docs: add v1.0.1 release notes, bump version to 1.0.1

- Replace tooltip with restart-required icon for better visibility
- Update descriptions to accurately reflect feature purpose
- Fix toast message to show correct restart notification
- Sync i18n keys across all supported languages

.gitignore

@@ -15,6 +15,7 @@ model_cache/
 # agent
 .opencode/
 .claude/
+.codex
 
 # Vue widgets development cache (but keep build output)
 vue-widgets/node_modules/

AGENTS.md

@@ -138,6 +138,13 @@ npm run test:coverage       # Generate coverage report
 - Run `python scripts/sync_translation_keys.py` after adding UI strings to `locales/en.json`
 - Symlinks require normalized paths
 
+## Git / Commit Messages
+
+- Follow the style of recent repository commits when writing commit messages
+- Prefer the repo's existing `feat(...)`, `fix(...)`, `chore:` style where applicable
+- If the user has provided a GitHub issue link or issue ID for the task, mention that issue in the commit message, for example `(#871)`
+- When unrelated local changes exist, stage and commit only the files relevant to the requested task
+
 ## Frontend UI Architecture
 
 ### 1. Standalone Web UI

__init__.py

@@ -7,6 +7,7 @@ try:  # pragma: no cover - import fallback for pytest collection
     from .py.nodes.prompt import PromptLM
     from .py.nodes.text import TextLM
     from .py.nodes.lora_stacker import LoraStackerLM
+    from .py.nodes.lora_stack_combiner import LoraStackCombinerLM
     from .py.nodes.save_image import SaveImageLM
     from .py.nodes.debug_metadata import DebugMetadataLM
     from .py.nodes.wanvideo_lora_select import WanVideoLoraSelectLM
@@ -39,6 +40,9 @@ except (
         "py.nodes.trigger_word_toggle"
     ).TriggerWordToggleLM
     LoraStackerLM = importlib.import_module("py.nodes.lora_stacker").LoraStackerLM
+    LoraStackCombinerLM = importlib.import_module(
+        "py.nodes.lora_stack_combiner"
+    ).LoraStackCombinerLM
     SaveImageLM = importlib.import_module("py.nodes.save_image").SaveImageLM
     DebugMetadataLM = importlib.import_module("py.nodes.debug_metadata").DebugMetadataLM
     WanVideoLoraSelectLM = importlib.import_module(
@@ -63,6 +67,7 @@ NODE_CLASS_MAPPINGS = {
     UNETLoaderLM.NAME: UNETLoaderLM,
     TriggerWordToggleLM.NAME: TriggerWordToggleLM,
     LoraStackerLM.NAME: LoraStackerLM,
+    LoraStackCombinerLM.NAME: LoraStackCombinerLM,
     SaveImageLM.NAME: SaveImageLM,
     DebugMetadataLM.NAME: DebugMetadataLM,
     WanVideoLoraSelectLM.NAME: WanVideoLoraSelectLM,

data/supporters.json

@@ -9,17 +9,17 @@
     "Insomnia Art Designs",
     "megakirbs",
     "Brennok",
-    "wackop",
     "2018cfh",
+    "W+K+White",
+    "wackop",
     "Takkan",
-    "stone9k",
+    "Carl G.",
     "$MetaSamsara",
     "itismyelement",
     "onesecondinosaur",
-    "Carl G.",
+    "stone9k",
     "Rosenthal",
     "Francisco Tatis",
-    "Tobi_Swagg",
     "Andrew Wilson",
     "Greybush",
     "Gooohokrbe",
@@ -29,18 +29,16 @@
     "VantAI",
     "runte3221",
     "FreelancerZ",
-    "Julian V",
     "Edgar Tejeda",
-    "Birdy",
     "Liam MacDougal",
     "Fraser Cross",
     "Polymorphic Indeterminate",
+    "Birdy",
     "Marc Whiffen",
-    "Kiba",
     "Jorge Hussni",
-    "Reno Lam",
+    "Kiba",
     "Skalabananen",
-    "esthe",
+    "Reno Lam",
     "sig",
     "Christian Byrne",
     "DM",
@@ -49,24 +47,22 @@
     "J\\B/ 8r0wns0n",
     "Snaggwort",
     "Arlecchino Shion",
+    "Charles Blakemore",
+    "Rob Williams",
     "ClockDaemon",
     "KD",
     "Omnidex",
     "Tyler Trebuchon",
     "Release Cabrakan",
-    "confiscated Zyra",
+    "Tobi_Swagg",
     "SG",
     "carozzz",
     "James Dooley",
     "zenbound",
     "Buzzard",
     "jmack",
-    "Adam Shaw",
-    "Tee Gee",
     "Mark Corneglio",
     "SarcasticHashtag",
-    "Anthony Rizzo",
-    "tarek helmi",
     "Cosmosis",
     "iamresist",
     "RedrockVP",
@@ -75,45 +71,34 @@
     "James Todd",
     "Steven Pfeiffer",
     "Tim",
-    "Timmy",
-    "Johnny",
     "Lisster",
     "Michael Wong",
     "Illrigger",
-    "whudunit",
     "Tom Corrigan",
     "JackieWang",
     "fnkylove",
+    "Julian V",
     "Steven Owens",
     "Yushio",
     "Vik71it",
-    "lh qwe",
     "Echo",
     "Lilleman",
     "Robert Stacey",
     "PM",
     "Todd Keck",
-    "Briton Heilbrun",
     "Mozzel",
     "Gingko Biloba",
-    "Felipe dos Santos",
-    "Penfore",
-    "BadassArabianMofo",
     "Sterilized",
+    "BadassArabianMofo",
     "Pascal Dahle",
-    "Markus",
     "quarz",
     "Greg",
-    "Douglas Gaspar",
+    "Penfore",
     "JSST",
-    "AlexDuKaNa",
-    "George",
+    "esthe",
     "lmsupporter",
-    "Phil",
-    "Charles Blakemore",
     "IamAyam",
     "wfpearl",
-    "Rob Williams",
     "Baekdoosixt",
     "Jonathan Ross",
     "Jack B Nimble",
@@ -125,127 +110,118 @@
     "contrite831",
     "Alex",
     "bh",
+    "confiscated Zyra",
     "Marlon Daniels",
     "Starkselle",
     "Aaron Bleuer",
     "LacesOut!",
-    "Graham Colehour",
+    "greebles",
+    "Adam Shaw",
+    "Tee Gee",
+    "Anthony Rizzo",
+    "tarek helmi",
     "M Postkasse",
-    "Tomohiro Baba",
-    "David Ortega",
     "ASLPro3D",
     "Jacob Hoehler",
     "FinalyFree",
     "Weasyl",
-    "Lex Song",
+    "Timmy",
+    "Johnny",
     "Cory Paza",
     "Tak",
     "Gonzalo Andre Allendes Lopez",
     "Zach Gonser",
     "Big Red",
-    "Jimmy Ledbetter",
+    "whudunit",
     "Luc Job",
     "dl0901dm",
     "Philip Hempel",
     "corde",
     "Nick Walker",
+    "lh qwe",
     "Bishoujoker",
     "conner",
     "aai",
-    "Yaboi",
+    "Briton Heilbrun",
     "Tori",
     "wildnut",
     "Princess Bright Eyes",
-    "Damon Cunliffe",
-    "CryptoTraderJK",
-    "Davaitamin",
     "AbstractAss",
+    "Felipe dos Santos",
     "ViperC",
+    "jean jahren",
     "Aleksander Wujczyk",
     "AM Kuro",
-    "jean jahren",
-    "Ran C",
-    "tedcor",
+    "Markus",
     "S Sang",
-    "MagnaInsomnia",
-    "Akira_HentAI",
     "Karl P.",
+    "Akira_HentAI",
+    "MagnaInsomnia",
     "Gordon Cole",
     "yuxz69",
-    "MadSpin",
+    "Douglas Gaspar",
+    "AlexDuKaNa",
+    "George",
     "andrew.tappan",
     "dw",
     "N/A",
     "The Spawn",
+    "Phil",
     "graysock",
     "Greenmoustache",
     "zounic",
-    "Gamalonia",
     "fancypants",
-    "Vir",
-    "Joboshy",
     "Digital",
     "JaxMax",
     "takyamtom",
-    "Bohemian Corporal",
     "奚明 刘",
-    "Dan",
-    "Seth Christensen",
     "Jwk0205",
     "Bro Xie",
-    "Draven T",
-    "yer fey",
+    "준희 김",
     "batblue",
     "carey6409",
     "Olive",
     "太郎 ゲーム",
     "Some Guy Named Barry",
-    "jinxedx",
-    "Aquatic Coffee",
     "Max Marklund",
+    "Tomohiro Baba",
+    "David Ortega",
     "AELOX",
-    "Dankin",
     "Nicfit23",
     "Noora",
-    "ethanfel",
     "wamekukyouzin",
     "drum matthieu",
     "Dogmaster",
     "Matt Wenzel",
     "Mattssn",
-    "Frank Nitty",
+    "Lex Song",
     "John Saveas",
-    "Focuschannel",
     "Christopher Michel",
     "Serge Bekenkamp",
+    "Jimmy Ledbetter",
     "LeoZero",
     "Antonio Pontes",
     "ApathyJones",
     "nahinahi9",
-    "Anthony Faxlandez",
     "Dustin Chen",
     "dan",
-    "Blackfish95",
+    "Yaboi",
     "Mouthlessman",
     "Steam Steam",
-    "Paul Kroll",
+    "Damon Cunliffe",
+    "CryptoTraderJK",
+    "Davaitamin",
     "otaku fra",
-    "semicolon drainpipe",
-    "Thesharingbrother",
+    "Ran C",
+    "tedcor",
     "Fotek Design",
-    "Bas Imagineer",
-    "Pat Hen",
-    "ResidentDeviant",
     "Adam Taylor",
-    "JC",
     "Weird_With_A_Beard",
-    "Prompt Pirate",
+    "MadSpin",
     "Pozadine1",
-    "uwutismxd",
     "Qarob",
     "AIGooner",
     "inbijiburu",
-    "decoy",
     "Luc",
     "ProtonPrince",
     "DiffDuck",
@@ -258,53 +234,54 @@
     "thesoftwaredruid",
     "wundershark",
     "mr_dinosaur",
-    "Tyrswood",
     "linnfrey",
-    "zenobeus",
-    "Jackthemind",
-    "Stryker",
+    "Gamalonia",
+    "Vir",
     "Pkrsky",
-    "raf8osz",
-    "blikkies",
+    "Joboshy",
+    "Bohemian Corporal",
+    "Dan",
     "Josef Lanzl",
+    "Seth Christensen",
     "Griffin Dahlberg",
-    "준희 김",
+    "Draven T",
+    "yer fey",
     "Error_Rule34_Not_found",
     "Gerald Welly",
-    "Shock Shockor",
     "Roslynd",
     "Geolog",
-    "Goldwaters",
+    "jinxedx",
     "Neco28",
-    "Zude",
+    "Aquatic Coffee",
+    "Dankin",
+    "ethanfel",
     "Cristian Vazquez",
-    "Kyler",
+    "Frank Nitty",
     "Magic Noob",
-    "aRtFuL_DodGeR",
-    "X",
+    "Focuschannel",
     "DougPeterson",
     "Jeff",
     "Bruce",
-    "CrimsonDX",
     "Kevin John Duck",
+    "Anthony Faxlandez",
     "Kevin Christopher",
     "Ouro Boros",
-    "DarkSunset",
+    "Blackfish95",
     "dd",
-    "Billy Gladky",
-    "Probis",
-    "shrshpp",
-    "Dušan Ryban",
-    "ItsGeneralButtNaked",
-    "sjon kreutz",
-    "Nimess",
+    "Paul Kroll",
+    "MiraiKuriyamaSy",
+    "semicolon drainpipe",
+    "Thesharingbrother",
+    "Bas Imagineer",
+    "Pat Hen",
     "John Statham",
-    "Youguang",
+    "ResidentDeviant",
     "Nihongasuki",
-    "Metryman55",
-    "andrewzpong",
-    "FrxzenSnxw",
-    "BossGame",
+    "JC",
+    "Prompt Pirate",
+    "uwutismxd",
+    "decoy",
+    "Tyrswood",
     "Ray Wing",
     "Ranzitho",
     "Gus",
@@ -316,7 +293,6 @@
     "WRL_SPR",
     "capn",
     "Joseph",
-    "lrdchs",
     "Mirko Katzula",
     "dan",
     "Piccio08",
@@ -326,51 +302,135 @@
     "Moon Knight",
     "몽타주",
     "Kland",
-    "Hailshem",
+    "zenobeus",
+    "Jackthemind",
     "ryoma",
-    "John Martin",
+    "Stryker",
+    "raf8osz",
+    "ElitaSSJ4",
+    "blikkies",
     "Chris",
     "Brian M",
     "Nerezza",
     "sanborondon",
-    "moranqianlong",
     "Taylor Funk",
     "aezin",
     "Thought2Form",
     "jcay015",
     "Kevin Picco",
     "Erik Lopez",
+    "Shock Shockor",
     "Mateo Curić",
-    "Haru Yotu",
+    "Goldwaters",
+    "Zude",
     "Eris3D",
     "m",
     "Pierce McBride",
     "Joshua Gray",
+    "Kyler",
     "Mikko Hemilä",
-    "Matura Arbeit",
+    "aRtFuL_DodGeR",
     "Jamie Ogletree",
-    "TBitz33",
-    "Emil Bernhoff",
     "a _",
-    "SendingRavens",
     "James Coleman",
+    "CrimsonDX",
     "Martial",
     "battu",
     "Emil Andersson",
     "Chad Idk",
-    "Michael Docherty",
+    "DarkSunset",
+    "Billy Gladky",
     "Yuji Kaneko",
+    "Probis",
+    "Dušan Ryban",
+    "ItsGeneralButtNaked",
+    "Jordan Shaw",
+    "Rops Alot",
+    "Sam",
+    "sjon kreutz",
+    "Nimess",
+    "SRDB",
+    "Ace Ventura",
+    "g unit",
+    "Youguang",
+    "Metryman55",
+    "andrewzpong",
+    "FrxzenSnxw",
+    "BossGame",
+    "lrdchs",
+    "momokai",
+    "Hailshem",
+    "kudari",
+    "Naomi Hale Danchi",
+    "dc7431",
+    "ken",
+    "Inversity",
+    "AIVORY3D",
+    "epicgamer0020690",
+    "Joshua Porrata",
+    "keemun",
+    "SuBu",
+    "RedPIXel",
+    "Kevinj",
+    "Wind",
+    "Nexus",
+    "Ramneek“Guy”Ashok",
+    "squid_actually",
+    "Nat_20",
+    "Edward Weeks",
+    "kyoumei",
+    "RadStorm04",
+    "JohnDoe42054",
+    "BillyHill",
+    "emyth",
+    "chriphost",
+    "KitKatM",
+    "socrasteeze",
+    "ResidentDeviant",
+    "gzmzmvp",
+    "Welkor",
+    "John Martin",
+    "Richard",
+    "Andrew",
+    "Robert Wegemund",
+    "Littlehuggy",
+    "moranqianlong",
+    "Gregory Kozhemiak",
+    "mrjuan",
+    "Brian Buie",
+    "Sadlip",
+    "Haru Yotu",
+    "Eric Whitney",
+    "Joey Callahan",
+    "Ivan Tadic",
+    "Mike Simone",
+    "Morgandel",
+    "Kyron Mahan",
+    "Matura Arbeit",
+    "Noah",
+    "Jacob McDaniel",
+    "X",
+    "Sloan Steddy",
+    "TBitz33",
+    "Anonym dkjglfleeoeldldldlkf",
+    "Temikus",
+    "Artokun",
+    "Michael Taylor",
+    "SendingRavens",
+    "Derek Baker",
+    "Michael Anthony Scott",
+    "Atilla Berke Pekduyar",
+    "Michael Docherty",
+    "Nathan",
+    "Decx _",
+    "Paul Hartsuyker",
     "elitassj",
     "Jacob Winter",
-    "Jordan Shaw",
-    "Sam",
-    "Rops Alot",
-    "SRDB",
-    "g unit",
-    "Ace Ventura",
+    "Distortik",
     "David",
     "Meilo",
     "Pen Bouryoung",
+    "四糸凜音",
     "shinonomeiro",
     "Snille",
     "MaartenAlbers",
@@ -378,101 +438,104 @@
     "xybrightsummer",
     "jreedatchison",
     "PhilW",
-    "momokai",
+    "Tree Tagger",
     "Janik",
-    "kudari",
-    "Naomi Hale Danchi",
-    "dc7431",
-    "ken",
-    "Inversity",
     "Crocket",
-    "AIVORY3D",
-    "epicgamer0020690",
-    "Joshua Porrata",
     "Cruel",
-    "keemun",
-    "SuBu",
-    "RedPIXel",
     "MRBlack",
-    "Kevinj",
-    "Wind",
-    "Nexus",
     "Mitchell Robson",
-    "Ramneek“Guy”Ashok",
-    "squid_actually",
-    "Nat_20",
     "Kiyoe",
-    "Edward Weeks",
-    "kyoumei",
-    "RadStorm04",
-    "JohnDoe42054",
-    "BillyHill",
     "humptynutz",
-    "emyth",
     "michael.isaza",
     "Kalnei",
-    "chriphost",
-    "KitKatM",
-    "socrasteeze",
-    "ResidentDeviant",
+    "Whitepinetrader",
+    "OrganicArtifact",
     "Scott",
-    "gzmzmvp",
-    "Welkor",
+    "MudkipMedkitz",
+    "deanbrian",
+    "POPPIN",
+    "Alex Wortman",
+    "Cody",
+    "Raku",
+    "smart.edge5178",
+    "emadsultan",
+    "InformedViewz",
+    "CHKeeho80",
+    "Bubbafett",
+    "leaf",
+    "Menard",
+    "Skyfire83",
+    "Adam Rinehart",
+    "D",
+    "Pitpe11",
+    "TheD1rtyD03",
+    "moonpetal",
+    "SomeDude",
+    "g9p0o",
+    "nanana",
+    "TheHolySheep",
+    "Monte Won",
+    "SpringBootisTrash",
+    "carsten",
+    "ikok",
+    "Buecyb99",
+    "4IXplr0r3r",
+    "dfklsjfkljslfjd",
     "hayden",
-    "Richard",
     "ahoystan",
     "Leland Saunders",
-    "Andrew",
+    "Wolfe7D1",
+    "Ink Temptation",
     "Bob Barker",
-    "Robert Wegemund",
-    "Littlehuggy",
-    "Gregory Kozhemiak",
-    "mrjuan",
+    "edk",
+    "Kalli Core",
     "Aeternyx",
-    "Brian Buie",
+    "elleshar666",
     "YOU SINWOO",
-    "Sadlip",
     "ja s",
-    "Eric Whitney",
     "Doug Mason",
-    "Joey Callahan",
-    "Ivan Tadic",
-    "y2Rxy7FdXzWo",
+    "Kauffy",
     "Jeremy Townsend",
-    "Mike Simone",
+    "EpicElric",
     "Sean voets",
     "Owen Gwosdz",
-    "Morgandel",
+    "John J Linehan",
+    "Elliot E",
     "Thomas Wanner",
-    "Kyron Mahan",
     "Theerat Jiramate",
-    "Noah",
-    "Jacob McDaniel",
+    "Edward Kennedy",
+    "Justin Blaylock",
+    "Devil Lude",
+    "Nick Kage",
     "kevin stoddard",
-    "Sloan Steddy",
     "Jack Dole",
+    "Vane Holzer",
+    "psytrax",
     "Ezokewn",
-    "Temikus",
-    "Artokun",
-    "Michael Taylor",
-    "Derek Baker",
-    "Michael Anthony Scott",
-    "Atilla Berke Pekduyar",
+    "hexxish",
+    "CptNeo",
+    "notedfakes",
     "Maso",
-    "Nathan",
-    "Decx _",
+    "Eric Ketchum",
+    "NICHOLAS BAXLEY",
+    "Michael Scott",
     "Kevin Wallace",
     "Matheus Couto",
-    "Paul Hartsuyker",
+    "Saya",
     "ChicRic",
     "mercur",
     "J C",
-    "Distortik",
+    "Ed Wang",
+    "Ryan Presley Ng",
+    "Wes Sims",
+    "Donor4115",
     "Yves Poezevara",
     "Teriak47",
     "Just me",
     "Raf Stahelin",
     "Вячеслав Маринин",
+    "Lyavph",
+    "Filippo Ferrari",
     "Cola Matthew",
     "OniNoKen",
     "Iain Wisely",
@@ -505,117 +568,100 @@
     "RevyHiep",
     "Captain_Swag",
     "obkircher",
-    "Tree Tagger",
     "gwyar",
     "D",
     "edgecase",
     "Neoxena",
     "mrmhalo",
     "dg",
-    "Whitepinetrader",
     "Maarten Harms",
-    "OrganicArtifact",
-    "四糸凜音",
-    "MudkipMedkitz",
     "Israel",
-    "deanbrian",
-    "POPPIN",
     "Muratoraccio",
     "SelfishMedic",
     "Ginnie",
-    "Alex Wortman",
-    "Cody",
     "adderleighn",
-    "Raku",
-    "smart.edge5178",
-    "emadsultan",
-    "InformedViewz",
-    "CHKeeho80",
-    "Bubbafett",
-    "leaf",
-    "Menard",
-    "Skyfire83",
-    "Adam Rinehart",
-    "D",
-    "Pitpe11",
-    "TheD1rtyD03",
     "EnragedAntelope",
-    "moonpetal",
-    "SomeDude",
-    "g9p0o",
-    "nanana",
-    "TheHolySheep",
-    "Monte Won",
-    "SpringBootisTrash",
-    "carsten",
-    "ikok",
-    "Buecyb99",
-    "4IXplr0r3r",
+    "Alan+Cano",
+    "FeralOpticsAI",
+    "Pavlaki",
+    "generic404",
+    "Mateusz+Kosela",
+    "Doug+Rintoul",
+    "Noor",
+    "Yorunai",
+    "Bula",
+    "quantenmecha",
+    "abattoirblues",
+    "Jason+Nash",
+    "BillyBoy84",
+    "DarkRoast",
+    "zounik",
+    "letzte",
+    "Nasty+Hobbit",
+    "SgtFluffles",
+    "lrdchs2",
+    "Duk3+Rand0m",
+    "KUJYAKU",
+    "NathenChoi",
+    "Thomas+Reck",
+    "Larses",
+    "cocona",
     "Coeur+de+cochon",
     "David Schenck",
     "han b",
     "Nico",
-    "Wolfe7D1",
     "Banana Joe",
     "_ G3n",
     "Donovan Jenkins",
-    "Ink Temptation",
-    "edk",
+    "JBsuede",
     "Michael Eid",
     "beersandbacon",
     "Maximilian Pyko",
     "Invis",
-    "Kalli Core",
     "Justin Houston",
+    "Time Valentine",
     "james",
-    "elleshar666",
     "OrochiNights",
     "Michael Zhu",
     "ACTUALLY_the_Real_Willem_Dafoe",
     "gonzalo",
     "Seraphy",
+    "Михал Михалыч",
     "雨の心 落",
+    "Matt",
     "AllTimeNoobie",
     "jumpd",
     "John C",
-    "Kauffy",
     "Rim",
     "Dismem",
-    "EpicElric",
-    "John J Linehan",
+    "Frogmilk",
+    "SPJ",
     "Xan Dionysus",
     "Nathan lee",
     "Mewtora",
-    "Elliot E",
     "Middo",
     "Forbidden Atelier",
-    "Edward Kennedy",
-    "Justin Blaylock",
+    "Bryan Rutkowski",
     "Adictedtohumping",
-    "Devil Lude",
-    "Nick Kage",
     "Towelie",
-    "Vane Holzer",
-    "psytrax",
     "Cyrus Fett",
     "Jean-françois SEMA",
     "Kurt",
-    "hexxish",
-    "giani kidd",
-    "CptNeo",
-    "notedfakes",
+    "max blo",
+    "Xenon Xue",
+    "JackJohnnyJim",
+    "Edward Ten Eyck",
     "Chase Kwon",
+    "Inyoshu",
     "Goober719",
-    "Eric Ketchum",
     "Chad Barnes",
-    "NICHOLAS BAXLEY",
-    "Michael Scott",
     "James Ming",
     "vanditking",
     "kripitonga",
     "Rizzi",
     "nimin",
     "OMAR LUCIANO",
+    "hannibal",
     "Jo+Example",
     "BrentBertram",
     "eumelzocker",
@@ -623,5 +669,5 @@
     "L C",
     "Dude"
   ],
-  "totalCount": 620
+  "totalCount": 666
 }

