services/agent/skills/enrich_hf_metadata/SKILL.md

@@ -0,0 +1,89 @@
+---
+name: enrich_hf_metadata
+title: "Enrich Metadata from HuggingFace"
+description: >
+  Parse the HuggingFace model card via LLM to extract description, trigger
+  words, base model, tags, and preview image URL.
+llm_required: true
+---
+
+You are an expert assistant for AI image generation models. Your task is to extract structured metadata from a HuggingFace model card (README.md).
+
+## Model Information
+
+- **Repository**: {{hf_url}}
+- **Model file path**: {{model_path}}
+- **Repository ID**: {{repo}}
+
+## Current Metadata (may be incomplete)
+
+```json
+{{current_metadata}}
+```
+
+## Available Base Models
+
+The following base models are currently valid in this system:
+{{base_models}}
+
+## HuggingFace README Content
+
+```
+{{readme_content}}
+```
+
+## Extraction Instructions
+
+Extract the following information from the README content above:
+
+### base_model
+The base model this LoRA/checkpoint was trained on. Use EXACTLY one of the names from the **Available Base Models** list above. Do not invent new names or use aliases.
+
+Check the YAML frontmatter (between --- markers) for `base_model:` first, then look at the description text and safetensors metadata. If you cannot determine it, return an empty string.
+
+### trigger_words
+The trigger words or activation prompts needed to use this LoRA. Look for:
+- `instance_prompt:` in the YAML frontmatter
+- Phrases like "trigger word:", "trigger:", "use this prompt:", "activation prompt:"
+- Example prompts at the start (usually the first word or phrase before any description)
+Return as an array of strings. If none found, return an empty array.
+
+### description
+A concise 1-2 sentence summary of what this model does. Extract from the "Model description" section or the first paragraph. Return empty string if the README is too minimal.
+
+### tags
+3-8 relevant tags for categorizing this model. Extract from:
+- The YAML frontmatter `tags:` list (often contains excellent categorization tags)
+- The model type (e.g. "lora", "checkpoint", "flux", "sdxl")
+- The style/subject (e.g. "anime", "photorealistic", "style", "character")
+All lowercase, no spaces. Return empty array if none found.
+
+### preview_url
+The URL of the most suitable preview image from the README. Look for image tags (e.g. `![alt](url)`) and the YAML frontmatter `widget:` section (which often has `output.url` fields). Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL as `https://huggingface.co/{{repo}}/resolve/main/{filename}`. If no suitable image is found, return an empty string.
+
+### confidence
+Your confidence level in the extracted data:
+- "high" — most fields were explicitly stated in the README
+- "medium" — some fields were inferred from context
+- "low" — most fields are guesses based on limited information
+
+## Output Format
+
+Return ONLY a JSON object with exactly these fields (no markdown fences, no extra text):
+
+```json
+{
+  "model_path": "{{model_path}}",
+  "base_model": "<canonical name or empty string>",
+  "trigger_words": ["<word1>", "<word2>"],
+  "description": "<1-2 sentence summary>",
+  "tags": ["<tag1>", "<tag2>"],
+  "preview_url": "<image URL or empty string>",
+  "confidence": "<high|medium|low>"
+}
+```
+
+Important:
+- Only include the JSON object, no other text
+- If a field cannot be determined, use an empty string or empty array
+- Do not fabricate information not supported by the README

py/services/aria2_downloader.py

@@ -84,6 +84,7 @@ class Aria2Downloader:
         self._transfers: Dict[str, Aria2Transfer] = {}
         self._poll_interval = 0.5
         self._state_store = Aria2TransferStateStore()
+        self._stderr_reader_task: Optional[asyncio.Task] = None
 
     @property
     def is_running(self) -> bool:
@@ -115,7 +116,7 @@ class Aria2Downloader:
 
         try:
             while True:
-                status = await self.get_status(download_id)
+                status = await self._get_status_with_retry(download_id)
                 if status is None:
                     return False, "aria2 download not found"
 
@@ -136,6 +137,35 @@ class Aria2Downloader:
         finally:
             self._transfers.pop(download_id, None)
 
+    async def _get_status_with_retry(
+        self, download_id: str, *, max_retries: int = 4, retry_delay: float = 3.0
+    ) -> Optional[Dict[str, Any]]:
+        """Call get_status with retry for transient RPC failures.
+
+        Only retries on :exc:`Aria2Error` (RPC-level failure).  Returns
+        ``None`` immediately when the download_id is not tracked (a missing
+        transfer is not a transient condition, so retrying is pointless).
+
+        A single failed RPC call should not immediately fail the download,
+        because aria2 may be temporarily busy (e.g. finalizing multiple
+        concurrent downloads) and a retry will often succeed.
+        """
+        last_exc: Optional[Exception] = None
+        for attempt in range(max_retries):
+            try:
+                return await self.get_status(download_id)
+            except Aria2Error as exc:
+                last_exc = exc
+                if attempt < max_retries - 1:
+                    logger.warning(
+                        "aria2 get_status transient failure (attempt %d/%d) for %s: %s",
+                        attempt + 1, max_retries, download_id, exc,
+                    )
+                    await asyncio.sleep(retry_delay)
+        raise Aria2Error(
+            f"Failed to query aria2 download status after {max_retries} attempts: {last_exc}"
+        ) from last_exc
+
     async def _schedule_download(
         self,
         url: str,
@@ -312,6 +342,16 @@ class Aria2Downloader:
     async def close(self) -> None:
         """Shut down the RPC process and session."""
 
+        # Cancel the background stderr reader first so it stops reading
+        # from the pipe before the subprocess is terminated.
+        if self._stderr_reader_task is not None:
+            self._stderr_reader_task.cancel()
+            try:
+                await asyncio.wait_for(self._stderr_reader_task, timeout=2.0)
+            except (asyncio.CancelledError, asyncio.TimeoutError):
+                pass
+            self._stderr_reader_task = None
+
         if self._rpc_session is not None:
             await self._rpc_session.close()
             self._rpc_session = None
@@ -331,6 +371,23 @@ class Aria2Downloader:
                 process.kill()
                 await process.wait()
 
+    async def _drain_stderr(self) -> None:
+        """Continuously drain aria2's stderr pipe so it never blocks.
+
+        When the 64 KB pipe buffer fills up, aria2's ``write()`` to stderr
+        blocks, which freezes the entire ``aria2c`` process — including its
+        RPC handler.  This background task reads lines from stderr as they
+        arrive and forwards them to Python's logger.
+        """
+        try:
+            assert self._process is not None and self._process.stderr is not None
+            async for line in self._process.stderr:
+                text = line.decode("utf-8", errors="replace").rstrip()
+                if text:
+                    logger.debug("aria2 stderr: %s", text)
+        except Exception:
+            pass
+
     async def _dispatch_progress(self, callback, snapshot: DownloadProgress) -> None:
         try:
             result = callback(snapshot, snapshot)
@@ -465,6 +522,17 @@ class Aria2Downloader:
 
             await self._wait_until_ready()
 
+            # Drain aria2's stderr in a background task so the pipe buffer
+            # never fills up.  If the pipe blocks, aria2 itself freezes and
+            # cannot respond to RPC — this was the root cause of the
+            # "Failed to query aria2 download status" timeout bug.
+            # Must start AFTER _wait_until_ready to avoid a race where the
+            # drain task consumes aria2's early-exit error message before
+            # _wait_until_ready can read it.
+            self._stderr_reader_task = asyncio.create_task(
+                self._drain_stderr()
+            )
+
     def _resolve_executable(self) -> str:
         settings = get_settings_manager()
         configured_path = (settings.get("aria2c_path") or "").strip()
@@ -584,7 +652,9 @@ class Aria2Downloader:
         if self._rpc_session is None or self._rpc_session.closed:
             async with self._rpc_session_lock:
                 if self._rpc_session is None or self._rpc_session.closed:
-                    timeout = aiohttp.ClientTimeout(total=30)
+                    timeout = aiohttp.ClientTimeout(
+                        total=None, sock_connect=10, sock_read=60
+                    )
                     self._rpc_session = aiohttp.ClientSession(timeout=timeout)
         return self._rpc_session
 

py/services/base_model_service.py

@@ -104,6 +104,100 @@ class BaseModelService(ABC):
         fetch_duration = time.perf_counter() - t0
         initial_count = len(sorted_data)
 
+        # Optionally filter by civitai model ID (shows all local versions of a specific model)
+        civitai_model_id = kwargs.get("civitai_model_id")
+        if civitai_model_id is not None:
+            sorted_data = [
+                item for item in sorted_data
+                if self._extract_model_id(item) == civitai_model_id
+            ]
+            # VLM mode: always sort by version ID descending (newest version first),
+            # regardless of the current sort_by preference.
+            sorted_data.sort(
+                key=lambda x: self._extract_version_id(x) or 0,
+                reverse=True,
+            )
+
+        # Optionally group by civitai modelId, showing only the latest version per model
+        dedup_lost = 0
+        if kwargs.get("group_by_model") and civitai_model_id is None:
+            # Determine whether to further sub-group by base model
+            # When version_grouping is "same_base", versions with different
+            # base models are effectively different groups — the dedup key
+            # needs to include base_model so the version count and VLM flow
+            # stay consistent (card shows correct count for its base model).
+            ufs = self.settings.get("version_grouping", "same_base")
+            group_by_base = ufs == "same_base"
+
+            dedup_map = {}  # (modelId [,base_model]) -> (item, version_id)
+            version_counter = {}  # same-key -> count
+            standalone = []
+            for item in sorted_data:
+                mid = self._extract_model_id(item)
+                if mid is None:
+                    standalone.append(item)
+                    continue
+                key = (mid, item.get("base_model") or "") if group_by_base else mid
+                # Count all versions per key
+                version_counter[key] = version_counter.get(key, 0) + 1
+                vid = self._extract_version_id(item) or 0
+                if key not in dedup_map or vid > dedup_map[key][1]:
+                    dedup_map[key] = (item, vid)
+            # Attach version_count to each surviving grouped item (shallow copy
+            # to avoid mutating cached dicts — the cache is shared across requests)
+            for key, (item, vid) in dedup_map.items():
+                item = dict(item)
+                item["version_count"] = version_counter[key]
+                dedup_map[key] = (item, vid)
+            dedup_lost = len(sorted_data) - (len(dedup_map) + len(standalone))
+            sorted_data = [entry[0] for entry in dedup_map.values()] + standalone
+
+        # Re-sort by version_count (grouped: after dedup; non-grouped: group internally, sort, expand)
+        if sort_params.key == "versions_count" and civitai_model_id is None:
+            reverse = sort_params.order == "desc"
+            if kwargs.get("group_by_model"):
+                # Grouped mode: items are already dedup'd with version_count attached
+                sorted_data.sort(
+                    key=lambda x: (
+                        x.get("version_count", 0),
+                        (x.get("model_name") or x.get("file_name") or "").lower(),
+                        x.get("file_path", "").lower(),
+                    ),
+                    reverse=reverse,
+                )
+            else:
+                # Non-grouped mode: group internally, sort groups by count, expand
+                # Respect the version_grouping setting (same logic as grouped dedup)
+                ufs = self.settings.get("version_grouping", "same_base")
+                group_by_base = ufs == "same_base"
+
+                model_groups: Dict[Any, List[Dict]] = {}
+                ungrouped_standalone: List[Dict] = []
+                for item in sorted_data:
+                    mid = self._extract_model_id(item)
+                    if mid is None:
+                        ungrouped_standalone.append(item)
+                        continue
+                    key = (mid, item.get("base_model") or "") if group_by_base else mid
+                    model_groups.setdefault(key, []).append(item)
+                # Sort versions within each group by version id descending
+                for items in model_groups.values():
+                    items.sort(
+                        key=lambda x: self._extract_version_id(x) or 0,
+                        reverse=True,
+                    )
+                # Sort groups by version count
+                sorted_groups = sorted(
+                    model_groups.values(),
+                    key=lambda items: len(items),
+                    reverse=reverse,
+                )
+                # Flatten: grouped items first, standalone items last
+                sorted_data = []
+                for items in sorted_groups:
+                    sorted_data.extend(items)
+                sorted_data.extend(ungrouped_standalone)
+
         t1 = time.perf_counter()
         if hash_filters:
             filtered_data = await self._apply_hash_filters(sorted_data, hash_filters)
@@ -172,7 +266,7 @@ class BaseModelService(ABC):
         overall_duration = time.perf_counter() - overall_start
         logger.debug(
             "%s.get_paginated_data took %.3fs (fetch: %.3fs, filter: %.3fs, update_filter: %.3fs, pagination: %.3fs, annotate: %.3fs). "
-            "Counts: initial=%d, post_filter=%d, final=%d",
+            "Counts: initial=%d, dedup=%d, post_filter=%d, final=%d",
             self.__class__.__name__,
             overall_duration,
             fetch_duration,
@@ -181,6 +275,7 @@ class BaseModelService(ABC):
             pagination_duration,
             annotate_duration,
             initial_count,
+            dedup_lost,
             post_filter_count,
             final_count,
         )
@@ -495,7 +590,7 @@ class BaseModelService(ABC):
         if not ordered_ids:
             return annotated
 
-        strategy_value = self.settings.get("update_flag_strategy")
+        strategy_value = self.settings.get("version_grouping")
         if isinstance(strategy_value, str) and strategy_value.strip():
             strategy = strategy_value.strip().lower()
         else:
@@ -696,8 +791,12 @@ class BaseModelService(ABC):
         }
 
     @abstractmethod
-    async def format_response(self, model_data: Dict) -> Dict:
-        """Format model data for API response - must be implemented by subclasses"""
+    async def format_response(self, model_data: Dict) -> Optional[Dict]:
+        """Format model data for API response - must be implemented by subclasses.
+
+        Subclasses should return None for corrupted entries so the handler
+        layer can filter them out. See issue #730.
+        """
         pass
 
     # Common service methods that delegate to scanner

py/services/batch_import_service.py

@@ -523,6 +523,10 @@ class BatchImportService:
                 if payload.get("checkpoint"):
                     metadata["checkpoint"] = payload["checkpoint"]
 
+                nsfw = payload.get("preview_nsfw_level")
+                if isinstance(nsfw, int) and nsfw > 0:
+                    metadata["preview_nsfw_level"] = nsfw
+
                 image_bytes = None
                 image_base64 = payload.get("image_base64")
 

py/services/checkpoint_service.py

@@ -1,6 +1,6 @@
 import os
 import logging
-from typing import Dict
+from typing import Dict, Optional
 
 from .base_model_service import BaseModelService
 from .auto_tag_service import extract_auto_tags
@@ -21,20 +21,37 @@ class CheckpointService(BaseModelService):
         """
         super().__init__("checkpoint", scanner, CheckpointMetadata, update_service=update_service)
     
-    async def format_response(self, checkpoint_data: Dict) -> Dict:
-        """Format Checkpoint data for API response"""
+    async def format_response(self, checkpoint_data: Dict) -> Optional[Dict]:
+        """Format Checkpoint data for API response.
+
+        Returns None when the entry is missing critical fields (corrupted cache
+        row), so the handler layer can filter it out. See issue #730.
+        """
+        # Guard against corrupted cache entries missing critical fields
+        file_path = checkpoint_data.get("file_path")
+        if not file_path or not isinstance(file_path, str):
+            logger.warning(
+                "Skipping corrupted checkpoint entry (missing file_path): %s",
+                checkpoint_data.get("file_name", "<unknown>"),
+            )
+            return None
+
         # Get sub_type from cache entry (new canonical field)
         sub_type = checkpoint_data.get("sub_type", "checkpoint")
-        
+
+        file_name = checkpoint_data.get("file_name") or ""
+        model_name = checkpoint_data.get("model_name") or file_name
+        folder = checkpoint_data.get("folder") or ""
+
         return {
-            "model_name": checkpoint_data["model_name"],
-            "file_name": checkpoint_data["file_name"],
+            "model_name": model_name,
+            "file_name": file_name,
             "preview_url": config.get_preview_static_url(checkpoint_data.get("preview_url", "")),
             "preview_nsfw_level": checkpoint_data.get("preview_nsfw_level", 0),
             "base_model": checkpoint_data.get("base_model", ""),
-            "folder": checkpoint_data["folder"],
+            "folder": folder,
             "sha256": checkpoint_data.get("sha256", ""),
-            "file_path": checkpoint_data["file_path"].replace(os.sep, "/"),
+            "file_path": file_path.replace(os.sep, "/"),
             "file_size": checkpoint_data.get("size", 0),
             "modified": checkpoint_data.get("modified", ""),
             "tags": checkpoint_data.get("tags", []),
@@ -48,6 +65,8 @@ class CheckpointService(BaseModelService):
             "skip_metadata_refresh": bool(checkpoint_data.get("skip_metadata_refresh", False)),
             "civitai": self.filter_civitai_data(checkpoint_data.get("civitai", {}), minimal=True),
             "auto_tags": checkpoint_data.get("auto_tags") or extract_auto_tags(checkpoint_data),
+            "version_count": checkpoint_data.get("version_count"),
+            "hf_url": checkpoint_data.get("hf_url", ""),
         }
     
     def find_duplicate_hashes(self) -> Dict:

py/services/civarchive_client.py

@@ -327,7 +327,7 @@ class CivArchiveClient:
             if resolved:
                 return resolved, None
 
-            logger.error("Error fetching version of CivArchive model by hash %s", model_hash[:10])
+            logger.debug("Error fetching version of CivArchive model by hash %s", model_hash[:10])
             return None, "No version data found"
 
         except RateLimitError:
@@ -417,7 +417,7 @@ class CivArchiveClient:
 
             if version_id is not None:
                 raw_id = version_data.get("id")
-                if raw_id != version_id:
+                if raw_id is not None and str(raw_id) != str(version_id):
                     logger.warning(
                         "Requested version %s doesn't match default version %s for model %s",
                         version_id,

py/services/civitai_base_model_service.py

@@ -196,6 +196,7 @@ class CivitaiBaseModelService:
             "ernie": "ERNI",
             "ernie turbo": "ETRB",
             "nucleus": "NUCL",
+            "krea 2": "KR2",
             "svd": "SVD",
             "ltxv": "LTXV",
             "ltxv2": "LTV2",
@@ -424,6 +425,7 @@ class CivitaiBaseModelService:
                 "Ernie",
                 "Ernie Turbo",
                 "Nucleus",
+                "Krea 2",
             ],
         }
 

py/services/civitai_client.py

@@ -56,7 +56,7 @@ class CivitaiClient:
         self._MAX_CACHE_ENTRIES = 500
 
     def _build_image_info_url(self, image_id: str) -> str:
-        return f"{self.base_url}/images?imageId={image_id}&nsfw=X"
+        return f"{self.base_url}/images?imageId={image_id}&nsfw=X&withMeta=true"
 
     async def _make_request(
         self,

py/services/download_manager.py

@@ -29,6 +29,7 @@ from .metadata_service import get_default_metadata_provider, get_metadata_provid
 from .downloader import get_downloader, DownloadProgress, DownloadStreamControl
 from .aria2_downloader import Aria2Error, get_aria2_downloader
 from .aria2_transfer_state import Aria2TransferStateStore
+from .download_queue_service import DownloadQueueService
 
 # Download to temporary file first
 import tempfile
@@ -360,6 +361,15 @@ class DownloadManager:
                     if self._active_downloads[task_id].get("transfer_backend") == "aria2":
                         await self._persist_aria2_state(task_id)
 
+                # Update SQLite queue status to 'downloading'
+                try:
+                    queue_service = await DownloadQueueService.get_instance()
+                    await queue_service.update_status(task_id, "downloading")
+                except Exception:
+                    logger.warning(
+                        "Failed to update queue status for %s", task_id, exc_info=True
+                    )
+
                 # Use original download implementation
                 try:
                     # Check for cancellation before starting
@@ -396,6 +406,22 @@ class DownloadManager:
                         if self._active_downloads[task_id].get("transfer_backend") == "aria2":
                             await self._persist_aria2_state(task_id)
 
+                    # Move queue item to history on completion
+                    try:
+                        queue_service = await DownloadQueueService.get_instance()
+                        await queue_service.complete_download(
+                            download_id=task_id,
+                            status=result.get("status", "completed") if result.get("success") else "failed",
+                            error=result.get("error") if not result.get("success") else None,
+                            file_path=result.get("file_path"),
+                            bytes_downloaded=self._active_downloads.get(task_id, {}).get("bytes_downloaded", 0),
+                            total_bytes=self._active_downloads.get(task_id, {}).get("total_bytes"),
+                        )
+                    except Exception:
+                        logger.warning(
+                            "Failed to complete queue item for %s", task_id, exc_info=True
+                        )
+
                     return result
                 except asyncio.CancelledError:
                     # Handle cancellation
@@ -404,6 +430,19 @@ class DownloadManager:
                         self._active_downloads[task_id]["bytes_per_second"] = 0.0
                         if self._active_downloads[task_id].get("transfer_backend") == "aria2":
                             await self._persist_aria2_state(task_id)
+
+                    # Move queue item to history as canceled
+                    try:
+                        queue_service = await DownloadQueueService.get_instance()
+                        await queue_service.complete_download(
+                            download_id=task_id,
+                            status="canceled",
+                        )
+                    except Exception:
+                        logger.warning(
+                            "Failed to cancel queue item for %s", task_id, exc_info=True
+                        )
+
                     logger.info(f"Download cancelled for task {task_id}")
                     raise
                 except Exception as e:
@@ -417,6 +456,22 @@ class DownloadManager:
                         self._active_downloads[task_id]["bytes_per_second"] = 0.0
                         if self._active_downloads[task_id].get("transfer_backend") == "aria2":
                             await self._persist_aria2_state(task_id)
+
+                    # Move queue item to history as failed
+                    try:
+                        queue_service = await DownloadQueueService.get_instance()
+                        await queue_service.complete_download(
+                            download_id=task_id,
+                            status="failed",
+                            error=str(e),
+                            bytes_downloaded=self._active_downloads.get(task_id, {}).get("bytes_downloaded", 0),
+                            total_bytes=self._active_downloads.get(task_id, {}).get("total_bytes"),
+                        )
+                    except Exception:
+                        logger.warning(
+                            "Failed to complete queue item for %s", task_id, exc_info=True
+                        )
+
                     return {"success": False, "error": str(e)}
         finally:
             # Schedule cleanup of download record after delay
@@ -1233,10 +1288,24 @@ class DownloadManager:
                     "download_id": download_id,
                 }
 