locales/de.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "NSFW-Inhalte unscharf stellen",
             "blurNsfwContentHelp": "Nicht jugendfreie (NSFW) Vorschaubilder unscharf stellen",
             "showOnlySfw": "Nur SFW-Ergebnisse anzeigen",
-            "showOnlySfwHelp": "Alle NSFW-Inhalte beim Durchsuchen und Suchen herausfiltern"
+            "showOnlySfwHelp": "Alle NSFW-Inhalte beim Durchsuchen und Suchen herausfiltern",
+            "matureBlurThreshold": "Schwelle für Unschärfe bei jugendgefährdenden Inhalten",
+            "matureBlurThresholdHelp": "Legen Sie fest, ab welcher Altersfreigabe die Unschärfe beginnt, wenn NSFW-Unschärfe aktiviert ist.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 und höher",
+                "r": "R und höher (Standard)",
+                "x": "X und höher",
+                "xxx": "Nur XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Videos bei Hover automatisch abspielen",
@@ -315,6 +323,24 @@
                 "saveFailed": "Übersprungene Pfade konnten nicht gespeichert werden: {message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "Downloads für Basismodelle überspringen",
+            "help": "Gilt für alle Download-Abläufe. Hier können nur unterstützte Basismodelle ausgewählt werden.",
+            "searchPlaceholder": "Basismodelle filtern...",
+            "empty": "Keine Basismodelle entsprechen der aktuellen Suche.",
+            "summary": {
+                "none": "Nichts ausgewählt",
+                "count": "{count} ausgewählt"
+            },
+            "actions": {
+                "edit": "Bearbeiten",
+                "collapse": "Einklappen",
+                "clear": "Löschen"
+            },
+            "validation": {
+                "saveFailed": "Ausgeschlossene Basismodelle konnten nicht gespeichert werden: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Anzeige-Dichte",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "Zusätzliche Ordnerpfade",
-            "help": "Fügen Sie zusätzliche Modellordner außerhalb der Standardpfade von ComfyUI hinzu. Diese Pfade werden separat gespeichert und zusammen mit den Standardordnern gescannt.",
-            "description": "Konfigurieren Sie zusätzliche Ordner zum Scannen von Modellen. Diese Pfade sind spezifisch für LoRA Manager und werden mit den Standardpfaden von ComfyUI zusammengeführt.",
+            "description": "Zusätzliche Modellstammverzeichnisse, die ausschließlich für LoRA Manager gelten. Laden Sie Modelle von Speicherorten außerhalb der Standardordner von ComfyUI – ideal für große Bibliotheken, die ComfyUI sonst verlangsamen würden.",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "LoRA-Pfade",
                 "checkpoint": "Checkpoint-Pfade",
@@ -376,7 +402,7 @@
                 "embedding": "Embedding-Pfade"
             },
             "pathPlaceholder": "/pfad/zu/extra/modellen",
-            "saveSuccess": "Zusätzliche Ordnerpfade aktualisiert.",
+            "saveSuccess": "Zusätzliche Ordnerpfade aktualisiert. Neustart erforderlich, um Änderungen anzuwenden.",
             "saveError": "Fehler beim Aktualisieren der zusätzlichen Ordnerpfade: {message}",
             "validation": {
                 "duplicatePath": "Dieser Pfad ist bereits konfiguriert"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "Metadaten-Aktualisierung für ausgewählte Modelle überspringen",
             "resumeMetadataRefresh": "Metadaten-Aktualisierung für ausgewählte Modelle fortsetzen",
             "deleteAll": "Alle Modelle löschen",
+            "downloadMissingLoras": "Fehlende LoRAs herunterladen",
             "clear": "Auswahl löschen",
             "skipMetadataRefreshCount": "Überspringen（{count} Modelle）",
             "resumeMetadataRefreshCount": "Fortsetzen（{count} Modelle）",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "In {otherType}-Ordner verschieben"
+            "moveToOtherTypeFolder": "In {otherType}-Ordner verschieben",
+            "sendToWorkflow": "[TODO: Translate] Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "Sidebar lösen",
         "switchToListView": "Zur Listenansicht wechseln",
         "switchToTreeView": "Zur Baumansicht wechseln",
-        "recursiveOn": "Unterordner durchsuchen",
-        "recursiveOff": "Nur aktuellen Ordner durchsuchen",
+        "recursiveOn": "Unterordner einbeziehen",
+        "recursiveOff": "Nur aktueller Ordner",
         "recursiveUnavailable": "Rekursive Suche ist nur in der Baumansicht verfügbar",
         "collapseAllDisabled": "Im Listenmodus nicht verfügbar",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "Basis-Modell aktualisieren",
             "cancel": "Abbrechen"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Fehlende LoRAs herunterladen",
+            "message": "{uniqueCount} einzigartige fehlende LoRAs gefunden (von insgesamt {totalCount} in ausgewählten Rezepten).",
+            "previewTitle": "Zu herunterladende LoRAs:",
+            "moreItems": "...und {count} weitere",
+            "note": "Dateien werden mit Standard-Pfad-Vorlagen heruntergeladen. Dies kann je nach Anzahl der LoRAs eine Weile dauern.",
+            "downloadButton": "{count} LoRA(s) herunterladen"
+        },
         "exampleAccess": {
             "title": "Lokale Beispielbilder",
             "message": "Keine lokalen Beispielbilder für dieses Modell gefunden. Ansichtsoptionen:",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "Auf Civitai anzeigen",
                 "viewOnCivitaiText": "Auf Civitai anzeigen",
                 "viewCreatorProfile": "Ersteller-Profil anzeigen",
-                "openFileLocation": "Dateispeicherort öffnen"
+                "openFileLocation": "Dateispeicherort öffnen",
+                "sendToWorkflow": "An ComfyUI senden",
+                "sendToWorkflowText": "An ComfyUI senden"
             },
             "openFileLocation": {
                 "success": "Dateispeicherort erfolgreich geöffnet",
@@ -1042,6 +1080,9 @@
                 "copied": "Pfad in die Zwischenablage kopiert: {{path}}",
                 "clipboardFallback": "Pfad: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "Kann nicht an ComfyUI senden: Kein Dateipfad verfügbar"
+            },
             "metadata": {
                 "version": "Version",
                 "fileName": "Dateiname",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "Rezept im Workflow ersetzt",
             "recipeFailedToSend": "Fehler beim Senden des Rezepts an den Workflow",
             "noMatchingNodes": "Keine kompatiblen Knoten im aktuellen Workflow verfügbar",
-            "noTargetNodeSelected": "Kein Zielknoten ausgewählt"
+            "noTargetNodeSelected": "Kein Zielknoten ausgewählt",
+            "modelUpdated": "Modell im Workflow aktualisiert",
+            "modelFailed": "Fehler beim Aktualisieren des Modellknotens"
         },
         "nodeSelector": {
             "recipe": "Rezept",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "Bitte wählen Sie eine Version aus",
             "versionExists": "Diese Version existiert bereits in Ihrer Bibliothek",
             "downloadCompleted": "Download erfolgreich abgeschlossen",
+            "downloadSkippedByBaseModel": "Download übersprungen, weil das Basismodell {baseModel} ausgeschlossen ist",
             "autoOrganizeSuccess": "Automatische Organisation für {count} {type} erfolgreich abgeschlossen",
             "autoOrganizePartialSuccess": "Automatische Organisation abgeschlossen: {success} verschoben, {failures} fehlgeschlagen von insgesamt {total} Modellen",
             "autoOrganizeFailed": "Automatische Organisation fehlgeschlagen: {error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "Rezeptname erfolgreich aktualisiert",
             "tagsUpdated": "Rezept-Tags erfolgreich aktualisiert",
             "sourceUrlUpdated": "Quell-URL erfolgreich aktualisiert",
+            "promptUpdated": "Prompt erfolgreich aktualisiert",
+            "negativePromptUpdated": "Negativer Prompt erfolgreich aktualisiert",
+            "promptEditorHint": "Drücken Sie Enter zum Speichern, Shift+Enter für neue Zeile",
             "noRecipeId": "Keine Rezept-ID verfügbar",
+            "sendToWorkflowFailed": "Fehler beim Senden des Rezepts an den Workflow: {message}",
             "copyFailed": "Fehler beim Kopieren der Rezept-Syntax: {message}",
             "noMissingLoras": "Keine fehlenden LoRAs zum Herunterladen",
             "missingLorasInfoFailed": "Fehler beim Abrufen der Informationen für fehlende LoRAs",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "Please enter at least one URL or file path",
             "batchImportNoDirectory": "Please enter a directory path",
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}"
+            "batchImportDirectorySelected": "Directory selected: {path}",
+            "noRecipesSelected": "Keine Rezepte ausgewählt",
+            "noMissingLorasInSelection": "Keine fehlenden LoRAs in ausgewählten Rezepten gefunden",
+            "noLoraRootConfigured": "Kein LoRA-Stammverzeichnis konfiguriert. Bitte legen Sie ein Standard-LoRA-Stammverzeichnis in den Einstellungen fest."
         },
         "models": {
             "noModelsSelected": "Keine Modelle ausgewählt",

locales/en.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "Blur NSFW Content",
             "blurNsfwContentHelp": "Blur mature (NSFW) content preview images",
             "showOnlySfw": "Show Only SFW Results",
-            "showOnlySfwHelp": "Filter out all NSFW content when browsing and searching"
+            "showOnlySfwHelp": "Filter out all NSFW content when browsing and searching",
+            "matureBlurThreshold": "Mature Blur Threshold",
+            "matureBlurThresholdHelp": "Set which rating level starts blur filtering when NSFW blur is enabled.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 and above",
+                "r": "R and above (default)",
+                "x": "X and above",
+                "xxx": "XXX only"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Autoplay Videos on Hover",
@@ -315,6 +323,24 @@
                 "saveFailed": "Unable to save skip paths: {message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "Skip downloads for base models",
+            "help": "When a model version uses one of these base models, LoRA Manager will skip the download before any file transfer starts. Applies to all download flows. Only supported base models can be selected here.",
+            "searchPlaceholder": "Filter base models...",
+            "empty": "No base models match the current search.",
+            "summary": {
+                "none": "None selected",
+                "count": "{count} selected"
+            },
+            "actions": {
+                "edit": "Edit",
+                "collapse": "Collapse",
+                "clear": "Clear"
+            },
+            "validation": {
+                "saveFailed": "Unable to save excluded base models: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Display Density",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "Extra Folder Paths",
-            "help": "Add additional model folders outside of ComfyUI's standard paths. These paths are stored separately and scanned alongside the default folders.",
-            "description": "Configure additional folders to scan for models. These paths are specific to LoRA Manager and will be merged with ComfyUI's default paths.",
+            "description": "Additional model root paths exclusive to LoRA Manager. Load models from locations outside ComfyUI's standard folders—ideal for large libraries that would otherwise slow down ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "LoRA Paths",
                 "checkpoint": "Checkpoint Paths",
@@ -376,7 +402,7 @@
                 "embedding": "Embedding Paths"
             },
             "pathPlaceholder": "/path/to/extra/models",
-            "saveSuccess": "Extra folder paths updated.",
+            "saveSuccess": "Extra folder paths updated. Restart required to apply changes.",
             "saveError": "Failed to update extra folder paths: {message}",
             "validation": {
                 "duplicatePath": "This path is already configured"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "Skip Metadata Refresh for Selected",
             "resumeMetadataRefresh": "Resume Metadata Refresh for Selected",
             "deleteAll": "Delete Selected Models",
+            "downloadMissingLoras": "Download Missing LoRAs",
             "clear": "Clear Selection",
             "skipMetadataRefreshCount": "Skip ({count} models)",
             "resumeMetadataRefreshCount": "Resume ({count} models)",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Move to {otherType} Folder"
+            "moveToOtherTypeFolder": "Move to {otherType} Folder",
+            "sendToWorkflow": "Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "Unpin Sidebar",
         "switchToListView": "Switch to List View",
         "switchToTreeView": "Switch to Tree View",
-        "recursiveOn": "Search subfolders",
-        "recursiveOff": "Search current folder only",
+        "recursiveOn": "Include subfolders",
+        "recursiveOff": "Current folder only",
         "recursiveUnavailable": "Recursive search is available in tree view only",
         "collapseAllDisabled": "Not available in list view",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "Update Base Model",
             "cancel": "Cancel"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Download Missing LoRAs",
+            "message": "Found {uniqueCount} unique missing LoRAs (from {totalCount} total across selected recipes).",
+            "previewTitle": "LoRAs to download:",
+            "moreItems": "...and {count} more",
+            "note": "Files will be downloaded using default path templates. This may take a while depending on the number of LoRAs.",
+            "downloadButton": "Download {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "Local Example Images",
             "message": "No local example images found for this model. View options:",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "View on Civitai",
                 "viewOnCivitaiText": "View on Civitai",
                 "viewCreatorProfile": "View Creator Profile",
-                "openFileLocation": "Open File Location"
+                "openFileLocation": "Open File Location",
+                "sendToWorkflow": "Send to ComfyUI",
+                "sendToWorkflowText": "Send to ComfyUI"
             },
             "openFileLocation": {
                 "success": "File location opened successfully",
@@ -1042,6 +1080,9 @@
                 "copied": "Path copied to clipboard: {{path}}",
                 "clipboardFallback": "Path: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "Unable to send to ComfyUI: No file path available"
+            },
             "metadata": {
                 "version": "Version",
                 "fileName": "File Name",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "Recipe replaced in workflow",
             "recipeFailedToSend": "Failed to send recipe to workflow",
             "noMatchingNodes": "No compatible nodes available in the current workflow",
-            "noTargetNodeSelected": "No target node selected"
+            "noTargetNodeSelected": "No target node selected",
+            "modelUpdated": "Model updated in workflow",
+            "modelFailed": "Failed to update model node"
         },
         "nodeSelector": {
             "recipe": "Recipe",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "Please select a version",
             "versionExists": "This version already exists in your library",
             "downloadCompleted": "Download completed successfully",
+            "downloadSkippedByBaseModel": "Skipped download because base model {baseModel} is excluded",
             "autoOrganizeSuccess": "Auto-organize completed successfully for {count} {type}",
             "autoOrganizePartialSuccess": "Auto-organize completed with {success} moved, {failures} failed out of {total} models",
             "autoOrganizeFailed": "Auto-organize failed: {error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "Recipe name updated successfully",
             "tagsUpdated": "Recipe tags updated successfully",
             "sourceUrlUpdated": "Source URL updated successfully",
+            "promptUpdated": "Prompt updated successfully",
+            "negativePromptUpdated": "Negative prompt updated successfully",
+            "promptEditorHint": "Press Enter to save, Shift+Enter for new line",
             "noRecipeId": "No recipe ID available",
+            "sendToWorkflowFailed": "Failed to send recipe to workflow: {message}",
             "copyFailed": "Error copying recipe syntax: {message}",
             "noMissingLoras": "No missing LoRAs to download",
             "missingLorasInfoFailed": "Failed to get information for missing LoRAs",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "Please enter at least one URL or file path",
             "batchImportNoDirectory": "Please enter a directory path",
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}"
+            "batchImportDirectorySelected": "Directory selected: {path}",
+            "noRecipesSelected": "No recipes selected",
+            "noMissingLorasInSelection": "No missing LoRAs found in selected recipes",
+            "noLoraRootConfigured": "No LoRA root directory configured. Please set a default LoRA root in settings."
         },
         "models": {
             "noModelsSelected": "No models selected",
@@ -1746,4 +1797,4 @@
             "retry": "Retry"
         }
     }
-}
+}

locales/es.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "Difuminar contenido NSFW",
             "blurNsfwContentHelp": "Difuminar imágenes de vista previa de contenido para adultos (NSFW)",
             "showOnlySfw": "Mostrar solo resultados SFW",
-            "showOnlySfwHelp": "Filtrar todo el contenido NSFW al navegar y buscar"
+            "showOnlySfwHelp": "Filtrar todo el contenido NSFW al navegar y buscar",
+            "matureBlurThreshold": "Umbral de difuminado para contenido adulto",
+            "matureBlurThresholdHelp": "Establecer a partir de qué nivel de clasificación comienza el filtrado por difuminado cuando el difuminado NSFW está habilitado.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 y superior",
+                "r": "R y superior (predeterminado)",
+                "x": "X y superior",
+                "xxx": "Solo XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Reproducir videos automáticamente al pasar el ratón",
@@ -315,6 +323,24 @@
                 "saveFailed": "No se pudieron guardar las rutas a omitir: {message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "Omitir descargas para modelos base",
+            "help": "Se aplica a todos los flujos de descarga. Aquí solo se pueden seleccionar modelos base compatibles.",
+            "searchPlaceholder": "Filtrar modelos base...",
+            "empty": "Ningún modelo base coincide con la búsqueda actual.",
+            "summary": {
+                "none": "Ninguno seleccionado",
+                "count": "{count} seleccionados"
+            },
+            "actions": {
+                "edit": "Editar",
+                "collapse": "Contraer",
+                "clear": "Limpiar"
+            },
+            "validation": {
+                "saveFailed": "No se pudieron guardar los modelos base excluidos: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Densidad de visualización",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "Rutas de carpetas adicionales",
-            "help": "Agregue carpetas de modelos adicionales fuera de las rutas estándar de ComfyUI. Estas rutas se almacenan por separado y se escanean junto con las carpetas predeterminadas.",
-            "description": "Configure carpetas adicionales para escanear modelos. Estas rutas son específicas de LoRA Manager y se fusionarán con las rutas predeterminadas de ComfyUI.",
+            "description": "Rutas raíz de modelos adicionales exclusivas para LoRA Manager. Cargue modelos desde ubicaciones fuera de las carpetas estándar de ComfyUI, ideal para bibliotecas grandes que de otro modo ralentizarían ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "Rutas de LoRA",
                 "checkpoint": "Rutas de Checkpoint",
@@ -376,7 +402,7 @@
                 "embedding": "Rutas de Embedding"
             },
             "pathPlaceholder": "/ruta/a/modelos/extra",
-            "saveSuccess": "Rutas de carpetas adicionales actualizadas.",
+            "saveSuccess": "Rutas de carpetas adicionales actualizadas. Se requiere reinicio para aplicar los cambios.",
             "saveError": "Error al actualizar las rutas de carpetas adicionales: {message}",
             "validation": {
                 "duplicatePath": "Esta ruta ya está configurada"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "Omitir actualización de metadatos para seleccionados",
             "resumeMetadataRefresh": "Reanudar actualización de metadatos para seleccionados",
             "deleteAll": "Eliminar todos los modelos",
+            "downloadMissingLoras": "Descargar LoRAs faltantes",
             "clear": "Limpiar selección",
             "skipMetadataRefreshCount": "Omitir（{count} modelos）",
             "resumeMetadataRefreshCount": "Reanudar（{count} modelos）",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Mover a la carpeta {otherType}"
+            "moveToOtherTypeFolder": "Mover a la carpeta {otherType}",
+            "sendToWorkflow": "[TODO: Translate] Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "Desfijar barra lateral",
         "switchToListView": "Cambiar a vista de lista",
         "switchToTreeView": "Cambiar a vista de árbol",
-        "recursiveOn": "Buscar en subcarpetas",
-        "recursiveOff": "Buscar solo en la carpeta actual",
+        "recursiveOn": "Incluir subcarpetas",
+        "recursiveOff": "Solo carpeta actual",
         "recursiveUnavailable": "La búsqueda recursiva solo está disponible en la vista en árbol",
         "collapseAllDisabled": "No disponible en vista de lista",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "Actualizar modelo base",
             "cancel": "Cancelar"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Descargar LoRAs faltantes",
+            "message": "Se encontraron {uniqueCount} LoRAs faltantes únicos (de {totalCount} en total entre las recetas seleccionadas).",
+            "previewTitle": "LoRAs para descargar:",
+            "moreItems": "...y {count} más",
+            "note": "Los archivos se descargarán usando las plantillas de ruta predeterminadas. Esto puede tomar un tiempo dependiendo del número de LoRAs.",
+            "downloadButton": "Descargar {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "Imágenes de ejemplo locales",
             "message": "No se encontraron imágenes de ejemplo locales para este modelo. Opciones de visualización:",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "Ver en Civitai",
                 "viewOnCivitaiText": "Ver en Civitai",
                 "viewCreatorProfile": "Ver perfil del creador",
-                "openFileLocation": "Abrir ubicación del archivo"
+                "openFileLocation": "Abrir ubicación del archivo",
+                "sendToWorkflow": "Enviar a ComfyUI",
+                "sendToWorkflowText": "Enviar a ComfyUI"
             },
             "openFileLocation": {
                 "success": "Ubicación del archivo abierta exitosamente",
@@ -1042,6 +1080,9 @@
                 "copied": "Ruta copiada al portapapeles: {{path}}",
                 "clipboardFallback": "Ruta: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "No se puede enviar a ComfyUI: no hay ruta de archivo disponible"
+            },
             "metadata": {
                 "version": "Versión",
                 "fileName": "Nombre de archivo",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "Receta reemplazada en el flujo de trabajo",
             "recipeFailedToSend": "Error al enviar receta al flujo de trabajo",
             "noMatchingNodes": "No hay nodos compatibles disponibles en el flujo de trabajo actual",
-            "noTargetNodeSelected": "No se ha seleccionado ningún nodo de destino"
+            "noTargetNodeSelected": "No se ha seleccionado ningún nodo de destino",
+            "modelUpdated": "Modelo actualizado en el flujo de trabajo",
+            "modelFailed": "Error al actualizar nodo de modelo"
         },
         "nodeSelector": {
             "recipe": "Receta",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "Por favor selecciona una versión",
             "versionExists": "Esta versión ya existe en tu biblioteca",
             "downloadCompleted": "Descarga completada exitosamente",
+            "downloadSkippedByBaseModel": "Descarga omitida porque el modelo base {baseModel} está excluido",
             "autoOrganizeSuccess": "Auto-organización completada exitosamente para {count} {type}",
             "autoOrganizePartialSuccess": "Auto-organización completada con {success} movidos, {failures} fallidos de un total de {total} modelos",
             "autoOrganizeFailed": "Auto-organización fallida: {error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "Nombre de receta actualizado exitosamente",
             "tagsUpdated": "Etiquetas de receta actualizadas exitosamente",
             "sourceUrlUpdated": "URL de origen actualizada exitosamente",
+            "promptUpdated": "Prompt actualizado exitosamente",
+            "negativePromptUpdated": "Prompt negativo actualizado exitosamente",
+            "promptEditorHint": "Presiona Enter para guardar, Shift+Enter para nueva línea",
             "noRecipeId": "No hay ID de receta disponible",
+            "sendToWorkflowFailed": "Error al enviar la receta al flujo de trabajo: {message}",
             "copyFailed": "Error copiando sintaxis de receta: {message}",
             "noMissingLoras": "No hay LoRAs faltantes para descargar",
             "missingLorasInfoFailed": "Error al obtener información de LoRAs faltantes",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "Please enter at least one URL or file path",
             "batchImportNoDirectory": "Please enter a directory path",
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}"
+            "batchImportDirectorySelected": "Directory selected: {path}",
+            "noRecipesSelected": "No se han seleccionado recetas",
+            "noMissingLorasInSelection": "No se encontraron LoRAs faltantes en las recetas seleccionadas",
+            "noLoraRootConfigured": "No se ha configurado el directorio raíz de LoRA. Por favor, establezca un directorio raíz de LoRA predeterminado en la configuración."
         },
         "models": {
             "noModelsSelected": "No hay modelos seleccionados",

locales/fr.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "Flouter le contenu NSFW",
             "blurNsfwContentHelp": "Flouter les images d'aperçu de contenu pour adultes (NSFW)",
             "showOnlySfw": "Afficher uniquement les résultats SFW",
-            "showOnlySfwHelp": "Filtrer tout le contenu NSFW lors de la navigation et de la recherche"
+            "showOnlySfwHelp": "Filtrer tout le contenu NSFW lors de la navigation et de la recherche",
+            "matureBlurThreshold": "Seuil de floutage pour contenu adulte",
+            "matureBlurThresholdHelp": "Définir à partir de quel niveau de classification le floutage s'applique lorsque le floutage NSFW est activé.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 et plus",
+                "r": "R et plus (par défaut)",
+                "x": "X et plus",
+                "xxx": "XXX uniquement"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Lecture automatique vidéo au survol",
@@ -315,6 +323,24 @@
                 "saveFailed": "Impossible d'enregistrer les chemins à ignorer : {message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "Ignorer les téléchargements pour certains modèles de base",
+            "help": "S’applique à tous les flux de téléchargement. Seuls les modèles de base pris en charge peuvent être sélectionnés ici.",
+            "searchPlaceholder": "Filtrer les modèles de base...",
+            "empty": "Aucun modèle de base ne correspond à la recherche actuelle.",
+            "summary": {
+                "none": "Aucune sélection",
+                "count": "{count} sélectionnés"
+            },
+            "actions": {
+                "edit": "Modifier",
+                "collapse": "Réduire",
+                "clear": "Effacer"
+            },
+            "validation": {
+                "saveFailed": "Impossible d’enregistrer les modèles de base exclus : {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Densité d'affichage",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "Chemins de dossiers supplémentaires",
-            "help": "Ajoutez des dossiers de modèles supplémentaires en dehors des chemins standard de ComfyUI. Ces chemins sont stockés séparément et analysés aux côtés des dossiers par défaut.",
-            "description": "Configurez des dossiers supplémentaires pour l'analyse de modèles. Ces chemins sont spécifiques à LoRA Manager et seront fusionnés avec les chemins par défaut de ComfyUI.",
+            "description": "Chemins racine de modèles supplémentaires exclusifs à LoRA Manager. Chargez des modèles depuis des emplacements en dehors des dossiers standard de ComfyUI, idéal pour les grandes bibliothèques qui ralentiraient autrement ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "Chemins LoRA",
                 "checkpoint": "Chemins Checkpoint",
@@ -376,7 +402,7 @@
                 "embedding": "Chemins Embedding"
             },
             "pathPlaceholder": "/chemin/vers/modèles/supplémentaires",
-            "saveSuccess": "Chemins de dossiers supplémentaires mis à jour.",
+            "saveSuccess": "Chemins de dossiers supplémentaires mis à jour. Redémarrage requis pour appliquer les changements.",
             "saveError": "Échec de la mise à jour des chemins de dossiers supplémentaires: {message}",
             "validation": {
                 "duplicatePath": "Ce chemin est déjà configuré"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "Ignorer l'actualisation des métadonnées pour la sélection",
             "resumeMetadataRefresh": "Reprendre l'actualisation des métadonnées pour la sélection",
             "deleteAll": "Supprimer tous les modèles",
+            "downloadMissingLoras": "Télécharger les LoRAs manquants",
             "clear": "Effacer la sélection",
             "skipMetadataRefreshCount": "Ignorer（{count} modèles）",
             "resumeMetadataRefreshCount": "Reprendre（{count} modèles）",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Déplacer vers le dossier {otherType}"
+            "moveToOtherTypeFolder": "Déplacer vers le dossier {otherType}",
+            "sendToWorkflow": "[TODO: Translate] Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "Désépingler la barre latérale",
         "switchToListView": "Passer en vue liste",
         "switchToTreeView": "Passer en vue arborescence",
-        "recursiveOn": "Rechercher dans les sous-dossiers",
-        "recursiveOff": "Rechercher uniquement dans le dossier actuel",
+        "recursiveOn": "Inclure les sous-dossiers",
+        "recursiveOff": "Dossier actuel uniquement",
         "recursiveUnavailable": "La recherche récursive n'est disponible qu'en vue arborescente",
         "collapseAllDisabled": "Non disponible en vue liste",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "Mettre à jour le modèle de base",
             "cancel": "Annuler"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Télécharger les LoRAs manquants",
+            "message": "{uniqueCount} LoRAs manquants uniques trouvés (sur un total de {totalCount} dans les recettes sélectionnées).",
+            "previewTitle": "LoRAs à télécharger :",
+            "moreItems": "...et {count} de plus",
+            "note": "Les fichiers seront téléchargés en utilisant les modèles de chemins par défaut. Cela peut prendre un certain temps selon le nombre de LoRAs.",
+            "downloadButton": "Télécharger {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "Images d'exemple locales",
             "message": "Aucune image d'exemple locale trouvée pour ce modèle. Options d'affichage :",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "Voir sur Civitai",
                 "viewOnCivitaiText": "Voir sur Civitai",
                 "viewCreatorProfile": "Voir le profil du créateur",
-                "openFileLocation": "Ouvrir l'emplacement du fichier"
+                "openFileLocation": "Ouvrir l'emplacement du fichier",
+                "sendToWorkflow": "Envoyer vers ComfyUI",
+                "sendToWorkflowText": "Envoyer vers ComfyUI"
             },
             "openFileLocation": {
                 "success": "Emplacement du fichier ouvert avec succès",
@@ -1042,6 +1080,9 @@
                 "copied": "Chemin copié dans le presse-papiers: {{path}}",
                 "clipboardFallback": "Chemin: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "Impossible d'envoyer vers ComfyUI : aucun chemin de fichier disponible"
+            },
             "metadata": {
                 "version": "Version",
                 "fileName": "Nom de fichier",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "Recipe remplacée dans le workflow",
             "recipeFailedToSend": "Échec de l'envoi de la recipe au workflow",
             "noMatchingNodes": "Aucun nœud compatible disponible dans le workflow actuel",
-            "noTargetNodeSelected": "Aucun nœud cible sélectionné"
+            "noTargetNodeSelected": "Aucun nœud cible sélectionné",
+            "modelUpdated": "Modèle mis à jour dans le workflow",
+            "modelFailed": "Échec de la mise à jour du nœud modèle"
         },
         "nodeSelector": {
             "recipe": "Recipe",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "Veuillez sélectionner une version",
             "versionExists": "Cette version existe déjà dans votre bibliothèque",
             "downloadCompleted": "Téléchargement terminé avec succès",
+            "downloadSkippedByBaseModel": "Téléchargement ignoré, car le modèle de base {baseModel} est exclu",
             "autoOrganizeSuccess": "Auto-organisation terminée avec succès pour {count} {type}",
             "autoOrganizePartialSuccess": "Auto-organisation terminée avec {success} déplacés, {failures} échecs sur {total} modèles",
             "autoOrganizeFailed": "Échec de l'auto-organisation : {error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "Nom de la recipe mis à jour avec succès",
             "tagsUpdated": "Tags de la recipe mis à jour avec succès",
             "sourceUrlUpdated": "URL source mise à jour avec succès",
+            "promptUpdated": "Prompt mis à jour avec succès",
+            "negativePromptUpdated": "Prompt négatif mis à jour avec succès",
+            "promptEditorHint": "Appuyez sur Entrée pour sauvegarder, Maj+Entrée pour nouvelle ligne",
             "noRecipeId": "Aucun ID de recipe disponible",
+            "sendToWorkflowFailed": "Échec de l'envoi de la recette vers le workflow : {message}",
             "copyFailed": "Erreur lors de la copie de la syntaxe de la recipe : {message}",
             "noMissingLoras": "Aucun LoRA manquant à télécharger",
             "missingLorasInfoFailed": "Échec de l'obtention des informations pour les LoRAs manquants",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "Please enter at least one URL or file path",
             "batchImportNoDirectory": "Please enter a directory path",
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}"
+            "batchImportDirectorySelected": "Directory selected: {path}",
+            "noRecipesSelected": "Aucune recette sélectionnée",
+            "noMissingLorasInSelection": "Aucun LoRA manquant trouvé dans les recettes sélectionnées",
+            "noLoraRootConfigured": "Aucun répertoire racine LoRA configuré. Veuillez définir un répertoire racine LoRA par défaut dans les paramètres."
         },
         "models": {
             "noModelsSelected": "Aucun modèle sélectionné",