-            # Check if this checkpoint should be treated as a diffusion model based on baseModel
+            # Check if this checkpoint should be treated as a diffusion model
+            # Priority: (1) any file has type "UNet" or "Diffusion Model",
+            #            (2) baseModel is in DIFFUSION_MODEL_BASE_MODELS
             is_diffusion_model = False
             if model_type == "checkpoint":
-                if base_model_value in DIFFUSION_MODEL_BASE_MODELS:
+                # Check file types first (more direct signal from CivitAI)
+                version_files = version_info.get("files", [])
+                for f in version_files:
+                    f_type = f.get("type", "")
+                    if f_type in ("UNet", "Diffusion Model"):
+                        is_diffusion_model = True
+                        logger.info(
+                            f"File type '{f_type}' detected, routing checkpoint to unet folder"
+                        )
+                        break
+
+                # Fallback to baseModel name check
+                if not is_diffusion_model and base_model_value in DIFFUSION_MODEL_BASE_MODELS:
                     is_diffusion_model = True
                     logger.info(
                         f"baseModel '{base_model_value}' is a known diffusion model, routing to unet folder"
@@ -1365,7 +1434,7 @@ class DownloadManager:
                             f
                             for f in files
                             if f.get("primary")
-                            and f.get("type") in ("Model", "Negative", "Diffusion Model")
+                            and f.get("type") in ("Model", "Negative", "Diffusion Model", "UNet")
                         ),
                         None,
                     )
@@ -1396,7 +1465,7 @@ class DownloadManager:
                     (
                         f
                         for f in files
-                        if f.get("primary") and f.get("type") in ("Model", "Negative", "Diffusion Model")
+                        if f.get("primary") and f.get("type") in ("Model", "Negative", "Diffusion Model", "UNet")
                     ),
                     None,
                 )
@@ -1974,7 +2043,21 @@ class DownloadManager:
                     break
 
                 last_error = result
-                if os.path.exists(save_path):
+                # For aria2: if the .aria2 control file is missing, aria2 considers
+                # the download complete.  A transient RPC failure may have made us
+                # think the download failed even though the file is fully on disk.
+                # Keep the file so a retry can find it already complete.
+                if (
+                    transfer_backend == "aria2"
+                    and os.path.exists(save_path)
+                    and not os.path.exists(f"{save_path}.aria2")
+                ):
+                    logger.warning(
+                        "aria2 download reported failure but .aria2 file is absent "
+                        "for %s — the file is likely complete.  Preserving it for retry.",
+                        save_path,
+                    )
+                elif os.path.exists(save_path):
                     try:
                         os.remove(save_path)
                     except Exception as e:

py/services/embedding_service.py

@@ -1,6 +1,6 @@
 import os
 import logging
-from typing import Dict
+from typing import Dict, Optional
 
 from .base_model_service import BaseModelService
 from .auto_tag_service import extract_auto_tags
@@ -21,20 +21,37 @@ class EmbeddingService(BaseModelService):
         """
         super().__init__("embedding", scanner, EmbeddingMetadata, update_service=update_service)
     
-    async def format_response(self, embedding_data: Dict) -> Dict:
-        """Format Embedding data for API response"""
+    async def format_response(self, embedding_data: Dict) -> Optional[Dict]:
+        """Format Embedding data for API response.
+
+        Returns None when the entry is missing critical fields (corrupted cache
+        row), so the handler layer can filter it out. See issue #730.
+        """
+        # Guard against corrupted cache entries missing critical fields
+        file_path = embedding_data.get("file_path")
+        if not file_path or not isinstance(file_path, str):
+            logger.warning(
+                "Skipping corrupted embedding entry (missing file_path): %s",
+                embedding_data.get("file_name", "<unknown>"),
+            )
+            return None
+
         # Get sub_type from cache entry (new canonical field)
         sub_type = embedding_data.get("sub_type", "embedding")
-        
+
+        file_name = embedding_data.get("file_name") or ""
+        model_name = embedding_data.get("model_name") or file_name
+        folder = embedding_data.get("folder") or ""
+
         return {
-            "model_name": embedding_data["model_name"],
-            "file_name": embedding_data["file_name"],
+            "model_name": model_name,
+            "file_name": file_name,
             "preview_url": config.get_preview_static_url(embedding_data.get("preview_url", "")),
             "preview_nsfw_level": embedding_data.get("preview_nsfw_level", 0),
             "base_model": embedding_data.get("base_model", ""),
-            "folder": embedding_data["folder"],
+            "folder": folder,
             "sha256": embedding_data.get("sha256", ""),
-            "file_path": embedding_data["file_path"].replace(os.sep, "/"),
+            "file_path": file_path.replace(os.sep, "/"),
             "file_size": embedding_data.get("size", 0),
             "modified": embedding_data.get("modified", ""),
             "tags": embedding_data.get("tags", []),
@@ -48,6 +65,8 @@ class EmbeddingService(BaseModelService):
             "skip_metadata_refresh": bool(embedding_data.get("skip_metadata_refresh", False)),
             "civitai": self.filter_civitai_data(embedding_data.get("civitai", {}), minimal=True),
             "auto_tags": embedding_data.get("auto_tags") or extract_auto_tags(embedding_data),
+            "version_count": embedding_data.get("version_count"),
+            "hf_url": embedding_data.get("hf_url", ""),
         }
     
     def find_duplicate_hashes(self) -> Dict:

py/services/errors.py

@@ -25,3 +25,21 @@ class ResourceNotFoundError(RuntimeError):
 
     pass
 
+
+class LLMNotConfiguredError(RuntimeError):
+    """Raised when an LLM-dependent operation is attempted but no provider is configured."""
+
+    pass
+
+
+class LLMRateLimitError(RateLimitError):
+    """Raised when the LLM provider rejects a request due to rate limiting."""
+
+    pass
+
+
+class LLMResponseError(RuntimeError):
+    """Raised when the LLM returns an unparseable or schema-invalid response."""
+
+    pass
+

py/services/llm_service.py

@@ -0,0 +1,321 @@
+"""Centralized LLM API client with BYOK (bring-your-own-key) provider support.
+
+Reads provider configuration from :class:`SettingsManager` and makes
+OpenAI-compatible ``/chat/completions`` calls.  Supports any provider that
+implements the OpenAI Chat Completions API surface area (OpenAI, Ollama,
+vLLM, LM Studio, etc.).
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from typing import Any, Dict, List, Optional
+
+import aiohttp
+
+from .errors import LLMNotConfiguredError, LLMRateLimitError, LLMResponseError
+
+logger = logging.getLogger(__name__)
+
+# Default API base URLs per provider
+_PROVIDER_DEFAULTS: Dict[str, str] = {
+    "openai": "https://api.openai.com/v1",
+    "ollama": "http://localhost:11434/v1",
+    # "custom" requires an explicit llm_api_base from the user
+}
+
+# Request timeout for LLM calls (seconds)
+_LLM_TIMEOUT = aiohttp.ClientTimeout(total=120)
+
+
+class LLMService:
+    """Centralized LLM API client.
+
+    All agent skills call LLMs through this service so that BYOK config,
+    retry logic, and error handling live in one place.
+    """
+
+    _instance: Optional["LLMService"] = None
+    _lock: asyncio.Lock = asyncio.Lock()
+
+    def __init__(self, settings_service) -> None:
+        self._settings = settings_service
+
+    # ------------------------------------------------------------------
+    # Singleton access
+    # ------------------------------------------------------------------
+
+    @classmethod
+    async def get_instance(cls) -> "LLMService":
+        """Return the lazily-initialised global ``LLMService`` instance."""
+
+        if cls._instance is None:
+            async with cls._lock:
+                if cls._instance is None:
+                    from .settings_manager import get_settings_manager
+
+                    cls._instance = cls(get_settings_manager())
+        return cls._instance
+
+    @classmethod
+    def reset_instance(cls) -> None:
+        """Reset the cached singleton — primarily for tests."""
+
+        cls._instance = None
+
+    # ------------------------------------------------------------------
+    # Configuration helpers
+    # ------------------------------------------------------------------
+
+    def _get_config(self) -> Dict[str, Any]:
+        """Read the current LLM configuration from settings."""
+
+        return {
+            "provider": self._settings.get("llm_provider", "openai"),
+            "api_key": self._settings.get("llm_api_key", ""),
+            "api_base": self._settings.get("llm_api_base", ""),
+            "model": self._settings.get("llm_model", ""),
+        }
+
+    def is_configured(self) -> bool:
+        """Return ``True`` when the LLM provider is minimally configured.
+
+        A provider is considered configured when ``llm_model`` is set and
+        (for non-Ollama) an API key is configured.
+        """
+
+        cfg = self._get_config()
+        has_model = bool(cfg["model"])
+        has_key = bool(cfg["api_key"]) or cfg["provider"] == "ollama"
+        return has_model and has_key
+
+    def _resolve_api_base(self, provider: str, api_base: str) -> str:
+        """Resolve the API base URL for the given provider."""
+
+        if api_base:
+            return api_base.rstrip("/")
+        return _PROVIDER_DEFAULTS.get(provider, "").rstrip("/")
+
+    def _build_headers(self, api_key: str) -> Dict[str, str]:
+        """Build HTTP headers for the LLM API request."""
+
+        headers = {"Content-Type": "application/json"}
+        if api_key:
+            headers["Authorization"] = f"Bearer {api_key}"
+        return headers
+
+    def _ensure_configured(self) -> Dict[str, Any]:
+        """Validate configuration and return it, or raise.
+
+        A provider is considered configured when ``llm_model`` is set and
+        (for non-Ollama) an API key is configured.
+        """
+
+        cfg = self._get_config()
+        has_model = bool(cfg["model"])
+        has_key = bool(cfg["api_key"]) or cfg["provider"] == "ollama"
+        if not (has_model and has_key):
+            parts = []
+            if not has_model:
+                parts.append("No LLM model specified")
+            if not has_key and cfg["provider"] != "ollama":
+                parts.append("No LLM API key configured")
+            detail = "; ".join(parts) if parts else "LLM provider is not configured"
+            raise LLMNotConfiguredError(
+                f"{detail}. Configure it in Settings → AI Provider."
+            )
+        return cfg
+
+    # ------------------------------------------------------------------
+    # Core API call
+    # ------------------------------------------------------------------
+
+    async def chat_completion(
+        self,
+        *,
+        messages: List[Dict[str, str]],
+        model: Optional[str] = None,
+        temperature: float = 0.3,
+        response_format: Optional[Dict[str, Any]] = None,
+        max_tokens: Optional[int] = None,
+        retry_on_rate_limit: bool = True,
+    ) -> Dict[str, Any]:
+        """Call the configured LLM provider's ``/chat/completions`` endpoint.
+
+        Args:
+            messages: OpenAI-format message list
+            model: Override the configured model name
+            temperature: Sampling temperature
+            response_format: Optional ``{"type": "json_object"}`` for structured output
+            max_tokens: Optional max output tokens
+            retry_on_rate_limit: Retry once after a 429 with backoff
+
+        Returns:
+            Dict with ``content`` (str), ``usage`` (dict), ``model`` (str)
+
+        Raises:
+            LLMNotConfiguredError: Provider not enabled / missing config
+            LLMRateLimitError: Rate limited and retry exhausted
+            LLMResponseError: Non-200 response or parse failure
+        """
+
+        cfg = self._ensure_configured()
+        api_base = self._resolve_api_base(cfg["provider"], cfg["api_base"])
+        url = f"{api_base}/chat/completions"
+        model_name = model or cfg["model"]
+
+        payload: Dict[str, Any] = {
+            "model": model_name,
+            "messages": messages,
+            "temperature": temperature,
+        }
+        if response_format is not None:
+            payload["response_format"] = response_format
+        if max_tokens is not None:
+            payload["max_tokens"] = max_tokens
+
+        headers = self._build_headers(cfg["api_key"])
+
+        attempt = 0
+        max_attempts = 2 if retry_on_rate_limit else 1
+        while attempt < max_attempts:
+            attempt += 1
+            try:
+                async with aiohttp.ClientSession(timeout=_LLM_TIMEOUT) as session:
+                    async with session.post(
+                        url, json=payload, headers=headers
+                    ) as resp:
+                        if resp.status == 429:
+                            if attempt < max_attempts:
+                                retry_after = float(
+                                    resp.headers.get("Retry-After", "5")
+                                )
+                                logger.warning(
+                                    "LLM rate limited, retrying after %.1fs",
+                                    retry_after,
+                                )
+                                await asyncio.sleep(retry_after)
+                                continue
+                            raise LLMRateLimitError(
+                                f"LLM provider rate limited (HTTP 429)",
+                                provider=cfg["provider"],
+                            )
+
+                        if resp.status != 200:
+                            body = await resp.text()
+                            raise LLMResponseError(
+                                f"LLM API returned HTTP {resp.status}: "
+                                f"{body[:500]}"
+                            )
+
+                        data = await resp.json()
+
+            except aiohttp.ClientError as exc:
+                raise LLMResponseError(f"Network error calling LLM API: {exc}") from exc
+
+            # Parse response
+            try:
+                content = data["choices"][0]["message"]["content"]
+                usage = data.get("usage", {})
+                return {
+                    "content": content,
+                    "usage": usage,
+                    "model": data.get("model", model_name),
+                }
+            except (KeyError, IndexError) as exc:
+                raise LLMResponseError(
+                    f"Unexpected LLM response structure: {json.dumps(data)[:500]}"
+                ) from exc
+
+        # Should not reach here, but satisfy type checker
+        raise LLMRateLimitError("Rate limit retry exhausted", provider=cfg["provider"])
+
+    # ------------------------------------------------------------------
+    # Structured output convenience
+    # ------------------------------------------------------------------
+
+    async def chat_completion_json(
+        self,
+        *,
+        system_prompt: str,
+        user_prompt: str,
+        model: Optional[str] = None,
+        temperature: float = 0.3,
+        max_tokens: Optional[int] = None,
+    ) -> Dict[str, Any]:
+        """Call the LLM and return parsed JSON.
+
+        Sends ``response_format: {"type": "json_object"}`` when the provider
+        supports it, and parses the response content as JSON.  If parsing
+        fails, retries once with a clarifying system message.
+
+        Args:
+            system_prompt: System-level instructions
+            user_prompt: User-level query
+            model: Override the configured model name
+            temperature: Sampling temperature
+            max_tokens: Optional max output tokens
+
+        Returns:
+            Parsed JSON dict from the LLM response
+
+        Raises:
+            LLMNotConfiguredError: Provider not configured
+            LLMRateLimitError: Rate limited
+            LLMResponseError: JSON parse failure after retry
+        """
+
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ]
+
+        # First attempt with JSON mode
+        result = await self.chat_completion(
+            messages=messages,
+            model=model,
+            temperature=temperature,
+            response_format={"type": "json_object"},
+            max_tokens=max_tokens,
+        )
+
+        try:
+            return json.loads(result["content"])
+        except (json.JSONDecodeError, TypeError) as exc:
+            logger.warning(
+                "LLM JSON parse failed on first attempt: %s. Retrying.", exc
+            )
+
+        # Retry with explicit instruction to return valid JSON
+        retry_messages = messages + [
+            {
+                "role": "assistant",
+                "content": result["content"],
+            },
+            {
+                "role": "user",
+                "content": (
+                    "The previous response could not be parsed as JSON. "
+                    "Please respond with ONLY a valid JSON object, no "
+                    "markdown fences or extra text."
+                ),
+            },
+        ]
+
+        result = await self.chat_completion(
+            messages=retry_messages,
+            model=model,
+            temperature=0.0,  # More deterministic for retry
+            response_format={"type": "json_object"},
+            max_tokens=max_tokens,
+        )
+
+        try:
+            return json.loads(result["content"])
+        except (json.JSONDecodeError, TypeError) as exc:
+            raise LLMResponseError(
+                f"LLM response could not be parsed as JSON after retry: {exc}\n"
+                f"Raw content: {result['content'][:500]}"
+            ) from exc

py/services/lora_service.py

@@ -24,23 +24,41 @@ class LoraService(BaseModelService):
         """
         super().__init__("lora", scanner, LoraMetadata, update_service=update_service)
 
-    async def format_response(self, lora_data: Dict) -> Dict:
-        """Format LoRA data for API response"""
+    async def format_response(self, lora_data: Dict) -> Optional[Dict]:
+        """Format LoRA data for API response.
+
+        Returns None when the entry is missing critical fields (corrupted cache
+        row), so the handler layer can filter it out instead of crashing the
+        whole listing request. See issue #730.
+        """
+        # Guard against corrupted cache entries missing critical fields
+        file_path = lora_data.get("file_path")
+        if not file_path or not isinstance(file_path, str):
+            logger.warning(
+                "Skipping corrupted LoRA entry (missing file_path): %s",
+                lora_data.get("file_name", "<unknown>"),
+            )
+            return None
+
         # Resolve sub_type using priority: sub_type > model_type > civitai.model.type > default
         # Normalize to lowercase for consistent API responses
         sub_type = resolve_sub_type(lora_data).lower()
 
+        file_name = lora_data.get("file_name") or ""
+        model_name = lora_data.get("model_name") or file_name
+        folder = lora_data.get("folder") or ""
+
         return {
-            "model_name": lora_data["model_name"],
-            "file_name": lora_data["file_name"],
+            "model_name": model_name,
+            "file_name": file_name,
             "preview_url": config.get_preview_static_url(
                 lora_data.get("preview_url", "")
             ),
             "preview_nsfw_level": lora_data.get("preview_nsfw_level", 0),
             "base_model": lora_data.get("base_model", ""),
-            "folder": lora_data["folder"],
+            "folder": folder,
             "sha256": lora_data.get("sha256", ""),
-            "file_path": lora_data["file_path"].replace(os.sep, "/"),
+            "file_path": file_path.replace(os.sep, "/"),
             "file_size": lora_data.get("size", 0),
             "modified": lora_data.get("modified", ""),
             "tags": lora_data.get("tags", []),
@@ -59,6 +77,8 @@ class LoraService(BaseModelService):
                 lora_data.get("civitai", {}), minimal=True
             ),
             "auto_tags": lora_data.get("auto_tags") or extract_auto_tags(lora_data),
+            "version_count": lora_data.get("version_count"),
+            "hf_url": lora_data.get("hf_url", ""),
         }
 
     async def _apply_specific_filters(self, data: List[Dict], **kwargs) -> List[Dict]:

py/services/metadata_sync_service.py

@@ -427,7 +427,18 @@ class MetadataSyncService:
         metadata = await metadata_loader(metadata_path)
 
         for key, value in updates.items():
-            if isinstance(value, dict) and isinstance(metadata.get(key), dict):
+            if key == "tags" and isinstance(value, list):
+                # Normalize tags: trim, lowercase, deduplicate
+                normalized = []
+                seen = set()
+                for tag in value:
+                    if isinstance(tag, str):
+                        t = tag.strip().lower()
+                        if t and t not in seen:
+                            normalized.append(t)
+                            seen.add(t)
+                metadata[key] = normalized
+            elif isinstance(value, dict) and isinstance(metadata.get(key), dict):
                 metadata[key].update(value)
             else:
                 metadata[key] = value

py/services/model_cache.py

@@ -18,6 +18,8 @@ SUPPORTED_SORT_MODES = [
     ('size', 'desc'),
     ('usage', 'asc'),
     ('usage', 'desc'),
+    ('versions_count', 'asc'),
+    ('versions_count', 'desc'),
 ]
 # Is this in use?
 
@@ -263,6 +265,17 @@ class ModelCache:
                 ),
                 reverse=reverse
             )
+        elif sort_key == 'versions_count':
+            # Pre-dedup sort: fall back to name sort.
+            # Actual re-sort by version_count happens in get_paginated_data after dedup.
+            result = natsorted(
+                data,
+                key=lambda x: (
+                    self._get_display_name(x).lower(),
+                    x.get('file_path', '').lower()
+                ),
+                reverse=reverse
+            )
         else:
             # Fallback: no sort
             result = list(data)

py/services/model_query.py

@@ -294,12 +294,14 @@ class ModelFilterSet:
                 for tag, state in tag_filters.items():
                     if not tag:
                         continue
+                    # Normalize to lowercase for case-insensitive matching
+                    normalized = tag.strip().lower()
                     if state == "exclude":
-                        exclude_tags.add(tag)
+                        exclude_tags.add(normalized)
                     else:
-                        include_tags.add(tag)
+                        include_tags.add(normalized)
             else:
-                include_tags = {tag for tag in tag_filters if tag}
+                include_tags = {tag.strip().lower() for tag in tag_filters if tag}
 
             if include_tags:
                 tag_logic = criteria.tag_logic.lower() if criteria.tag_logic else "any"
@@ -318,13 +320,17 @@ class ModelFilterSet:
                                 return True
                             # Otherwise, check if all non-special tags match
                             if non_special_tags:
-                                return all(tag in (item_tags or []) for tag in non_special_tags)
+                                # Case-insensitive: normalize item tags too
+                                normalized_item_tags = {t.strip().lower() for t in (item_tags or []) if isinstance(t, str)}
+                                return all(tag in normalized_item_tags for tag in non_special_tags)
                             return True
-                        # Normal case: all tags must match
-                        return all(tag in (item_tags or []) for tag in non_special_tags)
+                        # Normal case: all tags must match (case-insensitive)
+                        normalized_item_tags = {t.strip().lower() for t in (item_tags or []) if isinstance(t, str)}
+                        return all(tag in normalized_item_tags for tag in non_special_tags)
                     else:
-                        # OR logic (default): item must have ANY include tag
-                        return any(tag in include_tags for tag in (item_tags or []))
+                        # OR logic (default): item must have ANY include tag (case-insensitive)
+                        normalized_item_tags = {t.strip().lower() for t in (item_tags or []) if isinstance(t, str)}
+                        return bool(normalized_item_tags & include_tags)
 
                 items = [item for item in items if matches_include(item.get("tags"))]
 
@@ -333,7 +339,9 @@ class ModelFilterSet:
                 def matches_exclude(item_tags):
                     if not item_tags and "__no_tags__" in exclude_tags:
                         return True
-                    return any(tag in exclude_tags for tag in (item_tags or []))
+                    # Case-insensitive: normalize item tags
+                    normalized_item_tags = {t.strip().lower() for t in (item_tags or []) if isinstance(t, str)}
+                    return bool(normalized_item_tags & exclude_tags)
 
                 items = [
                     item for item in items if not matches_exclude(item.get("tags"))

py/services/model_scanner.py

@@ -248,6 +248,7 @@ class ModelScanner:
             'civitai': civitai_slim,
             'civitai_deleted': bool(get_value('civitai_deleted', False)),
             'skip_metadata_refresh': bool(get_value('skip_metadata_refresh', False)),
+            'hf_url': get_value('hf_url', '') or '',
         }
 
         license_source: Dict[str, Any] = {}
@@ -476,11 +477,20 @@ class ModelScanner:
             for tag in adjusted_item.get('tags') or []:
                 tags_count[tag] = tags_count.get(tag, 0) + 1
 
-        # Validate cache entries and check health
+        # Validate cache entries and check health.
+        # Always use the validated/repaired entries — even when there are no
+        # invalid entries, auto_repair may have filled in missing optional
+        # fields (model_name, file_name, folder) with safe defaults on a copied
+        # working_entry.  Without this unconditional replacement the repaired
+        # copies are discarded and None values propagate to format_response.
+        # See issue #730.
         valid_entries, invalid_entries = CacheEntryValidator.validate_batch(
             adjusted_raw_data, auto_repair=True
         )
 
+        # Always use the validated entries (repaired copies)
+        adjusted_raw_data = valid_entries
+
         if invalid_entries:
             monitor = CacheHealthMonitor()
             report = monitor.check_health(adjusted_raw_data, auto_repair=True)

py/services/model_update_service.py

@@ -724,6 +724,16 @@ class ModelUpdateService:
             "Refreshing update metadata for %d %s models", total_models, model_type
         )
 
+        # When filtering by folder, also collect the cross-folder version set
+        # so that versions already present in other folders are not reported
+        # as available updates. See issue #997.
+        all_local_versions: Optional[Dict[int, List[int]]] = None
+        if folder_path is not None:
+            all_local_versions = await self._collect_local_versions(
+                scanner,
+                target_model_ids=target_filter,
+            )
+
         results: Dict[int, ModelUpdateRecord] = {}
         prefetched: Dict[int, Mapping] = {}
 
@@ -762,6 +772,12 @@ class ModelUpdateService:
         for index, (model_id, version_ids) in enumerate(
             local_versions.items(), start=1
         ):
+            # Use cross-folder version IDs for is_in_library if available
+            all_vids: Sequence[int] = (
+                all_local_versions.get(model_id, [])
+                if all_local_versions is not None
+                else version_ids
+            )
             record = await self._refresh_single_model(
                 model_type,
                 model_id,
@@ -769,6 +785,7 @@ class ModelUpdateService:
                 metadata_provider,
                 force_refresh=force_refresh,
                 prefetched_response=prefetched.get(model_id),
+                all_local_version_ids=all_vids,
             )
             if scanner.is_cancelled():
                 logger.info(f"{model_type.capitalize()} Update Service: Refresh cancelled by user")
@@ -964,8 +981,16 @@ class ModelUpdateService:
         *,
         force_refresh: bool = False,
         prefetched_response: Optional[Mapping] = None,
+        all_local_version_ids: Optional[Sequence[int]] = None,
     ) -> Optional[ModelUpdateRecord]:
         normalized_local = self._normalize_sequence(local_versions)
+        # When folder-filtering, this carries the cross-folder version set
+        # for is_in_library; otherwise it falls back to normalized_local.
+        normalized_all = (
+            self._normalize_sequence(all_local_version_ids)
+            if all_local_version_ids is not None
+            else normalized_local
+        )
         now = time.time()
         async with self._lock:
             existing = self._get_record(model_type, model_id)
@@ -973,6 +998,7 @@ class ModelUpdateService:
                 record = self._merge_with_local_versions(
                     existing,
                     normalized_local,
+                    all_local_version_ids=normalized_all,
                 )
                 self._upsert_record(record)
                 return record
@@ -1048,6 +1074,7 @@ class ModelUpdateService:
                 record = self._merge_with_local_versions(
                     existing,
                     normalized_local,
+                    all_local_version_ids=normalized_all,
                 )
                 self._upsert_record(record)
                 return record
@@ -1059,6 +1086,7 @@ class ModelUpdateService:
                     model_type=model_type,
                     model_id=model_id,
                     last_checked_at=now,
+                    all_local_version_ids=normalized_all,
                 )
                 record = replace(record, should_ignore_model=True)
                 self._upsert_record(record)
@@ -1077,6 +1105,7 @@ class ModelUpdateService:
                     fetched_versions,
                     existing,
                     now,
+                    all_local_version_ids=normalized_all,
                 )
             else:
                 record = self._merge_with_local_versions(
@@ -1085,6 +1114,7 @@ class ModelUpdateService:
                     model_type=model_type,
                     model_id=model_id,
                     last_checked_at=existing.last_checked_at if existing else None,
+                    all_local_version_ids=normalized_all,
                 )
             self._upsert_record(record)
             return record
@@ -1322,12 +1352,20 @@ class ModelUpdateService:
         existing: Optional[ModelUpdateRecord],
         normalized_local: Sequence[int],
         *,
+        all_local_version_ids: Optional[Sequence[int]] = None,
         model_type: Optional[str] = None,
         model_id: Optional[int] = None,
         last_checked_at: Optional[float] = None,
         version_info: Optional[Mapping] = None,
     ) -> ModelUpdateRecord:
         local_set = set(normalized_local)
+        # When folder-filtering, also consider versions in other folders
+        # as in-library so they are not reported as available updates.
+        effective_local_set: set[int] = (
+            local_set | set(all_local_version_ids)
+            if all_local_version_ids is not None
+            else local_set
+        )
         versions: List[ModelVersionRecord] = []
         ignore_map: Dict[int, bool] = {}
         if existing:
@@ -1339,7 +1377,7 @@ class ModelUpdateService:
                 versions.append(
                     replace(
                         version,
-                        is_in_library=version.version_id in local_set,
+                        is_in_library=version.version_id in effective_local_set,
                     )
                 )
         elif model_type is None or model_id is None:
@@ -1386,8 +1424,17 @@ class ModelUpdateService:
         remote_versions: Sequence[ModelVersionRecord],
         existing: Optional[ModelUpdateRecord],
         timestamp: float,
+        *,
+        all_local_version_ids: Optional[Sequence[int]] = None,
     ) -> ModelUpdateRecord:
         local_set = set(local_versions)
+        # When folder-filtering, also consider versions in other folders
+        # as in-library so they are not reported as available updates.
+        effective_local_set: set[int] = (
+            local_set | set(all_local_version_ids)
+            if all_local_version_ids is not None
+            else local_set
+        )
         ignore_map = {version.version_id: version.should_ignore for version in existing.versions} if existing else {}
         preview_map = {version.version_id: version.preview_url for version in existing.versions} if existing else {}
         sort_map = {version.version_id: version.sort_index for version in existing.versions} if existing else {}
@@ -1406,7 +1453,7 @@ class ModelUpdateService:
                     released_at=remote_version.released_at,
                     size_bytes=remote_version.size_bytes,
                     preview_url=remote_version.preview_url or preview_map.get(version_id),
-                    is_in_library=version_id in local_set,
+                    is_in_library=version_id in effective_local_set,
                     should_ignore=ignore_map.get(version_id, remote_version.should_ignore),
                     sort_index=sort_map.get(version_id, index),
                     early_access_ends_at=remote_version.early_access_ends_at,

py/services/persistent_model_cache.py

@@ -57,6 +57,7 @@ class PersistentModelCache:
         "db_checked",
         "last_checked_at",
         "hash_status",
+        "hf_url",
     )
     _MODEL_UPDATE_COLUMNS: Tuple[str, ...] = _MODEL_COLUMNS[2:]
     _instances: Dict[str, "PersistentModelCache"] = {}
@@ -165,8 +166,8 @@ class PersistentModelCache:
 
             item = {
                 "file_path": file_path,
-                "file_name": row["file_name"],
-                "model_name": row["model_name"],
+                "file_name": row["file_name"] or "",
+                "model_name": row["model_name"] or "",
                 "folder": row["folder"] or "",
                 "size": row["size"] or 0,
                 "modified": row["modified"] or 0.0,
@@ -188,6 +189,7 @@ class PersistentModelCache:
                 "skip_metadata_refresh": bool(row["skip_metadata_refresh"]),
                 "license_flags": int(license_value),
                 "hash_status": row["hash_status"] or "completed",
+                "hf_url": row["hf_url"] or "",
             }
             raw_data.append(item)
 
@@ -452,6 +454,7 @@ class PersistentModelCache:
                             db_checked INTEGER,
                             last_checked_at REAL,
                             hash_status TEXT,
+                            hf_url TEXT DEFAULT '',
                             PRIMARY KEY (model_type, file_path)
                         );
 
@@ -500,6 +503,7 @@ class PersistentModelCache:
             # Persisting without explicit flags should assume CivitAI's documented defaults (0b111001 == 57).
             "license_flags": f"INTEGER DEFAULT {DEFAULT_LICENSE_FLAGS}",
             "hash_status": "TEXT DEFAULT 'completed'",
+            "hf_url": "TEXT DEFAULT ''",
         }
 
         for column, definition in required_columns.items():
@@ -548,19 +552,19 @@ class PersistentModelCache:
         return (
             model_type,
             item.get("file_path"),
-            item.get("file_name"),
-            item.get("model_name"),
-            item.get("folder"),
+            item.get("file_name") or "",
+            item.get("model_name") or "",
+            item.get("folder") or "",
             int(item.get("size") or 0),
             float(item.get("modified") or 0.0),
             (item.get("sha256") or "").lower() or None,
-            item.get("base_model"),
-            item.get("preview_url"),
+            item.get("base_model") or "",
+            item.get("preview_url") or "",
             int(item.get("preview_nsfw_level") or 0),
             1 if item.get("from_civitai", True) else 0,
             1 if item.get("favorite") else 0,
-            item.get("notes"),
-            item.get("usage_tips"),
+            item.get("notes") or "",
+            item.get("usage_tips") or "",
             metadata_source,
             civitai.get("id"),
             civitai.get("modelId"),
@@ -575,6 +579,7 @@ class PersistentModelCache:
             1 if item.get("db_checked") else 0,
             float(item.get("last_checked_at") or 0.0),
             item.get("hash_status", "completed"),
+            item.get("hf_url") or "",
         )
 
     def _insert_model_sql(self) -> str:

py/services/recipes/analysis_service.py

@@ -146,11 +146,38 @@ class RecipeAnalysisService:
                 ):
                     metadata = metadata["meta"]
 
-                # Include modelVersionIds from root level if available
-                # Civitai API returns modelVersionIds at root level, not in meta
+                # Include modelVersionIds from root level if available.
+                # CivitAI API returns modelVersionIds at root level, not in meta.
+                # When meta is null (None), create a minimal dict so downstream
+                # parsers can still discover LoRAs and checkpoints.
                 model_version_ids = image_info.get("modelVersionIds")
-                if model_version_ids and isinstance(metadata, dict):
-                    metadata["modelVersionIds"] = model_version_ids
+                if model_version_ids:
+                    if isinstance(metadata, dict):
+                        metadata["modelVersionIds"] = model_version_ids
+                    else:
+                        metadata = {"modelVersionIds": model_version_ids}
+
+                # Inject browsingLevel (canonical integer) so the recipe's
+                # preview_nsfw_level can be set, enabling proper NSFW blur
+                # of the preview image.  Fall back to nsfwLevel (string)
+                # when browsingLevel is absent.
+                if isinstance(metadata, dict):
+                    browsing_level = image_info.get("browsingLevel")
+                    nsfw_level_str = image_info.get("nsfwLevel")
+                    if isinstance(browsing_level, int) and browsing_level > 0:
+                        metadata["browsingLevel"] = browsing_level
+                    elif (
+                        isinstance(nsfw_level_str, str)
+                        and nsfw_level_str
+                        in (
+                            "PG", "PG13", "R", "X", "XXX", "Blocked",
+                        )
+                    ):
+                        from ...utils.constants import NSFW_LEVELS
+
+                        metadata["browsingLevel"] = NSFW_LEVELS.get(
+                            nsfw_level_str, 0
+                        )
 
                 # Validate that metadata contains meaningful recipe fields
                 # If not, treat as None to trigger EXIF extraction from downloaded image
@@ -171,12 +198,19 @@ class RecipeAnalysisService:
                 temp_path = self._create_temp_path(suffix=extension)
                 await self._download_image(url, temp_path)
 
-            if metadata is None and not is_video:
-                metadata = await asyncio.to_thread(
+            # Always extract EXIF from the downloaded image for generation
+            # params (prompt, negative prompt, sampler, steps, etc.).
+            # Previously this was gated on ``metadata is None``, but that
+            # skipped EXIF entirely when API metadata (modelVersionIds,
+            # browsingLevel) is present, losing all generation parameters.
+            exif_metadata = None
+            if not is_video:
+                exif_metadata = await asyncio.to_thread(
                     self._exif_utils.extract_image_metadata, temp_path
                 )
 
-            if not metadata and civitai_image_id and image_info:
+            # Fallback: try the original (non-optimized) image for EXIF data
+            if not exif_metadata and civitai_image_id and image_info:
                 original_url = image_info.get("url")
                 if original_url:
                     self._logger.debug(
@@ -187,15 +221,38 @@ class RecipeAnalysisService:
                     orig_temp_path = self._create_temp_path(suffix=".png")
                     try:
                         await self._download_image(original_url, orig_temp_path)
-                        metadata = await asyncio.to_thread(
+                        exif_metadata = await asyncio.to_thread(
                             self._exif_utils.extract_image_metadata,
                             orig_temp_path,
                         )
                     finally:
                         self._safe_cleanup(orig_temp_path)
 
+            # Parse EXIF data (typically a string like parameters/prompt/workflow)
+            # and API metadata (dict with modelVersionIds, browsingLevel) separately,
+            # then merge: API loras/checkpoint override, EXIF gen_params fill in gaps.
+            # This mirrors the two-pass approach in _do_import_from_url.
+            exif_parsed_result = None
+            if isinstance(exif_metadata, str):
+                exif_parser = self._recipe_parser_factory.create_parser(exif_metadata)
+                if exif_parser:
+                    exif_data = await exif_parser.parse_metadata(
+                        exif_metadata, recipe_scanner=recipe_scanner,
+                    )
+                    if exif_data and not exif_data.get("error"):
+                        exif_parsed_result = exif_data
+
+            # Merge API metadata (dict) with EXIF data (if dict) for the
+            # CivitaiApiMetadataParser.  If EXIF data is a string it was
+            # parsed above — don't try to merge a string into a dict.
+            merged = {}
+            if isinstance(exif_metadata, dict):
+                merged.update(exif_metadata)
+            if isinstance(metadata, dict):
+                merged.update(metadata)
+
             result = await self._parse_metadata(
-                metadata or {},
+                merged,
                 recipe_scanner=recipe_scanner,
                 image_path=temp_path,
                 include_image_base64=True,
@@ -203,13 +260,23 @@ class RecipeAnalysisService:
                 extension=extension,
             )
 
-            if civitai_image_id and image_info and not result.payload.get("error"):
-                mvid = image_info.get("modelVersionId")
-                if not mvid:
-                    mvids = image_info.get("modelVersionIds")
-                    if isinstance(mvids, list) and mvids:
-                        mvid = mvids[0]
+            # Merge EXIF string-parsed gen_params into the API result.
+            # API gen_params take priority (they come later via update).
+            if exif_parsed_result and not result.payload.get("error"):
+                exif_gp = exif_parsed_result.get("gen_params") or {}
+                result_gp = result.payload.get("gen_params") or {}
+                merged_gp = {**exif_gp, **result_gp}
+                if merged_gp:
+                    result.payload["gen_params"] = merged_gp
 
+            if civitai_image_id and image_info and not result.payload.get("error"):
+                # Use the metadata dict we built (may contain modelVersionIds
+                # and browsingLevel from the API root level).  Do NOT pass
+                # image_info.get("meta") — it is null for images whose meta
+                # lives at the root level only.  Also do NOT derive
+                # model_version_id from modelVersionIds[0] — that array mixes
+                # checkpoints, LoRAs, and other types without ordering
+                # guarantees; the parser already resolved them correctly.
                 recipe_for_enrich = {
                     "gen_params": result.payload.get("gen_params", {}),
                     "loras": result.payload.get("loras", []),
@@ -222,8 +289,10 @@ class RecipeAnalysisService:
                     recipe=recipe_for_enrich,
                     civitai_client=civitai_client,
                     request_params=None,
-                    prefetched_civitai_meta_raw=image_info.get("meta"),
-                    prefetched_model_version_id=mvid,
+                    prefetched_civitai_meta_raw=(
+                        metadata if isinstance(metadata, dict) else None
+                    ),
+                    prefetched_model_version_id=None,
                 )
 
                 result.payload["gen_params"] = recipe_for_enrich["gen_params"]
@@ -232,6 +301,12 @@ class RecipeAnalysisService:
                 if recipe_for_enrich.get("base_model"):
                     result.payload["base_model"] = recipe_for_enrich["base_model"]
 
+                # Extract browsingLevel from our constructed metadata for NSFW blur
+                if isinstance(metadata, dict):
+                    bl = metadata.get("browsingLevel")
+                    if isinstance(bl, int) and bl > 0:
+                        result.payload["preview_nsfw_level"] = bl
+
             return result
         finally:
             if temp_path:
@@ -314,6 +389,10 @@ class RecipeAnalysisService:
             "prompt_type",
             "positive",
             "negative",
+            # modelVersionIds is injected at the root level by CivitAI's image
+            # API when meta is null. It carries the version IDs of ALL models
+            # (checkpoint + LoRAs) used to generate the image.
+            "modelVersionIds",
         }
         return any(field in metadata for field in recipe_fields)
 

py/services/settings_manager.py

@@ -98,13 +98,20 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "lora_syntax_format": "legacy",
     "model_card_footer_action": "replace_preview",
     "show_version_on_card": True,
-    "update_flag_strategy": "same_base",
+    "version_grouping": "same_base",
     "auto_organize_exclusions": [],
     "metadata_refresh_skip_paths": [],
     "skip_previously_downloaded_model_versions": False,
     "download_skip_base_models": [],
     "backup_auto_enabled": True,
     "backup_retention_count": 5,
+    "use_new_license_icons": True,
+    "group_by_model": False,
+    # AI / LLM provider configuration (BYOK)
+    "llm_provider": "openai",  # "openai" | "ollama" | "custom"
+    "llm_api_key": "",
+    "llm_api_base": "",  # empty = provider default
+    "llm_model": "",  # e.g. "gpt-4o-mini"
 }
 
 
@@ -133,6 +140,9 @@ class SettingsManager:
         self._template_path = (
             Path(__file__).resolve().parents[2] / "settings.json.example"
         )
+        # Known placeholder value in settings.json.example; any file containing
+        # this value should be treated as "not configured".
+        self._TEMPLATE_PLACEHOLDER_API_KEY = "your_civitai_api_key_here"
         self.settings = self._load_settings()
         self._migrate_setting_keys()
         self._ensure_default_settings()
@@ -164,6 +174,12 @@ class SettingsManager:
                     self._original_disk_payload = copy.deepcopy(data)
                     if self._matches_template_payload(data):
                         self._preserve_disk_template = True
+                    # Clean up the template placeholder so it is not treated
+                    # as a real key (affects both the frontend boolean and
+                    # the downloader's Authorization header).
+                    placeholder = self._TEMPLATE_PLACEHOLDER_API_KEY
+                    if data.get("civitai_api_key") == placeholder:
+                        data["civitai_api_key"] = ""
                 return data
             except json.JSONDecodeError as exc:
                 logger.error("Failed to parse settings.json: %s", exc)
@@ -734,6 +750,7 @@ class SettingsManager:
             "includeTriggerWords": "include_trigger_words",
             "compactMode": "compact_mode",
             "modelCardFooterAction": "model_card_footer_action",
+            "update_flag_strategy": "version_grouping",
         }
 
         updated = False
@@ -861,6 +878,23 @@ class SettingsManager:
             self.settings["civitai_api_key"] = env_api_key
             self._save_settings()
 
+        # LLM provider overrides
+        llm_env_map = {
+            "LLM_API_KEY": "llm_api_key",
+            "LLM_MODEL": "llm_model",
+            "LLM_API_BASE": "llm_api_base",
+            "LLM_PROVIDER": "llm_provider",
+        }
+        llm_changed = False
+        for env_var, settings_key in llm_env_map.items():
+            env_val = os.environ.get(env_var)
+            if env_val:
+                logger.info("Found %s environment variable", env_var)
+                self.settings[settings_key] = env_val
+                llm_changed = True
+        if llm_changed:
+            self._save_settings()
+
     def _default_settings_actions(self) -> List[Dict[str, Any]]:
         return [
             {
@@ -1556,7 +1590,7 @@ class SettingsManager:
         previous_dir = os.path.dirname(previous_path) or target_dir
 
         if os.path.abspath(previous_path) != os.path.abspath(target_path):
-            self._copy_model_cache_directory(previous_dir, target_dir)
+            self._migrate_settings_directory_content(previous_dir, target_dir)
             logger.info("Switching settings file to: %s", target_path)
 
         self._pending_portable_switch = {"other_path": other_path}
@@ -1591,46 +1625,52 @@ class SettingsManager:
         finally:
             self._pending_portable_switch = None
 
-    def _copy_model_cache_directory(self, source_dir: str, target_dir: str) -> None:
-        """Copy model_cache artifacts when switching storage locations."""
+    def _migrate_settings_directory_content(
+        self, source_dir: str, target_dir: str
+    ) -> None:
+        """Migrate settings directory subdirectories when switching storage locations.
+
+        Copies the canonical subdirectories (cache, backups, logs, stats, wildcards)
+        from the old settings directory to the new one. Legacy cache artifacts
+        (model_cache, recipe_cache, etc.) are migrated lazily by
+        ``resolve_cache_path_with_migration`` on first access.
+
+        Args:
+            source_dir: The previous settings directory path.
+            target_dir: The new settings directory path.
+        """
 
         if not source_dir or not target_dir:
             return
 
-        source_cache_dir = os.path.join(source_dir, "model_cache")
-        target_cache_dir = os.path.join(target_dir, "model_cache")
-        if os.path.isdir(source_cache_dir) and os.path.abspath(
-            source_cache_dir
-        ) != os.path.abspath(target_cache_dir):
-            try:
-                shutil.copytree(
-                    source_cache_dir,
-                    target_cache_dir,
-                    dirs_exist_ok=True,
-                    ignore=shutil.ignore_patterns("*.sqlite-shm", "*.sqlite-wal"),
-                )
-            except Exception as exc:
-                logger.warning(
-                    "Failed to copy model_cache directory from %s to %s: %s",
-                    source_cache_dir,
-                    target_cache_dir,
-                    exc,
-                )
+        def _copy_dir(name: str) -> None:
+            source = os.path.join(source_dir, name)
+            target = os.path.join(target_dir, name)
+            if os.path.isdir(source) and os.path.abspath(source) != os.path.abspath(
+                target
+            ):
+                try:
+                    shutil.copytree(
+                        source,
+                        target,
+                        dirs_exist_ok=True,
+                        ignore=shutil.ignore_patterns("*.sqlite-shm", "*.sqlite-wal"),
+                    )
+                except Exception as exc:
+                    logger.warning(
+                        "Failed to copy directory %s from %s to %s: %s",
+                        name,
+                        source,
+                        target,
+                        exc,
+                    )
 
-        source_cache_file = os.path.join(source_dir, "model_cache.sqlite")
-        target_cache_file = os.path.join(target_dir, "model_cache.sqlite")
-        if os.path.isfile(source_cache_file) and os.path.abspath(
-            source_cache_file
-        ) != os.path.abspath(target_cache_file):
-            try:
-                shutil.copy2(source_cache_file, target_cache_file)
-            except Exception as exc:
-                logger.warning(
-                    "Failed to copy model_cache.sqlite from %s to %s: %s",
-                    source_cache_file,
-                    target_cache_file,
-                    exc,
-                )
+        # Managed subdirectories under settings_dir
+        _copy_dir("cache")
+        _copy_dir("backups")
+        _copy_dir("logs")
+        _copy_dir("stats")
+        _copy_dir("wildcards")
 
     def _get_user_config_directory(self) -> str:
         """Return the user configuration directory, falling back to ~/.config."""

py/services/tag_update_service.py

@@ -36,9 +36,9 @@ class TagUpdateService:
             if isinstance(tag, str) and tag.strip():
                 # Convert all tags to lowercase to avoid case sensitivity issues on Windows
                 normalized = tag.strip().lower()
-                if normalized.lower() not in existing_lower:
+                if normalized not in existing_lower:
                     existing_tags.append(normalized)
-                    existing_lower.append(normalized.lower())
+                    existing_lower.append(normalized)
                     tags_added.append(normalized)
 
         metadata["tags"] = existing_tags

py/utils/constants.py

@@ -47,6 +47,20 @@ SUPPORTED_MEDIA_EXTENSIONS = {
     "videos": [".mp4", ".webm"],
 }
 
+# Model weight file extensions recognised by scanners.
+# This is the union of all scanner extensions (lora, checkpoint, embedding).
+MODEL_FILE_EXTENSIONS = {
+    ".safetensors",
+    ".ckpt",
+    ".pt",
+    ".pt2",
+    ".bin",
+    ".pth",
+    ".pkl",
+    ".sft",
+    ".gguf",
+}
+
 # Valid sub-types for each scanner type
 VALID_LORA_SUB_TYPES = ["lora", "locon", "dora"]
 VALID_CHECKPOINT_SUB_TYPES = ["checkpoint", "diffusion_model"]
@@ -147,6 +161,8 @@ DIFFUSION_MODEL_BASE_MODELS = frozenset(
         "Qwen",
         "ZImageBase",
         "ZImageTurbo",
+        # Krea 2 — loaded via UNETLoader in ComfyUI
+        "Krea 2",
     ]
 )
 
@@ -213,5 +229,6 @@ SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS = frozenset(
         "Ernie",
         "Ernie Turbo",
         "Nucleus",
+        "Krea 2",
     ]
 )

py/utils/example_images_paths.py

@@ -12,6 +12,18 @@ from ..services.settings_manager import get_settings_manager
 
 _HEX_PATTERN = re.compile(r"[a-fA-F0-9]{64}")
 
+# Filesystem/metadata files that are never created by the example images system
+# and are safe to ignore during validation. The cleanup service only operates on
+# directories, so these files pose no data-loss risk.
+_SAFE_FILENAMES: frozenset[str] = frozenset({
+    ".DS_Store",       # macOS folder metadata
+    "Thumbs.db",       # Windows thumbnail cache
+    "desktop.ini",     # Windows folder customization
+    ".localized",      # macOS folder name localization
+    ".gitkeep",        # Placeholder to keep empty dirs in git
+    ".gitignore",      # Git ignore rules
+})
+
 logger = logging.getLogger(__name__)
 
 
@@ -180,6 +192,22 @@ def is_hash_folder(name: str) -> bool:
     return bool(_HEX_PATTERN.fullmatch(name or ""))
 
 
+def _is_safe_ignorable_entry(item: str, item_path: str) -> bool:
+    """Return True if *item* is a harmless system/hidden file we can skip.
+
+    These files are never created by the example images system and are safe to
+    ignore because the cleanup/delete operations only act on **directories**,
+    never on individual files (other than ``.download_progress.json``).
+    """
+    if item in _SAFE_FILENAMES:
+        return True
+    # Hide Unix hidden files (dotfiles) that are regular files,
+    # since the cleanup system never deletes or moves files.
+    if item.startswith(".") and os.path.isfile(item_path):
+        return True
+    return False
+
+
 def is_valid_example_images_root(folder_path: str) -> bool:
     """Check whether a folder looks like a dedicated example images root."""
 