locales/he.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "טשטש תוכן NSFW",
             "blurNsfwContentHelp": "טשטש תמונות תצוגה מקדימה של תוכן למבוגרים (NSFW)",
             "showOnlySfw": "הצג רק תוצאות SFW",
-            "showOnlySfwHelp": "סנן את כל התוכן ה-NSFW בעת גלישה וחיפוש"
+            "showOnlySfwHelp": "סנן את כל התוכן ה-NSFW בעת גלישה וחיפוש",
+            "matureBlurThreshold": "סף טשטוש תוכן מבוגרים",
+            "matureBlurThresholdHelp": "הגדר מאיזו רמת דירוג מתחיל סינון הטשטוש כאשר טשטוש NSFW מופעל.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 ומעלה",
+                "r": "R ומעלה (ברירת מחדל)",
+                "x": "X ומעלה",
+                "xxx": "XXX בלבד"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "נגן וידאו אוטומטית בריחוף",
@@ -315,6 +323,24 @@
                 "saveFailed": "לא ניתן לשמור נתיבי דילוג: {message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "דלג על הורדות עבור מודלי בסיס",
+            "help": "חל על כל תהליכי ההורדה. ניתן לבחור כאן רק מודלי בסיס נתמכים.",
+            "searchPlaceholder": "סנן מודלי בסיס...",
+            "empty": "אין מודלי בסיס התואמים לחיפוש הנוכחי.",
+            "summary": {
+                "none": "לא נבחר דבר",
+                "count": "{count} נבחרו"
+            },
+            "actions": {
+                "edit": "עריכה",
+                "collapse": "כווץ",
+                "clear": "נקה"
+            },
+            "validation": {
+                "saveFailed": "לא ניתן לשמור את מודלי הבסיס המוחרגים: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "צפיפות תצוגה",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "נתיבי תיקיות נוספים",
-            "help": "הוסף תיקיות מודלים נוספות מחוץ לנתיבים הסטנדרטיים של ComfyUI. נתיבים אלה נשמרים בנפרד ונסרקים לצד תיקיות ברירת המחדל.",
-            "description": "הגדר תיקיות נוספות לסריקת מודלים. נתיבים אלה ספציפיים ל-LoRA Manager וימוזגו עם נתיבי ברירת המחדל של ComfyUI.",
+            "description": "נתיבי שורש מודלים נוספים בלעדיים ל-LoRA Manager. טען מודלים ממיקומים מחוץ לתיקיות הסטנדרטיות של ComfyUI - אידיאלי לספריות גדולות שאחרת יאטו את ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "נתיבי LoRA",
                 "checkpoint": "נתיבי Checkpoint",
@@ -376,7 +402,7 @@
                 "embedding": "נתיבי Embedding"
             },
             "pathPlaceholder": "/נתיב/למודלים/נוספים",
-            "saveSuccess": "נתיבי תיקיות נוספים עודכנו.",
+            "saveSuccess": "נתיבי תיקיות נוספים עודכנו. נדרשת הפעלה מחדש כדי להחיל את השינויים.",
             "saveError": "נכשל בעדכון נתיבי תיקיות נוספים: {message}",
             "validation": {
                 "duplicatePath": "נתיב זה כבר מוגדר"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "דילוג על רענון מטא-נתונים לנבחרים",
             "resumeMetadataRefresh": "המשך רענון מטא-נתונים לנבחרים",
             "deleteAll": "מחק את כל המודלים",
+            "downloadMissingLoras": "הורדת LoRAs חסרים",
             "clear": "נקה בחירה",
             "skipMetadataRefreshCount": "דילוג（{count} מודלים）",
             "resumeMetadataRefreshCount": "המשך（{count} מודלים）",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "העבר לתיקיית {otherType}"
+            "moveToOtherTypeFolder": "העבר לתיקיית {otherType}",
+            "sendToWorkflow": "[TODO: Translate] Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "שחרר סרגל צד",
         "switchToListView": "עבור לתצוגת רשימה",
         "switchToTreeView": "תצוגת עץ",
-        "recursiveOn": "חיפוש בתיקיות משנה",
-        "recursiveOff": "חיפוש רק בתיקייה הנוכחית",
+        "recursiveOn": "כלול תיקיות משנה",
+        "recursiveOff": "רק התיקייה הנוכחית",
         "recursiveUnavailable": "חיפוש רקורסיבי זמין רק בתצוגת עץ",
         "collapseAllDisabled": "לא זמין בתצוגת רשימה",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "עדכן מודל בסיס",
             "cancel": "ביטול"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "הורדת LoRAs חסרים",
+            "message": "נמצאו {uniqueCount} LoRAs חסרים ייחודיים (מתוך {totalCount} בסך הכל במתכונים שנבחרו).",
+            "previewTitle": "LoRAs להורדה:",
+            "moreItems": "...ועוד {count}",
+            "note": "הקבצים יורדו באמצעות תבניות נתיב ברירת מחדל. זה עשוי לקחת זמן בהתאם למספר ה-LoRAs.",
+            "downloadButton": "הורד {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "תמונות דוגמה מקומיות",
             "message": "לא נמצאו תמונות דוגמה מקומיות למודל זה. אפשרויות צפייה:",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "הצג ב-Civitai",
                 "viewOnCivitaiText": "הצג ב-Civitai",
                 "viewCreatorProfile": "הצג פרופיל יוצר",
-                "openFileLocation": "פתח מיקום קובץ"
+                "openFileLocation": "פתח מיקום קובץ",
+                "sendToWorkflow": "שלח ל-ComfyUI",
+                "sendToWorkflowText": "שלח ל-ComfyUI"
             },
             "openFileLocation": {
                 "success": "מיקום הקובץ נפתח בהצלחה",
@@ -1042,6 +1080,9 @@
                 "copied": "הנתיב הועתק ללוח העריכה: {{path}}",
                 "clipboardFallback": "נתיב: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "לא ניתן לשלוח ל-ComfyUI: אין נתיב קובץ זמין"
+            },
             "metadata": {
                 "version": "גרסה",
                 "fileName": "שם קובץ",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "מתכון הוחלף ב-workflow",
             "recipeFailedToSend": "שליחת מתכון ל-workflow נכשלה",
             "noMatchingNodes": "אין צמתים תואמים זמינים ב-workflow הנוכחי",
-            "noTargetNodeSelected": "לא נבחר צומת יעד"
+            "noTargetNodeSelected": "לא נבחר צומת יעד",
+            "modelUpdated": "מודל עודכן ב-workflow",
+            "modelFailed": "עדכון צומת המודל נכשל"
         },
         "nodeSelector": {
             "recipe": "מתכון",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "אנא בחר גרסה",
             "versionExists": "גרסה זו כבר קיימת בספרייה שלך",
             "downloadCompleted": "ההורדה הושלמה בהצלחה",
+            "downloadSkippedByBaseModel": "ההורדה דולגה כי מודל הבסיס {baseModel} מוחרג",
             "autoOrganizeSuccess": "הארגון האוטומטי הושלם בהצלחה עבור {count} {type}",
             "autoOrganizePartialSuccess": "הארגון האוטומטי הושלם עם {success} שהועברו, {failures} שנכשלו מתוך {total} מודלים",
             "autoOrganizeFailed": "הארגון האוטומטי נכשל: {error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "שם המתכון עודכן בהצלחה",
             "tagsUpdated": "תגיות המתכון עודכנו בהצלחה",
             "sourceUrlUpdated": "כתובת ה-URL המקורית עודכנה בהצלחה",
+            "promptUpdated": "הפרומפט עודכן בהצלחה",
+            "negativePromptUpdated": "הפרומפט השלילי עודכן בהצלחה",
+            "promptEditorHint": "לחץ Enter לשמירה, Shift+Enter לשורה חדשה",
             "noRecipeId": "אין מזהה מתכון זמין",
+            "sendToWorkflowFailed": "נכשל שליחת המתכון ל-workflow: {message}",
             "copyFailed": "שגיאה בהעתקת תחביר המתכון: {message}",
             "noMissingLoras": "אין LoRAs חסרים להורדה",
             "missingLorasInfoFailed": "קבלת מידע עבור LoRAs חסרים נכשלה",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "Please enter at least one URL or file path",
             "batchImportNoDirectory": "Please enter a directory path",
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}"
+            "batchImportDirectorySelected": "Directory selected: {path}",
+            "noRecipesSelected": "לא נבחרו מתכונים",
+            "noMissingLorasInSelection": "לא נמצאו LoRAs חסרים במתכונים שנבחרו",
+            "noLoraRootConfigured": "תיקיית השורש של LoRA לא מוגדרת. אנא הגדר תיקיית שורש LoRA ברירת מחדל בהגדרות."
         },
         "models": {
             "noModelsSelected": "לא נבחרו מודלים",

locales/ja.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "NSFWコンテンツをぼかす",
             "blurNsfwContentHelp": "成人向け（NSFW）コンテンツのプレビュー画像をぼかします",
             "showOnlySfw": "SFWコンテンツのみ表示",
-            "showOnlySfwHelp": "閲覧と検索時にすべてのNSFWコンテンツを除外します"
+            "showOnlySfwHelp": "閲覧と検索時にすべてのNSFWコンテンツを除外します",
+            "matureBlurThreshold": "成人コンテンツぼかし閾値",
+            "matureBlurThresholdHelp": "NSFWぼかしが有効な場合、どのレーティングレベルからぼかしフィルタリングを開始するかを設定します。",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 以上",
+                "r": "R 以上（デフォルト）",
+                "x": "X 以上",
+                "xxx": "XXX のみ"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "ホバー時に動画を自動再生",
@@ -315,6 +323,24 @@
                 "saveFailed": "スキップパスの保存に失敗しました：{message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "ベースモデルのダウンロードをスキップ",
+            "help": "すべてのダウンロードフローに適用されます。ここでは対応しているベースモデルのみ選択できます。",
+            "searchPlaceholder": "ベースモデルを絞り込む...",
+            "empty": "現在の検索に一致するベースモデルはありません。",
+            "summary": {
+                "none": "未選択",
+                "count": "{count} 件を選択"
+            },
+            "actions": {
+                "edit": "編集",
+                "collapse": "折りたたむ",
+                "clear": "クリア"
+            },
+            "validation": {
+                "saveFailed": "除外するベースモデルを保存できませんでした: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "表示密度",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "追加フォルダーパス",
-            "help": "ComfyUIの標準パスの外部に追加のモデルフォルダを追加します。これらのパスは別々に保存され、デフォルトのフォルダと一緒にスキャンされます。",
-            "description": "モデルをスキャンするための追加フォルダを設定します。これらのパスはLoRA Manager固有であり、ComfyUIのデフォルトパスとマージされます。",
+            "description": "LoRA Manager専用の追加モデルルートパス。ComfyUIの標準フォルダー外の場所からモデルを読み込みます。ComfyUIの動作を低下させる可能性のある大規模ライブラリに最適です。",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "LoRAパス",
                 "checkpoint": "Checkpointパス",
@@ -376,7 +402,7 @@
                 "embedding": "Embeddingパス"
             },
             "pathPlaceholder": "/追加モデルへのパス",
-            "saveSuccess": "追加フォルダーパスを更新しました。",
+            "saveSuccess": "追加フォルダーパスを更新しました。変更を適用するには再起動が必要です。",
             "saveError": "追加フォルダーパスの更新に失敗しました: {message}",
             "validation": {
                 "duplicatePath": "このパスはすでに設定されています"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "選択したモデルのメタデータ更新をスキップ",
             "resumeMetadataRefresh": "選択したモデルのメタデータ更新を再開",
             "deleteAll": "すべてのモデルを削除",
+            "downloadMissingLoras": "不足している LoRA をダウンロード",
             "clear": "選択をクリア",
             "skipMetadataRefreshCount": "スキップ（{count}モデル）",
             "resumeMetadataRefreshCount": "再開（{count}モデル）",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "{otherType} フォルダに移動"
+            "moveToOtherTypeFolder": "{otherType} フォルダに移動",
+            "sendToWorkflow": "[TODO: Translate] Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "サイドバーの固定を解除",
         "switchToListView": "リストビューに切り替え",
         "switchToTreeView": "ツリー表示に切り替え",
-        "recursiveOn": "サブフォルダーを検索",
-        "recursiveOff": "現在のフォルダーのみを検索",
+        "recursiveOn": "サブフォルダーを含める",
+        "recursiveOff": "現在のフォルダーのみ",
         "recursiveUnavailable": "再帰検索はツリービューでのみ利用できます",
         "collapseAllDisabled": "リストビューでは利用できません",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "ベースモデルを更新",
             "cancel": "キャンセル"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "不足している LoRA をダウンロード",
+            "message": "選択したレシピから合計 {totalCount} 個中 {uniqueCount} 個のユニークな不足している LoRA が見つかりました。",
+            "previewTitle": "ダウンロードする LoRA:",
+            "moreItems": "...あと {count} 個",
+            "note": "ファイルはデフォルトのパステンプレートを使用してダウンロードされます。LoRA の数によっては時間がかかる場合があります。",
+            "downloadButton": "{count} 個の LoRA をダウンロード"
+        },
         "exampleAccess": {
             "title": "ローカル例画像",
             "message": "このモデルのローカル例画像が見つかりませんでした。表示オプション：",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "Civitaiで表示",
                 "viewOnCivitaiText": "Civitaiで表示",
                 "viewCreatorProfile": "作成者プロフィールを表示",
-                "openFileLocation": "ファイルの場所を開く"
+                "openFileLocation": "ファイルの場所を開く",
+                "sendToWorkflow": "ComfyUI に送信",
+                "sendToWorkflowText": "ComfyUI に送信"
             },
             "openFileLocation": {
                 "success": "ファイルの場所を正常に開きました",
@@ -1042,6 +1080,9 @@
                 "copied": "パスをクリップボードにコピーしました: {{path}}",
                 "clipboardFallback": "パス: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "ComfyUI に送信できません：ファイルパスがありません"
+            },
             "metadata": {
                 "version": "バージョン",
                 "fileName": "ファイル名",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "レシピがワークフローで置換されました",
             "recipeFailedToSend": "レシピをワークフローに送信できませんでした",
             "noMatchingNodes": "現在のワークフローには互換性のあるノードがありません",
-            "noTargetNodeSelected": "ターゲットノードが選択されていません"
+            "noTargetNodeSelected": "ターゲットノードが選択されていません",
+            "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"
         },
         "nodeSelector": {
             "recipe": "レシピ",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "バージョンを選択してください",
             "versionExists": "このバージョンは既にライブラリに存在します",
             "downloadCompleted": "ダウンロードが正常に完了しました",
+            "downloadSkippedByBaseModel": "ベースモデル {baseModel} が除外されているため、ダウンロードをスキップしました",
             "autoOrganizeSuccess": "{count} {type} の自動整理が正常に完了しました",
             "autoOrganizePartialSuccess": "自動整理が完了しました：{total} モデル中 {success} 移動、{failures} 失敗",
             "autoOrganizeFailed": "自動整理に失敗しました：{error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "レシピ名が正常に更新されました",
             "tagsUpdated": "レシピタグが正常に更新されました",
             "sourceUrlUpdated": "ソースURLが正常に更新されました",
+            "promptUpdated": "プロンプトが正常に更新されました",
+            "negativePromptUpdated": "ネガティブプロンプトが正常に更新されました",
+            "promptEditorHint": "Enterキーで保存、Shift+Enterで改行",
             "noRecipeId": "レシピIDが利用できません",
+            "sendToWorkflowFailed": "ワークフローへのレシピ送信に失敗しました：{message}",
             "copyFailed": "レシピ構文のコピーエラー：{message}",
             "noMissingLoras": "ダウンロードする不足LoRAがありません",
             "missingLorasInfoFailed": "不足LoRAの情報取得に失敗しました",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "Please enter at least one URL or file path",
             "batchImportNoDirectory": "Please enter a directory path",
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}"
+            "batchImportDirectorySelected": "Directory selected: {path}",
+            "noRecipesSelected": "レシピが選択されていません",
+            "noMissingLorasInSelection": "選択したレシピに不足している LoRA が見つかりませんでした",
+            "noLoraRootConfigured": "LoRA ルートディレクトリが設定されていません。設定でデフォルトの LoRA ルートを設定してください。"
         },
         "models": {
             "noModelsSelected": "モデルが選択されていません",

locales/ko.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "NSFW 콘텐츠 블러 처리",
             "blurNsfwContentHelp": "성인(NSFW) 콘텐츠 미리보기 이미지를 블러 처리합니다",
             "showOnlySfw": "SFW 결과만 표시",
-            "showOnlySfwHelp": "탐색 및 검색 시 모든 NSFW 콘텐츠를 필터링합니다"
+            "showOnlySfwHelp": "탐색 및 검색 시 모든 NSFW 콘텐츠를 필터링합니다",
+            "matureBlurThreshold": "성인 콘텐츠 블러 임계값",
+            "matureBlurThresholdHelp": "NSFW 블러가 활성화될 때 어떤 등급 레벨부터 블러 필터링을 시작할지 설정합니다.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 이상",
+                "r": "R 이상（기본값）",
+                "x": "X 이상",
+                "xxx": "XXX만"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "호버 시 비디오 자동 재생",
@@ -315,6 +323,24 @@
                 "saveFailed": "건너뛰기 경로를 저장할 수 없습니다: {message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "기본 모델 다운로드 건너뛰기",
+            "help": "모든 다운로드 흐름에 적용됩니다. 여기서는 지원되는 기본 모델만 선택할 수 있습니다.",
+            "searchPlaceholder": "기본 모델 필터링...",
+            "empty": "현재 검색과 일치하는 기본 모델이 없습니다.",
+            "summary": {
+                "none": "선택 없음",
+                "count": "{count}개 선택됨"
+            },
+            "actions": {
+                "edit": "편집",
+                "collapse": "접기",
+                "clear": "지우기"
+            },
+            "validation": {
+                "saveFailed": "제외된 기본 모델을 저장할 수 없습니다: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "표시 밀도",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "추가 폴다 경로",
-            "help": "ComfyUI의 표준 경로 외부에 추가 모델 폴드를 추가하세요. 이러한 경로는 별도로 저장되며 기본 폴와 함께 스캔됩니다.",
-            "description": "모델을 스캔하기 위한 추가 폴를 설정하세요. 이러한 경로는 LoRA Manager 특유의 것이며 ComfyUI의 기본 경로와 병합됩니다.",
+            "description": "LoRA Manager 전용 추가 모델 루트 경로입니다. ComfyUI의 표준 폴더 외부 위치에서 모델을 로드하여 대규모 라이브러리로 인한 성능 저하를 방지합니다.",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "LoRA 경로",
                 "checkpoint": "Checkpoint 경로",
@@ -376,7 +402,7 @@
                 "embedding": "Embedding 경로"
             },
             "pathPlaceholder": "/추가/모델/경로",
-            "saveSuccess": "추가 폴다 경로가 업데이트되었습니다.",
+            "saveSuccess": "추가 폴다 경로가 업데이트되었습니다. 변경 사항을 적용하려면 재시작이 필요합니다.",
             "saveError": "추가 폴다 경로 업데이트 실패: {message}",
             "validation": {
                 "duplicatePath": "이 경로는 이미 구성되어 있습니다"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "선택한 모델의 메타데이터 새로고침 건너뛰기",
             "resumeMetadataRefresh": "선택한 모델의 메타데이터 새로고침 재개",
             "deleteAll": "모든 모델 삭제",
+            "downloadMissingLoras": "누락된 LoRA 다운로드",
             "clear": "선택 지우기",
             "skipMetadataRefreshCount": "건너뛰기（{count}개 모델）",
             "resumeMetadataRefreshCount": "재개（{count}개 모델）",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "{otherType} 폴더로 이동"
+            "moveToOtherTypeFolder": "{otherType} 폴더로 이동",
+            "sendToWorkflow": "[TODO: Translate] Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "사이드바 고정 해제",
         "switchToListView": "목록 보기로 전환",
         "switchToTreeView": "트리 보기로 전환",
-        "recursiveOn": "하위 폴더 검색",
-        "recursiveOff": "현재 폴더만 검색",
+        "recursiveOn": "하위 폴더 포함",
+        "recursiveOff": "현재 폴더만",
         "recursiveUnavailable": "재귀 검색은 트리 보기에서만 사용할 수 있습니다",
         "collapseAllDisabled": "목록 보기에서는 사용할 수 없습니다",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "베이스 모델 업데이트",
             "cancel": "취소"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "누락된 LoRA 다운로드",
+            "message": "선택한 레시피에서 총 {totalCount}개 중 {uniqueCount}개의 고유한 누락된 LoRA를 찾았습니다.",
+            "previewTitle": "다운로드할 LoRA:",
+            "moreItems": "...그리고 {count}개 더",
+            "note": "파일은 기본 경로 템플릿을 사용하여 다운로드됩니다. LoRA의 수에 따라 다소 시간이 걸릴 수 있습니다.",
+            "downloadButton": "{count}개 LoRA 다운로드"
+        },
         "exampleAccess": {
             "title": "로컬 예시 이미지",
             "message": "이 모델의 로컬 예시 이미지를 찾을 수 없습니다. 보기 옵션:",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "Civitai에서 보기",
                 "viewOnCivitaiText": "Civitai에서 보기",
                 "viewCreatorProfile": "제작자 프로필 보기",
-                "openFileLocation": "파일 위치 열기"
+                "openFileLocation": "파일 위치 열기",
+                "sendToWorkflow": "ComfyUI로 보내기",
+                "sendToWorkflowText": "ComfyUI로 보내기"
             },
             "openFileLocation": {
                 "success": "파일 위치가 성공적으로 열렸습니다",
@@ -1042,6 +1080,9 @@
                 "copied": "경로가 클립보드에 복사되었습니다: {{path}}",
                 "clipboardFallback": "경로: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "ComfyUI로 보낼 수 없습니다: 파일 경로가 없습니다"
+            },
             "metadata": {
                 "version": "버전",
                 "fileName": "파일명",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "레시피가 워크플로에서 교체되었습니다",
             "recipeFailedToSend": "레시피를 워크플로로 전송하지 못했습니다",
             "noMatchingNodes": "현재 워크플로에서 호환되는 노드가 없습니다",
-            "noTargetNodeSelected": "대상 노드가 선택되지 않았습니다"
+            "noTargetNodeSelected": "대상 노드가 선택되지 않았습니다",
+            "modelUpdated": "모델이 워크플로에서 업데이트되었습니다",
+            "modelFailed": "모델 노드 업데이트 실패"
         },
         "nodeSelector": {
             "recipe": "레시피",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "버전을 선택해주세요",
             "versionExists": "이 버전은 이미 라이브러리에 있습니다",
             "downloadCompleted": "다운로드가 성공적으로 완료되었습니다",
+            "downloadSkippedByBaseModel": "기본 모델 {baseModel}이(가) 제외되어 다운로드를 건너뛰었습니다",
             "autoOrganizeSuccess": "{count}개의 {type}에 대해 자동 정리가 성공적으로 완료되었습니다",
             "autoOrganizePartialSuccess": "자동 정리 완료: 전체 {total}개 중 {success}개 이동, {failures}개 실패",
             "autoOrganizeFailed": "자동 정리 실패: {error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "레시피 이름이 성공적으로 업데이트되었습니다",
             "tagsUpdated": "레시피 태그가 성공적으로 업데이트되었습니다",
             "sourceUrlUpdated": "소스 URL이 성공적으로 업데이트되었습니다",
+            "promptUpdated": "프롬프트가 성공적으로 업데이트되었습니다",
+            "negativePromptUpdated": "네거티브 프롬프트가 성공적으로 업데이트되었습니다",
+            "promptEditorHint": "Enter 키를 눌러 저장, Shift+Enter로 새 줄",
             "noRecipeId": "사용 가능한 레시피 ID가 없습니다",
+            "sendToWorkflowFailed": "워크플로우에 레시피 보내기 실패: {message}",
             "copyFailed": "레시피 문법 복사 오류: {message}",
             "noMissingLoras": "다운로드할 누락된 LoRA가 없습니다",
             "missingLorasInfoFailed": "누락된 LoRA 정보를 가져오는데 실패했습니다",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "Please enter at least one URL or file path",
             "batchImportNoDirectory": "Please enter a directory path",
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}"
+            "batchImportDirectorySelected": "Directory selected: {path}",
+            "noRecipesSelected": "선택한 레시피가 없습니다",
+            "noMissingLorasInSelection": "선택한 레시피에서 누락된 LoRA를 찾을 수 없습니다",
+            "noLoraRootConfigured": "LoRA 루트 디렉토리가 구성되지 않았습니다. 설정에서 기본 LoRA 루트를 설정하세요."
         },
         "models": {
             "noModelsSelected": "선택된 모델이 없습니다",