@@ -190,9 +218,16 @@ def is_valid_example_images_root(folder_path: str) -> bool:
 
     for item in items:
         item_path = os.path.join(folder_path, item)
+
+        # .download_progress.json is an expected metadata file — check before
+        # the generic dotfile rule so it stays explicitly documented.
         if item == ".download_progress.json" and os.path.isfile(item_path):
             continue
 
+        # Skip harmless system/hidden files — cleanup only touches directories
+        if _is_safe_ignorable_entry(item, item_path):
+            continue
+
         if os.path.isdir(item_path):
             if is_hash_folder(item):
                 continue
@@ -211,6 +246,41 @@ def is_valid_example_images_root(folder_path: str) -> bool:
     return True
 
 
+def find_non_compliant_items_in_example_images_root(folder_path: str) -> list[str]:
+    """Return the names of items that prevent *folder_path* from being a valid
+    example images root, or an empty list if the folder is valid.
+
+    This mirrors ``is_valid_example_images_root`` but **returns** the offending
+    names instead of a boolean, so callers can produce actionable error messages.
+    """
+    try:
+        items = os.listdir(folder_path)
+    except OSError as exc:
+        return [f"<cannot list directory: {exc}>"]
+
+    offending: list[str] = []
+
+    for item in items:
+        item_path = os.path.join(folder_path, item)
+
+        # Same skip rules as is_valid_example_images_root
+        if item == ".download_progress.json" and os.path.isfile(item_path):
+            continue
+        if _is_safe_ignorable_entry(item, item_path):
+            continue
+        if os.path.isdir(item_path):
+            if is_hash_folder(item):
+                continue
+            if item == "_deleted":
+                continue
+            if _library_folder_has_only_hash_dirs(item_path):
+                continue
+
+        offending.append(item)
+
+    return offending
+
+
 def _library_folder_has_only_hash_dirs(path: str) -> bool:
     """Return True when a library subfolder only contains hash folders or metadata files."""
 

py/utils/file_utils.py

@@ -39,6 +39,9 @@ async def calculate_sha256(file_path: str) -> str:
     Uses ``posix_fadvise`` with ``POSIX_FADV_DONTNEED`` to avoid polluting the OS page
     cache — critical on WSL where cached file pages live inside the VM and are not
     accounted for in guest ``used`` memory, causing VmmemWSL to balloon.
+
+    On Windows/macOS where ``posix_fadvise`` is not available the hint is silently
+    skipped.
     """
     sha256_hash = hashlib.sha256()
     chunk_size = _get_hash_chunk_size_bytes()
@@ -48,7 +51,9 @@ async def calculate_sha256(file_path: str) -> str:
             sha256_hash.update(byte_block)
         # Evict pages after reading so the data doesn't linger in the kernel page
         # cache — on WSL this otherwise appears as unreclaimable VmmemWSL growth.
-        os.posix_fadvise(fd, 0, 0, os.POSIX_FADV_DONTNEED)
+        # Guard against platforms (Windows, macOS) that lack posix_fadvise.
+        if hasattr(os, "posix_fadvise") and hasattr(os, "POSIX_FADV_DONTNEED"):
+            os.posix_fadvise(fd, 0, 0, os.POSIX_FADV_DONTNEED)
     return sha256_hash.hexdigest()
 
 

py/utils/models.py

@@ -35,6 +35,9 @@ class BaseModelMetadata:
     metadata_source: Optional[str] = None  # Last provider that supplied metadata
     last_checked_at: float = 0  # Last checked timestamp
     hash_status: str = "completed"  # Hash calculation status: pending | calculating | completed | failed
+    trainedWords: List[str] = field(
+        default_factory=list
+    )  # Trigger words / activation prompts (source-agnostic)
     _unknown_fields: Dict[str, Any] = field(
         default_factory=dict, repr=False, compare=False
     )  # Store unknown fields
@@ -47,6 +50,9 @@ class BaseModelMetadata:
         if self.tags is None:
             self.tags = []
 
+        if self.trainedWords is None:
+            self.trainedWords = []
+
     @classmethod
     def from_dict(cls, data: Dict) -> "BaseModelMetadata":
         """Create instance from dictionary"""

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "1.1.2"
+version = "1.1.6"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

refs/civarc_api_model_data.json

@@ -1,134 +0,0 @@
-{
-    "id": 1746460,
-    "name": "Mixplin Style [Illustrious]",
-    "type": "LORA",
-    "description": "description",
-    "username": "Ty_Lee",
-    "downloadCount": 4207,
-    "favoriteCount": 0,
-    "commentCount": 8,
-    "ratingCount": 0,
-    "rating": 0,
-    "is_nsfw": true,
-    "nsfw_level": 31,
-    "createdAt": "2025-07-06T01:51:42.859Z",
-    "updatedAt": "2025-10-10T23:15:26.714Z",
-    "deletedAt": null,
-    "tags": [
-        "art",
-        "style",
-        "artist style",
-        "styles",
-        "mixplin",
-        "artiststyle"
-    ],
-    "creator_id": "Ty_Lee",
-    "creator_username": "Ty_Lee",
-    "creator_name": "Ty_Lee",
-    "creator_url": "/users/Ty_Lee",
-    "versions": [
-        {
-            "id": 2042594,
-            "name": "v2.0",
-            "href": "/models/1746460?modelVersionId=2042594"
-        },
-        {
-            "id": 1976567,
-            "name": "v1.0",
-            "href": "/models/1746460?modelVersionId=1976567"
-        }
-    ],
-    "version": {
-        "id": 1976567,
-        "modelId": 1746460,
-        "name": "v1.0",
-        "baseModel": "Illustrious",
-        "baseModelType": "Standard",
-        "description": null,
-        "downloadCount": 437,
-        "ratingCount": 0,
-        "rating": 0,
-        "is_nsfw": true,
-        "nsfw_level": 31,
-        "createdAt": "2025-07-05T10:17:28.716Z",
-        "updatedAt": "2025-10-10T23:15:26.756Z",
-        "deletedAt": null,
-        "files": [
-            {
-                "id": 1874043,
-                "name": "mxpln-illustrious-ty_lee.safetensors",
-                "type": "Model",
-                "sizeKB": 223124.37109375,
-                "downloadUrl": "https://civitai.com/api/download/models/1976567",
-                "modelId": 1746460,
-                "modelName": "Mixplin Style [Illustrious]",
-                "modelVersionId": 1976567,
-                "is_nsfw": true,
-                "nsfw_level": 31,
-                "sha256": "e2b7a280d6539556f23f380b3f71e4e22bc4524445c4c96526e117c6005c6ad3",
-                "createdAt": "2025-07-05T10:17:28.716Z",
-                "updatedAt": "2025-10-10T23:15:26.766Z",
-                "is_primary": false,
-                "mirrors": [
-                    {
-                        "filename": "mxpln-illustrious-ty_lee.safetensors",
-                        "url": "https://civitai.com/api/download/models/1976567",
-                        "source": "civitai",
-                        "model_id": 1746460,
-                        "model_version_id": 1976567,
-                        "deletedAt": null,
-                        "is_gated": false,
-                        "is_paid": false
-                    }
-                ]
-            }
-        ],
-        "images": [
-            {
-                "id": 86403595,
-                "url": "https://img.genur.art/sig/width:450/quality:85/aHR0cHM6Ly9jLmdlbnVyLmFydC9hNmE3Njc2YS0wMWQ3LTQ1YzAtOWEzYS1mNWJiYTU4MDNiMDE=",
-                "nsfwLevel": 1,
-                "width": 1560,
-                "height": 2280,
-                "hash": "U7G8Zp0w02%IA6%N00-;D]-W~VNG0nMw-.IV",
-                "type": "image",
-                "minor": false,
-                "poi": false,
-                "hasMeta": true,
-                "hasPositivePrompt": true,
-                "onSite": false,
-                "remixOfId": null,
-                "image_url": "https://img.genur.art/sig/width:450/quality:85/aHR0cHM6Ly9jLmdlbnVyLmFydC9hNmE3Njc2YS0wMWQ3LTQ1YzAtOWEzYS1mNWJiYTU4MDNiMDE=",
-                "link": "https://genur.art/posts/86403595"
-            }
-        ],
-        "trigger": [
-            "mxpln"
-        ],
-        "allow_download": true,
-        "download_url": "/api/download/models/1976567",
-        "platform_url": "https://civitai.com/models/1746460?modelVersionId=1976567",
-        "civitai_model_id": 1746460,
-        "civitai_model_version_id": 1976567,
-        "href": "/models/1746460?modelVersionId=1976567",
-        "mirrors": [
-            {
-                "platform": "tensorart",
-                "href": "/tensorart/models/904473536033245448/versions/904473536033245448",
-                "platform_url": "https://tensor.art/models/904473536033245448",
-                "name": "Mixplin Style MXP",
-                "version_name": "Mixplin",
-                "id": "904473536033245448",
-                "version_id": "904473536033245448"
-            }
-        ]
-    },
-    "platform": "civitai",
-    "platform_name": "CivitAI",
-    "meta": {
-        "title": "Mixplin Style [Illustrious] - v1.0 - CivitAI Archive",
-        "description": "Mixplin Style [Illustrious] v1.0 is a Illustrious LORA AI model created by Ty_Lee for generating images of art, style, artist style, styles, mixplin, artiststyle",
-        "image": "https://img.genur.art/sig/width:450/quality:85/aHR0cHM6Ly9jLmdlbnVyLmFydC9hNmE3Njc2YS0wMWQ3LTQ1YzAtOWEzYS1mNWJiYTU4MDNiMDE=",
-        "canonical": "https://civarchive.com/models/1746460?modelVersionId=1976567"
-    }
-}

refs/civitai.sql

@@ -1,38 +0,0 @@
-CREATE TABLE models (
-            id         INTEGER PRIMARY KEY,
-            name       TEXT NOT NULL,
-            type       TEXT NOT NULL,
-            username   TEXT,
-            data       TEXT NOT NULL,
-            created_at INTEGER NOT NULL,
-            updated_at INTEGER NOT NULL
-        ) STRICT;
-CREATE TABLE model_versions (
-            id           INTEGER PRIMARY KEY,
-            model_id     INTEGER NOT NULL,
-            position     INTEGER NOT NULL,
-            name         TEXT NOT NULL,
-            base_model   TEXT NOT NULL,
-            published_at INTEGER,
-            data         TEXT NOT NULL,
-            created_at   INTEGER NOT NULL,
-            updated_at   INTEGER NOT NULL
-        ) STRICT;
-CREATE INDEX model_versions_model_id_idx ON model_versions (model_id);
-CREATE TABLE model_files (
-            id               INTEGER PRIMARY KEY,
-            model_id         INTEGER NOT NULL,
-            version_id       INTEGER NOT NULL,
-            type             TEXT NOT NULL,
-            sha256           TEXT,
-            data             TEXT NOT NULL,
-            created_at       INTEGER NOT NULL,
-            updated_at       INTEGER NOT NULL
-        ) STRICT;
-CREATE INDEX model_files_model_id_idx ON model_files (model_id);
-CREATE INDEX model_files_version_id_idx ON model_files (version_id);
-CREATE TABLE archived_model_files (
-            file_id INTEGER PRIMARY KEY,
-            model_id INTEGER NOT NULL,
-            version_id INTEGER NOT NULL
-        ) STRICT;

refs/civitai_api_model_by_modelId.json

@@ -1,110 +0,0 @@
-{
-    "id": 1231067,
-    "name": "Vivid Impressions Storybook Style",
-    "description": "<h3 id=\"if-you'd-like-to-support-me-feel-free-to-visit-my-ko-fi-page.-please-share-your-images-using-the-&quot;+add-post&quot;-button-below.-it-supports-the-creators.-thanks!-nnfwkvfly\">If you'd like to support me, feel free to visit my <a target=\"_blank\" rel=\"ugc\" href=\"https://ko-fi.com/pixelpawsai\">Ko-Fi</a> page. ❤️<br /><br />Please share your images using the \"<span style=\"color:rgb(250, 82, 82)\">+add post</span>\" button below. It supports the creators. Thanks! 💕</h3><h3 id=\"if-you-like-my-lora-please-like-comment-or-donate-some-buzz.-much-appreciated!-vyeqok3go\">If you like my LoRA, please<span style=\"color:rgb(230, 73, 128)\"> </span><span style=\"color:rgb(250, 82, 82)\">like</span>, <span style=\"color:rgb(250, 82, 82)\">comment</span>, or <span style=\"color:#fa5252\">donate some Buzz</span>. Much appreciated! ❤️</h3><h3 id=\"-lo912t8rj\"></h3><h3 id=\"trigger-word:-ppstorybook-wlggllim2\"><strong><span style=\"color:rgb(253, 126, 20)\">Trigger word: </span></strong>ppstorybook</h3><h3 id=\"strength:-0.8-experiment-as-you-like-luvhks6za\"><strong><span style=\"color:rgb(253, 126, 20)\">Strength: </span></strong>0.8, experiment as you like</h3>",
-    "allowNoCredit": true,
-    "allowCommercialUse": [
-        "Image",
-        "RentCivit",
-        "Rent",
-        "Sell"
-    ],
-    "allowDerivatives": true,
-    "allowDifferentLicense": true,
-    "type": "LORA",
-    "minor": false,
-    "sfwOnly": false,
-    "poi": false,
-    "nsfw": false,
-    "nsfwLevel": 1,
-    "availability": "Public",
-    "cosmetic": null,
-    "supportsGeneration": true,
-    "stats": {
-        "downloadCount": 2183,
-        "favoriteCount": 0,
-        "thumbsUpCount": 416,
-        "thumbsDownCount": 0,
-        "commentCount": 12,
-        "ratingCount": 0,
-        "rating": 0,
-        "tippedAmountCount": 360
-    },
-    "creator": {
-        "username": "PixelPawsAI",
-        "image": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f3a1aa7c-0159-4dd8-884a-1e7ceb350f96/width=96/PixelPawsAI.jpeg"
-    },
-    "tags": [
-        "style",
-        "illustration",
-        "storybook"
-    ],
-    "modelVersions": [
-        {
-            "id": 1387174,
-            "index": 0,
-            "name": "v1.0",
-            "baseModel": "Flux.1 D",
-            "baseModelType": "Standard",
-            "createdAt": "2025-02-08T11:15:47.197Z",
-            "publishedAt": "2025-02-08T11:29:04.487Z",
-            "status": "Published",
-            "availability": "Public",
-            "nsfwLevel": 1,
-            "trainedWords": [
-                "ppstorybook"
-            ],
-            "covered": true,
-            "stats": {
-                "downloadCount": 2183,
-                "ratingCount": 0,
-                "rating": 0,
-                "thumbsUpCount": 416,
-                "thumbsDownCount": 0
-            },
-            "files": [
-                {
-                    "id": 1289799,
-                    "sizeKB": 18829.1484375,
-                    "name": "pp-storybook_rank2_bf16.safetensors",
-                    "type": "Model",
-                    "pickleScanResult": "Success",
-                    "pickleScanMessage": "No Pickle imports",
-                    "virusScanResult": "Success",
-                    "virusScanMessage": null,
-                    "scannedAt": "2025-02-08T11:21:04.247Z",
-                    "metadata": {
-                        "format": "SafeTensor"
-                    },
-                    "hashes": {
-                        "AutoV1": "F414C813",
-                        "AutoV2": "9753338AB6",
-                        "SHA256": "9753338AB693CA82BF89ED77A5D1912879E40051463EC6E330FB9866CE798668",
-                        "CRC32": "A65AE7B3",
-                        "BLAKE3": "A5F8AB95AC2486345E4ACCAE541FF19D97ED53EFB0A7CC9226636975A0437591",
-                        "AutoV3": "34A22376739D"
-                    },
-                    "downloadUrl": "https://civitai.com/api/download/models/1387174",
-                    "primary": true
-                }
-            ],
-            "images": [
-                {
-                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/42b875cf-c62b-41fa-a349-383b7f074351/original=true/56547310.jpeg",
-                    "nsfwLevel": 1,
-                    "width": 832,
-                    "height": 1216,
-                    "hash": "U5IiO6s-4Vn+0~EO^5xa00VsL#IU_O?E7yWC",
-                    "type": "image",
-                    "minor": false,
-                    "poi": false,
-                    "hasMeta": true,
-                    "hasPositivePrompt": true,
-                    "onSite": false,
-                    "remixOfId": null
-                }
-            ],
-            "downloadUrl": "https://civitai.com/api/download/models/1387174"
-        }
-    ]
-}

refs/civitai_api_model_by_versionId.json

@@ -1,100 +0,0 @@
-{
-    "id": 1387174,
-    "modelId": 1231067,
-    "name": "v1.0",
-    "createdAt": "2025-02-08T11:15:47.197Z",
-    "updatedAt": "2025-02-08T11:29:04.526Z",
-    "status": "Published",
-    "publishedAt": "2025-02-08T11:29:04.487Z",
-    "trainedWords": [
-        "ppstorybook"
-    ],
-    "trainingStatus": null,
-    "trainingDetails": null,
-    "baseModel": "Flux.1 D",
-    "baseModelType": null,
-    "earlyAccessEndsAt": null,
-    "earlyAccessConfig": null,
-    "description": null,
-    "uploadType": "Created",
-    "usageControl": "Download",
-    "air": "urn:air:flux1:lora:civitai:1231067@1387174",
-    "stats": {
-        "downloadCount": 1436,
-        "ratingCount": 0,
-        "rating": 0,
-        "thumbsUpCount": 316
-    },
-    "model": {
-        "name": "Vivid Impressions Storybook Style",
-        "type": "LORA",
-        "nsfw": false,
-        "poi": false
-    },
-    "files": [
-        {
-            "id": 1289799,
-            "sizeKB": 18829.1484375,
-            "name": "pp-storybook_rank2_bf16.safetensors",
-            "type": "Model",
-            "pickleScanResult": "Success",
-            "pickleScanMessage": "No Pickle imports",
-            "virusScanResult": "Success",
-            "virusScanMessage": null,
-            "scannedAt": "2025-02-08T11:21:04.247Z",
-            "metadata": {
-                "format": "SafeTensor",
-                "size": null,
-                "fp": null
-            },
-            "hashes": {
-                "AutoV1": "F414C813",
-                "AutoV2": "9753338AB6",
-                "SHA256": "9753338AB693CA82BF89ED77A5D1912879E40051463EC6E330FB9866CE798668",
-                "CRC32": "A65AE7B3",
-                "BLAKE3": "A5F8AB95AC2486345E4ACCAE541FF19D97ED53EFB0A7CC9226636975A0437591",
-                "AutoV3": "34A22376739D"
-            },
-            "primary": true,
-            "downloadUrl": "https://civitai.com/api/download/models/1387174"
-        }
-    ],
-    "images": [
-        {
-            "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/42b875cf-c62b-41fa-a349-383b7f074351/width=832/56547310.jpeg",
-            "nsfwLevel": 1,
-            "width": 832,
-            "height": 1216,
-            "hash": "U5IiO6s-4Vn+0~EO^5xa00VsL#IU_O?E7yWC",
-            "type": "image",
-            "metadata": {
-                "hash": "U5IiO6s-4Vn+0~EO^5xa00VsL#IU_O?E7yWC",
-                "size": 1361590,
-                "width": 832,
-                "height": 1216
-            },
-            "meta": {
-                "Size": "832x1216",
-                "seed": 1116375220995209,
-                "Model": "flux_dev_fp8",
-                "steps": 23,
-                "hashes": {
-                    "model": ""
-                },
-                "prompt": "ppstorybook,A dreamy bunny hopping across a rainbow bridge, with fluffy clouds surrounding it and tiny birds flying alongside, rendered in a magical, soft-focus style with pastel hues and glowing accents.",
-                "Version": "ComfyUI",
-                "sampler": "DPM++ 2M",
-                "cfgScale": 3.5,
-                "clipSkip": 1,
-                "resources": [],
-                "Model hash": ""
-            },
-            "availability": "Public",
-            "hasMeta": true,
-            "hasPositivePrompt": true,
-            "onSite": false,
-            "remixOfId": null
-        }
-    ],
-    "downloadUrl": "https://civitai.com/api/download/models/1387174"
-}