locales/ru.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "Размывать NSFW контент",
             "blurNsfwContentHelp": "Размывать превью изображений контента для взрослых (NSFW)",
             "showOnlySfw": "Показывать только SFW результаты",
-            "showOnlySfwHelp": "Фильтровать весь NSFW контент при просмотре и поиске"
+            "showOnlySfwHelp": "Фильтровать весь NSFW контент при просмотре и поиске",
+            "matureBlurThreshold": "Порог размытия взрослого контента",
+            "matureBlurThresholdHelp": "Установить, с какого уровня рейтинга начинается размытие при включенном размытии NSFW.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 и выше",
+                "r": "R и выше (по умолчанию)",
+                "x": "X и выше",
+                "xxx": "Только XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Автовоспроизведение видео при наведении",
@@ -315,6 +323,24 @@
                 "saveFailed": "Не удалось сохранить пути для пропуска: {message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "Пропускать загрузки для базовых моделей",
+            "help": "Применяется ко всем сценариям загрузки. Здесь можно выбрать только поддерживаемые базовые модели.",
+            "searchPlaceholder": "Фильтровать базовые модели...",
+            "empty": "Нет базовых моделей, соответствующих текущему поиску.",
+            "summary": {
+                "none": "Ничего не выбрано",
+                "count": "Выбрано: {count}"
+            },
+            "actions": {
+                "edit": "Изменить",
+                "collapse": "Свернуть",
+                "clear": "Очистить"
+            },
+            "validation": {
+                "saveFailed": "Не удалось сохранить исключённые базовые модели: {message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "Плотность отображения",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "Дополнительные пути к папкам",
-            "help": "Добавьте дополнительные папки моделей за пределами стандартных путей ComfyUI. Эти пути хранятся отдельно и сканируются вместе с папками по умолчанию.",
-            "description": "Настройте дополнительные папки для сканирования моделей. Эти пути специфичны для LoRA Manager и будут объединены с путями по умолчанию ComfyUI.",
+            "description": "Дополнительные корневые пути моделей, эксклюзивные для LoRA Manager. Загружайте модели из расположений за пределами стандартных папок ComfyUI — идеально подходит для больших библиотек, которые иначе замедлили бы ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "Пути LoRA",
                 "checkpoint": "Пути Checkpoint",
@@ -376,7 +402,7 @@
                 "embedding": "Пути Embedding"
             },
             "pathPlaceholder": "/путь/к/дополнительным/моделям",
-            "saveSuccess": "Дополнительные пути к папкам обновлены.",
+            "saveSuccess": "Дополнительные пути к папкам обновлены. Требуется перезапуск для применения изменений.",
             "saveError": "Не удалось обновить дополнительные пути к папкам: {message}",
             "validation": {
                 "duplicatePath": "Этот путь уже настроен"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "Пропустить обновление метаданных для выбранных",
             "resumeMetadataRefresh": "Возобновить обновление метаданных для выбранных",
             "deleteAll": "Удалить все модели",
+            "downloadMissingLoras": "Скачать отсутствующие LoRAs",
             "clear": "Очистить выбор",
             "skipMetadataRefreshCount": "Пропустить（{count} моделей）",
             "resumeMetadataRefreshCount": "Возобновить（{count} моделей）",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Переместить в папку {otherType}"
+            "moveToOtherTypeFolder": "Переместить в папку {otherType}",
+            "sendToWorkflow": "[TODO: Translate] Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "Открепить боковую панель",
         "switchToListView": "Переключить на вид списка",
         "switchToTreeView": "Переключить на древовидный вид",
-        "recursiveOn": "Искать во вложенных папках",
-        "recursiveOff": "Искать только в текущей папке",
+        "recursiveOn": "Включать вложенные папки",
+        "recursiveOff": "Только текущая папка",
         "recursiveUnavailable": "Рекурсивный поиск доступен только в режиме дерева",
         "collapseAllDisabled": "Недоступно в виде списка",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "Обновить базовую модель",
             "cancel": "Отмена"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Скачать отсутствующие LoRAs",
+            "message": "Найдено {uniqueCount} уникальных отсутствующих LoRAs (из {totalCount} всего в выбранных рецептах).",
+            "previewTitle": "LoRAs для скачивания:",
+            "moreItems": "...и еще {count}",
+            "note": "Файлы будут скачаны с использованием шаблонов путей по умолчанию. Это может занять некоторое время в зависимости от количества LoRAs.",
+            "downloadButton": "Скачать {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "Локальные примеры изображений",
             "message": "Локальные примеры изображений для этой модели не найдены. Варианты просмотра:",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "Посмотреть на Civitai",
                 "viewOnCivitaiText": "Посмотреть на Civitai",
                 "viewCreatorProfile": "Посмотреть профиль создателя",
-                "openFileLocation": "Открыть расположение файла"
+                "openFileLocation": "Открыть расположение файла",
+                "sendToWorkflow": "Отправить в ComfyUI",
+                "sendToWorkflowText": "Отправить в ComfyUI"
             },
             "openFileLocation": {
                 "success": "Расположение файла успешно открыто",
@@ -1042,6 +1080,9 @@
                 "copied": "Путь скопирован в буфер обмена: {{path}}",
                 "clipboardFallback": "Путь: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "Невозможно отправить в ComfyUI: путь к файлу недоступен"
+            },
             "metadata": {
                 "version": "Версия",
                 "fileName": "Имя файла",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "Рецепт заменён в workflow",
             "recipeFailedToSend": "Не удалось отправить рецепт в workflow",
             "noMatchingNodes": "В текущем workflow нет совместимых узлов",
-            "noTargetNodeSelected": "Целевой узел не выбран"
+            "noTargetNodeSelected": "Целевой узел не выбран",
+            "modelUpdated": "Модель обновлена в workflow",
+            "modelFailed": "Не удалось обновить узел модели"
         },
         "nodeSelector": {
             "recipe": "Рецепт",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "Пожалуйста, выберите версию",
             "versionExists": "Эта версия уже существует в вашей библиотеке",
             "downloadCompleted": "Загрузка успешно завершена",
+            "downloadSkippedByBaseModel": "Загрузка пропущена, потому что базовая модель {baseModel} исключена",
             "autoOrganizeSuccess": "Автоматическая организация успешно завершена для {count} {type}",
             "autoOrganizePartialSuccess": "Автоматическая организация завершена: перемещено {success}, не удалось {failures} из {total} моделей",
             "autoOrganizeFailed": "Ошибка автоматической организации: {error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "Название рецепта успешно обновлено",
             "tagsUpdated": "Теги рецепта успешно обновлены",
             "sourceUrlUpdated": "Исходный URL успешно обновлен",
+            "promptUpdated": "Промпт успешно обновлён",
+            "negativePromptUpdated": "Негативный промпт успешно обновлён",
+            "promptEditorHint": "Нажмите Enter для сохранения, Shift+Enter для новой строки",
             "noRecipeId": "ID рецепта недоступен",
+            "sendToWorkflowFailed": "Не удалось отправить рецепт в рабочий процесс: {message}",
             "copyFailed": "Ошибка копирования синтаксиса рецепта: {message}",
             "noMissingLoras": "Нет отсутствующих LoRAs для загрузки",
             "missingLorasInfoFailed": "Не удалось получить информацию для отсутствующих LoRAs",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "Please enter at least one URL or file path",
             "batchImportNoDirectory": "Please enter a directory path",
             "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}"
+            "batchImportDirectorySelected": "Directory selected: {path}",
+            "noRecipesSelected": "Рецепты не выбраны",
+            "noMissingLorasInSelection": "В выбранных рецептах не найдены отсутствующие LoRAs",
+            "noLoraRootConfigured": "Корневой каталог LoRA не настроен. Пожалуйста, установите корневой каталог LoRA по умолчанию в настройках."
         },
         "models": {
             "noModelsSelected": "Модели не выбраны",

locales/zh-CN.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "模糊 NSFW 内容",
             "blurNsfwContentHelp": "模糊成熟（NSFW）内容预览图片",
             "showOnlySfw": "仅显示 SFW 结果",
-            "showOnlySfwHelp": "浏览和搜索时过滤所有 NSFW 内容"
+            "showOnlySfwHelp": "浏览和搜索时过滤所有 NSFW 内容",
+            "matureBlurThreshold": "成人内容模糊阈值",
+            "matureBlurThresholdHelp": "设置当启用 NSFW 模糊时，从哪个评级级别开始模糊过滤。",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 及以上",
+                "r": "R 及以上（默认）",
+                "x": "X 及以上",
+                "xxx": "仅 XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "悬停时自动播放视频",
@@ -315,6 +323,24 @@
                 "saveFailed": "无法保存跳过路径：{message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "跳过这些基础模型的下载",
+            "help": "适用于所有下载流程。这里只能选择受支持的基础模型。",
+            "searchPlaceholder": "筛选基础模型...",
+            "empty": "没有与当前搜索匹配的基础模型。",
+            "summary": {
+                "none": "未选择",
+                "count": "已选择 {count} 项"
+            },
+            "actions": {
+                "edit": "编辑",
+                "collapse": "收起",
+                "clear": "清空"
+            },
+            "validation": {
+                "saveFailed": "无法保存已排除的基础模型：{message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "显示密度",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "额外文件夹路径",
-            "help": "在 ComfyUI 的标准路径之外添加额外的模型文件夹。这些路径单独存储，并与默认文件夹一起扫描。",
-            "description": "配置额外的文件夹以扫描模型。这些路径是 LoRA Manager 特有的，将与 ComfyUI 的默认路径合并。",
+            "description": "LoRA Manager 专属的额外模型根目录。从 ComfyUI 标准文件夹之外的位置加载模型，特别适合管理大型模型库，避免影响 ComfyUI 性能。",
+            "restartRequired": "需要重启才能生效",
             "modelTypes": {
                 "lora": "LoRA 路径",
                 "checkpoint": "Checkpoint 路径",
@@ -376,7 +402,7 @@
                 "embedding": "Embedding 路径"
             },
             "pathPlaceholder": "/额外/模型/路径",
-            "saveSuccess": "额外文件夹路径已更新。",
+            "saveSuccess": "额外文件夹路径已更新，需要重启才能生效。",
             "saveError": "更新额外文件夹路径失败：{message}",
             "validation": {
                 "duplicatePath": "此路径已配置"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "跳过所选模型的元数据刷新",
             "resumeMetadataRefresh": "恢复所选模型的元数据刷新",
             "deleteAll": "删除选中模型",
+            "downloadMissingLoras": "下载缺失的 LoRAs",
             "clear": "清除选择",
             "skipMetadataRefreshCount": "跳过（{count} 个模型）",
             "resumeMetadataRefreshCount": "恢复（{count} 个模型）",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "移动到 {otherType} 文件夹"
+            "moveToOtherTypeFolder": "移动到 {otherType} 文件夹",
+            "sendToWorkflow": "[TODO: Translate] Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "取消固定侧边栏",
         "switchToListView": "切换到列表视图",
         "switchToTreeView": "切换到树状视图",
-        "recursiveOn": "搜索子文件夹",
-        "recursiveOff": "仅搜索当前文件夹",
+        "recursiveOn": "包含子文件夹",
+        "recursiveOff": "仅当前文件夹",
         "recursiveUnavailable": "仅在树形视图中可使用递归搜索",
         "collapseAllDisabled": "列表视图下不可用",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "更新基础模型",
             "cancel": "取消"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "下载缺失的 LoRAs",
+            "message": "发现 {uniqueCount} 个独特的缺失 LoRAs（从选定配方中的 {totalCount} 个总数）。",
+            "previewTitle": "要下载的 LoRAs：",
+            "moreItems": "...还有 {count} 个",
+            "note": "文件将使用默认路径模板下载。根据 LoRAs 的数量，这可能需要一些时间。",
+            "downloadButton": "下载 {count} 个 LoRA(s)"
+        },
         "exampleAccess": {
             "title": "本地示例图片",
             "message": "未找到此模型的本地示例图片。可选操作：",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
                 "viewCreatorProfile": "查看创作者主页",
-                "openFileLocation": "打开文件位置"
+                "openFileLocation": "打开文件位置",
+                "sendToWorkflow": "发送到 ComfyUI",
+                "sendToWorkflowText": "发送到 ComfyUI"
             },
             "openFileLocation": {
                 "success": "文件位置已成功打开",
@@ -1042,6 +1080,9 @@
                 "copied": "路径已复制到剪贴板：{{path}}",
                 "clipboardFallback": "路径：{{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "无法发送到 ComfyUI：没有可用的文件路径"
+            },
             "metadata": {
                 "version": "版本",
                 "fileName": "文件名",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "配方已替换到工作流",
             "recipeFailedToSend": "发送配方到工作流失败",
             "noMatchingNodes": "当前工作流中没有兼容的节点",
-            "noTargetNodeSelected": "未选择目标节点"
+            "noTargetNodeSelected": "未选择目标节点",
+            "modelUpdated": "模型已更新到工作流",
+            "modelFailed": "更新模型节点失败"
         },
         "nodeSelector": {
             "recipe": "配方",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "请选择版本",
             "versionExists": "该版本已存在于你的库中",
             "downloadCompleted": "下载成功完成",
+            "downloadSkippedByBaseModel": "由于基础模型 {baseModel} 已被排除，已跳过下载",
             "autoOrganizeSuccess": "自动整理已成功完成，共 {count} 个 {type}",
             "autoOrganizePartialSuccess": "自动整理完成：已移动 {success} 个，{failures} 个失败，共 {total} 个模型",
             "autoOrganizeFailed": "自动整理失败：{error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "配方名称更新成功",
             "tagsUpdated": "配方标签更新成功",
             "sourceUrlUpdated": "来源 URL 更新成功",
+            "promptUpdated": "提示词更新成功",
+            "negativePromptUpdated": "负面提示词更新成功",
+            "promptEditorHint": "按 Enter 保存，Shift+Enter 换行",
             "noRecipeId": "无配方 ID",
+            "sendToWorkflowFailed": "发送配方到工作流失败：{message}",
             "copyFailed": "复制配方语法出错：{message}",
             "noMissingLoras": "没有缺失的 LoRA 可下载",
             "missingLorasInfoFailed": "获取缺失 LoRA 信息失败",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "请输入至少一个 URL 或文件路径",
             "batchImportNoDirectory": "请输入目录路径",
             "batchImportBrowseFailed": "浏览目录失败：{message}",
-            "batchImportDirectorySelected": "已选择目录：{path}"
+            "batchImportDirectorySelected": "已选择目录：{path}",
+            "noRecipesSelected": "未选择任何配方",
+            "noMissingLorasInSelection": "在选定的配方中未找到缺失的 LoRAs",
+            "noLoraRootConfigured": "未配置 LoRA 根目录。请在设置中设置默认的 LoRA 根目录。"
         },
         "models": {
             "noModelsSelected": "未选中模型",

locales/zh-TW.json

@@ -291,7 +291,15 @@
             "blurNsfwContent": "模糊 NSFW 內容",
             "blurNsfwContentHelp": "模糊成熟（NSFW）內容預覽圖片",
             "showOnlySfw": "僅顯示 SFW 結果",
-            "showOnlySfwHelp": "瀏覽和搜尋時過濾所有 NSFW 內容"
+            "showOnlySfwHelp": "瀏覽和搜尋時過濾所有 NSFW 內容",
+            "matureBlurThreshold": "成人內容模糊閾值",
+            "matureBlurThresholdHelp": "設定當啟用 NSFW 模糊時，從哪個評級級別開始模糊過濾。",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 及以上",
+                "r": "R 及以上（預設）",
+                "x": "X 及以上",
+                "xxx": "僅 XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "滑鼠懸停自動播放影片",
@@ -315,6 +323,24 @@
                 "saveFailed": "無法儲存跳過路徑：{message}"
             }
         },
+        "downloadSkipBaseModels": {
+            "label": "跳過這些基礎模型的下載",
+            "help": "適用於所有下載流程。這裡只能選擇受支援的基礎模型。",
+            "searchPlaceholder": "篩選基礎模型...",
+            "empty": "沒有符合目前搜尋條件的基礎模型。",
+            "summary": {
+                "none": "未選擇",
+                "count": "已選擇 {count} 項"
+            },
+            "actions": {
+                "edit": "編輯",
+                "collapse": "收起",
+                "clear": "清空"
+            },
+            "validation": {
+                "saveFailed": "無法儲存已排除的基礎模型：{message}"
+            }
+        },
         "layoutSettings": {
             "displayDensity": "顯示密度",
             "displayDensityOptions": {
@@ -367,8 +393,8 @@
         },
         "extraFolderPaths": {
             "title": "額外資料夾路徑",
-            "help": "在 ComfyUI 的標準路徑之外新增額外的模型資料夾。這些路徑單獨儲存，並與預設資料夾一起掃描。",
-            "description": "設定額外的資料夾以掃描模型。這些路徑是 LoRA Manager 特有的，將與 ComfyUI 的預設路徑合併。",
+            "description": "LoRA Manager 專屬的額外模型根目錄。從 ComfyUI 標準資料夾之外的位置載入模型，特別適合管理大型模型庫，避免影響 ComfyUI 效能。",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "LoRA 路徑",
                 "checkpoint": "Checkpoint 路徑",
@@ -376,7 +402,7 @@
                 "embedding": "Embedding 路徑"
             },
             "pathPlaceholder": "/額外/模型/路徑",
-            "saveSuccess": "額外資料夾路徑已更新。",
+            "saveSuccess": "額外資料夾路徑已更新，需要重啟才能生效。",
             "saveError": "更新額外資料夾路徑失敗：{message}",
             "validation": {
                 "duplicatePath": "此路徑已設定"
@@ -575,6 +601,7 @@
             "skipMetadataRefresh": "跳過所選模型的元數據更新",
             "resumeMetadataRefresh": "恢復所選模型的元數據更新",
             "deleteAll": "刪除全部模型",
+            "downloadMissingLoras": "下載缺失的 LoRAs",
             "clear": "清除選取",
             "skipMetadataRefreshCount": "跳過（{count} 個模型）",
             "resumeMetadataRefreshCount": "恢復（{count} 個模型）",
@@ -799,7 +826,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "移動到 {otherType} 資料夾"
+            "moveToOtherTypeFolder": "移動到 {otherType} 資料夾",
+            "sendToWorkflow": "[TODO: Translate] Send to Workflow"
         }
     },
     "embeddings": {
@@ -812,8 +840,8 @@
         "unpinSidebar": "取消固定側邊欄",
         "switchToListView": "切換至列表檢視",
         "switchToTreeView": "切換到樹狀檢視",
-        "recursiveOn": "搜尋子資料夾",
-        "recursiveOff": "僅搜尋目前資料夾",
+        "recursiveOn": "包含子資料夾",
+        "recursiveOff": "僅目前資料夾",
         "recursiveUnavailable": "遞迴搜尋僅能在樹狀檢視中使用",
         "collapseAllDisabled": "列表檢視下不可用",
         "dragDrop": {
@@ -983,6 +1011,14 @@
             "save": "更新基礎模型",
             "cancel": "取消"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "下載缺失的 LoRAs",
+            "message": "發現 {uniqueCount} 個獨特的缺失 LoRAs（從選取食譜中的 {totalCount} 個總數）。",
+            "previewTitle": "要下載的 LoRAs：",
+            "moreItems": "...還有 {count} 個",
+            "note": "檔案將使用預設路徑模板下載。根據 LoRAs 的數量，這可能需要一些時間。",
+            "downloadButton": "下載 {count} 個 LoRA(s)"
+        },
         "exampleAccess": {
             "title": "本機範例圖片",
             "message": "此模型未找到本機範例圖片。可選擇：",
@@ -1034,7 +1070,9 @@
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
                 "viewCreatorProfile": "查看創作者個人檔案",
-                "openFileLocation": "開啟檔案位置"
+                "openFileLocation": "開啟檔案位置",
+                "sendToWorkflow": "傳送到 ComfyUI",
+                "sendToWorkflowText": "傳送到 ComfyUI"
             },
             "openFileLocation": {
                 "success": "檔案位置已成功開啟",
@@ -1042,6 +1080,9 @@
                 "copied": "路徑已複製到剪貼簿：{{path}}",
                 "clipboardFallback": "路徑：{{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "無法傳送到 ComfyUI：沒有可用的檔案路徑"
+            },
             "metadata": {
                 "version": "版本",
                 "fileName": "檔案名稱",
@@ -1299,7 +1340,9 @@
             "recipeReplaced": "配方已取代於工作流",
             "recipeFailedToSend": "傳送配方到工作流失敗",
             "noMatchingNodes": "目前工作流程中沒有相容的節點",
-            "noTargetNodeSelected": "未選擇目標節點"
+            "noTargetNodeSelected": "未選擇目標節點",
+            "modelUpdated": "模型已更新到工作流",
+            "modelFailed": "更新模型節點失敗"
         },
         "nodeSelector": {
             "recipe": "配方",
@@ -1450,6 +1493,7 @@
             "pleaseSelectVersion": "請選擇一個版本",
             "versionExists": "此版本已存在於您的庫中",
             "downloadCompleted": "下載成功完成",
+            "downloadSkippedByBaseModel": "由於基礎模型 {baseModel} 已被排除，已跳過下載",
             "autoOrganizeSuccess": "自動整理已成功完成，共 {count} 個 {type} 已整理",
             "autoOrganizePartialSuccess": "自動整理完成：已移動 {success} 個，{failures} 個失敗，共 {total} 個模型",
             "autoOrganizeFailed": "自動整理失敗：{error}",
@@ -1469,7 +1513,11 @@
             "nameUpdated": "配方名稱已更新",
             "tagsUpdated": "配方標籤已更新",
             "sourceUrlUpdated": "來源網址已更新",
+            "promptUpdated": "提示詞更新成功",
+            "negativePromptUpdated": "負面提示詞更新成功",
+            "promptEditorHint": "按 Enter 儲存，Shift+Enter 換行",
             "noRecipeId": "無配方 ID",
+            "sendToWorkflowFailed": "傳送配方到工作流失敗：{message}",
             "copyFailed": "複製配方語法錯誤：{message}",
             "noMissingLoras": "無缺少的 LoRA 可下載",
             "missingLorasInfoFailed": "取得缺少 LoRA 資訊失敗",
@@ -1507,7 +1555,10 @@
             "batchImportNoUrls": "請輸入至少一個 URL 或檔案路徑",
             "batchImportNoDirectory": "請輸入目錄路徑",
             "batchImportBrowseFailed": "瀏覽目錄失敗：{message}",
-            "batchImportDirectorySelected": "已選擇目錄：{path}"
+            "batchImportDirectorySelected": "已選擇目錄：{path}",
+            "noRecipesSelected": "未選取任何食譜",
+            "noMissingLorasInSelection": "在選取的食譜中未找到缺失的 LoRAs",
+            "noLoraRootConfigured": "未配置 LoRA 根目錄。請在設定中設定預設的 LoRA 根目錄。"
         },
         "models": {
             "noModelsSelected": "未選擇模型",

py/config.py

@@ -25,6 +25,31 @@ standalone_mode = (
 logger = logging.getLogger(__name__)
 
 
+def _resolve_valid_default_root(
+    current: str, primary_paths: List[str], name: str
+) -> str:
+    """Return a valid default root from the current primary path set."""
+
+    valid_paths = [path for path in primary_paths if isinstance(path, str) and path.strip()]
+    if not valid_paths:
+        return ""
+
+    if current in valid_paths:
+        return current
+
+    if current:
+        logger.info(
+            "Repaired stale %s from '%s' to '%s'",
+            name,
+            current,
+            valid_paths[0],
+        )
+    else:
+        logger.info("Auto-setting %s to '%s'", name, valid_paths[0])
+
+    return valid_paths[0]
+
+
 def _normalize_folder_paths_for_comparison(
     folder_paths: Mapping[str, Iterable[str]],
 ) -> Dict[str, Set[str]]:
@@ -197,25 +222,23 @@ class Config:
                         "Failed to rename legacy 'default' library: %s", rename_error
                     )
 
-            default_lora_root = comfy_library.get("default_lora_root", "")
-            if not default_lora_root and len(self.loras_roots) == 1:
-                default_lora_root = self.loras_roots[0]
+            default_lora_root = _resolve_valid_default_root(
+                comfy_library.get("default_lora_root", ""),
+                list(self.loras_roots or []),
+                "default_lora_root",
+            )
 
-            default_checkpoint_root = comfy_library.get("default_checkpoint_root", "")
-            if (
-                not default_checkpoint_root
-                and self.checkpoints_roots
-                and len(self.checkpoints_roots) == 1
-            ):
-                default_checkpoint_root = self.checkpoints_roots[0]
+            default_checkpoint_root = _resolve_valid_default_root(
+                comfy_library.get("default_checkpoint_root", ""),
+                list(self.checkpoints_roots or []),
+                "default_checkpoint_root",
+            )
 