refs/civitai_comfy_metadata.json

@@ -1,153 +0,0 @@
-{
-    "resource-stack": {
-      "class_type": "CheckpointLoaderSimple",
-      "inputs": { "ckpt_name": "urn:air:sdxl:checkpoint:civitai:827184@1410435" }
-    },
-    "resource-stack-1": {
-      "class_type": "LoraLoader",
-      "inputs": {
-        "lora_name": "urn:air:sdxl:lora:civitai:1107767@1253442",
-        "strength_model": 1,
-        "strength_clip": 1,
-        "model": ["resource-stack", 0],
-        "clip": ["resource-stack", 1]
-      }
-    },
-    "resource-stack-2": {
-      "class_type": "LoraLoader",
-      "inputs": {
-        "lora_name": "urn:air:sdxl:lora:civitai:1342708@1516344",
-        "strength_model": 1,
-        "strength_clip": 1,
-        "model": ["resource-stack-1", 0],
-        "clip": ["resource-stack-1", 1]
-      }
-    },
-    "resource-stack-3": {
-      "class_type": "LoraLoader",
-      "inputs": {
-        "lora_name": "urn:air:sdxl:lora:civitai:122359@135867",
-        "strength_model": 1.55,
-        "strength_clip": 1,
-        "model": ["resource-stack-2", 0],
-        "clip": ["resource-stack-2", 1]
-      }
-    },
-    "6": {
-      "class_type": "smZ CLIPTextEncode",
-      "inputs": {
-        "text": "masterpiece, best quality, amazing quality, detailed setting, detailed background, 1girl, yunyun (konosuba), nude, red eyes, hair ornament, braid, hair between eyes,low twintails, pink ribbon, bow, hair bow, pussy, frilled skirt, layered skirt, belt, pink thighhighs, (pussy juice), large insertion, vaginal tugging, pussy grip, detailed skin, detailed soles, stretched pussy, feet in stockings, ass, nipples, medium breasts, french kiss, anus, shocked, nervous, penis awe,  BREAK Professor\u0027s office, college student, pornographic, 1boy, close eyes, (musscular male, detailed large cock), vaginal sex, college office setting, ass grab, fucking, riding, cowgirl, erotic, side view, deep fucking",
-        "parser": "comfy",
-        "text_g": "",
-        "text_l": "",
-        "ascore": 2.5,
-        "width": 0,
-        "height": 0,
-        "crop_w": 0,
-        "crop_h": 0,
-        "target_width": 0,
-        "target_height": 0,
-        "smZ_steps": 1,
-        "mean_normalization": true,
-        "multi_conditioning": true,
-        "use_old_emphasis_implementation": false,
-        "with_SDXL": false,
-        "clip": ["resource-stack-3", 1]
-      },
-      "_meta": { "title": "Positive" }
-    },
-    "7": {
-      "class_type": "smZ CLIPTextEncode",
-      "inputs": {
-        "text": "bad quality,worst quality,worst detail,sketch,censor",
-        "parser": "comfy",
-        "text_g": "",
-        "text_l": "",
-        "ascore": 2.5,
-        "width": 0,
-        "height": 0,
-        "crop_w": 0,
-        "crop_h": 0,
-        "target_width": 0,
-        "target_height": 0,
-        "smZ_steps": 1,
-        "mean_normalization": true,
-        "multi_conditioning": true,
-        "use_old_emphasis_implementation": false,
-        "with_SDXL": false,
-        "clip": ["resource-stack-3", 1]
-      },
-      "_meta": { "title": "Negative" }
-    },
-    "20": {
-      "class_type": "UpscaleModelLoader",
-      "inputs": { "model_name": "urn:air:other:upscaler:civitai:147759@164821" },
-      "_meta": { "title": "Load Upscale Model" }
-    },
-    "17": {
-      "class_type": "LoadImage",
-      "inputs": {
-        "image": "https://orchestration.civitai.com/v2/consumer/blobs/5KZ6358TW8CNEGPZKD08NVDB30",
-        "upload": "image"
-      },
-      "_meta": { "title": "Image Load" }
-    },
-    "19": {
-      "class_type": "ImageUpscaleWithModel",
-      "inputs": { "upscale_model": ["20", 0], "image": ["17", 0] },
-      "_meta": { "title": "Upscale Image (using Model)" }
-    },
-    "23": {
-      "class_type": "ImageScale",
-      "inputs": {
-        "upscale_method": "nearest-exact",
-        "crop": "disabled",
-        "width": 1280,
-        "height": 1856,
-        "image": ["19", 0]
-      },
-      "_meta": { "title": "Upscale Image" }
-    },
-    "21": {
-      "class_type": "VAEEncode",
-      "inputs": { "pixels": ["23", 0], "vae": ["resource-stack", 2] },
-      "_meta": { "title": "VAE Encode" }
-    },
-    "11": {
-      "class_type": "KSampler",
-      "inputs": {
-        "sampler_name": "euler_ancestral",
-        "scheduler": "normal",
-        "seed": 2088370631,
-        "steps": 47,
-        "cfg": 6.5,
-        "denoise": 0.3,
-        "model": ["resource-stack-3", 0],
-        "positive": ["6", 0],
-        "negative": ["7", 0],
-        "latent_image": ["21", 0]
-      },
-      "_meta": { "title": "KSampler" }
-    },
-    "13": {
-      "class_type": "VAEDecode",
-      "inputs": { "samples": ["11", 0], "vae": ["resource-stack", 2] },
-      "_meta": { "title": "VAE Decode" }
-    },
-    "12": {
-      "class_type": "SaveImage",
-      "inputs": { "filename_prefix": "ComfyUI", "images": ["13", 0] },
-      "_meta": { "title": "Save Image" }
-    },
-    "extra": {
-      "airs": [
-        "urn:air:other:upscaler:civitai:147759@164821",
-        "urn:air:sdxl:checkpoint:civitai:827184@1410435",
-        "urn:air:sdxl:lora:civitai:1107767@1253442",
-        "urn:air:sdxl:lora:civitai:1342708@1516344",
-        "urn:air:sdxl:lora:civitai:122359@135867"
-      ]
-    },
-    "extraMetadata": "{\u0022prompt\u0022:\u0022masterpiece, best quality, amazing quality, detailed setting, detailed background, 1girl, yunyun (konosuba), nude, red eyes, hair ornament, braid, hair between eyes,low twintails, pink ribbon, bow, hair bow, pussy, frilled skirt, layered skirt, belt, pink thighhighs, (pussy juice), large insertion, vaginal tugging, pussy grip, detailed skin, detailed soles, stretched pussy, feet in stockings, ass, nipples, medium breasts, french kiss, anus, shocked, nervous, penis awe,  BREAK Professor\u0027s office, college student, pornographic, 1boy, close eyes, (musscular male, detailed large cock), vaginal sex, college office setting, ass grab, fucking, riding, cowgirl, erotic, side view, deep fucking\u0022,\u0022negativePrompt\u0022:\u0022bad quality,worst quality,worst detail,sketch,censor\u0022,\u0022steps\u0022:47,\u0022cfgScale\u0022:6.5,\u0022sampler\u0022:\u0022euler_ancestral\u0022,\u0022workflowId\u0022:\u0022img2img-hires\u0022,\u0022resources\u0022:[{\u0022modelVersionId\u0022:1410435,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:1410435,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:1253442,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:1516344,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:135867,\u0022strength\u0022:1.55}],\u0022remixOfId\u0022:32140259}"
-  }
-  

refs/jpeg_civitai_exif_userComment_example

@@ -1,18 +0,0 @@
-a dynamic and dramatic digital artwork featuring a stylized anthropomorphic white tiger with striking yellow eyes. The tiger is depicted in a powerful stance, wielding a katana with one hand raised above its head. Its fur is detailed with black stripes, and its mane flows wildly, blending with the stormy background. The scene is set amidst swirling dark clouds and flashes of lightning, enhancing the sense of movement and energy. The composition is vertical, with the tiger positioned centrally, creating a sense of depth and intensity. The color palette is dominated by shades of blue, gray, and white, with bright highlights from the lightning. The overall style is reminiscent of fantasy or manga art, with a focus on dynamic action and dramatic lighting.
-Negative prompt:
-Steps: 30, Sampler: Undefined, CFG scale: 3.5, Seed: 90300501, Size: 832x1216, Clip skip: 2, Created Date: 2025-03-05T13:51:18.1770234Z, Civitai resources: [{"type":"checkpoint","modelVersionId":691639,"modelName":"FLUX","modelVersionName":"Dev"},{"type":"lora","weight":0.4,"modelVersionId":1202162,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Gothic Lines"},{"type":"lora","weight":0.8,"modelVersionId":1470588,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Retro"},{"type":"lora","weight":0.75,"modelVersionId":746484,"modelName":"Elden Ring - Yoshitaka Amano","modelVersionName":"V1"},{"type":"lora","weight":0.2,"modelVersionId":914935,"modelName":"Ink-style","modelVersionName":"ink-dynamic"},{"type":"lora","weight":0.2,"modelVersionId":1189379,"modelName":"Painterly Fantasy by ChronoKnight - [FLUX \u0026 IL]","modelVersionName":"FLUX"},{"type":"lora","weight":0.2,"modelVersionId":757030,"modelName":"Mezzotint Artstyle for Flux - by Ethanar","modelVersionName":"V1"}], Civitai metadata: {}
-
-masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject,
-dynamic angle, dutch angle, from below, epic half body portrait, gritty, wabi sabi, looking at viewer, woman is a geisha, parted lips,
-holographic skin, holofoil glitter, faint, glowing, ethereal, neon hair, glowing hair, otherworldly glow, she is dangerous
-<lora:ck-shadow-circuit-IL:0.78>, <lora:ck-nc-cyberpunk-IL-000011:0.4>, <lora:ck-neon-retrowave-IL:0.2>, <lora:ck-yoneyama-mai-IL-000014:0.4>
-Negative prompt: score_6, score_5, score_4, bad quality, worst quality, worst detail, sketch, censorship, furry, window, headphones,
-Steps: 30, Sampler: Euler a, Schedule type: Simple, CFG scale: 7, Seed: 1405717592, Size: 832x1216, Model hash: 1ad6ca7f70, Model: waiNSFWIllustrious_v100, Denoising strength: 0.35, Hires CFG Scale: 5, Hires upscale: 1.3, Hires steps: 20, Hires upscaler: 4x-AnimeSharp, Lora hashes: "ck-shadow-circuit-IL: 88e247aa8c3d, ck-nc-cyberpunk-IL-000011: 935e6755554c, ck-neon-retrowave-IL: edafb9df7da1, ck-yoneyama-mai-IL-000014: 1b9305692a2e", Version: f2.0.1v1.10.1-1.10.1, Diffusion in Low Bits: Automatic (fp16 LoRA)
-
-Masterpiece, best quality, high quality, newest, highres, 8K, HDR, absurdres, 1girl, solo, futuristic warrior, sleek exosuit with glowing energy cores, long braided hair flowing behind, gripping a high-tech bow with an energy arrow drawn, standing on a floating platform overlooking a massive space station, planets and nebulae in the distance, soft glow from distant stars, cinematic depth, foreshortening, dynamic pose, dramatic sci-fi lighting.
-Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
-Steps: 20, Sampler: euler_ancestral_karras, CFG scale: 8.0, Seed: 691121152183439, Model: il\waiNSFWIllustrious_v110.safetensors, Model hash: c3688ee04c, Lora_0 Model name: iLLMythAn1m3Style.safetensors, Lora_0 Model hash: ba7a040786, Lora_0 Strength model: 1.0, Lora_0 Strength clip: 1.0, Hashes: {"model": "c3688ee04c", "lora:iLLMythAn1m3Style": "ba7a040786"}
-
-Immerse yourself in the enchanting journey, where harmonious transmutation of Bauhaus art unites photographic precision and contemporary illustration, capturing an enthralling blend between vivid abstract nature and urban landscapes. Let your eyes be captivated by a kaleidoscope of rich, deep reds and yellows, entwined with intriguing shades that beckon a somber atmosphere. As your spirit ventures along this haunting path, witness the mysterious, high-angle perspective dominated by scattered clouds – granting you a mesmerizing glimpse into the ever-transforming realm of metamorphosing environments. ,<lora:flux/fav/ck-charcoal-drawing-000014.safetensors:1.0:1.0>   
-Negative prompt:
-Steps: 20, Sampler: Euler, CFG scale: 3.5, Seed: 885491426361006, Size: 832x1216, Model hash: 4610115bb0, Model: flux_dev, Hashes: {"LORA:flux/fav/ck-charcoal-drawing-000014.safetensors": "34d36c17c1", "model": "4610115bb0"}, Version: ComfyUI

refs/meta_format.txt

@@ -1,3 +0,0 @@
-In this ethereal masterpiece, metallic sculptures juxtapose effortlessly against a subtle backdrop of misty neutral hues. Exquisite curvatures and geometric shapes converge harmoniously, creating an illuminating realm of polished metallic surfaces. Shimmering copper, gleaming silver, and lustrous gold hues dance in perfect balance, highlighting the intricate play of light and shadow cast upon these celestial forms.  A halo of diffused radiance envelops each piece, enhancing their textured depths and metallic brilliance while allowing delicate details to emerge from obscurity. The composition conveys a serene yet mesmerizing atmosphere, as if suspended in a dreamlike limbo between reality and fantasy. The tantalizing interplay of colors within this transcendent realm creates a profound sense of depth and grandeur that invites the viewer into an enchanting voyage through abstract metallic beauty.  This captivating artwork evokes emotions of boundless curiosity and reverence reminiscent of the timeless works by artists such as Giorgio de Chirico or Paul Klee, while asserting a unique, modern artistic sensibility. With every observation, a new nuance unfolds, as if a never-ending story waiting to be discovered through the lens of metallic artistry.
-Negative prompt:
-Steps: 25, Sampler: dpmpp_2m_sgm_uniform, Seed: 471889513588087, Model: Fluxmania V5P.safetensors, Model hash: 8ae0583b06, VAE: ae.sft, VAE hash: afc8e28272, Lora_0 Model name: ArtVador I.safetensors, Lora_0 Model hash: 08f7133a58, Lora_0 Strength model: 0.65, Lora_0 Strength clip: 0.65, Lora_1 Model name: Kaoru Yamada.safetensors, Lora_1 Model hash: d4893f7202, Lora_1 Strength model: 0.75, Lora_1 Strength clip: 0.75, Hashes: {"model": "8ae0583b06", "vae": "afc8e28272", "lora:ArtVador I": "08f7133a58", "lora:Kaoru Yamada": "d4893f7202"}

refs/recipe.json

@@ -1,33 +0,0 @@
-{
-    "id": "42803a29-02dc-49e1-b798-27da70e8b408",
-    "file_path": "/home/miao/workspace/ComfyUI/models/loras/recipes/test/42803a29-02dc-49e1-b798-27da70e8b408.webp",
-    "title": "masterpiece, best quality, amazing quality, very aesthetic, detailed eyes, perfect",
-    "modified": 1754897325.0507245,
-    "created_date": 1754897325.0507245,
-    "base_model": "Illustrious",
-    "loras": [
-        {
-            "file_name": "",
-            "hash": "1b5b763d83961bb5745f3af8271ba83f1d4fd69c16278dae6d5b4e194bdde97a",
-            "strength": 1.0,
-            "modelVersionId": 2007092,
-            "modelName": "Pony: People's Works +",
-            "modelVersionName": "v8_Illusv1.0",
-            "isDeleted": false,
-            "exclude": false
-        }
-    ],
-    "gen_params": {
-        "prompt": "masterpiece, best quality, amazing quality, very aesthetic, detailed eyes, perfect eyes, realistic eyes,\n(flat colors:1.5), (anime:1.5), (lineart:1.5),\nclose-up, solo, tongue, 1girl, food, (saliva:0.1), open mouth, candy, simple background, blue background, large lollipop, tongue out, fade background, lips, hand up, holding, looking at viewer, licking, seductive, half-closed eyes,",
-        "negative_prompt": "shiny skin,",
-        "steps": 19,
-        "sampler": "Euler a",
-        "cfg_scale": 5,
-        "seed": 1765271748,
-        "size": "832x1216",
-        "clip_skip": 2
-    },
-    "fingerprint": "1b5b763d83961bb5745f3af8271ba83f1d4fd69c16278dae6d5b4e194bdde97a:1.0",
-    "source_path": "https://civitai.com/images/92427432",
-    "folder": "test"
-}

refs/target_version.json

@@ -1,42 +0,0 @@
-{
-  "id": 2269146,
-  "modelId": 2004760,
-  "name": "v1.0 Illustrious",
-  "nsfwLevel": 1,
-  "trainedWords": ["PencilSketchDaal"],
-  "baseModel": "Illustrious",
-  "description": "<p>Illustrious. Your pencil may vary with your checkpoint. </p>",
-  "model": {
-    "name": "Pencil Sketch Anime",
-    "type": "LORA",
-    "nsfw": false,
-    "description": "description",
-    "tags": ["style"],
-    "allowNoCredit": true,
-    "allowCommercialUse": ["Sell"],
-    "allowDerivatives": true,
-    "allowDifferentLicense": true
-  },
-  "files": [
-    {
-      "id": 2161260,
-      "sizeKB": 223106.37890625,
-      "name": "Pencil-Sketch-Illustrious.safetensors",
-      "type": "Model",
-      "hashes": {
-        "SHA256": "2C70479CD673B0FE056EAF4FD97C7F33A39F14853805431AC9AB84226ECE3B82"
-      },
-      "primary": true,
-      "downloadUrl": "https://civitai.com/api/download/models/2269146",
-      "mirrors": {}
-    }
-  ],
-  "images": [
-    {},
-    {}
-  ],
-  "creator": {
-    "username": "Daalis",
-    "image": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/eb245b49-edc8-4ed6-ad7b-6d61eb8c51de/width=96/Daalis.jpeg"
-  }
-}

refs/version.json

@@ -1,91 +0,0 @@
-{
-  "id": 1255556,
-  "modelId": 1117241,
-  "name": "v1.0",
-  "createdAt": "2025-01-08T06:13:08.839Z",
-  "updatedAt": "2025-01-08T06:28:54.156Z",
-  "status": "Published",
-  "publishedAt": "2025-01-08T06:28:54.155Z",
-  "trainedWords": ["in the style of ppWhimsy"],
-  "trainingStatus": null,
-  "trainingDetails": null,
-  "baseModel": "Flux.1 D",
-  "baseModelType": "Standard",
-  "earlyAccessEndsAt": null,
-  "earlyAccessConfig": null,
-  "description": null,
-  "uploadType": "Created",
-  "usageControl": "Download",
-  "air": "urn:air:flux1:lora:civitai:1117241@1255556",
-  "stats": {
-    "downloadCount": 210,
-    "ratingCount": 0,
-    "rating": 0,
-    "thumbsUpCount": 26
-  },
-  "model": {
-    "name": "Enchanted Whimsy style (Flux)",
-    "type": "LORA",
-    "nsfw": false,
-    "poi": false
-  },
-  "files": [
-    {
-      "id": 1160774,
-      "sizeKB": 38828.8125,
-      "name": "pp-enchanted-whimsy.safetensors",
-      "type": "Model",
-      "pickleScanResult": "Success",
-      "pickleScanMessage": "No Pickle imports",
-      "virusScanResult": "Success",
-      "virusScanMessage": null,
-      "scannedAt": "2025-01-08T06:16:27.731Z",
-      "metadata": {
-        "format": "SafeTensor",
-        "size": null,
-        "fp": null
-      },
-      "hashes": {
-        "AutoV1": "40CAF049",
-        "AutoV2": "3202778C3E",
-        "SHA256": "3202778C3EBE5CF7EBE5FC51561DEAE8611F4362036EB7C02EFA033C705E6240",
-        "CRC32": "69DCD953",
-        "BLAKE3": "ED04580DDB1AD36D8B87F4B0800F5930C7E5D4A7269BDC2BE26ED77EA1A34697",
-        "AutoV3": "BF82986F8597"
-      },
-      "primary": true,
-      "downloadUrl": "https://civitai.com/api/download/models/1255556"
-    }
-  ],
-  "images": [
-    {
-      "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/707aef9b-36fb-46c2-ac41-adcab539d3a6/width=832/50270101.jpeg",
-      "nsfwLevel": 1,
-      "width": 832,
-      "height": 1216,
-      "hash": "U7Am@@$^J3%100R;pLR.M]tQ-ps+?wRiVrof",
-      "type": "image",
-      "metadata": {
-        "hash": "U7Am@@$^J3%100R;pLR.M]tQ-ps+?wRiVrof",
-        "size": 702313,
-        "width": 832,
-        "height": 1216
-      },
-      "minor": false,
-      "poi": false,
-      "meta": {
-        "prompt": "in the style of ppWhimsy, a close-up of a boy with a crown of ferns and tiny horns, his eyes wide with wonder as a family of glowing hedgehogs nestle in his hands, their spines shimmering with soft pastel colors"
-      },
-      "availability": "Public",
-      "hasMeta": true,
-      "hasPositivePrompt": true,
-      "onSite": false,
-      "remixOfId": null
-    }
-  ],
-  "downloadUrl": "https://civitai.com/api/download/models/1255556",
-  "creator": {
-    "username": "PixelPawsAI",
-    "image": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f3a1aa7c-0159-4dd8-884a-1e7ceb350f96/width=96/PixelPawsAI.jpeg"
-  }
-}

static/css/components/batch-import-modal.css

@@ -349,8 +349,8 @@
 }
 
 .progress-percentage {
-    font-size: 1.2em;
-    font-weight: 600;
+    font-size: var(--text-lg);
+    font-weight: var(--weight-semibold);
     color: var(--lora-accent);
 }
 
@@ -365,9 +365,9 @@
 
 .progress-bar {
     height: 100%;
-    background: linear-gradient(90deg, var(--lora-accent), oklch(from var(--lora-accent) calc(l + 0.1) c h));
-    border-radius: 4px;
-    transition: width 0.3s ease;
+    background: var(--lora-accent);
+    border-radius: var(--border-radius-xs);
+    transition: width var(--transition-base);
 }
 
 /* Progress Stats */
@@ -389,27 +389,26 @@
 }
 
 .stat-item.success {
-    border-left: 3px solid #00B87A;
+    border-left: 4px solid var(--color-success);
 }
 
 .stat-item.failed {
-    border-left: 3px solid var(--lora-error);
+    border-left: 4px solid var(--color-error);
 }
 
 .stat-item.skipped {
-    border-left: 3px solid var(--lora-warning);
+    border-left: 4px solid var(--color-warning);
 }
 
 .stat-label {
-    font-size: 0.8em;
-    color: var(--text-color);
-    opacity: 0.7;
+    font-size: var(--text-xs);
+    color: var(--text-secondary);
     margin-bottom: 4px;
 }
 
 .stat-value {
-    font-size: 1.4em;
-    font-weight: 600;
+    font-size: var(--text-lg);
+    font-weight: var(--weight-semibold);
     color: var(--text-color);
 }
 
@@ -425,8 +424,7 @@
 }
 
 .current-item-label {
-    color: var(--text-color);
-    opacity: 0.7;
+    color: var(--text-secondary);
     flex-shrink: 0;
 }
 
@@ -449,27 +447,29 @@
 }
 
 .results-header {
-    text-align: center;
+    display: flex;
+    align-items: center;
+    gap: var(--space-2);
     margin-bottom: var(--space-3);
 }
 
 .results-icon {
-    font-size: 3em;
-    color: #00B87A;
-    margin-bottom: var(--space-1);
+    font-size: var(--text-xl);
+    color: var(--color-success);
+    flex-shrink: 0;
 }
 
 .results-icon.warning {
-    color: var(--lora-warning);
+    color: var(--color-warning);
 }
 
 .results-icon.error {
-    color: var(--lora-error);
+    color: var(--color-error);
 }
 
 .results-title {
-    font-size: 1.3em;
-    font-weight: 600;
+    font-size: var(--text-lg);
+    font-weight: var(--weight-semibold);
     color: var(--text-color);
 }
 
@@ -493,27 +493,26 @@
 }
 
 .result-card.success {
-    border-left: 3px solid #00B87A;
+    border-left: 4px solid var(--color-success);
 }
 
 .result-card.failed {
-    border-left: 3px solid var(--lora-error);
+    border-left: 4px solid var(--color-error);
 }
 
 .result-card.skipped {
-    border-left: 3px solid var(--lora-warning);
+    border-left: 4px solid var(--color-warning);
 }
 
 .result-label {
-    font-size: 0.8em;
-    color: var(--text-color);
-    opacity: 0.7;
+    font-size: var(--text-xs);
+    color: var(--text-secondary);
     margin-bottom: 4px;
 }
 
 .result-value {
-    font-size: 1.4em;
-    font-weight: 600;
+    font-size: var(--text-lg);
+    font-weight: var(--weight-semibold);
     color: var(--text-color);
 }
 
@@ -527,13 +526,13 @@
     display: flex;
     align-items: center;
     justify-content: center;
-    gap: 8px;
-    padding: 10px;
+    gap: var(--space-2);
+    padding: var(--space-2);
     cursor: pointer;
     color: var(--lora-accent);
-    font-weight: 500;
+    font-weight: var(--weight-medium);
     border-radius: var(--border-radius-xs);
-    transition: background 0.2s;
+    transition: background var(--transition-base);
 }
 
 .details-toggle:hover {
@@ -541,7 +540,7 @@
 }
 
 .details-toggle i {
-    transition: transform 0.2s;
+    transition: transform var(--transition-base);
 }
 
 .details-toggle.expanded i {
@@ -561,10 +560,10 @@
 .result-item {
     display: flex;
     align-items: center;
-    gap: 10px;
-    padding: 10px 12px;
+    gap: var(--space-2);
+    padding: var(--space-2) var(--space-3);
     border-bottom: 1px solid var(--border-color);
-    font-size: 0.9em;
+    font-size: var(--text-sm);
 }
 
 .result-item:last-child {
@@ -572,28 +571,23 @@
 }
 
 .result-item-status {
-    width: 24px;
-    height: 24px;
-    border-radius: 50%;
     display: flex;
     align-items: center;
     justify-content: center;
-    font-size: 0.8em;
+    font-size: var(--text-sm);
+    flex-shrink: 0;
 }
 
 .result-item-status.success {
-    background: oklch(from #00B87A l c h / 0.2);
-    color: #00B87A;
+    color: var(--color-success);
 }
 
 .result-item-status.failed {
-    background: oklch(from var(--lora-error) l c h / 0.2);
-    color: var(--lora-error);
+    color: var(--color-error);
 }
 
 .result-item-status.skipped {
-    background: oklch(from var(--lora-warning) l c h / 0.2);
-    color: var(--lora-warning);
+    color: var(--color-warning);
 }
 
 .result-item-info {
@@ -610,8 +604,8 @@
 }
 
 .result-item-error {
-    font-size: 0.8em;
-    color: var(--lora-error);
+    font-size: var(--text-xs);
+    color: var(--color-error);
     margin-top: 2px;
 }
 
@@ -661,11 +655,11 @@
 
 /* Completed State */
 .batch-progress-container.completed .progress-bar {
-    background: #00B87A;
+    background: var(--color-success);
 }
 
 .batch-progress-container.completed .status-icon {
-    color: #00B87A;
+    color: var(--color-success);
 }
 
 .batch-progress-container.completed .status-icon i {

static/css/components/bulk.css

@@ -1,6 +1,7 @@
 /* Style for selected cards */
 .model-card.selected {
-    box-shadow: 0 0 0 2px var(--lora-accent);
+    outline: 2px solid var(--lora-accent);
+    outline-offset: -2px;
     position: relative;
 }
 

static/css/components/card.css

@@ -509,6 +509,50 @@
     background: rgba(0,0,0,0.18); /* Optional: subtle background for contrast */
 }
 
+/* Clickable version count link (shown in group-by-model mode) */
+.version-count-link {
+    display: inline-block;
+    color: var(--color-accent);
+    text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.5);
+    font-size: 0.85em;
+    line-height: 1.4;
+    margin-top: 2px;
+    border: 1px solid var(--color-accent-border);
+    border-radius: var(--border-radius-xs);
+    padding: 1px 6px;
+    background: var(--color-accent-subtle);
+    cursor: pointer;
+    transition: background 0.15s ease, border-color 0.15s ease;
+}
+.version-count-link:hover {
+    background: var(--color-accent-border);
+    border-color: var(--color-accent-transparent);
+}
+
+/* Medium density adjustments for version count link */
+.medium-density .version-count-link {
+    font-size: 0.8em;
+}
+
+.medium-density .badge-version-unit .version-count-link {
+    max-width: 90px;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+/* Compact density adjustments for version count link */
+.compact-density .version-count-link {
+    font-size: 0.75em;
+}
+
+.compact-density .badge-version-unit .version-count-link {
+    max-width: 70px;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
 /* Version row — flex container for badges + version names */
 .version-row {
     display: flex;
@@ -690,6 +734,21 @@ body.hide-card-version .hl-badge {
     }
 }
 
+/* Grid-scoped loading overlay (replaces full-page overlay for VirtualScroller refreshes) */
+.grid-loading-overlay {
+    position: absolute;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: var(--lora-bg-transparent, oklch(0% 0 0 / 0.3));
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    z-index: 100;
+    pointer-events: none;
+}
+
 /* Add after the existing .model-card:hover styles */
 
 @keyframes update-pulse {

static/css/components/duplicates.css

@@ -5,10 +5,10 @@
     position: sticky; /* Keep the sticky position */
     top: var(--space-1);
     width: 100%;
-    background-color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1); /* Use accent color with low opacity */
+    background-color: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h) / 0.1); /* Use accent color with low opacity */
     color: var(--text-color);
-    border-top: 1px solid oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.3); /* Add top border with accent color */
-    border-bottom: 1px solid oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.4); /* Make bottom border stronger */
+    border-top: 1px solid oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h) / 0.3); /* Add top border with accent color */
+    border-bottom: 1px solid oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h) / 0.4); /* Make bottom border stronger */
     z-index: var(--z-overlay);
     padding: 12px 0;
     box-shadow: var(--shadow-lg); /* Stronger shadow */
@@ -41,7 +41,7 @@
 
 .duplicates-banner i.fa-exclamation-triangle {
     font-size: 18px;
-    color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    color: oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h));
 }
 
 .duplicates-banner .banner-actions {
@@ -70,7 +70,7 @@
 
 .duplicates-banner button.btn-exit-mode:hover {
     background-color: var(--bg-color);
-    border-color: var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h);
+    border-color: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h));
     transform: translateY(-1px);
 }
 
@@ -92,7 +92,7 @@
 }
 
 .duplicates-banner button:hover {
-    border-color: var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h);
+    border-color: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h));
     background: var(--bg-color);
     transform: translateY(-1px);
     box-shadow: var(--shadow-sm);
@@ -117,7 +117,7 @@
 /* Duplicate groups */
 .duplicate-group {
     position: relative;
-    border: 2px solid oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    border: 2px solid oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h));
     border-radius: var(--border-radius-base);
     padding: 16px;
     margin-bottom: 24px;
@@ -152,7 +152,7 @@
     display: flex;
     justify-content: space-between;
     align-items: center;
-    border-left: 4px solid oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h)); /* Add accent border on the left */
+    border-left: 4px solid oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h)); /* Add accent border on the left */
 }
 
 .duplicate-group-header span:last-child {
@@ -180,7 +180,7 @@
 }
 
 .duplicate-group-header button:hover {
-    border-color: var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h);
+    border-color: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h));
     background: var(--bg-color);
     transform: translateY(-1px);
     box-shadow: var(--shadow-sm);
@@ -235,7 +235,7 @@
 }
 
 .group-toggle-btn:hover {
-    border-color: var(--lora-accent-l) var(--lora-accent-c) var (--lora-accent-h);
+    border-color: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h));
     transform: translateY(-1px);
     box-shadow: var(--shadow-sm);
 }
@@ -247,16 +247,16 @@
 }
 
 .model-card.duplicate:hover {
-    border-color: var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h);
+    border-color: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h));
 }
 
 .model-card.duplicate.latest {
     border-style: solid;
-    border-color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    border-color: oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h));
 }
 
 .model-card.duplicate-selected {
-    border: 2px solid oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
+    border: 2px solid oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h));
     box-shadow: var(--shadow-md);
 }
 
@@ -276,7 +276,7 @@
     position: absolute;
     top: 10px;
     left: 10px;
-    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
+    background: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h));
     color: white;
     font-size: 12px;
     padding: 2px 6px;
@@ -328,7 +328,7 @@
     margin-top: 8px;
     padding-top: 8px;
     border-top: 1px dashed var(--border-color);
-    color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    color: oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h));
     font-weight: bold;
     word-break: break-all; /* Ensure long hashes wrap properly */
 }
@@ -351,12 +351,12 @@
 }
 
 .verification-badge.verified {
-    background-color: oklch(70% 0.2 140); /* Green for verified */
+    background-color: var(--color-success); /* Green for verified */
     color: white;
 }
 
 .verification-badge.mismatch {
-    background-color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    background-color: oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h));
     color: white;
 }
 
@@ -366,7 +366,7 @@
 
 /* Hash Mismatch Styling */
 .model-card.duplicate.hash-mismatch {
-    border: 2px dashed oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    border: 2px dashed oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h));
     opacity: 0.85;
     position: relative;
 }
@@ -380,8 +380,8 @@
     bottom: 0;
     background: repeating-linear-gradient(
         45deg,
-        oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h) / 0.05),
-        oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h) / 0.05) 10px,
+        oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h) / 0.05),
+        oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h) / 0.05) 10px,
         transparent 10px,
         transparent 20px
     );
@@ -398,7 +398,7 @@
     position: absolute;
     top: 10px;
     left: 10px; /* Changed from right:10px to left:10px */
-    background: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    background: oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h));
     color: white;
     font-size: 12px;
     padding: 3px 8px;
@@ -417,7 +417,7 @@
     margin-top: 8px;
     padding-top: 8px;
     border-top: 1px dashed var(--border-color);
-    color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    color: oklch(var(--color-warning-l) var(--color-warning-c) var(--color-warning-h));
     font-weight: bold;
 }
 
@@ -437,7 +437,7 @@
 
 .btn-verify-hashes:hover {
     background: var(--bg-color);
-    border-color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
+    border-color: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h));
     transform: translateY(-1px);
 }
 
@@ -498,7 +498,7 @@
 
 .help-icon:hover {
     opacity: 1;
-    color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
+    color: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h));
 }
 
 /* Help tooltip */
@@ -573,7 +573,7 @@
 /* In dark mode, add additional distinction */
 html[data-theme="dark"] .duplicates-banner {
     box-shadow: var(--shadow-dark-lg); /* Stronger shadow in dark mode */
-    background-color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.15); /* Slightly stronger background in dark mode */
+    background-color: oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h) / 0.15); /* Slightly stronger background in dark mode */
 }
 
 html[data-theme="dark"] .duplicate-group {
@@ -598,11 +598,11 @@ html[data-theme="dark"] .help-tooltip {
     background: var(--lora-accent);
     color: white;
     border-color: var(--lora-accent);
-    box-shadow: 0 0 0 2px oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.25);
+    box-shadow: 0 0 0 2px oklch(var(--color-accent-l) var(--color-accent-c) var(--color-accent-h) / 0.25);
     position: relative;
     z-index: 5;
 }
 
 #findDuplicatesBtn.active:hover {
-    background: oklch(calc(var(--lora-accent-l) - 5%) var(--lora-accent-c) var(--lora-accent-h));
+    background: oklch(calc(var(--color-accent-l) - 5%) var(--color-accent-c) var(--color-accent-h));
 }

static/css/components/header.css

@@ -149,7 +149,7 @@
   width: 100%;
   padding: 0.5rem 0.75rem;
   padding-left: 2.25rem !important;
-  padding-right: 5rem !important;
+  padding-right: 6.75rem !important; /* clear room for options + filter + clear/cue toggles */
   border: none;
   background: transparent;
   color: var(--text-color);
@@ -190,6 +190,81 @@
   right: 2.25rem;
 }
 
+/* Clear button: sit immediately left of the search-options toggle */
+.header-search .search-clear {
+  position: absolute;
+  right: 4.25rem; /* 2.25rem (options toggle) + 28px toggle width + 4px gap */
+  top: 50%;
+  transform: translateY(-50%);
+  width: 28px;
+  height: 28px;
+  display: none;
+  align-items: center;
+  justify-content: center;
+  background: transparent;
+  border: none;
+  color: var(--text-muted);
+  cursor: pointer;
+  border-radius: var(--border-radius-xs, 4px);
+  padding: 0;
+  line-height: 1;
+  transition: background-color var(--transition-base), color var(--transition-base);
+}
+
+.header-search .search-clear.visible {
+  display: flex;
+}
+
+.header-search .search-clear:hover {
+  background: color-mix(in oklch, var(--text-muted) 15%, transparent);
+  color: var(--lora-accent);
+}
+
+/* Keyboard shortcut cue: shown when search is empty, hidden when typing */
+.header-search .search-shortcut-cue {
+  position: absolute;
+  right: 4.25rem; /* same slot as clear button */
+  top: 50%;
+  transform: translateY(-50%);
+  display: flex;
+  align-items: center;
+  gap: 2px;
+  pointer-events: none;
+  font-family: inherit;
+  font-size: 0.7rem;
+  line-height: 1;
+  color: var(--text-muted);
+  opacity: 0.7;
+  white-space: nowrap;
+  transition: opacity 0.2s ease;
+}
+
+.header-search .search-shortcut-cue kbd {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  min-width: 18px;
+  height: 18px;
+  padding: 0 4px;
+  font-family: inherit;
+  font-size: 0.68rem;
+  font-weight: 500;
+  color: var(--text-muted);
+  /* Subtle tint derived from text color so it adapts to both light & dark themes */
+  background: color-mix(in oklch, var(--text-muted) 12%, transparent);
+  border: 1px solid color-mix(in oklch, var(--text-muted) 25%, transparent);
+  border-radius: var(--border-radius-xs, 3px);
+  line-height: 1;
+}
+
+.header-search .search-shortcut-cue.hidden {
+  display: none;
+}
+
+.header-search.disabled .search-shortcut-cue {
+  display: none;
+}
+
 .header-search .search-options-toggle:hover,
 .header-search .search-filter-toggle:hover,
 .header-search .search-filter-toggle:focus-visible {
@@ -283,7 +358,6 @@
 
 .theme-toggle {
   position: relative;
-  /* Ensure relative positioning for the container */
 }
 
 .theme-toggle .light-icon,
@@ -293,17 +367,14 @@
   top: 50%;
   left: 50%;
   transform: translate(-50%, -50%);
-  /* Center perfectly */
   opacity: 0;
   transition: opacity 0.3s ease;
 }
 
-/* Default state shows dark icon */
 .theme-toggle .dark-icon {
   opacity: 1;
 }
 
-/* Light theme shows light icon */
 .theme-toggle.theme-light .light-icon {
   opacity: 1;
 }
@@ -313,7 +384,6 @@
   opacity: 0;
 }
 
-/* Dark theme shows dark icon */
 .theme-toggle.theme-dark .dark-icon {
   opacity: 1;
 }
@@ -323,7 +393,6 @@
   opacity: 0;
 }
 
-/* Auto theme shows auto icon */
 .theme-toggle.theme-auto .auto-icon {
   opacity: 1;
 }
@@ -333,6 +402,201 @@
   opacity: 0;
 }
 
+.theme-popover {
+  display: none;
+  position: fixed;
+  background: var(--surface-base, #ffffff);
+  border: 1px solid var(--border-base, #e0e0e0);
+  border-radius: var(--radius-md, 8px);
+  box-shadow: var(--shadow-xl, 0 4px 16px rgba(0, 0, 0, 0.15));
+  padding: 12px;
+  min-width: 220px;
+  z-index: calc(var(--z-overlay) + 1);
+  animation: theme-popover-in 0.15s ease-out;
+}
+
+.theme-popover.active {
+  display: block;
+}
+
+@keyframes theme-popover-in {
+  from {
+    opacity: 0;
+    transform: translateY(-4px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+.theme-popover-section {
+  display: flex;
+  flex-direction: column;
+  gap: 8px;
+}
+
+.theme-popover-label {
+  font-size: 0.7rem;
+  font-weight: 600;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  color: var(--text-secondary, #6c757d);
+}
+
+.theme-popover-divider {
+  height: 1px;
+  background: var(--border-base, #e0e0e0);
+  margin: 10px 0;
+}
+
+.theme-popover-modes {
+  display: flex;
+  gap: 6px;
+}
+
+.theme-mode-btn {
+  flex: 1;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 4px;
+  padding: 8px 4px;
+  border: 1px solid var(--border-base, #e0e0e0);
+  border-radius: var(--radius-sm, 6px);
+  background: var(--surface-elevated, #ffffff);
+  color: var(--text-primary, #333333);
+  cursor: pointer;
+  font-size: 0.75rem;
+  transition: background-color var(--transition-base, 200ms ease),
+              border-color var(--transition-base, 200ms ease),
+              color var(--transition-base, 200ms ease);
+}
+
+.theme-mode-btn i {
+  font-size: 0.9rem;
+}
+
+.theme-mode-btn:hover {
+  background: var(--surface-hover, oklch(95% 0.02 256));
+  border-color: var(--color-accent, oklch(68% 0.28 256));
+}
+
+.theme-mode-btn.active {
+  background: var(--color-accent-subtle, oklch(68% 0.28 256 / 0.12));
+  border-color: var(--color-accent, oklch(68% 0.28 256));
+  color: var(--color-accent, oklch(68% 0.28 256));
+}
+
+.theme-popover-presets {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  gap: 6px;
+}
+
+.theme-preset-btn {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 4px;
+  padding: 8px 4px;
+  border: 1px solid var(--border-base, #e0e0e0);
+  border-radius: var(--radius-sm, 6px);
+  background: var(--surface-elevated, #ffffff);
+  color: var(--text-primary, #333333);
+  cursor: pointer;
+  font-size: 0.7rem;
+  transition: background-color var(--transition-base, 200ms ease),
+              border-color var(--transition-base, 200ms ease),
+              color var(--transition-base, 200ms ease);
+}
+
+.theme-preset-btn:hover {
+  background: var(--surface-hover, oklch(95% 0.02 256));
+  border-color: var(--color-accent, oklch(68% 0.28 256));
+}
+
+.theme-preset-btn.active {
+  background: var(--color-accent-subtle, oklch(68% 0.28 256 / 0.12));
+  border-color: var(--color-accent, oklch(68% 0.28 256));
+  color: var(--color-accent, oklch(68% 0.28 256));
+}
+
+.preset-swatch {
+  display: inline-block;
+  width: 22px;
+  height: 22px;
+  border-radius: var(--radius-xs, 4px);
+  border: 1px solid var(--border-subtle, oklch(72% 0.03 256 / 0.45));
+  flex-shrink: 0;
+  transition: transform var(--transition-base, 200ms ease),
+              box-shadow var(--transition-base, 200ms ease);
+}
+
+/* Solid accent colors — each swatch shows the theme's accent color directly.
+   This matches the app's flat, token-driven design language instead of using
+   decorative gradients that clash with the matte aesthetic. */
+
+.preset-swatch-default {
+  background: oklch(68% 0.28 256);
+}
+
+.preset-swatch-nord {
+  background: oklch(62% 0.18 213);
+}
+
+.preset-swatch-midnight {
+  background: oklch(52% 0.15 300);
+}
+
+.preset-swatch-monokai {
+  background: oklch(72% 0.24 190);
+}
+
+.preset-swatch-dracula {
+  background: oklch(68% 0.24 265);
+}
+
+.preset-swatch-solarized {
+  background: oklch(55% 0.18 175);
+}
+
+.theme-preset-btn.active .preset-swatch {
+  box-shadow: 0 0 0 2px var(--color-accent, oklch(68% 0.28 256));
+}
+
+.theme-preset-btn:hover .preset-swatch {
+  transform: scale(1.08);
+}
+
+/* Dark mode: use each preset's dark-mode accent lightness for visibility.
+   These match the --color-accent-l values from [data-theme="dark"][data-theme-preset="..."]
+   in tokens/colors.css so the swatch accurately previews what the theme looks like. */
+
+[data-theme="dark"] .preset-swatch-default {
+  background: oklch(68% 0.28 256);
+}
+
+[data-theme="dark"] .preset-swatch-nord {
+  background: oklch(68% 0.18 213);
+}
+
+[data-theme="dark"] .preset-swatch-midnight {
+  background: oklch(68% 0.14 300);
+}
+
+[data-theme="dark"] .preset-swatch-monokai {
+  background: oklch(72% 0.24 190);
+}
+
+[data-theme="dark"] .preset-swatch-dracula {
+  background: oklch(72% 0.24 265);
+}
+
+[data-theme="dark"] .preset-swatch-solarized {
+  background: oklch(60% 0.18 175);
+}
+
 /* Badge styling */
 .update-badge {
   position: absolute;

static/css/components/import-modal.css

@@ -211,7 +211,7 @@
 
 .lora-item.is-early-access {
     background: rgba(0, 184, 122, 0.05);
-    border-left: 4px solid #00B87A;
+    border-left: 4px solid var(--color-success);
 }
 
 .lora-item.missing-locally {
@@ -310,7 +310,7 @@
 
 .missing-lora-item.is-early-access {
     background: rgba(0, 184, 122, 0.05);
-    border-left: 3px solid #00B87A;
+    border-left: 3px solid var(--color-success);
     padding-left: 10px;
 }
 
@@ -630,7 +630,7 @@
     gap: 12px;
     padding: 12px 16px;
     background: rgba(0, 184, 122, 0.1);
-    border: 1px solid #00B87A;
+    border: 1px solid var(--color-success);
     border-radius: var(--border-radius-sm);
     color: var(--text-color);
     margin-bottom: var(--space-2);
@@ -646,7 +646,7 @@
 
 /* Specific styling for the early access warning container in import modal */
 .early-access-warning .warning-icon {
-    color: #00B87A;
+    color: var(--color-success);
     font-size: 1.2em;
 }
 

static/css/components/keyboard-nav.css

@@ -1,96 +0,0 @@
-/* Keyboard navigation indicator and help */
-.keyboard-nav-hint {
-    display: inline-flex;
-    align-items: center;
-    justify-content: center;
-    position: relative;
-    width: 32px;
-    height: 32px;
-    border-radius: 50%;
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    color: var(--text-color);
-    cursor: help;
-    transition: var(--transition-base);
-    margin-left: 8px;
-}
-
-.keyboard-nav-hint:hover {
-    background: var(--lora-accent);
-    color: white;
-    transform: translateY(-2px);
-    box-shadow: var(--shadow-sm);
-}
-
-.keyboard-nav-hint i {
-    font-size: 14px;
-}
-
-/* Tooltip styling */
-.tooltip {
-    position: relative;
-}
-
-.tooltip .tooltiptext {
-    visibility: hidden;
-    width: 240px;
-    background-color: var(--lora-surface);
-    color: var(--text-color);
-    text-align: center;
-    border-radius: var(--border-radius-xs);
-    padding: 8px;
-    position: absolute;
-    z-index: 9999; /* Ensure tooltip appears above cards */
-    right: 120%; /* Position tooltip to the left of the icon */
-    top: 50%; /* Vertically center */
-    transform: translateY(-15%); /* Vertically center */
-    opacity: 0;
-    transition: opacity 0.3s;
-    box-shadow: var(--shadow-lg);
-    border: 1px solid var(--lora-border);
-    font-size: 0.85em;
-    line-height: 1.4;
-}
-
-.tooltip .tooltiptext::after {
-    content: "";
-    position: absolute;
-    top: 50%; /* Vertically center arrow */
-    left: 100%; /* Arrow on the right side */
-    margin-top: -5px;
-    border-width: 5px;
-    border-style: solid;
-    border-color: transparent transparent transparent var(--lora-border); /* Arrow points right */
-}
-
-.tooltip:hover .tooltiptext {
-    visibility: visible;
-    opacity: 1;
-}
-
-/* Keyboard shortcuts table */
-.keyboard-shortcuts {
-    width: 100%;
-    border-collapse: collapse;
-    margin-top: 5px;
-}
-
-.keyboard-shortcuts td {
-    padding: 4px;
-    text-align: left;
-}
-
-.keyboard-shortcuts td:first-child {
-    font-weight: bold;
-    width: 40%;
-}
-
-.key {
-    display: inline-block;
-    background: var(--bg-color);
-    border: 1px solid var(--border-color);
-    border-radius: 3px;
-    padding: 1px 5px;
-    font-size: 0.8em;
-    box-shadow: var(--shadow-xs);
-}

static/css/components/lora-modal/lora-modal.css

@@ -72,6 +72,10 @@
     margin-left: auto;
 }
 
+.modal-header-actions .license-permissions {
+    margin-left: auto;
+}
+
 .license-restrictions {
     display: flex;
     align-items: center;
@@ -95,6 +99,41 @@
     transform: translateY(-1px);
 }
 
+/* Set 2 — New style permission indicators */
+.license-permissions {
+    display: flex;
+    gap: 4px;
+    align-items: center;
+}
+
+.license-icon-new {
+    width: 22px;
+    height: 22px;
+    display: inline-block;
+    border-radius: 4px;
+    background-color: var(--text-muted);
+    -webkit-mask: var(--license-icon-image) center/contain no-repeat;
+    mask: var(--license-icon-image) center/contain no-repeat;
+    transition: background-color 0.2s ease, transform 0.2s ease;
+    cursor: default;
+    outline: 2px solid transparent;
+    outline-offset: 1px;
+}
+
+.license-icon-new.allowed {
+    background-color: var(--color-success, #40c057);
+    outline-color: color-mix(in oklch, var(--color-success, #40c057) 30%, transparent);
+}
+
+.license-icon-new.denied {
+    background-color: var(--color-error, #fa5252);
+    outline-color: color-mix(in oklch, var(--color-error, #fa5252) 30%, transparent);
+}
+
+.license-icon-new:hover {
+    transform: translateY(-1px);
+}
+
 /* Info Grid */
 .info-grid {
     display: grid;
@@ -405,16 +444,161 @@
     flex: 1;
 }
 