-            default_embedding_root = comfy_library.get("default_embedding_root", "")
-            if (
-                not default_embedding_root
-                and self.embeddings_roots
-                and len(self.embeddings_roots) == 1
-            ):
-                default_embedding_root = self.embeddings_roots[0]
+            default_embedding_root = _resolve_valid_default_root(
+                comfy_library.get("default_embedding_root", ""),
+                list(self.embeddings_roots or []),
+                "default_embedding_root",
+            )
 
             metadata = dict(comfy_library.get("metadata", {}))
             metadata.setdefault("display_name", "ComfyUI")
@@ -705,6 +728,122 @@ class Config:
 
         return unique_paths
 
+    @staticmethod
+    def _normalize_path_for_comparison(
+        path: str, *, resolve_realpath: bool = False
+    ) -> str:
+        """Normalize a path for equality checks across platforms."""
+        candidate = os.path.realpath(path) if resolve_realpath else path
+        return os.path.normcase(os.path.normpath(candidate)).replace(os.sep, "/")
+
+    def _filter_overlapping_extra_lora_paths(
+        self,
+        primary_paths: Iterable[str],
+        extra_paths: Iterable[str],
+    ) -> List[str]:
+        """Drop extra LoRA paths that resolve to the same physical location as primary roots."""
+
+        primary_map = {
+            self._normalize_path_for_comparison(path, resolve_realpath=True): path
+            for path in primary_paths
+            if isinstance(path, str) and path.strip() and os.path.exists(path)
+        }
+        primary_symlink_map = self._collect_first_level_symlink_targets(primary_paths)
+        filtered: List[str] = []
+
+        for original_path in extra_paths:
+            if not isinstance(original_path, str):
+                continue
+
+            stripped = original_path.strip()
+            if not stripped:
+                continue
+            if not os.path.exists(stripped):
+                continue
+
+            real_path = self._normalize_path_for_comparison(
+                stripped,
+                resolve_realpath=True,
+            )
+            normalized_path = os.path.normpath(stripped).replace(os.sep, "/")
+            primary_path = primary_map.get(real_path)
+            if primary_path:
+                # Config loading should stay tolerant of existing invalid state and warn.
+                logger.warning(
+                    "Detected the same LoRA folder in both ComfyUI model paths and "
+                    "LoRA Manager Extra Folder Paths. This can cause duplicate items or "
+                    "other unexpected behavior, and it usually means the path setup is "
+                    "not doing what you intended. LoRA Manager will keep the ComfyUI "
+                    "path and ignore this Extra Folder Paths entry: '%s'. Please review "
+                    "your path settings and remove the duplicate entry.",
+                    normalized_path,
+                )
+                continue
+
+            symlink_path = primary_symlink_map.get(real_path)
+            if symlink_path:
+                # Config loading should stay tolerant of existing invalid state and warn.
+                logger.warning(
+                    "Detected the same LoRA folder in both ComfyUI model paths and "
+                    "LoRA Manager Extra Folder Paths. This can cause duplicate items or "
+                    "other unexpected behavior, and it usually means the path setup is "
+                    "not doing what you intended. LoRA Manager will keep the ComfyUI "
+                    "path and ignore this Extra Folder Paths entry: '%s'. Please review "
+                    "your path settings and remove the duplicate entry.",
+                    normalized_path,
+                )
+                continue
+
+            filtered.append(stripped)
+
+        return filtered
+
+    def _collect_first_level_symlink_targets(
+        self, roots: Iterable[str]
+    ) -> Dict[str, str]:
+        """Return real-path -> link-path mappings for first-level symlinks under the given roots."""
+
+        targets: Dict[str, str] = {}
+        for root in roots:
+            if not isinstance(root, str):
+                continue
+            stripped_root = root.strip()
+            if not stripped_root or not os.path.isdir(stripped_root):
+                continue
+
+            try:
+                with os.scandir(stripped_root) as iterator:
+                    for entry in iterator:
+                        try:
+                            if not self._entry_is_symlink(entry):
+                                continue
+                            target_path = os.path.realpath(entry.path)
+                            if not os.path.isdir(target_path):
+                                continue
+
+                            normalized_target = self._normalize_path_for_comparison(
+                                target_path,
+                                resolve_realpath=True,
+                            )
+                            normalized_link = os.path.normpath(entry.path).replace(
+                                os.sep, "/"
+                            )
+                            targets.setdefault(normalized_target, normalized_link)
+                        except Exception as inner_exc:
+                            logger.debug(
+                                "Error collecting LoRA symlink target for %s: %s",
+                                entry.path,
+                                inner_exc,
+                            )
+            except Exception as exc:
+                logger.debug(
+                    "Error scanning first-level LoRA symlinks in %s: %s",
+                    stripped_root,
+                    exc,
+                )
+
+        return targets
+
     def _prepare_checkpoint_paths(
         self, checkpoint_paths: Iterable[str], unet_paths: Iterable[str]
     ) -> Tuple[List[str], List[str], List[str]]:
@@ -796,7 +935,11 @@ class Config:
         extra_unet_paths = extra_paths.get("unet", []) or []
         extra_embedding_paths = extra_paths.get("embeddings", []) or []
 
-        self.extra_loras_roots = self._prepare_lora_paths(extra_lora_paths)
+        filtered_extra_lora_paths = self._filter_overlapping_extra_lora_paths(
+            self.loras_roots,
+            extra_lora_paths,
+        )
+        self.extra_loras_roots = self._prepare_lora_paths(filtered_extra_lora_paths)
         (
             _,
             self.extra_checkpoints_roots,

py/nodes/lora_cycler.py

@@ -8,6 +8,7 @@ and tracks the cycle progress which persists across workflow save/load.
 
 import logging
 import os
+
 from ..utils.utils import get_lora_info
 
 logger = logging.getLogger(__name__)
@@ -54,6 +55,9 @@ class LoraCyclerLM:
         current_index = cycler_config.get("current_index", 1)  # 1-based
         model_strength = float(cycler_config.get("model_strength", 1.0))
         clip_strength = float(cycler_config.get("clip_strength", 1.0))
+        use_same_clip_strength = cycler_config.get("use_same_clip_strength", True)
+        use_preset_strength = cycler_config.get("use_preset_strength", False)
+        preset_strength_scale = float(cycler_config.get("preset_strength_scale", 1.0))
         sort_by = "filename"
 
         # Include "no lora" option
@@ -131,6 +135,39 @@ class LoraCyclerLM:
             else:
                 # Normalize path separators
                 lora_path = lora_path.replace("/", os.sep)
+
+                if use_preset_strength:
+                    lora_metadata = await lora_service.get_lora_metadata_by_filename(
+                        current_lora["file_name"]
+                    )
+                    if lora_metadata:
+                        recommended_strength = (
+                            lora_service.get_recommended_strength_from_lora_data(
+                                lora_metadata
+                            )
+                        )
+                        if recommended_strength is not None:
+                            model_strength = round(
+                                recommended_strength * preset_strength_scale, 2
+                            )
+
+                        if use_same_clip_strength:
+                            clip_strength = model_strength
+                        else:
+                            recommended_clip_strength = (
+                                lora_service.get_recommended_clip_strength_from_lora_data(
+                                    lora_metadata
+                                )
+                            )
+                            if recommended_clip_strength is not None:
+                                clip_strength = round(
+                                    recommended_clip_strength * preset_strength_scale, 2
+                                )
+                    elif use_same_clip_strength:
+                        clip_strength = model_strength
+                elif use_same_clip_strength:
+                    clip_strength = model_strength
+
                 lora_stack = [(lora_path, model_strength, clip_strength)]
 
         # Calculate next index (wrap to 1 if at end)

py/nodes/lora_loader.py

@@ -1,22 +1,138 @@
+import importlib
 import logging
 import re
-import comfy.utils # type: ignore
-import comfy.sd # type: ignore
+
+import comfy.sd  # type: ignore
+import comfy.utils  # type: ignore
+
 from ..utils.utils import get_lora_info_absolute
-from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list, nunchaku_load_lora
+from .utils import (
+    FlexibleOptionalInputType,
+    any_type,
+    detect_nunchaku_model_kind,
+    extract_lora_name,
+    get_loras_list,
+    nunchaku_load_lora,
+)
 
 logger = logging.getLogger(__name__)
 
+
+def _get_nunchaku_load_qwen_loras():
+    try:
+        module = importlib.import_module(".nunchaku_qwen", __package__)
+    except ImportError as exc:
+        raise RuntimeError(
+            "Qwen-Image LoRA loading requires the ComfyUI runtime with its torch dependency available."
+        ) from exc
+    return module.nunchaku_load_qwen_loras
+
+
+def _collect_stack_entries(lora_stack):
+    entries = []
+    if not lora_stack:
+        return entries
+
+    for lora_path, model_strength, clip_strength in lora_stack:
+        lora_name = extract_lora_name(lora_path)
+        absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
+        entries.append({
+            "name": lora_name,
+            "absolute_path": absolute_lora_path,
+            "input_path": lora_path,
+            "model_strength": float(model_strength),
+            "clip_strength": float(clip_strength),
+            "trigger_words": trigger_words,
+        })
+    return entries
+
+
+def _collect_widget_entries(kwargs):
+    entries = []
+    for lora in get_loras_list(kwargs):
+        if not lora.get("active", False):
+            continue
+        lora_name = lora["name"]
+        model_strength = float(lora["strength"])
+        clip_strength = float(lora.get("clipStrength", model_strength))
+        lora_path, trigger_words = get_lora_info_absolute(lora_name)
+        entries.append({
+            "name": lora_name,
+            "absolute_path": lora_path,
+            "input_path": lora_path,
+            "model_strength": model_strength,
+            "clip_strength": clip_strength,
+            "trigger_words": trigger_words,
+        })
+    return entries
+
+
+def _format_loaded_loras(loaded_loras):
+    formatted_loras = []
+    for item in loaded_loras:
+        if item["include_clip_strength"]:
+            formatted_loras.append(
+                f"<lora:{item['name']}:{item['model_strength']}:{item['clip_strength']}>"
+            )
+        else:
+            formatted_loras.append(f"<lora:{item['name']}:{item['model_strength']}>")
+    return " ".join(formatted_loras)
+
+
+def _apply_entries(model, clip, lora_entries, nunchaku_model_kind):
+    loaded_loras = []
+    all_trigger_words = []
+
+    if nunchaku_model_kind == "qwen_image":
+        nunchaku_load_qwen_loras = _get_nunchaku_load_qwen_loras()
+        qwen_lora_configs = []
+        for entry in lora_entries:
+            qwen_lora_configs.append((entry["absolute_path"], entry["model_strength"]))
+            loaded_loras.append({
+                "name": entry["name"],
+                "model_strength": entry["model_strength"],
+                "clip_strength": entry["model_strength"],
+                "include_clip_strength": False,
+            })
+            all_trigger_words.extend(entry["trigger_words"])
+        if qwen_lora_configs:
+            model = nunchaku_load_qwen_loras(model, qwen_lora_configs)
+        return model, clip, loaded_loras, all_trigger_words
+
+    for entry in lora_entries:
+        if nunchaku_model_kind == "flux":
+            model = nunchaku_load_lora(model, entry["input_path"], entry["model_strength"])
+        else:
+            lora = comfy.utils.load_torch_file(entry["absolute_path"], safe_load=True)
+            model, clip = comfy.sd.load_lora_for_models(
+                model,
+                clip,
+                lora,
+                entry["model_strength"],
+                entry["clip_strength"],
+            )
+
+        include_clip_strength = nunchaku_model_kind is None and abs(entry["model_strength"] - entry["clip_strength"]) > 0.001
+        loaded_loras.append({
+            "name": entry["name"],
+            "model_strength": entry["model_strength"],
+            "clip_strength": entry["clip_strength"],
+            "include_clip_strength": include_clip_strength,
+        })
+        all_trigger_words.extend(entry["trigger_words"])
+
+    return model, clip, loaded_loras, all_trigger_words
+
+
 class LoraLoaderLM:
     NAME = "Lora Loader (LoraManager)"
     CATEGORY = "Lora Manager/loaders"
-    
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "model": ("MODEL",),
-                # "clip": ("CLIP",),
                 "text": ("AUTOCOMPLETE_TEXT_LORAS", {
                     "placeholder": "Search LoRAs to add...",
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
@@ -28,114 +144,30 @@ class LoraLoaderLM:
     RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras"
-    
+
     def load_loras(self, model, text, **kwargs):
         """Loads multiple LoRAs based on the kwargs input and lora_stack."""
-        loaded_loras = []
-        all_trigger_words = []
-        
-        clip = kwargs.get('clip', None)
-        lora_stack = kwargs.get('lora_stack', None)
-        
-        # Check if model is a Nunchaku Flux model - simplified approach
-        is_nunchaku_model = False
-        
-        try:
-            model_wrapper = model.model.diffusion_model
-            # Check if model is a Nunchaku Flux model using only class name
-            if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
-                is_nunchaku_model = True
-                logger.info("Detected Nunchaku Flux model")
-        except (AttributeError, TypeError):
-            # Not a model with the expected structure
-            pass
-        
-        # First process lora_stack if available
-        if lora_stack:
-            for lora_path, model_strength, clip_strength in lora_stack:
-                # Extract lora name and convert to absolute path
-                # lora_stack stores relative paths, but load_torch_file needs absolute paths
-                lora_name = extract_lora_name(lora_path)
-                absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
-                
-                # Apply the LoRA using the appropriate loader
-                if is_nunchaku_model:
-                    # Use our custom function for Flux models
-                    model = nunchaku_load_lora(model, lora_path, model_strength)
-                    # clip remains unchanged for Nunchaku models
-                else:
-                    # Use lower-level API to load LoRA directly without folder_paths validation
-                    lora = comfy.utils.load_torch_file(absolute_lora_path, safe_load=True)
-                    model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
-                
-                all_trigger_words.extend(trigger_words)
-                # Add clip strength to output if different from model strength (except for Nunchaku models)
-                if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
-                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
-                else:
-                    loaded_loras.append(f"{lora_name}: {model_strength}")
-        
-        # Then process loras from kwargs with support for both old and new formats
-        loras_list = get_loras_list(kwargs)
-        for lora in loras_list:
-            if not lora.get('active', False):
-                continue
-                
-            lora_name = lora['name']
-            model_strength = float(lora['strength'])
-            # Get clip strength - use model strength as default if not specified
-            clip_strength = float(lora.get('clipStrength', model_strength))
-            
-            # Get lora path and trigger words
-            lora_path, trigger_words = get_lora_info_absolute(lora_name)
-            
-            # Apply the LoRA using the appropriate loader
-            if is_nunchaku_model:
-                # For Nunchaku models, use our custom function
-                model = nunchaku_load_lora(model, lora_path, model_strength)
-                # clip remains unchanged
-            else:
-                # Use lower-level API to load LoRA directly without folder_paths validation
-                lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
-                model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
-            
-            # Include clip strength in output if different from model strength and not a Nunchaku model
-            if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
-                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
-            else:
-                loaded_loras.append(f"{lora_name}: {model_strength}")
-            
-            # Add trigger words to collection
-            all_trigger_words.extend(trigger_words)
-        
-        # use ',, ' to separate trigger words for group mode
-        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-        
-        # Format loaded_loras with support for both formats
-        formatted_loras = []
-        for item in loaded_loras:
-            parts = item.split(":")
-            lora_name = parts[0]
-            strength_parts = parts[1].strip().split(",")
-            
-            if len(strength_parts) > 1:
-                # Different model and clip strengths
-                model_str = strength_parts[0].strip()
-                clip_str = strength_parts[1].strip()
-                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
-            else:
-                # Same strength for both
-                model_str = strength_parts[0].strip()
-                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
-                
-        formatted_loras_text = " ".join(formatted_loras)
+        del text
+        clip = kwargs.get("clip", None)
+        lora_entries = _collect_stack_entries(kwargs.get("lora_stack", None))
+        lora_entries.extend(_collect_widget_entries(kwargs))
 
+        nunchaku_model_kind = detect_nunchaku_model_kind(model)
+        if nunchaku_model_kind == "flux":
+            logger.info("Detected Nunchaku Flux model")
+        elif nunchaku_model_kind == "qwen_image":
+            logger.info("Detected Nunchaku Qwen-Image model")
+
+        model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+        formatted_loras_text = _format_loaded_loras(loaded_loras)
         return (model, clip, trigger_words_text, formatted_loras_text)
 
+
 class LoraTextLoaderLM:
     NAME = "LoRA Text Loader (LoraManager)"
     CATEGORY = "Lora Manager/loaders"
-    
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -143,131 +175,55 @@ class LoraTextLoaderLM:
                 "model": ("MODEL",),
                 "lora_syntax": ("STRING", {
                     "forceInput": True,
-                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation"
+                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                 }),
             },
             "optional": {
                 "clip": ("CLIP",),
                 "lora_stack": ("LORA_STACK",),
-            }
+            },
         }
 
     RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras_from_text"
-    
+
     def parse_lora_syntax(self, text):
         """Parse LoRA syntax from text input."""
-        # Pattern to match <lora:name:strength> or <lora:name:model_strength:clip_strength>
-        pattern = r'<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>'
+        pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
         matches = re.findall(pattern, text, re.IGNORECASE)
-        
+
         loras = []
         for match in matches:
-            lora_name = match[0]
             model_strength = float(match[1])
-            clip_strength = float(match[2]) if match[2] else model_strength
-            
             loras.append({
-                'name': lora_name,
-                'model_strength': model_strength,
-                'clip_strength': clip_strength
+                "name": match[0],
+                "model_strength": model_strength,
+                "clip_strength": float(match[2]) if match[2] else model_strength,
             })
-        
         return loras
-    
+
     def load_loras_from_text(self, model, lora_syntax, clip=None, lora_stack=None):
         """Load LoRAs based on text syntax input."""
-        loaded_loras = []
-        all_trigger_words = []
-        
-        # Check if model is a Nunchaku Flux model - simplified approach
-        is_nunchaku_model = False
-        
-        try:
-            model_wrapper = model.model.diffusion_model
-            # Check if model is a Nunchaku Flux model using only class name
-            if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
-                is_nunchaku_model = True
-                logger.info("Detected Nunchaku Flux model")
-        except (AttributeError, TypeError):
-            # Not a model with the expected structure
-            pass
-        
-        # First process lora_stack if available
-        if lora_stack:
-            for lora_path, model_strength, clip_strength in lora_stack:
-                # Extract lora name and convert to absolute path
-                # lora_stack stores relative paths, but load_torch_file needs absolute paths
-                lora_name = extract_lora_name(lora_path)
-                absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
-                
-                # Apply the LoRA using the appropriate loader
-                if is_nunchaku_model:
-                    # Use our custom function for Flux models
-                    model = nunchaku_load_lora(model, lora_path, model_strength)
-                    # clip remains unchanged for Nunchaku models
-                else:
-                    # Use lower-level API to load LoRA directly without folder_paths validation
-                    lora = comfy.utils.load_torch_file(absolute_lora_path, safe_load=True)
-                    model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
-                
-                all_trigger_words.extend(trigger_words)
-                # Add clip strength to output if different from model strength (except for Nunchaku models)
-                if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
-                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
-                else:
-                    loaded_loras.append(f"{lora_name}: {model_strength}")
-        
-        # Parse and process LoRAs from text syntax
-        parsed_loras = self.parse_lora_syntax(lora_syntax)
-        for lora in parsed_loras:
-            lora_name = lora['name']
-            model_strength = lora['model_strength']
-            clip_strength = lora['clip_strength']
-            
-            # Get lora path and trigger words
-            lora_path, trigger_words = get_lora_info_absolute(lora_name)
-            
-            # Apply the LoRA using the appropriate loader
-            if is_nunchaku_model:
-                # For Nunchaku models, use our custom function
-                model = nunchaku_load_lora(model, lora_path, model_strength)
-                # clip remains unchanged
-            else:
-                # Use lower-level API to load LoRA directly without folder_paths validation
-                lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
-                model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
-            
-            # Include clip strength in output if different from model strength and not a Nunchaku model
-            if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
-                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
-            else:
-                loaded_loras.append(f"{lora_name}: {model_strength}")
-            
-            # Add trigger words to collection
-            all_trigger_words.extend(trigger_words)
-        
-        # use ',, ' to separate trigger words for group mode
+        lora_entries = _collect_stack_entries(lora_stack)
+        for lora in self.parse_lora_syntax(lora_syntax):
+            lora_path, trigger_words = get_lora_info_absolute(lora["name"])
+            lora_entries.append({
+                "name": lora["name"],
+                "absolute_path": lora_path,
+                "input_path": lora_path,
+                "model_strength": lora["model_strength"],
+                "clip_strength": lora["clip_strength"],
+                "trigger_words": trigger_words,
+            })
+
+        nunchaku_model_kind = detect_nunchaku_model_kind(model)
+        if nunchaku_model_kind == "flux":
+            logger.info("Detected Nunchaku Flux model")
+        elif nunchaku_model_kind == "qwen_image":
+            logger.info("Detected Nunchaku Qwen-Image model")
+
+        model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-
-        # Format loaded_loras with support for both formats
-        formatted_loras = []
-        for item in loaded_loras:
-            parts = item.split(":")
-            lora_name = parts[0].strip()
-            strength_parts = parts[1].strip().split(",")
-            
-            if len(strength_parts) > 1:
-                # Different model and clip strengths
-                model_str = strength_parts[0].strip()
-                clip_str = strength_parts[1].strip()
-                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
-            else:
-                # Same strength for both
-                model_str = strength_parts[0].strip()
-                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
-                
-        formatted_loras_text = " ".join(formatted_loras)
-
-        return (model, clip, trigger_words_text, formatted_loras_text)
+        formatted_loras_text = _format_loaded_loras(loaded_loras)
+        return (model, clip, trigger_words_text, formatted_loras_text)

py/nodes/lora_stack_combiner.py

@@ -0,0 +1,26 @@
+class LoraStackCombinerLM:
+    NAME = "Lora Stack Combiner (LoraManager)"
+    CATEGORY = "Lora Manager/stackers"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "lora_stack_a": ("LORA_STACK",),
+                "lora_stack_b": ("LORA_STACK",),
+            },
+        }
+
+    RETURN_TYPES = ("LORA_STACK",)
+    RETURN_NAMES = ("LORA_STACK",)
+    FUNCTION = "combine_stacks"
+
+    def combine_stacks(self, lora_stack_a, lora_stack_b):
+        combined_stack = []
+
+        if lora_stack_a:
+            combined_stack.extend(lora_stack_a)
+        if lora_stack_b:
+            combined_stack.extend(lora_stack_b)
+
+        return (combined_stack,)

py/nodes/nunchaku_qwen.py

@@ -0,0 +1,570 @@
+from __future__ import annotations
+
+"""Qwen-Image LoRA support for Nunchaku models.
+
+Portions of the LoRA mapping/application logic in this file are adapted from
+ComfyUI-QwenImageLoraLoader by GitHub user ussoewwin:
+https://github.com/ussoewwin/ComfyUI-QwenImageLoraLoader
+
+The upstream project is licensed under Apache License 2.0.
+"""
+
+import copy
+import logging
+import os
+import re
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Union
+
+import comfy.utils  # type: ignore
+import folder_paths  # type: ignore
+import torch
+import torch.nn as nn
+from safetensors import safe_open
+
+from nunchaku.lora.flux.nunchaku_converter import (
+    pack_lowrank_weight,
+    unpack_lowrank_weight,
+)
+
+logger = logging.getLogger(__name__)
+
+KEY_MAPPING = [
+    (re.compile(r"^(layers)[._](\d+)[._]attention[._]to[._]([qkv])$"), r"\1.\2.attention.to_qkv", "qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(layers)[._](\d+)[._]feed_forward[._](w1|w3)$"), r"\1.\2.feed_forward.net.0.proj", "glu", lambda m: m.group(3)),
+    (re.compile(r"^(layers)[._](\d+)[._]feed_forward[._]w2$"), r"\1.\2.feed_forward.net.2", "regular", None),
+    (re.compile(r"^(layers)[._](\d+)[._](.*)$"), r"\1.\2.\3", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]to[._]([qkv])$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._](q|k|v)[._]proj$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]add[._](q|k|v)[._]proj$"), r"\1.\2.attn.add_qkv_proj", "add_qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]out[._]proj[._]context$"), r"\1.\2.attn.to_add_out", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]out[._]proj$"), r"\1.\2.attn.to_out.0", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]to[._]out$"), r"\1.\2.attn.to_out.0", "regular", None),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]attn[._]to[._]([qkv])$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]attn[._]to[._]out$"), r"\1.\2.attn.to_out", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff[._]net[._]0(?:[._]proj)?$"), r"\1.\2.mlp_fc1", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff[._]net[._]2$"), r"\1.\2.mlp_fc2", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff_context[._]net[._]0(?:[._]proj)?$"), r"\1.\2.mlp_context_fc1", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff_context[._]net[._]2$"), r"\1.\2.mlp_context_fc2", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mlp)[._](net)[._](0)[._](proj)$"), r"\1.\2.\3.\4.\5.\6", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mlp)[._](net)[._](2)$"), r"\1.\2.\3.\4.\5", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mlp)[._](net)[._](0)[._](proj)$"), r"\1.\2.\3.\4.\5.\6", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mlp)[._](net)[._](2)$"), r"\1.\2.\3.\4.\5", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mod)[._](1)$"), r"\1.\2.\3.\4", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mod)[._](1)$"), r"\1.\2.\3.\4", "regular", None),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]proj[._]out$"), r"\1.\2.proj_out", "single_proj_out", None),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]proj[._]mlp$"), r"\1.\2.mlp_fc1", "regular", None),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]norm[._]linear$"), r"\1.\2.norm.linear", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]norm1[._]linear$"), r"\1.\2.norm1.linear", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]norm1_context[._]linear$"), r"\1.\2.norm1_context.linear", "regular", None),
+    (re.compile(r"^(img_in)$"), r"\1", "regular", None),
+    (re.compile(r"^(txt_in)$"), r"\1", "regular", None),
+    (re.compile(r"^(proj_out)$"), r"\1", "regular", None),
+    (re.compile(r"^(norm_out)[._](linear)$"), r"\1.\2", "regular", None),
+    (re.compile(r"^(time_text_embed)[._](timestep_embedder)[._](linear_1)$"), r"\1.\2.\3", "regular", None),
+    (re.compile(r"^(time_text_embed)[._](timestep_embedder)[._](linear_2)$"), r"\1.\2.\3", "regular", None),
+]
+
+_RE_LORA_SUFFIX = re.compile(r"\.(?P<tag>lora(?:[._](?:A|B|down|up)))(?:\.[^.]+)*\.weight$")
+_RE_ALPHA_SUFFIX = re.compile(r"\.(?:alpha|lora_alpha)(?:\.[^.]+)*$")
+
+
+def _rename_layer_underscore_layer_name(old_name: str) -> str:
+    rules = [
+        (r"_(\d+)_attn_to_out_(\d+)", r".\1.attn.to_out.\2"),
+        (r"_(\d+)_img_mlp_net_(\d+)_proj", r".\1.img_mlp.net.\2.proj"),
+        (r"_(\d+)_txt_mlp_net_(\d+)_proj", r".\1.txt_mlp.net.\2.proj"),
+        (r"_(\d+)_img_mlp_net_(\d+)", r".\1.img_mlp.net.\2"),
+        (r"_(\d+)_txt_mlp_net_(\d+)", r".\1.txt_mlp.net.\2"),
+        (r"_(\d+)_img_mod_(\d+)", r".\1.img_mod.\2"),
+        (r"_(\d+)_txt_mod_(\d+)", r".\1.txt_mod.\2"),
+        (r"_(\d+)_attn_", r".\1.attn."),
+    ]
+    new_name = old_name
+    for pattern, replacement in rules:
+        new_name = re.sub(pattern, replacement, new_name)
+    return new_name
+
+
+def _is_indexable_module(module):
+    return isinstance(module, (nn.ModuleList, nn.Sequential, list, tuple))
+
+
+def _get_module_by_name(model: nn.Module, name: str) -> Optional[nn.Module]:
+    if not name:
+        return model
+    module = model
+    for part in name.split("."):
+        if not part:
+            continue
+        if hasattr(module, part):
+            module = getattr(module, part)
+        elif part.isdigit() and _is_indexable_module(module):
+            try:
+                module = module[int(part)]
+            except (IndexError, TypeError):
+                return None
+        else:
+            return None
+    return module
+
+
+def _resolve_module_name(model: nn.Module, name: str) -> Tuple[str, Optional[nn.Module]]:
+    module = _get_module_by_name(model, name)
+    if module is not None:
+        return name, module
+
+    replacements = [
+        (".attn.to_out.0", ".attn.to_out"),
+        (".attention.to_qkv", ".attention.qkv"),
+        (".attention.to_out.0", ".attention.out"),
+        (".feed_forward.net.0.proj", ".feed_forward.w13"),
+        (".feed_forward.net.2", ".feed_forward.w2"),
+        (".ff.net.0.proj", ".mlp_fc1"),
+        (".ff.net.2", ".mlp_fc2"),
+        (".ff_context.net.0.proj", ".mlp_context_fc1"),
+        (".ff_context.net.2", ".mlp_context_fc2"),
+    ]
+    for src, dst in replacements:
+        if src in name:
+            alt = name.replace(src, dst)
+            module = _get_module_by_name(model, alt)
+            if module is not None:
+                return alt, module
+    return name, None
+
+
+def _classify_and_map_key(key: str) -> Optional[Tuple[str, str, Optional[str], str]]:
+    normalized = key
+    if normalized.startswith("transformer."):
+        normalized = normalized[len("transformer."):]
+    if normalized.startswith("diffusion_model."):
+        normalized = normalized[len("diffusion_model."):]
+    if normalized.startswith("lora_unet_"):
+        normalized = _rename_layer_underscore_layer_name(normalized[len("lora_unet_"):])
+
+    match = _RE_LORA_SUFFIX.search(normalized)
+    if match:
+        tag = match.group("tag")
+        base = normalized[:match.start()]
+        ab = "A" if ("lora_A" in tag or tag.endswith(".A") or "down" in tag) else "B"
+    else:
+        match = _RE_ALPHA_SUFFIX.search(normalized)
+        if not match:
+            return None
+        base = normalized[:match.start()]
+        ab = "alpha"
+
+    for pattern, template, group, comp_fn in KEY_MAPPING:
+        key_match = pattern.match(base)
+        if key_match:
+            return group, key_match.expand(template), comp_fn(key_match) if comp_fn else None, ab
+    return None
+
+
+def _detect_lora_format(lora_state_dict: Dict[str, torch.Tensor]) -> bool:
+    standard_patterns = (
+        ".lora_up.",
+        ".lora_down.",
+        ".lora_A.",
+        ".lora_B.",
+        ".lora.up.",
+        ".lora.down.",
+        ".lora.A.",
+        ".lora.B.",
+    )
+    return any(pattern in key for key in lora_state_dict for pattern in standard_patterns)
+
+
+def _load_lora_state_dict(path_or_dict: Union[str, Path, Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+    if isinstance(path_or_dict, dict):
+        return path_or_dict
+    path = Path(path_or_dict)
+    if path.suffix == ".safetensors":
+        state_dict: Dict[str, torch.Tensor] = {}
+        with safe_open(path, framework="pt", device="cpu") as handle:
+            for key in handle.keys():
+                state_dict[key] = handle.get_tensor(key)
+        return state_dict
+    return comfy.utils.load_torch_file(str(path), safe_load=True)
+
+
+def _fuse_glu_lora(glu_weights: Dict[str, torch.Tensor]) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
+    if "w1_A" not in glu_weights or "w3_A" not in glu_weights:
+        return None, None, None
+    a_w1, b_w1 = glu_weights["w1_A"], glu_weights["w1_B"]
+    a_w3, b_w3 = glu_weights["w3_A"], glu_weights["w3_B"]
+    if a_w1.shape[1] != a_w3.shape[1]:
+        return None, None, None
+    a_fused = torch.cat([a_w1, a_w3], dim=0)
+    out1, out3 = b_w1.shape[0], b_w3.shape[0]
+    rank1, rank3 = b_w1.shape[1], b_w3.shape[1]
+    b_fused = torch.zeros(out1 + out3, rank1 + rank3, dtype=b_w1.dtype, device=b_w1.device)
+    b_fused[:out1, :rank1] = b_w1
+    b_fused[out1:, rank1:] = b_w3
+    return a_fused, b_fused, glu_weights.get("w1_alpha")
+
+
+def _fuse_qkv_lora(qkv_weights: Dict[str, torch.Tensor], model: Optional[nn.Module] = None, base_key: Optional[str] = None) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
+    required_keys = ["Q_A", "Q_B", "K_A", "K_B", "V_A", "V_B"]
+    if not all(key in qkv_weights for key in required_keys):
+        return None, None, None
+    a_q, a_k, a_v = qkv_weights["Q_A"], qkv_weights["K_A"], qkv_weights["V_A"]
+    b_q, b_k, b_v = qkv_weights["Q_B"], qkv_weights["K_B"], qkv_weights["V_B"]
+    if not (a_q.shape == a_k.shape == a_v.shape):
+        return None, None, None
+    if not (b_q.shape[1] == b_k.shape[1] == b_v.shape[1]):
+        return None, None, None
+
+    out_features = None
+    if model is not None and base_key is not None:
+        _, module = _resolve_module_name(model, base_key)
+        out_features = getattr(module, "out_features", None) if module is not None else None
+
+    alpha_fused = None
+    alpha_q = qkv_weights.get("Q_alpha")
+    alpha_k = qkv_weights.get("K_alpha")
+    alpha_v = qkv_weights.get("V_alpha")
+    if alpha_q is not None and alpha_k is not None and alpha_v is not None and alpha_q.item() == alpha_k.item() == alpha_v.item():
+        alpha_fused = alpha_q
+
+    a_fused = torch.cat([a_q, a_k, a_v], dim=0)
+    rank = b_q.shape[1]
+    out_q, out_k, out_v = b_q.shape[0], b_k.shape[0], b_v.shape[0]
+    total_out = out_features if out_features is not None else out_q + out_k + out_v
+    b_fused = torch.zeros(total_out, 3 * rank, dtype=b_q.dtype, device=b_q.device)
+    b_fused[:out_q, :rank] = b_q
+    b_fused[out_q:out_q + out_k, rank:2 * rank] = b_k
+    b_fused[out_q + out_k:out_q + out_k + out_v, 2 * rank:] = b_v
+    return a_fused, b_fused, alpha_fused
+
+
+def _handle_proj_out_split(lora_dict: Dict[str, Dict[str, torch.Tensor]], base_key: str, model: nn.Module) -> Tuple[Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]], List[str]]:
+    result: Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]] = {}
+    consumed: List[str] = []
+    match = re.search(r"single_transformer_blocks\.(\d+)", base_key)
+    if not match or base_key not in lora_dict:
+        return result, consumed
+    block_idx = match.group(1)
+    block = _get_module_by_name(model, f"single_transformer_blocks.{block_idx}")
+    if block is None:
+        return result, consumed
+    a_full = lora_dict[base_key].get("A")
+    b_full = lora_dict[base_key].get("B")
+    alpha = lora_dict[base_key].get("alpha")
+    attn_to_out = getattr(getattr(block, "attn", None), "to_out", None)
+    mlp_fc2 = getattr(block, "mlp_fc2", None)
+    if a_full is None or b_full is None or attn_to_out is None or mlp_fc2 is None:
+        return result, consumed
+    attn_in = getattr(attn_to_out, "in_features", None)
+    mlp_in = getattr(mlp_fc2, "in_features", None)
+    if attn_in is None or mlp_in is None or a_full.shape[1] != attn_in + mlp_in:
+        return result, consumed
+    result[f"single_transformer_blocks.{block_idx}.attn.to_out"] = (a_full[:, :attn_in], b_full.clone(), alpha)
+    result[f"single_transformer_blocks.{block_idx}.mlp_fc2"] = (a_full[:, attn_in:], b_full.clone(), alpha)
+    consumed.append(base_key)
+    return result, consumed
+
+
+def _apply_lora_to_module(module: nn.Module, a_tensor: torch.Tensor, b_tensor: torch.Tensor, module_name: str, model: nn.Module) -> None:
+    if not hasattr(module, "in_features") or not hasattr(module, "out_features"):
+        raise ValueError(f"{module_name}: unsupported module without in/out features")
+    if a_tensor.shape[1] != module.in_features or b_tensor.shape[0] != module.out_features:
+        raise ValueError(f"{module_name}: LoRA shape mismatch")
+
+    if module.__class__.__name__ == "AWQW4A16Linear" and hasattr(module, "qweight"):
+        if not hasattr(module, "_lora_original_forward"):
+            module._lora_original_forward = module.forward
+        if not hasattr(module, "_nunchaku_lora_bundle"):
+            module._nunchaku_lora_bundle = []
+        module._nunchaku_lora_bundle.append((a_tensor, b_tensor))
+
+        def _awq_lora_forward(x, *args, **kwargs):
+            out = module._lora_original_forward(x, *args, **kwargs)
+            x_flat = x.reshape(-1, module.in_features)
+            for local_a, local_b in module._nunchaku_lora_bundle:
+                local_a = local_a.to(device=out.device, dtype=out.dtype)
+                local_b = local_b.to(device=out.device, dtype=out.dtype)
+                lora_term = (x_flat @ local_a.transpose(0, 1)) @ local_b.transpose(0, 1)
+                try:
+                    out = out + lora_term.reshape(out.shape)
+                except Exception:
+                    pass
+            return out
+
+        module.forward = _awq_lora_forward
+        if not hasattr(model, "_lora_slots"):
+            model._lora_slots = {}
+        model._lora_slots[module_name] = {"type": "awq_w4a16"}
+        return
+
+    if hasattr(module, "proj_down") and hasattr(module, "proj_up"):
+        proj_down = unpack_lowrank_weight(module.proj_down.data, down=True)
+        proj_up = unpack_lowrank_weight(module.proj_up.data, down=False)
+        base_rank = proj_down.shape[0] if proj_down.shape[1] == module.in_features else proj_down.shape[1]
+        if proj_down.shape[1] == module.in_features:
+            updated_down = torch.cat([proj_down, a_tensor], dim=0)
+            axis_down = 0
+        else:
+            updated_down = torch.cat([proj_down, a_tensor.T], dim=1)
+            axis_down = 1
+        updated_up = torch.cat([proj_up, b_tensor], dim=1)
+        module.proj_down.data = pack_lowrank_weight(updated_down, down=True)
+        module.proj_up.data = pack_lowrank_weight(updated_up, down=False)
+        module.rank = base_rank + a_tensor.shape[0]
+        if not hasattr(model, "_lora_slots"):
+            model._lora_slots = {}
+        model._lora_slots[module_name] = {
+            "type": "nunchaku",
+            "base_rank": base_rank,
+            "axis_down": axis_down,
+        }
+        return
+
+    if isinstance(module, nn.Linear):
+        if not hasattr(model, "_lora_slots"):
+            model._lora_slots = {}
+        if module_name not in model._lora_slots:
+            model._lora_slots[module_name] = {
+                "type": "linear",
+                "original_weight": module.weight.detach().cpu().clone(),
+            }
+        module.weight.data.add_((b_tensor @ a_tensor).to(dtype=module.weight.dtype, device=module.weight.device))
+        return
+
+    raise ValueError(f"{module_name}: unsupported module type {type(module)}")
+
+
+def reset_lora_v2(model: nn.Module) -> None:
+    slots = getattr(model, "_lora_slots", None)
+    if not slots:
+        return
+    for name, info in list(slots.items()):
+        module = _get_module_by_name(model, name)
+        if module is None:
+            continue
+        module_type = info.get("type", "nunchaku")
+        if module_type == "nunchaku":
+            base_rank = info["base_rank"]
+            proj_down = unpack_lowrank_weight(module.proj_down.data, down=True)
+            proj_up = unpack_lowrank_weight(module.proj_up.data, down=False)
+            if info.get("axis_down", 0) == 0:
+                proj_down = proj_down[:base_rank, :].clone()
+            else:
+                proj_down = proj_down[:, :base_rank].clone()
+            proj_up = proj_up[:, :base_rank].clone()
+            module.proj_down.data = pack_lowrank_weight(proj_down, down=True)
+            module.proj_up.data = pack_lowrank_weight(proj_up, down=False)
+            module.rank = base_rank
+        elif module_type == "linear" and "original_weight" in info:
+            module.weight.data.copy_(info["original_weight"].to(device=module.weight.device, dtype=module.weight.dtype))
+        elif module_type == "awq_w4a16":
+            if hasattr(module, "_lora_original_forward"):
+                module.forward = module._lora_original_forward
+            for attr in ("_lora_original_forward", "_nunchaku_lora_bundle"):
+                if hasattr(module, attr):
+                    delattr(module, attr)
+    model._lora_slots = {}
+
+
+def compose_loras_v2(model: nn.Module, lora_configs: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]], apply_awq_mod: bool = True) -> bool:
+    del apply_awq_mod  # retained for interface compatibility
+    reset_lora_v2(model)
+    aggregated_weights: Dict[str, List[Dict[str, object]]] = defaultdict(list)
+    saw_supported_format = False
+    unresolved_targets = 0
+
+    for index, (path_or_dict, strength) in enumerate(lora_configs):
+        if abs(strength) < 1e-5:
+            continue
+        lora_name = str(path_or_dict) if not isinstance(path_or_dict, dict) else f"lora_{index}"
+        lora_state_dict = _load_lora_state_dict(path_or_dict)
+        if not lora_state_dict or not _detect_lora_format(lora_state_dict):
+            logger.warning("Skipping unsupported Qwen LoRA: %s", lora_name)
+            continue
+        saw_supported_format = True
+
+        grouped_weights: Dict[str, Dict[str, torch.Tensor]] = defaultdict(dict)
+        for key, value in lora_state_dict.items():
+            parsed = _classify_and_map_key(key)
+            if parsed is None:
+                continue
+            group, base_key, component, ab = parsed
+            if component and ab:
+                grouped_weights[base_key][f"{component}_{ab}"] = value
+            else:
+                grouped_weights[base_key][ab] = value
+
+        processed_groups: Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]] = {}
+        handled: set[str] = set()
+        for base_key, weights in grouped_weights.items():
+            if base_key in handled:
+                continue
+            a_tensor = b_tensor = alpha = None
+            if "qkv" in base_key or "add_qkv_proj" in base_key:
+                a_tensor, b_tensor, alpha = _fuse_qkv_lora(weights, model=model, base_key=base_key)
+            elif "w1_A" in weights or "w3_A" in weights:
+                a_tensor, b_tensor, alpha = _fuse_glu_lora(weights)
+            elif ".proj_out" in base_key and "single_transformer_blocks" in base_key:
+                split_map, consumed = _handle_proj_out_split(grouped_weights, base_key, model)
+                processed_groups.update(split_map)
+                handled.update(consumed)
+                continue
+            else:
+                a_tensor, b_tensor, alpha = weights.get("A"), weights.get("B"), weights.get("alpha")
+            if a_tensor is not None and b_tensor is not None:
+                processed_groups[base_key] = (a_tensor, b_tensor, alpha)
+
+        for module_name, (a_tensor, b_tensor, alpha) in processed_groups.items():
+            aggregated_weights[module_name].append({
+                "A": a_tensor,
+                "B": b_tensor,
+                "alpha": alpha,
+                "strength": strength,
+            })
+
+    for module_name, weight_list in aggregated_weights.items():
+        resolved_name, module = _resolve_module_name(model, module_name)
+        if module is None:
+            logger.warning("Skipping unresolved Qwen LoRA target: %s", module_name)
+            unresolved_targets += 1
+            continue
+        all_a = []
+        all_b_scaled = []
+        for item in weight_list:
+            a_tensor = item["A"]
+            b_tensor = item["B"]
+            alpha = item["alpha"]
+            strength = float(item["strength"])
+            rank = a_tensor.shape[0]
+            scale = strength * ((alpha / rank) if alpha is not None else 1.0)
+            if module.__class__.__name__ == "AWQW4A16Linear" and hasattr(module, "qweight"):
+                target_dtype = torch.float16
+                target_device = module.qweight.device
+            elif hasattr(module, "proj_down"):
+                target_dtype = module.proj_down.dtype
+                target_device = module.proj_down.device
+            elif hasattr(module, "weight"):
+                target_dtype = module.weight.dtype
+                target_device = module.weight.device
+            else:
+                target_dtype = torch.float16
+                target_device = "cuda" if torch.cuda.is_available() else "cpu"
+            all_a.append(a_tensor.to(dtype=target_dtype, device=target_device))
+            all_b_scaled.append((b_tensor * scale).to(dtype=target_dtype, device=target_device))
+        if not all_a:
+            continue
+        _apply_lora_to_module(module, torch.cat(all_a, dim=0), torch.cat(all_b_scaled, dim=1), resolved_name, model)
+
+    slot_count = len(getattr(model, "_lora_slots", {}) or {})
+    logger.info(
+        "Qwen LoRA composition finished: requested=%d supported=%s applied_targets=%d unresolved=%d",
+        len(lora_configs),
+        saw_supported_format,
+        slot_count,
+        unresolved_targets,
+    )
+    return saw_supported_format
+
+
+class ComfyQwenImageWrapperLM(nn.Module):
+    def __init__(self, model: nn.Module, config=None, apply_awq_mod: bool = True):
+        super().__init__()
+        self.model = model
+        self.config = {} if config is None else config
+        self.dtype = next(model.parameters()).dtype
+        self.loras: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]] = []
+        self._applied_loras: Optional[List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]]] = None
+        self.apply_awq_mod = apply_awq_mod
+
+    def __getattr__(self, name):
+        try:
+            inner = object.__getattribute__(self, "_modules").get("model")
+        except (AttributeError, KeyError):
+            inner = None
+        if inner is None:
+            raise AttributeError(f"{type(self).__name__!s} has no attribute {name}")
+        if name == "model":
+            return inner
+        return getattr(inner, name)
+
+    def process_img(self, *args, **kwargs):
+        return self.model.process_img(*args, **kwargs)
+
+    def _ensure_composed(self):
+        if self._applied_loras != self.loras or (not self.loras and getattr(self.model, "_lora_slots", None)):
+            is_supported_format = compose_loras_v2(self.model, self.loras, apply_awq_mod=self.apply_awq_mod)
+            self._applied_loras = self.loras.copy()
+            has_slots = bool(getattr(self.model, "_lora_slots", None))
+            if self.loras and is_supported_format and not has_slots:
+                logger.warning("Qwen LoRA compose produced 0 target modules. Resetting and retrying once.")
+                reset_lora_v2(self.model)
+                compose_loras_v2(self.model, self.loras, apply_awq_mod=self.apply_awq_mod)
+                has_slots = bool(getattr(self.model, "_lora_slots", None))
+                logger.info("Qwen LoRA retry result: applied_targets=%d", len(getattr(self.model, "_lora_slots", {}) or {}))
+
+            offload_manager = getattr(self.model, "offload_manager", None)
+            if offload_manager is not None:
+                offload_settings = {
+                    "num_blocks_on_gpu": getattr(offload_manager, "num_blocks_on_gpu", 1),
+                    "use_pin_memory": getattr(offload_manager, "use_pin_memory", False),
+                }
+                logger.info(
+                    "Rebuilding Qwen offload manager after LoRA compose: num_blocks_on_gpu=%s use_pin_memory=%s",
+                    offload_settings["num_blocks_on_gpu"],
+                    offload_settings["use_pin_memory"],
+                )
+                self.model.set_offload(False)
+                self.model.set_offload(True, **offload_settings)
+
+    def forward(self, *args, **kwargs):
+        self._ensure_composed()
+        return self.model(*args, **kwargs)
+
+
+def _get_qwen_wrapper_and_transformer(model):
+    model_wrapper = model.model.diffusion_model
+    if hasattr(model_wrapper, "model") and hasattr(model_wrapper, "loras"):
+        transformer = model_wrapper.model
+        if transformer.__class__.__name__.endswith("NunchakuQwenImageTransformer2DModel"):
+            return model_wrapper, transformer
+    if model_wrapper.__class__.__name__.endswith("NunchakuQwenImageTransformer2DModel"):
+        wrapped_model = ComfyQwenImageWrapperLM(model_wrapper, getattr(model_wrapper, "config", {}))
+        model.model.diffusion_model = wrapped_model
+        return wrapped_model, wrapped_model.model
+    raise TypeError(f"This LoRA loader only works with Nunchaku Qwen Image models, but got {type(model_wrapper).__name__}.")
+
+
+def nunchaku_load_qwen_loras(model, lora_configs: List[Tuple[str, float]], apply_awq_mod: bool = True):
+    model_wrapper, transformer = _get_qwen_wrapper_and_transformer(model)
+    model_wrapper.apply_awq_mod = apply_awq_mod
+
+    saved_config = None
+    if hasattr(model, "model") and hasattr(model.model, "model_config"):
+        saved_config = model.model.model_config
+        model.model.model_config = None
+
+    model_wrapper.model = None
+    try:
+        ret_model = copy.deepcopy(model)
+    finally:
+        if saved_config is not None:
+            model.model.model_config = saved_config
+        model_wrapper.model = transformer
+
+    ret_model_wrapper = ret_model.model.diffusion_model
+    if saved_config is not None:
+        ret_model.model.model_config = saved_config
+    ret_model_wrapper.model = transformer
+    ret_model_wrapper.apply_awq_mod = apply_awq_mod
+    ret_model_wrapper.loras = list(getattr(model_wrapper, "loras", []))
+
+    for lora_name, lora_strength in lora_configs:
+        lora_path = lora_name if os.path.isfile(lora_name) else folder_paths.get_full_path("loras", lora_name)
+        if not lora_path or not os.path.isfile(lora_path):
+            logger.warning("Skipping Qwen LoRA '%s' because it could not be found", lora_name)
+            continue
+        ret_model_wrapper.loras.append((lora_path, lora_strength))
+
+    return ret_model

py/nodes/save_image.py

@@ -72,6 +72,13 @@ class SaveImageLM:
                         "tooltip": "Embeds the complete workflow data into the image metadata. Only works with PNG and WebP formats.",
                     },
                 ),
+                "save_with_metadata": (
+                    "BOOLEAN",
+                    {
+                        "default": True,
+                        "tooltip": "When enabled, embeds generation parameters into the saved image metadata. Disable to skip writing generation metadata.",
+                    },
+                ),
                 "add_counter_to_filename": (
                     "BOOLEAN",
                     {
@@ -350,6 +357,7 @@ class SaveImageLM:
         lossless_webp=True,
         quality=100,
         embed_workflow=False,
+        save_with_metadata=True,
         add_counter_to_filename=True,
     ):
         """Save images with metadata"""
@@ -421,7 +429,7 @@ class SaveImageLM:
             try:
                 if file_format == "png":
                     assert pnginfo is not None
-                    if metadata:
+                    if save_with_metadata and metadata:
                         pnginfo.add_text("parameters", metadata)
                     if embed_workflow and extra_pnginfo is not None:
                         workflow_json = json.dumps(extra_pnginfo["workflow"])
@@ -430,7 +438,7 @@ class SaveImageLM:
                     img.save(file_path, format="PNG", **save_kwargs)
                 elif file_format == "jpeg":
                     # For JPEG, use piexif
-                    if metadata:
+                    if save_with_metadata and metadata:
                         try:
                             exif_dict = {
                                 "Exif": {
@@ -448,7 +456,7 @@ class SaveImageLM:
                         # For WebP, use piexif for metadata
                         exif_dict = {}
 
-                        if metadata:
+                        if save_with_metadata and metadata:
                             exif_dict["Exif"] = {
                                 piexif.ExifIFD.UserComment: b"UNICODE\0"
                                 + metadata.encode("utf-16be")
@@ -489,6 +497,7 @@ class SaveImageLM:
         lossless_webp=True,
         quality=100,
         embed_workflow=False,
+        save_with_metadata=True,
         add_counter_to_filename=True,
     ):
         """Process and save image with metadata"""
@@ -516,7 +525,11 @@ class SaveImageLM:
             lossless_webp,
             quality,
             embed_workflow,
+            save_with_metadata,
             add_counter_to_filename,
         )
 
-        return (images,)
+        return {
+            "result": (images,),
+            "ui": {"images": results},
+        }

py/nodes/utils.py

@@ -158,3 +158,24 @@ def nunchaku_load_lora(model, lora_name, lora_strength):
             ret_model.model.model_config.unet_config["in_channels"] = new_in_channels
 
     return ret_model
+
+
+def detect_nunchaku_model_kind(model):
+    """Return the supported Nunchaku model kind for a Comfy model, if any."""
+    try:
+        model_wrapper = model.model.diffusion_model
+    except (AttributeError, TypeError):
+        return None
+
+    wrapper_name = model_wrapper.__class__.__name__
+    if wrapper_name == "ComfyFluxWrapper":
+        return "flux"
+
+    inner_model = getattr(model_wrapper, "model", None)
+    inner_name = inner_model.__class__.__name__ if inner_model is not None else ""
+    if wrapper_name.endswith("NunchakuQwenImageTransformer2DModel"):
+        return "qwen_image"
+    if inner_name.endswith("NunchakuQwenImageTransformer2DModel"):
+        return "qwen_image"
+
+    return None

py/recipes/parsers/civitai_image.py

@@ -42,6 +42,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 "height",
                 "Model",
                 "Model hash",
+                "modelVersionIds",
             )
             return any(key in payload for key in civitai_image_fields)
 