-.base-model-selector {
-    width: 100%;
-    padding: 3px 5px;
+/* ── Base Model Search Dropdown ─────────────────────────────────────────── */
+
+.base-model-search-wrapper {
+    position: relative;
+    flex: 1;
+    min-width: 0;
+    z-index: 100;
+}
+
+.base-model-search-input-wrapper {
+    display: flex;
+    align-items: center;
     background: var(--bg-color);
     border: 1px solid var(--lora-accent);
     border-radius: var(--border-radius-xs);
+    padding: 0 6px;
+    gap: 4px;
+}
+
+.base-model-search-input-wrapper .search-icon {
+    color: var(--text-color);
+    opacity: 0.45;
+    font-size: 12px;
+    flex-shrink: 0;
+    pointer-events: none;
+    /* Reset global .search-icon rules from search-filter.css */
+    position: static;
+    right: auto;
+    top: auto;
+    transform: none;
+}
+
+.base-model-search-input {
+    flex: 1;
+    background: transparent;
+    border: none;
+    outline: none;
     color: var(--text-color);
     font-size: 0.9em;
-    outline: none;
-    margin-right: var(--space-1);
+    padding: 3px 0;
+    width: 100%;
+    min-width: 0;
+}
+
+.base-model-search-input::placeholder {
+    color: var(--text-color);
+    opacity: 0.35;
+}
+
+.base-model-dropdown {
+    position: absolute;
+    top: 100%;
+    left: -1px;
+    right: -1px;
+    max-height: 270px;
+    overflow-y: auto;
+    background: var(--bg-color);
+    border: 1px solid var(--lora-border);
+    border-top: none;
+    border-radius: 0 0 var(--border-radius-xs) var(--border-radius-xs);
+    box-shadow: 0 8px 24px rgba(0, 0, 0, 0.22);
+    z-index: 101;
+}
+
+[data-theme="dark"] .base-model-dropdown {
+    box-shadow: 0 8px 28px rgba(0, 0, 0, 0.5);
+}
+
+/* Dropdown scrollbar styling */
+.base-model-dropdown::-webkit-scrollbar {
+    width: 6px;
+}
+
+.base-model-dropdown::-webkit-scrollbar-thumb {
+    background: var(--lora-border);
+    border-radius: 3px;
+}
+
+.base-model-dropdown::-webkit-scrollbar-track {
+    background: transparent;
+}
+
+/* Section */
+.base-model-dropdown-section {
+    border-bottom: 1px solid var(--lora-border);
+}
+
+.base-model-dropdown-section:last-child {
+    border-bottom: none;
+}
+
+/* Section header */
+.base-model-dropdown-header {
+    padding: 5px 10px;
+    font-size: 0.72em;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.08em;
+    color: var(--text-color);
+    opacity: 0.5;
+    background: var(--surface-subtle);
+    position: sticky;
+    top: 0;
+    z-index: 1;
+}
+
+.base-model-dropdown-header.suggested-header {
+    color: var(--lora-accent);
+    opacity: 1;
+    background: oklch(from var(--lora-accent) l c h / 0.08);
+}
+
+.base-model-dropdown-header.suggested-header i {
+    margin-right: 4px;
+    font-size: 0.85em;
+}
+
+/* Dropdown items */
+.base-model-dropdown-item {
+    padding: 5px 12px;
+    cursor: pointer;
+    font-size: 0.9em;
+    color: var(--text-color);
+    transition: background 0.1s;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+.base-model-dropdown-item:hover {
+    background: oklch(from var(--lora-accent) l c h / 0.1);
+}
+
+.base-model-dropdown-item.active {
+    background: oklch(from var(--lora-accent) l c h / 0.16);
+}
+
+.base-model-dropdown-item.selected {
+    font-weight: 600;
+}
+
+.base-model-dropdown-item.selected::after {
+    content: '✓';
+    float: right;
+    color: var(--lora-accent);
+    margin-left: 8px;
+}
+
+/* Empty state */
+.base-model-dropdown-empty {
+    padding: 18px 12px;
+    text-align: center;
+    color: var(--text-color);
+    opacity: 0.4;
+    font-size: 0.88em;
 }
 
 .size-wrapper {

static/css/components/lora-modal/showcase.css

@@ -229,6 +229,19 @@
   gap: 10px;
 }
 
+/* Header row for params section */
+.metadata-row.params-row {
+    flex-direction: column;
+}
+
+.metadata-row.params-row .param-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    gap: 8px;
+    margin-bottom: 4px;
+}
+
 /* Styling for parameters tags */
 .params-tags {
   display: flex;
@@ -272,13 +285,25 @@
   margin-top: var(--space-2);
 }
 
+.metadata-row.prompt-row .param-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 8px;
+  margin-bottom: 4px;
+}
+
+.metadata-row.prompt-row .param-actions {
+  display: flex;
+  align-items: center;
+  gap: 4px;
+}
+
 .metadata-label {
   font-weight: 600;
   color: var(--text-color);
   opacity: 0.8;
   font-size: 0.85em;
-  display: block;
-  margin-bottom: 4px;
 }
 
 .metadata-prompt-wrapper {
@@ -286,7 +311,7 @@
   background: var(--lora-surface);
   border: 1px solid var(--lora-border);
   border-radius: var(--border-radius-xs);
-  padding: 6px 30px 6px 8px;
+  padding: 6px 8px;
   margin-top: 2px;
   max-height: 80px; /* Reduced from 120px */
   overflow-y: auto;
@@ -302,22 +327,26 @@
   white-space: pre-wrap;
 }
 
-.copy-prompt-btn {
-  position: absolute;
-  top: 6px;
-  right: 6px;
+.copy-prompt-btn,
+.send-prompt-btn,
+.send-params-btn {
   background: transparent;
   border: none;
   color: var(--text-color);
   opacity: 0.6;
   cursor: pointer;
-  padding: 3px;
+  padding: 3px 6px;
+  border-radius: var(--border-radius-xs);
   transition: var(--transition-base);
+  font-size: 0.9em;
 }
 
-.copy-prompt-btn:hover {
+.copy-prompt-btn:hover,
+.send-prompt-btn:hover,
+.send-params-btn:hover {
   opacity: 1;
   color: var(--lora-accent);
+  background: var(--lora-surface);
 }
 
 /* Scrollbar styling for metadata panel */

static/css/components/lora-modal/tag.css

@@ -17,6 +17,8 @@
   flex-wrap: nowrap;
   gap: 6px;
   align-items: center;
+  min-width: 0;
+  overflow: hidden;
 }
 
 .model-tag-compact {
@@ -28,6 +30,9 @@
   font-size: 0.75em;
   color: var(--text-color);
   white-space: nowrap;
+  max-width: 150px;
+  overflow: hidden;
+  text-overflow: ellipsis;
 }
 
 /* Style for empty tags placeholder */
@@ -118,8 +123,9 @@
 /* Model Tags Edit Mode */
 .model-tags-header {
   display: flex;
-  justify-content: space-between;
+  justify-content: flex-start;
   align-items: center;
+  overflow: hidden;
 }
 
 .edit-tags-btn {
@@ -132,6 +138,7 @@
   border-radius: var(--border-radius-xs);
   transition: var(--transition-base);
   margin-left: var(--space-1);
+  flex-shrink: 0;
 }
 
 .edit-tags-btn.visible,

static/css/components/metadata-refresh-result.css

@@ -24,11 +24,6 @@
     min-width: 130px;
 }
 
-.stat-card > i {
-    font-size: 1.25em;
-    flex-shrink: 0;
-}
-
 .stat-card-body {
     display: flex;
     flex-direction: column;
@@ -52,40 +47,20 @@
     border-left-color: var(--color-success);
 }
 
-.stat-card-success > i {
-    color: var(--color-success);
-}
-
 .stat-card-failure {
     border-left-color: var(--color-error);
 }
 
-.stat-card-failure > i {
-    color: var(--color-error);
-}
-
 .stat-card-skipped {
     border-left-color: var(--color-warning);
 }
 
-.stat-card-skipped > i {
-    color: var(--color-warning);
-}
-
 .stat-card-total {
-    border-left-color: var(--color-info);
-}
-
-.stat-card-total > i {
-    color: var(--color-info);
+    border-left-color: var(--lora-border);
 }
 
 .stat-card-time {
-    border-left-color: var(--color-accent);
-}
-
-.stat-card-time > i {
-    color: var(--color-accent);
+    border-left-color: var(--lora-border);
 }
 
 .refresh-failures-section {
@@ -122,7 +97,7 @@
     position: sticky;
     top: 0;
     background: var(--lora-surface);
-    border-bottom: 2px solid var(--lora-border);
+    border-bottom: 1px solid var(--lora-border);
     padding: var(--space-1) var(--space-2);
     text-align: left;
     font-weight: var(--weight-semibold);

static/css/components/modal/download-modal.css

@@ -821,4 +821,66 @@
 
 [data-theme="dark"] .batch-preview-item {
     background: var(--lora-surface);
-}
+}
+
+.hf-badge {
+    display: inline-block;
+    padding: 1px 6px;
+    border-radius: 8px;
+    background: oklch(0.55 0.12 250 / 0.15);
+    color: oklch(0.7 0.12 250);
+    font-size: 0.75em;
+    font-weight: 600;
+    margin-left: 4px;
+}
+
+
+/* Checkbox inside HF batch preview items */
+.batch-preview-checkbox {
+    width: 18px;
+    height: 18px;
+    cursor: pointer;
+    accent-color: var(--lora-accent);
+    flex-shrink: 0;
+    padding: 0;
+    border: none;
+    margin: 0;
+}
+
+/* Select All toolbar in batch preview */
+.batch-preview-select-all {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 8px 12px;
+    border-bottom: 1px solid var(--border-color);
+    background: var(--lora-surface);
+    cursor: pointer;
+    position: sticky;
+    top: 0;
+    z-index: 1;
+}
+
+.batch-preview-select-all input[type="checkbox"] {
+    width: 18px;
+    height: 18px;
+    cursor: pointer;
+    accent-color: var(--lora-accent);
+    flex-shrink: 0;
+    padding: 0;
+    border: none;
+    margin: 0;
+}
+
+.batch-preview-select-all label {
+    cursor: pointer;
+    font-size: 0.9em;
+    color: var(--text-color);
+    font-weight: 500;
+    margin: 0;
+    user-select: none;
+}
+
+[data-theme="dark"] .batch-preview-select-all {
+    background: var(--lora-surface);
+}

static/css/components/modal/settings-modal.css

@@ -335,7 +335,12 @@
     }
 }
 
-/* API key input specific styles */
+/* API key input — CSS masking (prevents Chrome password manager triggers) */
+.api-key-masked {
+    -webkit-text-security: disc;
+}
+
+/* API key input specific styles (shared with proxy password) */
 .api-key-input {
     width: 100%; /* Take full width of parent */
     position: relative;
@@ -345,7 +350,7 @@
 
 .api-key-input input {
     width: 100%;
-    padding: 6px 40px 6px 10px; /* Add left padding */
+    padding: 6px 40px 6px 10px; /* Right padding for eye button */
     height: 32px;
     box-sizing: border-box;
     border-radius: var(--border-radius-xs);
@@ -353,6 +358,13 @@
     background-color: var(--lora-surface);
     color: var(--text-color);
     font-size: 0.95em;
+    transition: border-color 0.2s ease, box-shadow 0.2s ease;
+}
+
+.api-key-input input:focus {
+    border-color: var(--lora-accent);
+    outline: none;
+    box-shadow: 0 0 0 2px rgba(var(--lora-accent-rgb, 79, 70, 229), 0.1);
 }
 
 .api-key-input .toggle-visibility {
@@ -364,12 +376,98 @@
     opacity: 0.6;
     cursor: pointer;
     padding: 4px 8px;
+    transition: opacity 0.2s ease;
 }
 
 .api-key-input .toggle-visibility:hover {
     opacity: 1;
 }
 
+/* API key item — stack status/edit views vertically for smooth cross-fade */
+.api-key-item .setting-control {
+    flex-direction: column;
+    align-items: flex-end;
+}
+
+/* API key status display (shown when not editing) */
+.api-key-status {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    width: 100%;
+    justify-content: flex-end;
+    transition: opacity 0.2s ease, transform 0.2s ease, max-height 0.25s ease;
+    max-height: 80px;
+    overflow: hidden;
+}
+
+.api-key-status.is-hidden {
+    opacity: 0;
+    max-height: 0;
+    transform: translateY(-4px);
+    pointer-events: none;
+    margin: 0;
+}
+
+.api-key-status-text {
+    display: inline-flex;
+    align-items: center;
+    gap: 4px;
+    font-size: 0.95em;
+    white-space: nowrap;
+    transition: color 0.2s ease;
+}
+
+/* Status color modifiers — replace inline styles */
+.api-key-status--configured .fa-check-circle {
+    color: var(--lora-success);
+}
+
+.api-key-status--unconfigured .fa-times-circle {
+    color: var(--lora-error);
+}
+
+/* Utility classes for status icon colors (used by JS) */
+.text-success {
+    color: var(--lora-success);
+}
+
+.text-error {
+    color: var(--lora-error);
+}
+
+/* API key inline edit container — flex row with input + buttons */
+.api-key-edit {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    width: 100%;
+    justify-content: flex-end;
+    transition: opacity 0.2s ease, transform 0.2s ease, max-height 0.25s ease;
+    max-height: 80px;
+    overflow: hidden;
+}
+
+.api-key-edit.is-hidden {
+    opacity: 0;
+    max-height: 0;
+    transform: translateY(-4px);
+    pointer-events: none;
+    margin: 0;
+}
+
+.api-key-edit .api-key-input {
+    flex: 1;
+    min-width: 0;
+}
+
+.api-key-edit .primary-btn,
+.api-key-edit .secondary-btn {
+    height: 32px;
+    flex-shrink: 0;
+    white-space: nowrap;
+}
+
 /* Text input wrapper styles for consistent input styling */
 .text-input-wrapper {
     width: 100%;
@@ -823,54 +921,107 @@
 }
 
 .range-control input[type="range"] {
+    --range-fill: 40%;
     width: 120px;
-    height: 4px;
+    height: 6px;
     -webkit-appearance: none;
     appearance: none;
-    background: var(--border-color);
-    border-radius: 2px;
+    background: linear-gradient(
+        to right,
+        var(--lora-accent) 0%,
+        var(--lora-accent) var(--range-fill),
+        var(--border-color) var(--range-fill),
+        var(--border-color) 100%
+    );
+    border-radius: var(--radius-full);
     outline: none;
     cursor: pointer;
     flex-shrink: 0;
+    transition: background 0.3s ease;
+}
+
+.range-control input[type="range"]:focus-visible {
+    outline: none;
 }
 
 .range-control input[type="range"]::-webkit-slider-thumb {
     -webkit-appearance: none;
     appearance: none;
-    width: 16px;
-    height: 16px;
+    width: 18px;
+    height: 18px;
     border-radius: 50%;
     background: var(--lora-accent);
     cursor: pointer;
     border: 2px solid var(--lora-surface);
-    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
-    transition: transform 0.15s ease;
+    box-shadow: var(--shadow-md);
+    transition: transform var(--transition-bounce), box-shadow 0.2s ease;
 }
 
 .range-control input[type="range"]::-webkit-slider-thumb:hover {
-    transform: scale(1.15);
+    transform: scale(1.2);
+    box-shadow: var(--shadow-md), 0 0 0 4px var(--color-accent-subtle);
+}
+
+.range-control input[type="range"]::-webkit-slider-thumb:active {
+    transform: scale(1.1);
+    box-shadow: var(--shadow-md), 0 0 0 6px var(--color-accent-subtle);
+}
+
+.range-control input[type="range"]:focus-visible::-webkit-slider-thumb {
+    box-shadow: var(--shadow-md), 0 0 0 3px var(--color-accent-subtle);
 }
 
 .range-control input[type="range"]::-moz-range-thumb {
-    width: 16px;
-    height: 16px;
+    width: 18px;
+    height: 18px;
     border-radius: 50%;
     background: var(--lora-accent);
     cursor: pointer;
     border: 2px solid var(--lora-surface);
-    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
+    box-shadow: var(--shadow-md);
+    transition: transform var(--transition-bounce), box-shadow 0.2s ease;
+}
+
+.range-control input[type="range"]::-moz-range-thumb:hover {
+    transform: scale(1.2);
+    box-shadow: var(--shadow-md), 0 0 0 4px var(--color-accent-subtle);
+}
+
+.range-control input[type="range"]::-moz-range-thumb:active {
+    transform: scale(1.1);
+    box-shadow: var(--shadow-md), 0 0 0 6px var(--color-accent-subtle);
+}
+
+.range-control input[type="range"]::-moz-range-track {
+    height: 6px;
+    border-radius: var(--radius-full);
+    background: var(--border-color);
 }
 
 .range-control .range-value {
     min-width: 36px;
     text-align: center;
-    font-size: 0.9em;
-    font-weight: 600;
-    color: var(--text-color);
+    font-size: 0.85em;
+    font-weight: 700;
+    color: var(--lora-accent);
     font-variant-numeric: tabular-nums;
+    background: var(--surface-subtle);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+    letter-spacing: 0.02em;
 }
 
 [data-theme="dark"] .range-control input[type="range"] {
+    background: linear-gradient(
+        to right,
+        var(--lora-accent) 0%,
+        var(--lora-accent) var(--range-fill),
+        rgba(255, 255, 255, 0.15) var(--range-fill),
+        rgba(255, 255, 255, 0.15) 100%
+    );
+}
+
+[data-theme="dark"] .range-control input[type="range"]::-moz-range-track {
     background: rgba(255, 255, 255, 0.15);
 }
 

static/css/components/recipe-modal.css

@@ -9,6 +9,10 @@
     position: relative;
 }
 
+#recipeTagsContainer {
+    width: 100%;
+}
+
 .recipe-modal-header h2 {
     margin: 0 0 var(--space-1);
     padding: var(--space-1);
@@ -95,127 +99,11 @@
     min-width: 0;
 }
 
-.content-editor.tags-editor input {
-    font-size: 0.9em;
-}
-
 /* Remove obsolete button styles */
 .editor-actions {
     display: none;
 }
 
-/* Special styling for tags content */
-.tags-content {
-    display: flex;
-    align-items: center;
-    flex-wrap: nowrap;
-    gap: 8px;
-}
-
-.tags-display {
-    display: flex;
-    flex-wrap: nowrap;
-    gap: 6px;
-    align-items: center;
-    flex: 1;
-    min-width: 0;
-    overflow: hidden;
-}
-
-.no-tags {
-    font-size: 0.85em;
-    color: var(--text-color);
-    opacity: 0.6;
-    font-style: italic;
-}
-
-/* Recipe Tags styles */
-.recipe-tags-container {
-    position: relative;
-    margin-top: 0;
-    margin-bottom: 10px;
-}
-
-.recipe-tags-compact {
-    display: flex;
-    flex-wrap: nowrap;
-    gap: 6px;
-    align-items: center;
-}
-
-.recipe-tag-compact {
-    background: var(--surface-subtle);
-    border: 1px solid rgba(0, 0, 0, 0.1);
-    border-radius: var(--border-radius-xs);
-    padding: 2px 8px;
-    font-size: 0.75em;
-    color: var(--text-color);
-    white-space: nowrap;
-}
-
-[data-theme="dark"] .recipe-tag-compact {
-    background: var(--surface-subtle);
-    border: 1px solid var(--lora-border);
-}
-
-.recipe-tag-more {
-    background: var(--lora-accent);
-    color: var(--lora-text);
-    border-radius: var(--border-radius-xs);
-    padding: 2px 8px;
-    font-size: 0.75em;
-    cursor: pointer;
-    white-space: nowrap;
-    font-weight: 500;
-}
-
-.recipe-tags-tooltip {
-    position: absolute;
-    top: calc(100% + 8px);
-    left: 0;
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-sm);
-    box-shadow: var(--shadow-dropdown);
-    padding: 10px 14px;
-    max-width: 400px;
-    z-index: 10;
-    opacity: 0;
-    visibility: hidden;
-    transform: translateY(-4px);
-    transition: var(--transition-base);
-    pointer-events: none;
-}
-
-.recipe-tags-tooltip.visible {
-    opacity: 1;
-    visibility: visible;
-    transform: translateY(0);
-    pointer-events: auto;
-}
-
-.tooltip-content {
-    display: flex;
-    flex-wrap: wrap;
-    gap: 6px;
-    max-height: 200px;
-    overflow-y: auto;
-}
-
-.tooltip-tag {
-    background: var(--surface-hover);
-    border: 1px solid rgba(0, 0, 0, 0.1);
-    border-radius: var(--border-radius-xs);
-    padding: 3px 8px;
-    font-size: 0.75em;
-    color: var(--text-color);
-}
-
-[data-theme="dark"] .tooltip-tag {
-    background: var(--surface-hover);
-    border: 1px solid var(--lora-border);
-}
-
 #recipeModal .modal-content {
     display: flex;
     flex-direction: column;
@@ -1153,7 +1041,7 @@
         max-height: 2.4em;
     }
 
-    .recipe-tags-container {
+    #recipeTagsContainer {
         margin-bottom: 6px;
     }
 

static/css/components/sidebar.css

@@ -114,11 +114,10 @@
 .sidebar-hidden-indicator {
     position: fixed;
     left: 0;
-    top: 50%;
-    transform: translateY(-50%);
+    top: 68px; /* Align with sidebar header */
     z-index: var(--z-overlay);
     width: 14px;
-    height: 44px;
+    height: 48px;
     display: flex;
     align-items: center;
     justify-content: center;
@@ -135,7 +134,7 @@
 }
 
 .sidebar-hidden-indicator i {
-    font-size: 9px;
+    font-size: 11px;
     color: var(--text-muted);
     transition: color 0.15s ease;
 }
@@ -144,6 +143,21 @@
     color: white;
 }
 
+/* Subtle breathing animation for first-time discovery */
+@keyframes sidebarBreathing {
+    0%, 100% { opacity: 0.3; }
+    50%      { opacity: 0.65; }
+}
+
+.sidebar-hidden-indicator.breathing {
+    animation: sidebarBreathing 2.5s ease-in-out infinite;
+    animation-delay: 0.5s;
+}
+
+.sidebar-hidden-indicator.breathing:hover {
+    animation: none;
+}
+
 .sidebar-hidden-indicator-tooltip {
     position: absolute;
     left: 100%;

static/css/layout.css

@@ -264,6 +264,174 @@
     box-shadow: 0 0 0 2px oklch(var(--lora-accent) / 0.15);
 }
 