@@ -429,6 +430,65 @@ 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
+            ):
+                for version_id in metadata["modelVersionIds"]:
+                    version_id_str = str(version_id)
+
+                    # Skip if we've already added this LoRA by version ID
+                    if version_id_str in added_loras:
+                        continue
+
+                    # Initialize lora entry with version ID
+                    lora_entry = {
+                        "id": version_id,
+                        "modelId": 0,
+                        "name": "Unknown LoRA",
+                        "version": "",
+                        "type": "lora",
+                        "weight": 1.0,
+                        "existsLocally": False,
+                        "thumbnailUrl": "/loras_static/images/no-preview.png",
+                        "baseModel": "",
+                        "size": 0,
+                        "downloadUrl": "",
+                        "isDeleted": False,
+                    }
+
+                    # Fetch model info from Civitai
+                    if metadata_provider and version_id_str:
+                        try:
+                            civitai_info = (
+                                await metadata_provider.get_model_version_info(
+                                    version_id_str
+                                )
+                            )
+
+                            populated_entry = await self.populate_lora_from_civitai(
+                                lora_entry,
+                                civitai_info,
+                                recipe_scanner,
+                                base_model_counts,
+                            )
+
+                            if populated_entry is None:
+                                continue  # Skip invalid LoRA types
+
+                            lora_entry = populated_entry
+                        except Exception as e:
+                            logger.error(
+                                f"Error fetching Civitai info for model version {version_id}: {e}"
+                            )
+
+                    # Track this LoRA for deduplication
+                    if version_id_str:
+                        added_loras[version_id_str] = len(result["loras"])
+
+                    result["loras"].append(lora_entry)
+
             # If we found LoRA hashes in the metadata but haven't already
             # populated entries for them, fall back to creating LoRAs from
             # the hashes section. Some Civitai image responses only include

py/routes/handlers/base_model_handlers.py

@@ -0,0 +1,141 @@
+"""Handlers for base model related endpoints."""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Awaitable, Callable, Dict
+
+from aiohttp import web
+
+from ...services.civitai_base_model_service import get_civitai_base_model_service
+
+logger = logging.getLogger(__name__)
+
+
+class BaseModelHandlerSet:
+    """Collection of handlers for base model operations."""
+
+    def __init__(
+        self,
+        base_model_service_factory: Callable[[], Any] = get_civitai_base_model_service,
+    ) -> None:
+        self._base_model_service_factory = base_model_service_factory
+
+    def to_route_mapping(
+        self,
+    ) -> Dict[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
+        """Return mapping of route names to handler methods."""
+        return {
+            "get_base_models": self.get_base_models,
+            "refresh_base_models": self.refresh_base_models,
+            "get_base_model_categories": self.get_base_model_categories,
+            "get_base_model_cache_status": self.get_base_model_cache_status,
+        }
+
+    async def get_base_models(self, request: web.Request) -> web.Response:
+        """Get merged base models (hardcoded + remote from Civitai).
+
+        Query Parameters:
+            refresh: If 'true', force refresh from API
+
+        Returns:
+            JSON response with:
+            - models: List of base model names
+            - source: 'cache', 'api', or 'fallback'
+            - last_updated: ISO timestamp
+            - counts: hardcoded_count, remote_count, merged_count
+        """
+        try:
+            service = await self._base_model_service_factory()
+
+            # Check for refresh parameter
+            force_refresh = request.query.get("refresh", "").lower() == "true"
+
+            result = await service.get_base_models(force_refresh=force_refresh)
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "data": result,
+                }
+            )
+
+        except Exception as e:
+            logger.error(f"Error in get_base_models: {e}")
+            return web.json_response(
+                {"success": False, "error": str(e)},
+                status=500,
+            )
+
+    async def refresh_base_models(self, request: web.Request) -> web.Response:
+        """Force refresh base models from Civitai API.
+
+        Returns:
+            JSON response with refreshed data
+        """
+        try:
+            service = await self._base_model_service_factory()
+            result = await service.refresh_cache()
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "data": result,
+                    "message": "Base models cache refreshed successfully",
+                }
+            )
+
+        except Exception as e:
+            logger.error(f"Error in refresh_base_models: {e}")
+            return web.json_response(
+                {"success": False, "error": str(e)},
+                status=500,
+            )
+
+    async def get_base_model_categories(self, request: web.Request) -> web.Response:
+        """Get categorized base models.
+
+        Returns:
+            JSON response with categorized models
+        """
+        try:
+            service = await self._base_model_service_factory()
+            categories = service.get_model_categories()
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "data": categories,
+                }
+            )
+
+        except Exception as e:
+            logger.error(f"Error in get_base_model_categories: {e}")
+            return web.json_response(
+                {"success": False, "error": str(e)},
+                status=500,
+            )
+
+    async def get_base_model_cache_status(self, request: web.Request) -> web.Response:
+        """Get cache status for base models.
+
+        Returns:
+            JSON response with cache status
+        """
+        try:
+            service = await self._base_model_service_factory()
+            status = service.get_cache_status()
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "data": status,
+                }
+            )
+
+        except Exception as e:
+            logger.error(f"Error in get_base_model_cache_status: {e}")
+            return web.json_response(
+                {"success": False, "error": str(e)},
+                status=500,
+            )

py/routes/handlers/misc_handlers.py

@@ -40,6 +40,7 @@ from ...utils.civitai_utils import rewrite_preview_url
 from ...utils.example_images_paths import is_valid_example_images_root
 from ...utils.lora_metadata import extract_trained_words
 from ...utils.usage_stats import UsageStats
+from .base_model_handlers import BaseModelHandlerSet
 
 logger = logging.getLogger(__name__)
 
@@ -1618,6 +1619,7 @@ class MiscHandlerSet:
         custom_words: CustomWordsHandler,
         supporters: SupportersHandler,
         example_workflows: ExampleWorkflowsHandler,
+        base_model: BaseModelHandlerSet,
     ) -> None:
         self.health = health
         self.settings = settings
@@ -1632,6 +1634,7 @@ class MiscHandlerSet:
         self.custom_words = custom_words
         self.supporters = supporters
         self.example_workflows = example_workflows
+        self.base_model = base_model
 
     def to_route_mapping(
         self,
@@ -1663,6 +1666,11 @@ 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,
+            # Base model handlers
+            "get_base_models": self.base_model.get_base_models,
+            "refresh_base_models": self.base_model.refresh_base_models,
+            "get_base_model_categories": self.base_model.get_base_model_categories,
+            "get_base_model_cache_status": self.base_model.get_base_model_cache_status,
         }
 
 

py/routes/handlers/recipe_handlers.py

@@ -81,6 +81,7 @@ class RecipeHandlerSet:
             "bulk_delete": self.management.bulk_delete,
             "save_recipe_from_widget": self.management.save_recipe_from_widget,
             "get_recipes_for_lora": self.query.get_recipes_for_lora,
+            "get_recipes_for_checkpoint": self.query.get_recipes_for_checkpoint,
             "scan_recipes": self.query.scan_recipes,
             "move_recipe": self.management.move_recipe,
             "repair_recipes": self.management.repair_recipes,
@@ -218,6 +219,7 @@ class RecipeListingHandler:
                 filters["tags"] = tag_filters
 
             lora_hash = request.query.get("lora_hash")
+            checkpoint_hash = request.query.get("checkpoint_hash")
 
             result = await recipe_scanner.get_paginated_data(
                 page=page,
@@ -227,6 +229,7 @@ class RecipeListingHandler:
                 filters=filters,
                 search_options=search_options,
                 lora_hash=lora_hash,
+                checkpoint_hash=checkpoint_hash,
                 folder=folder,
                 recursive=recursive,
             )
@@ -423,6 +426,28 @@ class RecipeQueryHandler:
             self._logger.error("Error getting recipes for Lora: %s", exc)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def get_recipes_for_checkpoint(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+            recipe_scanner = self._recipe_scanner_getter()
+            if recipe_scanner is None:
+                raise RuntimeError("Recipe scanner unavailable")
+
+            checkpoint_hash = request.query.get("hash")
+            if not checkpoint_hash:
+                return web.json_response(
+                    {"success": False, "error": "Checkpoint hash is required"},
+                    status=400,
+                )
+
+            matching_recipes = await recipe_scanner.get_recipes_for_checkpoint(
+                checkpoint_hash
+            )
+            return web.json_response({"success": True, "recipes": matching_recipes})
+        except Exception as exc:
+            self._logger.error("Error getting recipes for checkpoint: %s", exc)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
     async def scan_recipes(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()

py/routes/misc_route_registrar.py

@@ -56,6 +56,15 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition(
         "GET", "/api/lm/example-workflows/{filename}", "get_example_workflow"
     ),
+    # Base model management routes
+    RouteDefinition("GET", "/api/lm/base-models", "get_base_models"),
+    RouteDefinition("POST", "/api/lm/base-models/refresh", "refresh_base_models"),
+    RouteDefinition(
+        "GET", "/api/lm/base-models/categories", "get_base_model_categories"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/base-models/cache-status", "get_base_model_cache_status"
+    ),
 )
 
 

py/routes/misc_routes.py

@@ -35,6 +35,7 @@ from .handlers.misc_handlers import (
     UsageStatsHandler,
     build_service_registry_adapter,
 )
+from .handlers.base_model_handlers import BaseModelHandlerSet
 from .misc_route_registrar import MiscRouteRegistrar
 
 logger = logging.getLogger(__name__)
@@ -128,6 +129,7 @@ class MiscRoutes:
         custom_words = CustomWordsHandler()
         supporters = SupportersHandler()
         example_workflows = ExampleWorkflowsHandler()
+        base_model = BaseModelHandlerSet()
 
         return self._handler_set_factory(
             health=health,
@@ -143,6 +145,7 @@ class MiscRoutes:
             custom_words=custom_words,
             supporters=supporters,
             example_workflows=example_workflows,
+            base_model=base_model,
         )
 
 

py/routes/recipe_route_registrar.py

@@ -51,6 +51,9 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
         "POST", "/api/lm/recipes/save-from-widget", "save_recipe_from_widget"
     ),
     RouteDefinition("GET", "/api/lm/recipes/for-lora", "get_recipes_for_lora"),
+    RouteDefinition(
+        "GET", "/api/lm/recipes/for-checkpoint", "get_recipes_for_checkpoint"
+    ),
     RouteDefinition("GET", "/api/lm/recipes/scan", "scan_recipes"),
     RouteDefinition("POST", "/api/lm/recipes/repair", "repair_recipes"),
     RouteDefinition("POST", "/api/lm/recipes/cancel-repair", "cancel_repair"),

py/services/civitai_base_model_service.py

@@ -0,0 +1,430 @@
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import re
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+from ..utils.constants import SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS
+from .downloader import get_downloader
+
+logger = logging.getLogger(__name__)
+
+
+class CivitaiBaseModelService:
+    """Service for fetching and managing Civitai base models.
+
+    This service provides:
+    - Fetching base models from Civitai API
+    - Caching with TTL (7 days default)
+    - Merging hardcoded and remote base models
+    - Generating abbreviations for new/unknown models
+    """
+
+    _instance: Optional[CivitaiBaseModelService] = None
+    _lock = asyncio.Lock()
+
+    # Default TTL for cache in seconds (7 days)
+    DEFAULT_CACHE_TTL = 7 * 24 * 60 * 60
+
+    # Civitai API endpoint for enums
+    CIVITAI_ENUMS_URL = "https://civitai.com/api/v1/enums"
+
+    @classmethod
+    async def get_instance(cls) -> CivitaiBaseModelService:
+        """Get singleton instance of the service."""
+        async with cls._lock:
+            if cls._instance is None:
+                cls._instance = cls()
+            return cls._instance
+
+    def __init__(self):
+        """Initialize the service."""
+        if hasattr(self, "_initialized"):
+            return
+        self._initialized = True
+
+        # Cache storage
+        self._cache: Optional[Dict[str, Any]] = None
+        self._cache_timestamp: Optional[datetime] = None
+        self._cache_ttl = self.DEFAULT_CACHE_TTL
+
+        # Hardcoded models for fallback
+        self._hardcoded_models = set(SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS)
+
+        logger.info("CivitaiBaseModelService initialized")
+
+    async def get_base_models(self, force_refresh: bool = False) -> Dict[str, Any]:
+        """Get merged base models (hardcoded + remote).
+
+        Args:
+            force_refresh: If True, fetch from API regardless of cache state.
+
+        Returns:
+            Dictionary containing:
+            - models: List of merged base model names
+            - source: 'cache', 'api', or 'fallback'
+            - last_updated: ISO timestamp of last successful API fetch
+            - hardcoded_count: Number of hardcoded models
+            - remote_count: Number of remote models
+            - merged_count: Total unique models
+        """
+        # Check if cache is valid
+        if not force_refresh and self._is_cache_valid():
+            logger.debug("Returning cached base models")
+            return self._build_response("cache")
+
+        # Try to fetch from API
+        try:
+            remote_models = await self._fetch_from_civitai()
+            if remote_models:
+                self._update_cache(remote_models)
+                return self._build_response("api")
+        except Exception as e:
+            logger.error(f"Failed to fetch base models from Civitai: {e}")
+
+        # Fallback to hardcoded models
+        return self._build_response("fallback")
+
+    async def refresh_cache(self) -> Dict[str, Any]:
+        """Force refresh the cache from Civitai API.
+
+        Returns:
+            Response dict same as get_base_models()
+        """
+        return await self.get_base_models(force_refresh=True)
+
+    def get_cache_status(self) -> Dict[str, Any]:
+        """Get current cache status.
+
+        Returns:
+            Dictionary containing:
+            - has_cache: Whether cache exists
+            - last_updated: ISO timestamp or None
+            - is_expired: Whether cache is expired
+            - ttl_seconds: TTL in seconds
+            - age_seconds: Age of cache in seconds (if exists)
+        """
+        if self._cache is None or self._cache_timestamp is None:
+            return {
+                "has_cache": False,
+                "last_updated": None,
+                "is_expired": True,
+                "ttl_seconds": self._cache_ttl,
+                "age_seconds": None,
+            }
+
+        age = (datetime.now(timezone.utc) - self._cache_timestamp).total_seconds()
+        return {
+            "has_cache": True,
+            "last_updated": self._cache_timestamp.isoformat(),
+            "is_expired": age > self._cache_ttl,
+            "ttl_seconds": self._cache_ttl,
+            "age_seconds": int(age),
+        }
+
+    def generate_abbreviation(self, model_name: str) -> str:
+        """Generate abbreviation for a base model name.
+
+        Algorithm:
+        1. Extract version patterns (e.g., "2.5" from "Wan Video 2.5")
+        2. Extract main acronym (e.g., "SD" from "SD 1.5")
+        3. Handle special cases (Flux, Wan, etc.)
+        4. Fallback to first letters of words (max 4 chars)
+
+        Args:
+            model_name: Full base model name
+
+        Returns:
+            Generated abbreviation (max 4 characters)
+        """
+        if not model_name or not isinstance(model_name, str):
+            return "OTH"
+
+        name = model_name.strip()
+        if not name:
+            return "OTH"
+
+        # Check if it's already in hardcoded abbreviations
+        # This is a simplified check - in practice you'd have a mapping
+        lower_name = name.lower()
+
+        # Special cases
+        special_cases = {
+            "sd 1.4": "SD1",
+            "sd 1.5": "SD1",
+            "sd 1.5 lcm": "SD1",
+            "sd 1.5 hyper": "SD1",
+            "sd 2.0": "SD2",
+            "sd 2.1": "SD2",
+            "sd 3": "SD3",
+            "sd 3.5": "SD3",
+            "sd 3.5 medium": "SD3",
+            "sd 3.5 large": "SD3",
+            "sd 3.5 large turbo": "SD3",
+            "sdxl 1.0": "XL",
+            "sdxl lightning": "XL",
+            "sdxl hyper": "XL",
+            "flux.1 d": "F1D",
+            "flux.1 s": "F1S",
+            "flux.1 krea": "F1KR",
+            "flux.1 kontext": "F1KX",
+            "flux.2 d": "F2D",
+            "flux.2 klein 9b": "FK9",
+            "flux.2 klein 9b-base": "FK9B",
+            "flux.2 klein 4b": "FK4",
+            "flux.2 klein 4b-base": "FK4B",
+            "auraflow": "AF",
+            "chroma": "CHR",
+            "pixart a": "PXA",
+            "pixart e": "PXE",
+            "hunyuan 1": "HY",
+            "hunyuan video": "HYV",
+            "lumina": "L",
+            "kolors": "KLR",
+            "noobai": "NAI",
+            "illustrious": "IL",
+            "pony": "PONY",
+            "pony v7": "PNY7",
+            "hidream": "HID",
+            "qwen": "QWEN",
+            "zimageturbo": "ZIT",
+            "zimagebase": "ZIB",
+            "anima": "ANI",
+            "svd": "SVD",
+            "ltxv": "LTXV",
+            "ltxv2": "LTV2",
+            "ltxv 2.3": "LTX",
+            "cogvideox": "CVX",
+            "mochi": "MCHI",
+            "wan video": "WAN",
+            "wan video 1.3b t2v": "WAN",
+            "wan video 14b t2v": "WAN",
+            "wan video 14b i2v 480p": "WAN",
+            "wan video 14b i2v 720p": "WAN",
+            "wan video 2.2 ti2v-5b": "WAN",
+            "wan video 2.2 t2v-a14b": "WAN",
+            "wan video 2.2 i2v-a14b": "WAN",
+            "wan video 2.5 t2v": "WAN",
+            "wan video 2.5 i2v": "WAN",
+        }
+
+        if lower_name in special_cases:
+            return special_cases[lower_name]
+
+        # Try to extract acronym from version pattern
+        # e.g., "Model Name 2.5" -> "MN25"
+        version_match = re.search(r"(\d+(?:\.\d+)?)", name)
+        version = version_match.group(1) if version_match else ""
+
+        # Remove version and common words
+        words = re.sub(r"\d+(?:\.\d+)?", "", name)
+        words = re.sub(
+            r"\b(model|video|diffusion|checkpoint|textualinversion)\b",
+            "",
+            words,
+            flags=re.I,
+        )
+        words = words.strip()
+
+        # Get first letters of remaining words
+        tokens = re.findall(r"[A-Za-z]+", words)
+        if tokens:
+            # Build abbreviation from first letters
+            abbrev = "".join(token[0].upper() for token in tokens)
+            # Add version if present
+            if version:
+                # Clean version (remove dots for abbreviation)
+                version_clean = version.replace(".", "")
+                abbrev = abbrev[: 4 - len(version_clean)] + version_clean
+            return abbrev[:4]
+
+        # Final fallback: just take first 4 alphanumeric chars
+        alphanumeric = re.sub(r"[^A-Za-z0-9]", "", name)
+        if alphanumeric:
+            return alphanumeric[:4].upper()
+
+        return "OTH"
+
+    async def _fetch_from_civitai(self) -> Optional[Set[str]]:
+        """Fetch base models from Civitai API.
+
+        Returns:
+            Set of base model names, or None if failed
+        """
+        try:
+            downloader = await get_downloader()
+            success, result = await downloader.make_request(
+                "GET",
+                self.CIVITAI_ENUMS_URL,
+                use_auth=False,  # enums endpoint doesn't require auth
+            )
+
+            if not success:
+                logger.warning(f"Failed to fetch enums from Civitai: {result}")
+                return None
+
+            if isinstance(result, str):
+                data = json.loads(result)
+            else:
+                data = result
+
+            # Extract base models from response
+            base_models = set()
+
+            # Use ActiveBaseModel if available (recommended active models)
+            if "ActiveBaseModel" in data:
+                base_models.update(data["ActiveBaseModel"])
+                logger.info(f"Fetched {len(base_models)} models from ActiveBaseModel")
+            # Fallback to full BaseModel list
+            elif "BaseModel" in data:
+                base_models.update(data["BaseModel"])
+                logger.info(f"Fetched {len(base_models)} models from BaseModel")
+            else:
+                logger.warning("No base model data found in Civitai response")
+                return None
+
+            return base_models
+
+        except Exception as e:
+            logger.error(f"Error fetching from Civitai: {e}")
+            return None
+
+    def _update_cache(self, remote_models: Set[str]) -> None:
+        """Update internal cache with fetched models.
+
+        Args:
+            remote_models: Set of base model names from API
+        """
+        self._cache = {
+            "remote_models": sorted(remote_models),
+            "hardcoded_models": sorted(self._hardcoded_models),
+        }
+        self._cache_timestamp = datetime.now(timezone.utc)
+        logger.info(f"Cache updated with {len(remote_models)} remote models")
+
+    def _is_cache_valid(self) -> bool:
+        """Check if current cache is valid (not expired).
+
+        Returns:
+            True if cache exists and is not expired
+        """
+        if self._cache is None or self._cache_timestamp is None:
+            return False
+
+        age = (datetime.now(timezone.utc) - self._cache_timestamp).total_seconds()
+        return age <= self._cache_ttl
+
+    def _build_response(self, source: str) -> Dict[str, Any]:
+        """Build response dictionary.
+
+        Args:
+            source: 'cache', 'api', or 'fallback'
+
+        Returns:
+            Response dictionary
+        """
+        if source == "fallback" or self._cache is None:
+            # Use only hardcoded models
+            merged = sorted(self._hardcoded_models)
+            return {
+                "models": merged,
+                "source": source,
+                "last_updated": None,
+                "hardcoded_count": len(self._hardcoded_models),
+                "remote_count": 0,
+                "merged_count": len(merged),
+            }
+
+        # Merge hardcoded and remote models
+        remote_set = set(self._cache.get("remote_models", []))
+        merged = sorted(self._hardcoded_models | remote_set)
+
+        return {
+            "models": merged,
+            "source": source,
+            "last_updated": self._cache_timestamp.isoformat()
+            if self._cache_timestamp
+            else None,
+            "hardcoded_count": len(self._hardcoded_models),
+            "remote_count": len(remote_set),
+            "merged_count": len(merged),
+        }
+
+    def get_model_categories(self) -> Dict[str, List[str]]:
+        """Get categorized base models.
+
+        Returns:
+            Dictionary mapping category names to lists of model names
+        """
+        # Define category patterns
+        categories = {
+            "Stable Diffusion 1.x": ["SD 1.4", "SD 1.5", "SD 1.5 LCM", "SD 1.5 Hyper"],
+            "Stable Diffusion 2.x": ["SD 2.0", "SD 2.1"],
+            "Stable Diffusion 3.x": [
+                "SD 3",
+                "SD 3.5",
+                "SD 3.5 Medium",
+                "SD 3.5 Large",
+                "SD 3.5 Large Turbo",
+            ],
+            "SDXL": ["SDXL 1.0", "SDXL Lightning", "SDXL Hyper"],
+            "Flux Models": [
+                "Flux.1 D",
+                "Flux.1 S",
+                "Flux.1 Krea",
+                "Flux.1 Kontext",
+                "Flux.2 D",
+                "Flux.2 Klein 9B",
+                "Flux.2 Klein 9B-base",
+                "Flux.2 Klein 4B",
+                "Flux.2 Klein 4B-base",
+            ],
+            "Video Models": [
+                "SVD",
+                "LTXV",
+                "LTXV2",
+                "LTXV 2.3",
+                "CogVideoX",
+                "Mochi",
+                "Hunyuan Video",
+                "Wan Video",
+                "Wan Video 1.3B t2v",
+                "Wan Video 14B t2v",
+                "Wan Video 14B i2v 480p",
+                "Wan Video 14B i2v 720p",
+                "Wan Video 2.2 TI2V-5B",
+                "Wan Video 2.2 T2V-A14B",
+                "Wan Video 2.2 I2V-A14B",
+                "Wan Video 2.5 T2V",
+                "Wan Video 2.5 I2V",
+            ],
+            "Other Models": [
+                "Illustrious",
+                "Pony",
+                "Pony V7",
+                "HiDream",
+                "Qwen",
+                "AuraFlow",
+                "Chroma",
+                "ZImageTurbo",
+                "ZImageBase",
+                "PixArt a",
+                "PixArt E",
+                "Hunyuan 1",
+                "Lumina",
+                "Kolors",
+                "NoobAI",
+                "Anima",
+            ],
+        }
+
+        return categories
+
+
+# Convenience function for getting the singleton instance
+async def get_civitai_base_model_service() -> CivitaiBaseModelService:
+    """Get the singleton instance of CivitaiBaseModelService."""
+    return await CivitaiBaseModelService.get_instance()

py/services/download_manager.py

@@ -13,10 +13,11 @@ from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
 from ..utils.constants import (
     CARD_PREVIEW_WIDTH,
     DIFFUSION_MODEL_BASE_MODELS,
+    SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS,
     VALID_LORA_TYPES,
 )
 from ..utils.civitai_utils import rewrite_preview_url
-from ..utils.preview_selection import select_preview_media
+from ..utils.preview_selection import resolve_mature_threshold, select_preview_media
 from ..utils.utils import sanitize_folder_name
 from ..utils.exif_utils import ExifUtils
 from ..utils.metadata_manager import MetadataManager
@@ -228,7 +229,9 @@ class DownloadManager:
                     # Update status based on result
                     if task_id in self._active_downloads:
                         self._active_downloads[task_id]["status"] = (
-                            "completed" if result["success"] else "failed"
+                            result.get("status", "completed")
+                            if result["success"]
+                            else "failed"
                         )
                         if not result["success"]:
                             self._active_downloads[task_id]["error"] = result.get(
@@ -352,10 +355,54 @@ class DownloadManager:
                     "error": f'Model type "{model_type_from_info}" is not supported for download',
                 }
 
+            excluded_base_models = get_settings_manager().get_download_skip_base_models()
+            base_model_value = version_info.get("baseModel", "")
+            if (
+                isinstance(base_model_value, str)
+                and base_model_value in SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS
+                and base_model_value in excluded_base_models
+            ):
+                file_name = ""
+                files = version_info.get("files")
+                if isinstance(files, list):
+                    primary_file = next(
+                        (
+                            file_info
+                            for file_info in files
+                            if isinstance(file_info, dict) and file_info.get("primary")
+                        ),
+                        None,
+                    )
+                    selected_file = primary_file
+                    if selected_file is None:
+                        selected_file = next(
+                            (file_info for file_info in files if isinstance(file_info, dict)),
+                            None,
+                        )
+                    if isinstance(selected_file, dict):
+                        raw_file_name = selected_file.get("name", "")
+                        if isinstance(raw_file_name, str):
+                            file_name = raw_file_name.strip()
+
+                message = (
+                    f"Skipped download for '{file_name or version_info.get('name') or f'model_version:{model_version_id or model_id}'}' "
+                    f"because base model '{base_model_value}' is excluded in settings"
+                )
+                logger.info(message)
+                return {
+                    "success": True,
+                    "skipped": True,
+                    "status": "skipped",
+                    "reason": "base_model_excluded",
+                    "message": message,
+                    "base_model": base_model_value,
+                    "file_name": file_name,
+                    "download_id": download_id,
+                }
+
             # Check if this checkpoint should be treated as a diffusion model based on baseModel
             is_diffusion_model = False
             if model_type == "checkpoint":
-                base_model_value = version_info.get("baseModel", "")
                 if base_model_value in DIFFUSION_MODEL_BASE_MODELS:
                     is_diffusion_model = True
                     logger.info(
@@ -846,9 +893,13 @@ class DownloadManager:
                 blur_mature_content = bool(
                     settings_manager.get("blur_mature_content", True)
                 )
+                mature_threshold = resolve_mature_threshold(
+                    {"mature_blur_level": settings_manager.get("mature_blur_level", "R")}
+                )
                 selected_image, nsfw_level = select_preview_media(
                     images,
                     blur_mature_content=blur_mature_content,
+                    mature_threshold=mature_threshold,
                 )
 
                 preview_url = selected_image.get("url") if selected_image else None

py/services/lora_service.py

@@ -1,5 +1,6 @@
-import os
 import logging
+import json
+import os
 from typing import Dict, List, Optional
 
 from .base_model_service import BaseModelService
@@ -278,6 +279,42 @@ class LoraService(BaseModelService):
 
         return None
 
+    @staticmethod
+    def get_recommended_strength_from_lora_data(lora_data: Dict) -> Optional[float]:
+        """Parse usage_tips JSON and extract recommended model strength."""
+        try:
+            usage_tips = lora_data.get("usage_tips", "")
+            if not usage_tips:
+                return None
+            tips_data = json.loads(usage_tips)
+            return tips_data.get("strength")
+        except (json.JSONDecodeError, TypeError, AttributeError):
+            return None
+
+    @staticmethod
+    def get_recommended_clip_strength_from_lora_data(
+        lora_data: Dict,
+    ) -> Optional[float]:
+        """Parse usage_tips JSON and extract recommended clip strength."""
+        try:
+            usage_tips = lora_data.get("usage_tips", "")
+            if not usage_tips:
+                return None
+            tips_data = json.loads(usage_tips)
+            return tips_data.get("clipStrength")
+        except (json.JSONDecodeError, TypeError, AttributeError):
+            return None
+
+    async def get_lora_metadata_by_filename(self, filename: str) -> Optional[Dict]:
+        """Return cached raw metadata for a LoRA matching the given filename."""
+        cache = await self.scanner.get_cached_data(force_refresh=False)
+
+        for lora in cache.raw_data if cache else []:
+            if lora.get("file_name") == filename:
+                return lora
+
+        return None
+
     def find_duplicate_hashes(self) -> Dict:
         """Find LoRAs with duplicate SHA256 hashes"""
         return self.scanner._hash_index.get_duplicate_hashes()
@@ -328,34 +365,10 @@ class LoraService(BaseModelService):
             List of LoRA dicts with randomized strengths
         """
         import random
-        import json
-
         # Use a local Random instance to avoid affecting global random state
         # This ensures each execution with a different seed produces different results
         rng = random.Random(seed)
 