+/* Disabled sort dropdown — used when VLM custom filter is active */
+.control-group select:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+    background-color: var(--bg-color);
+    border-color: var(--border-color);
+    box-shadow: none;
+    transform: none;
+}
+
+.control-group select:disabled:hover {
+    border-color: var(--border-color);
+    background-color: var(--bg-color);
+    transform: none;
+    box-shadow: none;
+}
+
+/* === Sort dropdown — decoupled trigger width ===========================
+   The native <select> sizes its trigger to the widest <option>, wasting
+   horizontal space when a short option is selected. This custom trigger
+   sizes to the currently selected text only; the dropdown menu sizes to
+   its content independently. The native <select> is kept in the DOM
+   (visually hidden) so existing JS that reads/writes `.value` / `.disabled`
+   and dynamically adds/removes <option>s keeps working. */
+
+.sort-dropdown-group {
+    position: relative;
+    display: flex;
+}
+
+.sort-trigger {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    min-width: 100px;
+    max-width: 240px;
+    padding: 4px 10px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background: var(--card-bg);
+    color: var(--text-color);
+    font-size: 0.85em;
+    cursor: pointer;
+    transition: var(--transition-base);
+    box-shadow: var(--shadow-xs);
+}
+
+.sort-trigger:hover,
+.sort-trigger:focus-visible {
+    border-color: var(--lora-accent);
+    background: var(--bg-color);
+    transform: translateY(-1px);
+    box-shadow: var(--shadow-lg);
+    outline: none;
+}
+
+.sort-trigger:active {
+    transform: translateY(0);
+    box-shadow: var(--shadow-xs);
+}
+
+.sort-trigger__label {
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.sort-trigger__caret {
+    opacity: 0.8;
+    transition: transform var(--transition-base);
+    flex-shrink: 0;
+}
+
+.sort-dropdown-group.active .sort-trigger__caret {
+    transform: rotate(180deg);
+}
+
+.sort-dropdown-group.active .sort-trigger {
+    border-color: var(--lora-accent);
+    box-shadow: 0 0 0 2px color-mix(in oklch, var(--lora-accent) 15%, transparent);
+}
+
+/* Disabled state — mirrors the native :disabled look (used when VLM is active) */
+.sort-dropdown-group.is-disabled .sort-trigger {
+    opacity: 0.5;
+    cursor: not-allowed;
+    pointer-events: none;
+    background: var(--bg-color);
+    border-color: var(--border-color);
+    box-shadow: none;
+    transform: none;
+}
+
+/* Dropdown menu — sizes to its content, independent of trigger width.
+   Inherits base .dropdown-menu styling; capped for very long i18n text. */
+.sort-dropdown-menu {
+    min-width: max-content;
+    max-width: 320px;
+    width: max-content;
+}
+
+/* Optgroup label rendered as a section header */
+.sort-dropdown-group .sort-optgroup-label {
+    padding: 8px 12px 4px;
+    font-size: 0.75em;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.04em;
+    color: var(--text-muted);
+    cursor: default;
+    user-select: none;
+}
+
+.sort-dropdown-group .sort-optgroup-label:first-child {
+    padding-top: 4px;
+}
+
+/* Option items */
+.sort-dropdown-group .sort-option {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 6px 12px;
+    color: var(--text-color);
+    cursor: pointer;
+    transition: background-color 0.2s ease;
+    white-space: nowrap;
+}
+
+.sort-dropdown-group .sort-option::before {
+    content: '';
+    width: 14px;
+    flex-shrink: 0;
+    text-align: center;
+    font-weight: 700;
+}
+
+.sort-dropdown-group .sort-option:hover {
+    background-color: color-mix(in oklch, var(--lora-accent) 10%, transparent);
+}
+
+.sort-dropdown-group .sort-option.is-selected {
+    color: var(--lora-accent);
+    font-weight: 600;
+}
+
+.sort-dropdown-group .sort-option.is-selected::before {
+    content: '\2713';
+    color: var(--lora-accent);
+}
+
+/* Visually hidden native <select> — kept in the DOM for programmatic access.
+   High-specificity selector overrides .control-group select { min-width: 100px }. */
+.control-group .sort-select-native {
+    position: absolute;
+    width: 1px;
+    height: 1px;
+    min-width: 0;
+    padding: 0;
+    margin: -1px;
+    overflow: hidden;
+    clip: rect(0, 0, 0, 0);
+    white-space: nowrap;
+    border: 0;
+    opacity: 0;
+    pointer-events: none;
+}
+
 /* Ensure hidden class works properly */
 .hidden {
     display: none !important;

static/css/onboarding.css

@@ -27,8 +27,8 @@
     transition: var(--transition-slow);
     /* Add glow effect */
     box-shadow: 
-        0 0 0 2px rgba(24, 144, 255, 0.3),
-        0 0 20px rgba(24, 144, 255, 0.2),
+        0 0 0 2px color-mix(in oklch, var(--color-accent) 30%, transparent),
+        0 0 20px color-mix(in oklch, var(--color-accent) 20%, transparent),
         inset 0 0 0 1px rgba(255, 255, 255, 0.1);
 }
 
@@ -221,14 +221,14 @@
 @keyframes onboarding-pulse {
     0%, 100% {
         box-shadow: 
-            0 0 0 2px rgba(24, 144, 255, 0.4),
-            0 0 20px rgba(24, 144, 255, 0.3),
+            0 0 0 2px color-mix(in oklch, var(--color-accent) 40%, transparent),
+            0 0 20px color-mix(in oklch, var(--color-accent) 30%, transparent),
             inset 0 0 0 1px rgba(255, 255, 255, 0.1);
     }
     50% {
         box-shadow: 
-            0 0 0 4px rgba(24, 144, 255, 0.6),
-            0 0 30px rgba(24, 144, 255, 0.4),
+            0 0 0 4px color-mix(in oklch, var(--color-accent) 60%, transparent),
+            0 0 30px color-mix(in oklch, var(--color-accent) 40%, transparent),
             inset 0 0 0 1px rgba(255, 255, 255, 0.2);
     }
 }

static/css/style.css

@@ -36,7 +36,7 @@
 @import 'components/initialization.css';
 @import 'components/progress-panel.css';
 @import 'components/duplicates.css'; /* Add duplicates component */
-@import 'components/keyboard-nav.css'; /* Add keyboard navigation component */
+
 @import 'components/statistics.css'; /* Add statistics component */
 @import 'components/sidebar.css'; /* Add sidebar component */
 @import 'components/media-viewer.css';
@@ -59,3 +59,5 @@
 .initialization-notice .loading-spinner {
     margin-bottom: var(--space-2);
 }
+
+/* ---------- reused from shared styles ---------- */

static/css/tokens/colors.css

@@ -37,13 +37,13 @@
   --color-error-border: color-mix(in oklch, var(--color-error) 50%, transparent);
 
   --color-info: oklch(var(--color-info-l) var(--color-info-c) var(--color-info-h));
-  --color-info-bg: oklch(72% 0.2 220);
-  --color-info-text: oklch(28% 0.03 220);
-  --color-info-glow: oklch(72% 0.2 220 / 0.28);
+  --color-info-bg: oklch(var(--color-info-l) var(--color-info-c) var(--color-info-h));
+  --color-info-text: oklch(28% 0.03 var(--color-info-h));
+  --color-info-glow: oklch(var(--color-info-l) var(--color-info-c) var(--color-info-h) / 0.28);
 
-  --color-skip-refresh-bg: oklch(82% 0.12 45);
-  --color-skip-refresh-text: oklch(35% 0.02 45);
-  --color-skip-refresh-glow: oklch(82% 0.12 45 / 0.15);
+  --color-skip-refresh-bg: oklch(82% 0.12 var(--color-warning-h));
+  --color-skip-refresh-text: oklch(35% 0.02 var(--color-warning-h));
+  --color-skip-refresh-glow: oklch(82% 0.12 var(--color-warning-h) / 0.15);
 }
 
 :root {
@@ -106,12 +106,360 @@
   --status-info-bg: oklch(50% 0.10 190 / 0.25);
   --status-info-border: oklch(55% 0.12 195 / 0.3);
 
-  --color-info-bg: oklch(62% 0.18 220);
-  --color-info-text: oklch(98% 0.02 240);
-  --color-info-glow: oklch(62% 0.18 220 / 0.4);
+  --color-info-bg: oklch(62% 0.18 var(--color-info-h));
+  --color-info-text: oklch(98% 0.02 var(--color-info-h));
+  --color-info-glow: oklch(62% 0.18 var(--color-info-h) / 0.4);
 
   --color-error-bg: color-mix(in oklch, var(--color-error) 15%, transparent);
   --color-error-border: color-mix(in oklch, var(--color-error) 40%, transparent);
 
   --favorite-color: #ffc107;
 }
+
+/* ── Preset: Nord ──────────────────────────────────────────── */
+
+[data-theme-preset="nord"] {
+  --color-accent-h: 213;
+  --color-accent-c: 0.18;
+  --color-accent-l: 62%;
+  --color-warning-h: 35;
+  --color-warning-c: 0.18;
+  --color-success-h: 130;
+  --color-error-l: 62%;
+  --color-error-c: 0.22;
+  --color-error-h: 5;
+  --color-info-h: 195;
+  --color-info-c: 0.18;
+
+  --bg-base: oklch(96% 0.01 240);
+  --bg-elevated: oklch(98% 0.008 240 / 0.95);
+  --bg-hover: oklch(93% 0.02 240);
+  --bg-disabled: oklch(92% 0.01 240);
+
+  --text-primary: oklch(22% 0.03 260);
+  --text-secondary: oklch(48% 0.03 260);
+  --text-inverse: oklch(97% 0.01 240);
+
+  --surface-base: oklch(97% 0.01 240);
+  --surface-elevated: oklch(98% 0.008 240 / 0.95);
+  --surface-hover: oklch(93% 0.02 240);
+  --surface-subtle: oklch(0% 0 0 / 0.03);
+
+  --border-base: oklch(82% 0.03 240);
+  --border-subtle: oklch(82% 0.03 240 / 0.45);
+
+  --favorite-color: oklch(72% 0.14 85);
+  --favorite-glow: oklch(72% 0.14 85 / 0.5);
+}
+
+[data-theme="dark"][data-theme-preset="nord"] {
+  --color-accent-h: 213;
+  --color-accent-c: 0.18;
+  --color-accent-l: 68%;
+  --color-warning-h: 35;
+  --color-warning-c: 0.18;
+  --color-success-h: 130;
+  --color-error-l: 65%;
+  --color-error-c: 0.22;
+  --color-error-h: 5;
+  --color-info-h: 195;
+  --color-info-c: 0.18;
+
+  --bg-base: oklch(20% 0.03 260);
+  --bg-elevated: oklch(24% 0.03 260 / 0.98);
+  --bg-hover: oklch(30% 0.03 260);
+  --bg-disabled: oklch(30% 0.02 260);
+
+  --text-primary: oklch(87% 0.02 240);
+  --text-secondary: oklch(68% 0.02 240);
+  --text-inverse: oklch(20% 0.03 260);
+
+  --surface-base: oklch(26% 0.03 260);
+  --surface-elevated: oklch(24% 0.03 260 / 0.98);
+  --surface-hover: oklch(30% 0.03 260);
+  --surface-subtle: oklch(100% 0 0 / 0.03);
+
+  --border-base: oklch(38% 0.03 260);
+  --border-subtle: oklch(87% 0.02 240 / 0.15);
+
+  --favorite-color: oklch(78% 0.15 85);
+  --favorite-glow: oklch(78% 0.15 85 / 0.5);
+}
+
+/* ── Preset: Midnight ───────────────────────────────────────── */
+
+[data-theme-preset="midnight"] {
+  --color-accent-h: 300;
+  --color-accent-c: 0.15;
+  --color-accent-l: 52%;
+  --color-warning-h: 50;
+  --color-warning-c: 0.18;
+  --color-success-h: 135;
+  --color-error-h: 5;
+  --color-error-l: 62%;
+  --color-error-c: 0.22;
+  --color-info-h: 195;
+  --color-info-c: 0.12;
+
+  --bg-base: oklch(96% 0.01 255);
+  --bg-elevated: oklch(98% 0.008 255 / 0.95);
+  --bg-hover: oklch(93% 0.02 255);
+  --bg-disabled: oklch(92% 0.01 255);
+
+  --text-primary: oklch(22% 0.03 260);
+  --text-secondary: oklch(48% 0.03 260);
+  --text-inverse: oklch(97% 0.01 255);
+
+  --surface-base: oklch(97% 0.01 255);
+  --surface-elevated: oklch(98% 0.008 255 / 0.95);
+  --surface-hover: oklch(93% 0.02 255);
+  --surface-subtle: oklch(0% 0 0 / 0.03);
+
+  --border-base: oklch(80% 0.03 255);
+  --border-subtle: oklch(80% 0.03 255 / 0.45);
+
+  --favorite-color: oklch(72% 0.16 85);
+  --favorite-glow: oklch(72% 0.16 85 / 0.5);
+}
+
+[data-theme="dark"][data-theme-preset="midnight"] {
+  --color-accent-h: 300;
+  --color-accent-c: 0.14;
+  --color-accent-l: 68%;
+  --color-warning-h: 50;
+  --color-warning-c: 0.18;
+  --color-success-h: 135;
+  --color-error-h: 5;
+  --color-error-l: 65%;
+  --color-error-c: 0.22;
+  --color-info-h: 195;
+  --color-info-c: 0.12;
+
+  --bg-base: oklch(18% 0.03 260);
+  --bg-elevated: oklch(22% 0.03 260 / 0.98);
+  --bg-hover: oklch(28% 0.03 260);
+  --bg-disabled: oklch(28% 0.02 260);
+
+  --text-primary: oklch(88% 0.02 255);
+  --text-secondary: oklch(68% 0.02 255);
+  --text-inverse: oklch(18% 0.03 260);
+
+  --surface-base: oklch(24% 0.03 260);
+  --surface-elevated: oklch(22% 0.03 260 / 0.98);
+  --surface-hover: oklch(28% 0.03 260);
+  --surface-subtle: oklch(100% 0 0 / 0.03);
+
+  --border-base: oklch(36% 0.03 260);
+  --border-subtle: oklch(88% 0.02 255 / 0.15);
+
+  --favorite-color: oklch(78% 0.16 85);
+  --favorite-glow: oklch(78% 0.16 85 / 0.5);
+}
+
+/* ── Preset: Monokai ───────────────────────────────────────── */
+
+[data-theme-preset="monokai"] {
+  --color-accent-h: 190;
+  --color-accent-c: 0.24;
+  --color-accent-l: 72%;
+  --color-warning-h: 50;
+  --color-warning-c: 0.22;
+  --color-success-h: 140;
+  --color-error-l: 60%;
+  --color-error-c: 0.22;
+  --color-error-h: 340;
+  --color-info-h: 250;
+
+  --bg-base: oklch(96% 0.01 80);
+  --bg-elevated: oklch(98% 0.005 80 / 0.95);
+  --bg-hover: oklch(93% 0.015 80);
+  --bg-disabled: oklch(92% 0.01 80);
+
+  --text-primary: oklch(20% 0.02 100);
+  --text-secondary: oklch(45% 0.02 100);
+  --text-inverse: oklch(97% 0.01 80);
+
+  --surface-base: oklch(97% 0.008 80);
+  --surface-elevated: oklch(98% 0.005 80 / 0.95);
+  --surface-hover: oklch(93% 0.015 80);
+  --surface-subtle: oklch(0% 0 0 / 0.03);
+
+  --border-base: oklch(80% 0.02 80);
+  --border-subtle: oklch(80% 0.02 80 / 0.45);
+
+  --favorite-color: oklch(72% 0.16 85);
+  --favorite-glow: oklch(72% 0.16 85 / 0.5);
+}
+
+[data-theme="dark"][data-theme-preset="monokai"] {
+  --color-accent-h: 190;
+  --color-accent-c: 0.24;
+  --color-accent-l: 72%;
+  --color-warning-h: 50;
+  --color-warning-c: 0.22;
+  --color-success-h: 140;
+  --color-error-l: 65%;
+  --color-error-c: 0.22;
+  --color-error-h: 340;
+  --color-info-h: 250;
+
+  --bg-base: oklch(18% 0.02 100);
+  --bg-elevated: oklch(22% 0.02 100 / 0.98);
+  --bg-hover: oklch(28% 0.025 100);
+  --bg-disabled: oklch(28% 0.015 100);
+
+  --text-primary: oklch(90% 0.02 80);
+  --text-secondary: oklch(70% 0.02 80);
+  --text-inverse: oklch(18% 0.02 100);
+
+  --surface-base: oklch(24% 0.02 100);
+  --surface-elevated: oklch(22% 0.02 100 / 0.98);
+  --surface-hover: oklch(28% 0.025 100);
+  --surface-subtle: oklch(100% 0 0 / 0.03);
+
+  --border-base: oklch(36% 0.02 100);
+  --border-subtle: oklch(90% 0.02 80 / 0.15);
+
+  --favorite-color: oklch(78% 0.16 85);
+  --favorite-glow: oklch(78% 0.16 85 / 0.5);
+}
+
+/* ── Preset: Dracula ───────────────────────────────────────── */
+
+[data-theme-preset="dracula"] {
+  --color-accent-h: 265;
+  --color-accent-c: 0.24;
+  --color-accent-l: 68%;
+  --color-warning-h: 45;
+  --color-warning-c: 0.22;
+  --color-success-h: 135;
+  --color-error-l: 62%;
+  --color-error-c: 0.22;
+  --color-error-h: 350;
+  --color-info-h: 195;
+
+  --bg-base: oklch(96% 0.01 290);
+  --bg-elevated: oklch(98% 0.008 290 / 0.95);
+  --bg-hover: oklch(93% 0.02 290);
+  --bg-disabled: oklch(92% 0.01 290);
+
+  --text-primary: oklch(22% 0.04 290);
+  --text-secondary: oklch(48% 0.04 290);
+  --text-inverse: oklch(97% 0.01 290);
+
+  --surface-base: oklch(97% 0.01 290);
+  --surface-elevated: oklch(98% 0.008 290 / 0.95);
+  --surface-hover: oklch(93% 0.02 290);
+  --surface-subtle: oklch(0% 0 0 / 0.03);
+
+  --border-base: oklch(80% 0.04 290);
+  --border-subtle: oklch(80% 0.04 290 / 0.45);
+
+  --favorite-color: oklch(72% 0.16 85);
+  --favorite-glow: oklch(72% 0.16 85 / 0.5);
+}
+
+[data-theme="dark"][data-theme-preset="dracula"] {
+  --color-accent-h: 265;
+  --color-accent-c: 0.24;
+  --color-accent-l: 72%;
+  --color-warning-h: 45;
+  --color-warning-c: 0.22;
+  --color-success-h: 135;
+  --color-error-l: 65%;
+  --color-error-c: 0.22;
+  --color-error-h: 350;
+  --color-info-h: 195;
+
+  --bg-base: oklch(18% 0.04 290);
+  --bg-elevated: oklch(22% 0.04 290 / 0.98);
+  --bg-hover: oklch(28% 0.04 290);
+  --bg-disabled: oklch(28% 0.03 290);
+
+  --text-primary: oklch(90% 0.02 290);
+  --text-secondary: oklch(70% 0.03 290);
+  --text-inverse: oklch(18% 0.04 290);
+
+  --surface-base: oklch(24% 0.04 290);
+  --surface-elevated: oklch(22% 0.04 290 / 0.98);
+  --surface-hover: oklch(28% 0.04 290);
+  --surface-subtle: oklch(100% 0 0 / 0.03);
+
+  --border-base: oklch(36% 0.04 290);
+  --border-subtle: oklch(90% 0.02 290 / 0.15);
+
+  --favorite-color: oklch(78% 0.16 85);
+  --favorite-glow: oklch(78% 0.16 85 / 0.5);
+}
+
+/* ── Preset: Solarized ─────────────────────────────────────── */
+
+[data-theme-preset="solarized"] {
+  --color-accent-h: 175;
+  --color-accent-c: 0.18;
+  --color-accent-l: 55%;
+  --color-warning-h: 45;
+  --color-warning-c: 0.20;
+  --color-success-h: 68;
+  --color-error-l: 62%;
+  --color-error-c: 0.22;
+  --color-error-h: 5;
+  --color-info-h: 220;
+  --color-info-c: 0.16;
+  --color-info-l: 68%;
+
+  --bg-base: oklch(95% 0.03 85);
+  --bg-elevated: oklch(97% 0.025 85 / 0.95);
+  --bg-hover: oklch(91% 0.035 85);
+  --bg-disabled: oklch(90% 0.025 85);
+
+  --text-primary: oklch(30% 0.06 200);
+  --text-secondary: oklch(50% 0.04 200);
+  --text-inverse: oklch(95% 0.03 85);
+
+  --surface-base: oklch(96% 0.025 85);
+  --surface-elevated: oklch(97% 0.025 85 / 0.95);
+  --surface-hover: oklch(91% 0.035 85);
+  --surface-subtle: oklch(0% 0 0 / 0.03);
+
+  --border-base: oklch(78% 0.04 85);
+  --border-subtle: oklch(78% 0.04 85 / 0.45);
+
+  --favorite-color: oklch(68% 0.16 75);
+  --favorite-glow: oklch(68% 0.16 75 / 0.5);
+}
+
+[data-theme="dark"][data-theme-preset="solarized"] {
+  --color-accent-h: 175;
+  --color-accent-c: 0.18;
+  --color-accent-l: 60%;
+  --color-warning-h: 45;
+  --color-warning-c: 0.20;
+  --color-success-h: 68;
+  --color-error-l: 65%;
+  --color-error-c: 0.22;
+  --color-error-h: 5;
+  --color-info-h: 220;
+  --color-info-c: 0.16;
+  --color-info-l: 68%;
+
+  --bg-base: oklch(18% 0.05 200);
+  --bg-elevated: oklch(22% 0.05 200 / 0.98);
+  --bg-hover: oklch(28% 0.05 200);
+  --bg-disabled: oklch(28% 0.04 200);
+
+  --text-primary: oklch(72% 0.03 85);
+  --text-secondary: oklch(62% 0.03 85);
+  --text-inverse: oklch(18% 0.05 200);
+
+  --surface-base: oklch(24% 0.05 200);
+  --surface-elevated: oklch(22% 0.05 200 / 0.98);
+  --surface-hover: oklch(28% 0.05 200);
+  --surface-subtle: oklch(100% 0 0 / 0.03);
+
+  --border-base: oklch(36% 0.04 200);
+  --border-subtle: oklch(72% 0.03 85 / 0.15);
+
+  --favorite-color: oklch(72% 0.16 75);
+  --favorite-glow: oklch(72% 0.16 75 / 0.5);
+}

static/images/tabler/brush.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="icon icon-tabler icons-tabler-outline icon-tabler-brush"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M3 21v-4a4 4 0 1 1 4 4h-4" /><path d="M21 3a16 16 0 0 0 -12.8 10.2" /><path d="M21 3a16 16 0 0 1 -10.2 12.8" /><path d="M10.6 9a9 9 0 0 1 4.4 4.4" /></svg>

static/images/tabler/currency-dollar.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="icon icon-tabler icons-tabler-outline icon-tabler-currency-dollar"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M16.7 8a3 3 0 0 0 -2.7 -2h-4a3 3 0 0 0 0 6h4a3 3 0 0 1 0 6h-4a3 3 0 0 1 -2.7 -2" /><path d="M12 3v3m0 12v3" /></svg>

static/images/tabler/git-merge.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="icon icon-tabler icons-tabler-outline icon-tabler-git-merge"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M5 18a2 2 0 1 0 4 0a2 2 0 1 0 -4 0" /><path d="M5 6a2 2 0 1 0 4 0a2 2 0 1 0 -4 0" /><path d="M15 12a2 2 0 1 0 4 0a2 2 0 1 0 -4 0" /><path d="M7 8l0 8" /><path d="M7 8a4 4 0 0 0 4 4h4" /></svg>

static/images/tabler/license.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="icon icon-tabler icons-tabler-outline icon-tabler-license"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M15 21h-9a3 3 0 0 1 -3 -3v-1h10v2a2 2 0 0 0 4 0v-14a2 2 0 1 1 2 2h-2m2 -4h-11a3 3 0 0 0 -3 3v11" /><path d="M9 7l4 0" /><path d="M9 11l4 0" /></svg>

static/images/tabler/user.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="icon icon-tabler icons-tabler-outline icon-tabler-user"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M8 7a4 4 0 1 0 8 0a4 4 0 0 0 -8 0" /><path d="M6 21v-2a4 4 0 0 1 4 -4h4a4 4 0 0 1 4 4v2" /></svg>

static/js/api/apiConfig.js

@@ -190,6 +190,12 @@ export const DOWNLOAD_ENDPOINTS = {
     exampleImages: '/api/lm/force-download-example-images' // New endpoint for downloading example images
 };
 
+// Hugging Face API endpoints
+export const HF_ENDPOINTS = {
+    repoFiles: '/api/lm/hf-repo-files',
+    download: '/api/lm/download-hf-model',
+};
+
 // WebSocket endpoints
 export const WS_ENDPOINTS = {
     fetchProgress: '/ws/fetch-progress'