-        def get_recommended_strength(lora_data: Dict) -> Optional[float]:
-            """Parse usage_tips JSON and extract recommended strength"""
-            try:
-                usage_tips = lora_data.get("usage_tips", "")
-                if not usage_tips:
-                    return None
-                tips_data = json.loads(usage_tips)
-                return tips_data.get("strength")
-            except (json.JSONDecodeError, TypeError, AttributeError):
-                return None
-
-        def get_recommended_clip_strength(lora_data: Dict) -> Optional[float]:
-            """Parse usage_tips JSON and extract recommended clip strength"""
-            try:
-                usage_tips = lora_data.get("usage_tips", "")
-                if not usage_tips:
-                    return None
-                tips_data = json.loads(usage_tips)
-                return tips_data.get("clipStrength")
-            except (json.JSONDecodeError, TypeError, AttributeError):
-                return None
-
         if locked_loras is None:
             locked_loras = []
 
@@ -403,7 +416,9 @@ class LoraService(BaseModelService):
         result_loras = []
         for lora in selected:
             if use_recommended_strength:
-                recommended_strength = get_recommended_strength(lora)
+                recommended_strength = self.get_recommended_strength_from_lora_data(
+                    lora
+                )
                 if recommended_strength is not None:
                     scale = rng.uniform(
                         recommended_strength_scale_min, recommended_strength_scale_max
@@ -421,7 +436,9 @@ class LoraService(BaseModelService):
             if use_same_clip_strength:
                 clip_str = model_str
             elif use_recommended_strength:
-                recommended_clip_strength = get_recommended_clip_strength(lora)
+                recommended_clip_strength = (
+                    self.get_recommended_clip_strength_from_lora_data(lora)
+                )
                 if recommended_clip_strength is not None:
                     scale = rng.uniform(
                         recommended_strength_scale_min, recommended_strength_scale_max

py/services/model_scanner.py

@@ -732,18 +732,23 @@ class ModelScanner:
             # Get current cached file paths
             cached_paths = {item['file_path'] for item in self._cache.raw_data}
             path_to_item = {item['file_path']: item for item in self._cache.raw_data}
+            cached_real_paths = {}
+            for cached_path in cached_paths:
+                try:
+                    cached_real_paths.setdefault(os.path.realpath(cached_path), cached_path)
+                except Exception:
+                    continue
             
             # Track found files and new files
             found_paths = set()
             new_files = []
+            visited_real_paths = set()
+            discovered_real_files = set()
             
             # Scan all model roots
             for root_path in self.get_model_roots():
                 if not os.path.exists(root_path):
                     continue
-                    
-                # Track visited real paths to avoid symlink loops
-                visited_real_paths = set()
                 
                 # Recursively scan directory
                 for root, _, files in os.walk(root_path, followlinks=True):
@@ -757,12 +762,18 @@ class ModelScanner:
                         if ext in self.file_extensions:
                             # Construct paths exactly as they would be in cache
                             file_path = os.path.join(root, file).replace(os.sep, '/')
+                            real_file_path = os.path.realpath(os.path.join(root, file))
                             
                             # Check if this file is already in cache
                             if file_path in cached_paths:
                                 found_paths.add(file_path)
                                 continue
 
+                            cached_real_match = cached_real_paths.get(real_file_path)
+                            if cached_real_match:
+                                found_paths.add(cached_real_match)
+                                continue
+
                             if file_path in self._excluded_models:
                                 continue
                                 
@@ -778,6 +789,10 @@ class ModelScanner:
                                 if matched:
                                     continue
                                 
+                            if real_file_path in discovered_real_files:
+                                continue
+
+                            discovered_real_files.add(real_file_path)
                             # This is a new file to process
                             new_files.append(file_path)
                     
@@ -1099,6 +1114,8 @@ class ModelScanner:
         tags_count: Dict[str, int] = {}
         excluded_models: List[str] = []
         processed_files = 0
+        processed_real_files: Set[str] = set()
+        visited_real_dirs: Set[str] = set()
 
         async def handle_progress() -> None:
             if progress_callback is None:
@@ -1115,9 +1132,10 @@ class ModelScanner:
 
             try:
                 real_path = os.path.realpath(current_path)
-                if real_path in visited_paths:
+                if real_path in visited_paths or real_path in visited_real_dirs:
                     return
                 visited_paths.add(real_path)
+                visited_real_dirs.add(real_path)
 
                 with os.scandir(current_path) as iterator:
                     entries = list(iterator)
@@ -1130,6 +1148,11 @@ class ModelScanner:
                                 continue
 
                             file_path = entry.path.replace(os.sep, "/")
+                            real_file_path = os.path.realpath(entry.path)
+                            if real_file_path in processed_real_files:
+                                continue
+
+                            processed_real_files.add(real_file_path)
                             result = await self._process_model_file(
                                 file_path,
                                 root_path,

py/services/model_update_service.py

@@ -13,7 +13,7 @@ from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence
 from .errors import RateLimitError, ResourceNotFoundError
 from .settings_manager import get_settings_manager
 from ..utils.civitai_utils import rewrite_preview_url
-from ..utils.preview_selection import select_preview_media
+from ..utils.preview_selection import resolve_mature_threshold, select_preview_media
 
 logger = logging.getLogger(__name__)
 
@@ -1252,14 +1252,23 @@ class ModelUpdateService:
             return None
 
         blur_mature_content = True
+        mature_threshold = resolve_mature_threshold({"mature_blur_level": "R"})
         settings = getattr(self, "_settings", None)
         if settings is not None and hasattr(settings, "get"):
             try:
                 blur_mature_content = bool(settings.get("blur_mature_content", True))
+                mature_threshold = resolve_mature_threshold(
+                    {"mature_blur_level": settings.get("mature_blur_level", "R")}
+                )
             except Exception:  # pragma: no cover - defensive guard
                 blur_mature_content = True
+                mature_threshold = resolve_mature_threshold({"mature_blur_level": "R"})
 
-        selected, _ = select_preview_media(candidates, blur_mature_content=blur_mature_content)
+        selected, _ = select_preview_media(
+            candidates,
+            blur_mature_content=blur_mature_content,
+            mature_threshold=mature_threshold,
+        )
         if not selected:
             return None
 

py/services/preview_asset_service.py

@@ -9,7 +9,7 @@ from urllib.parse import urlparse
 
 from ..utils.constants import CARD_PREVIEW_WIDTH, PREVIEW_EXTENSIONS
 from ..utils.civitai_utils import rewrite_preview_url
-from ..utils.preview_selection import select_preview_media
+from ..utils.preview_selection import resolve_mature_threshold, select_preview_media
 from .settings_manager import get_settings_manager
 
 logger = logging.getLogger(__name__)
@@ -49,9 +49,13 @@ class PreviewAssetService:
         blur_mature_content = bool(
             settings_manager.get("blur_mature_content", True)
         )
+        mature_threshold = resolve_mature_threshold(
+            {"mature_blur_level": settings_manager.get("mature_blur_level", "R")}
+        )
         first_preview, nsfw_level = select_preview_media(
             images,
             blur_mature_content=blur_mature_content,
+            mature_threshold=mature_threshold,
         )
 
         if not first_preview:
@@ -216,4 +220,3 @@ class PreviewAssetService:
         if "webm" in content_type:
             return ".webm"
         return ".mp4"
-

py/services/recipe_scanner.py

@@ -1615,6 +1615,9 @@ class RecipeScanner:
     ) -> Optional[Dict[str, Any]]:
         """Coerce legacy or malformed checkpoint entries into a dict."""
 
+        if checkpoint_raw is None:
+            return None
+
         if isinstance(checkpoint_raw, dict):
             return dict(checkpoint_raw)
 
@@ -1632,9 +1635,6 @@ class RecipeScanner:
                 "file_name": file_name,
             }
 
-        logger.warning(
-            "Unexpected checkpoint payload type %s", type(checkpoint_raw).__name__
-        )
         return None
 
     def _enrich_checkpoint_entry(self, checkpoint: Dict[str, Any]) -> Dict[str, Any]:
@@ -1790,6 +1790,7 @@ class RecipeScanner:
         filters: dict = None,
         search_options: dict = None,
         lora_hash: str = None,
+        checkpoint_hash: str = None,
         bypass_filters: bool = True,
         folder: str | None = None,
         recursive: bool = True,
@@ -1804,7 +1805,8 @@ class RecipeScanner:
             filters: Dictionary of filters to apply
             search_options: Dictionary of search options to apply
             lora_hash: Optional SHA256 hash of a LoRA to filter recipes by
-            bypass_filters: If True, ignore other filters when a lora_hash is provided
+            checkpoint_hash: Optional SHA256 hash of a checkpoint to filter recipes by
+            bypass_filters: If True, ignore other filters when a hash filter is provided
             folder: Optional folder filter relative to recipes directory
             recursive: Whether to include recipes in subfolders of the selected folder
         """
@@ -1852,9 +1854,23 @@ class RecipeScanner:
                 # Skip other filters if bypass_filters is True
                 pass
             # Otherwise continue with normal filtering after applying LoRA hash filter
+        elif checkpoint_hash:
+            normalized_checkpoint_hash = checkpoint_hash.lower()
+            filtered_data = [
+                item
+                for item in filtered_data
+                if isinstance(item.get("checkpoint"), dict)
+                and (item["checkpoint"].get("hash", "") or "").lower()
+                == normalized_checkpoint_hash
+            ]
 
-        # Skip further filtering if we're only filtering by LoRA hash with bypass enabled
-        if not (lora_hash and bypass_filters):
+            if bypass_filters:
+                pass
+
+        has_hash_filter = bool(lora_hash or checkpoint_hash)
+
+        # Skip further filtering if we're only filtering by model hash with bypass enabled
+        if not (has_hash_filter and bypass_filters):
             # Apply folder filter before other criteria
             if folder is not None:
                 normalized_folder = folder.strip("/")
@@ -2334,6 +2350,38 @@ class RecipeScanner:
 
         return matching_recipes
 
+    async def get_recipes_for_checkpoint(
+        self, checkpoint_hash: str
+    ) -> List[Dict[str, Any]]:
+        """Return recipes that reference a given checkpoint hash."""
+
+        if not checkpoint_hash:
+            return []
+
+        normalized_hash = checkpoint_hash.lower()
+        cache = await self.get_cached_data()
+        matching_recipes: List[Dict[str, Any]] = []
+
+        for recipe in cache.raw_data:
+            checkpoint = self._normalize_checkpoint_entry(recipe.get("checkpoint"))
+            if not checkpoint:
+                continue
+
+            enriched_checkpoint = self._enrich_checkpoint_entry(dict(checkpoint))
+            if (enriched_checkpoint.get("hash") or "").lower() != normalized_hash:
+                continue
+
+            recipe_copy = {**recipe}
+            recipe_copy["checkpoint"] = enriched_checkpoint
+            recipe_copy["loras"] = [
+                self._enrich_lora_entry(dict(entry))
+                for entry in recipe.get("loras", [])
+            ]
+            recipe_copy["file_url"] = self._format_file_url(recipe.get("file_path"))
+            matching_recipes.append(recipe_copy)
+
+        return matching_recipes
+
     async def get_recipe_syntax_tokens(self, recipe_id: str) -> List[str]:
         """Build LoRA syntax tokens for a recipe."""
 

py/services/recipes/analysis_service.py

@@ -143,6 +143,12 @@ class RecipeAnalysisService:
                 ):
                     metadata = metadata["meta"]
 
+                # Include modelVersionIds from root level if available
+                # Civitai API returns modelVersionIds at root level, not in meta
+                model_version_ids = image_info.get("modelVersionIds")
+                if model_version_ids and isinstance(metadata, dict):
+                    metadata["modelVersionIds"] = model_version_ids
+
                 # Validate that metadata contains meaningful recipe fields
                 # If not, treat as None to trigger EXIF extraction from downloaded image
                 if isinstance(metadata, dict) and not self._has_recipe_fields(metadata):

py/services/recipes/persistence_service.py

@@ -173,11 +173,23 @@ class RecipePersistenceService:
     async def update_recipe(self, *, recipe_scanner, recipe_id: str, updates: dict[str, Any]) -> PersistenceResult:
         """Update persisted metadata for a recipe."""
 
-        if not any(key in updates for key in ("title", "tags", "source_path", "preview_nsfw_level", "favorite")):
+        allowed_fields = (
+            "title",
+            "tags",
+            "source_path",
+            "preview_nsfw_level",
+            "favorite",
+            "gen_params",
+        )
+
+        if not any(key in updates for key in allowed_fields):
             raise RecipeValidationError(
-                "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level or favorite)"
+                "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level or favorite or gen_params)"
             )
 
+        if "gen_params" in updates and not isinstance(updates["gen_params"], dict):
+            raise RecipeValidationError("gen_params must be an object")
+
         success = await recipe_scanner.update_recipe_metadata(recipe_id, updates)
         if not success:
             raise RecipeNotFoundError("Recipe not found or update failed")

py/services/settings_manager.py

@@ -7,12 +7,31 @@ import logging
 from pathlib import Path
 from datetime import datetime, timezone
 from threading import Lock
-from typing import Any, Awaitable, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple
+from typing import (
+    Any,
+    Awaitable,
+    Dict,
+    Iterable,
+    List,
+    Mapping,
+    Optional,
+    Sequence,
+    Tuple,
+)
 
 from platformdirs import user_config_dir
 
-from ..utils.constants import DEFAULT_HASH_CHUNK_SIZE_MB, DEFAULT_PRIORITY_TAG_CONFIG
-from ..utils.settings_paths import APP_NAME, ensure_settings_file, get_legacy_settings_path
+from ..utils.constants import (
+    DEFAULT_HASH_CHUNK_SIZE_MB,
+    DEFAULT_PRIORITY_TAG_CONFIG,
+    SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS,
+)
+from ..utils.preview_selection import VALID_MATURE_BLUR_LEVELS
+from ..utils.settings_paths import (
+    APP_NAME,
+    ensure_settings_file,
+    get_legacy_settings_path,
+)
 from ..utils.tag_priorities import (
     PriorityTagEntry,
     collect_canonical_tags,
@@ -59,6 +78,7 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "optimize_example_images": True,
     "auto_download_example_images": False,
     "blur_mature_content": True,
+    "mature_blur_level": "R",
     "autoplay_on_hover": False,
     "display_density": "default",
     "card_info_display": "always",
@@ -71,6 +91,7 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "update_flag_strategy": "same_base",
     "auto_organize_exclusions": [],
     "metadata_refresh_skip_paths": [],
+    "download_skip_base_models": [],
 }
 
 
@@ -87,7 +108,9 @@ class SettingsManager:
         self._template_payload_cache_loaded = False
         self._original_disk_payload: Optional[Dict[str, Any]] = None
         self._preserve_disk_template = False
-        self._template_path = Path(__file__).resolve().parents[2] / "settings.json.example"
+        self._template_path = (
+            Path(__file__).resolve().parents[2] / "settings.json.example"
+        )
         self.settings = self._load_settings()
         self._migrate_setting_keys()
         self._ensure_default_settings()
@@ -113,7 +136,7 @@ class SettingsManager:
         """Load settings from file"""
         if os.path.exists(self.settings_file):
             try:
-                with open(self.settings_file, 'r', encoding='utf-8') as f:
+                with open(self.settings_file, "r", encoding="utf-8") as f:
                     data = json.load(f)
                 if isinstance(data, dict):
                     self._original_disk_payload = copy.deepcopy(data)
@@ -191,7 +214,9 @@ class SettingsManager:
             return None
 
         if not isinstance(data, dict):
-            logger.debug("settings.json.example is not a JSON object; ignoring template")
+            logger.debug(
+                "settings.json.example is not a JSON object; ignoring template"
+            )
             return None
 
         self._template_payload_cache = copy.deepcopy(data)
@@ -267,13 +292,38 @@ class SettingsManager:
             normalized_skip_paths = self.normalize_metadata_refresh_skip_paths(
                 self.settings.get("metadata_refresh_skip_paths")
             )
-            if normalized_skip_paths != self.settings.get("metadata_refresh_skip_paths"):
+            if normalized_skip_paths != self.settings.get(
+                "metadata_refresh_skip_paths"
+            ):
                 self.settings["metadata_refresh_skip_paths"] = normalized_skip_paths
                 updated_existing = True
         else:
             self.settings["metadata_refresh_skip_paths"] = []
             inserted_defaults = True
 
+        if "download_skip_base_models" in self.settings:
+            normalized_skip_base_models = self.normalize_download_skip_base_models(
+                self.settings.get("download_skip_base_models")
+            )
+            if normalized_skip_base_models != self.settings.get(
+                "download_skip_base_models"
+            ):
+                self.settings["download_skip_base_models"] = normalized_skip_base_models
+                updated_existing = True
+        else:
+            self.settings["download_skip_base_models"] = []
+            inserted_defaults = True
+
+        had_mature_level = "mature_blur_level" in self.settings
+        raw_mature_level = self.settings.get("mature_blur_level")
+        normalized_mature_level = self.normalize_mature_blur_level(raw_mature_level)
+        if normalized_mature_level != raw_mature_level:
+            self.settings["mature_blur_level"] = normalized_mature_level
+            if had_mature_level:
+                updated_existing = True
+            else:
+                inserted_defaults = True
+
         for key, value in defaults.items():
             if key == "priority_tags":
                 continue
@@ -298,19 +348,19 @@ class SettingsManager:
         raw_top_level_paths = self.settings.get("folder_paths", {})
         normalized_top_level_paths: Dict[str, List[str]] = {}
         if isinstance(raw_top_level_paths, Mapping):
-            normalized_top_level_paths = self._normalize_folder_paths(raw_top_level_paths)
+            normalized_top_level_paths = self._normalize_folder_paths(
+                raw_top_level_paths
+            )
             if normalized_top_level_paths != raw_top_level_paths:
-                self.settings["folder_paths"] = copy.deepcopy(normalized_top_level_paths)
+                self.settings["folder_paths"] = copy.deepcopy(
+                    normalized_top_level_paths
+                )
 
         top_level_has_paths = self._has_configured_paths(normalized_top_level_paths)
 
         needs_library_bootstrap = not isinstance(libraries, dict) or not libraries
 
-        if (
-            not needs_library_bootstrap
-            and top_level_has_paths
-            and len(libraries) == 1
-        ):
+        if not needs_library_bootstrap and top_level_has_paths and len(libraries) == 1:
             only_library_payload = next(iter(libraries.values()))
             if isinstance(only_library_payload, Mapping):
                 folder_payload = only_library_payload.get("folder_paths")
@@ -322,7 +372,9 @@ class SettingsManager:
             library_payload = self._build_library_payload(
                 folder_paths=normalized_top_level_paths,
                 default_lora_root=self.settings.get("default_lora_root", ""),
-                default_checkpoint_root=self.settings.get("default_checkpoint_root", ""),
+                default_checkpoint_root=self.settings.get(
+                    "default_checkpoint_root", ""
+                ),
                 default_unet_root=self.settings.get("default_unet_root", ""),
                 default_embedding_root=self.settings.get("default_embedding_root", ""),
             )
@@ -344,7 +396,11 @@ class SettingsManager:
 
             if target_name:
                 candidate_payload = libraries.get(target_name)
-                if isinstance(candidate_payload, Mapping) and not self._has_configured_paths(candidate_payload.get("folder_paths")):
+                if isinstance(
+                    candidate_payload, Mapping
+                ) and not self._has_configured_paths(
+                    candidate_payload.get("folder_paths")
+                ):
                     seed_library_name = target_name
 
         sanitized_libraries: Dict[str, Dict[str, Any]] = {}
@@ -403,11 +459,17 @@ class SettingsManager:
         active_library = libraries.get(active_name, {})
         folder_paths = copy.deepcopy(active_library.get("folder_paths", {}))
         self.settings["folder_paths"] = folder_paths
-        self.settings["extra_folder_paths"] = copy.deepcopy(active_library.get("extra_folder_paths", {}))
+        self.settings["extra_folder_paths"] = copy.deepcopy(
+            active_library.get("extra_folder_paths", {})
+        )
         self.settings["default_lora_root"] = active_library.get("default_lora_root", "")
-        self.settings["default_checkpoint_root"] = active_library.get("default_checkpoint_root", "")
+        self.settings["default_checkpoint_root"] = active_library.get(
+            "default_checkpoint_root", ""
+        )
         self.settings["default_unet_root"] = active_library.get("default_unet_root", "")
-        self.settings["default_embedding_root"] = active_library.get("default_embedding_root", "")
+        self.settings["default_embedding_root"] = active_library.get(
+            "default_embedding_root", ""
+        )
 
         if save:
             self._save_settings()
@@ -436,7 +498,9 @@ class SettingsManager:
             payload.setdefault("folder_paths", {})
 
         if extra_folder_paths is not None:
-            payload["extra_folder_paths"] = self._normalize_folder_paths(extra_folder_paths)
+            payload["extra_folder_paths"] = self._normalize_folder_paths(
+                extra_folder_paths
+            )
         else:
             payload.setdefault("extra_folder_paths", {})
 
@@ -545,7 +609,9 @@ class SettingsManager:
                 }
                 overlap = existing.intersection(new_paths.keys())
                 if overlap:
-                    collisions = ", ".join(sorted(new_paths[value] for value in overlap))
+                    collisions = ", ".join(
+                        sorted(new_paths[value] for value in overlap)
+                    )
                     raise ValueError(
                         f"Folder path(s) {collisions} already assigned to library '{other_name}'"
                     )
@@ -580,19 +646,31 @@ class SettingsManager:
                 library["extra_folder_paths"] = normalized_extra_paths
                 changed = True
 
-        if default_lora_root is not None and library.get("default_lora_root") != default_lora_root:
+        if (
+            default_lora_root is not None
+            and library.get("default_lora_root") != default_lora_root
+        ):
             library["default_lora_root"] = default_lora_root
             changed = True
 
-        if default_checkpoint_root is not None and library.get("default_checkpoint_root") != default_checkpoint_root:
+        if (
+            default_checkpoint_root is not None
+            and library.get("default_checkpoint_root") != default_checkpoint_root
+        ):
             library["default_checkpoint_root"] = default_checkpoint_root
             changed = True
 
-        if default_unet_root is not None and library.get("default_unet_root") != default_unet_root:
+        if (
+            default_unet_root is not None
+            and library.get("default_unet_root") != default_unet_root
+        ):
             library["default_unet_root"] = default_unet_root
             changed = True
 
-        if default_embedding_root is not None and library.get("default_embedding_root") != default_embedding_root:
+        if (
+            default_embedding_root is not None
+            and library.get("default_embedding_root") != default_embedding_root
+        ):
             library["default_embedding_root"] = default_embedding_root
             changed = True
 
@@ -605,15 +683,16 @@ class SettingsManager:
     def _migrate_setting_keys(self) -> None:
         """Migrate legacy camelCase setting keys to snake_case"""
         key_migrations = {
-            'optimizeExampleImages': 'optimize_example_images',
-            'autoDownloadExampleImages': 'auto_download_example_images',
-            'blurMatureContent': 'blur_mature_content',
-            'autoplayOnHover': 'autoplay_on_hover',
-            'displayDensity': 'display_density',
-            'cardInfoDisplay': 'card_info_display',
-            'includeTriggerWords': 'include_trigger_words',
-            'compactMode': 'compact_mode',
-            'modelCardFooterAction': 'model_card_footer_action',
+            "optimizeExampleImages": "optimize_example_images",
+            "autoDownloadExampleImages": "auto_download_example_images",
+            "blurMatureContent": "blur_mature_content",
+            "matureBlurLevel": "mature_blur_level",
+            "autoplayOnHover": "autoplay_on_hover",
+            "displayDensity": "display_density",
+            "cardInfoDisplay": "card_info_display",
+            "includeTriggerWords": "include_trigger_words",
+            "compactMode": "compact_mode",
+            "modelCardFooterAction": "model_card_footer_action",
         }
 
         updated = False
@@ -630,65 +709,77 @@ class SettingsManager:
 
     def _migrate_download_path_template(self):
         """Migrate old download_path_template to new download_path_templates"""
-        old_template = self.settings.get('download_path_template')
-        templates = self.settings.get('download_path_templates')
+        old_template = self.settings.get("download_path_template")
+        templates = self.settings.get("download_path_templates")
 
         # If old template exists and new templates don't exist, migrate
         if old_template is not None and not templates:
             logger.info("Migrating download_path_template to download_path_templates")
-            self.settings['download_path_templates'] = {
-                'lora': old_template,
-                'checkpoint': old_template,
-                'embedding': old_template
+            self.settings["download_path_templates"] = {
+                "lora": old_template,
+                "checkpoint": old_template,
+                "embedding": old_template,
             }
             # Remove old setting
-            del self.settings['download_path_template']
+            del self.settings["download_path_template"]
             self._save_settings()
             logger.info("Migration completed")
 
     def _auto_set_default_roots(self):
-        """Auto set default root paths when the current default is unset or not among the options.
+        """Ensure default root paths always point at a current valid root.
 
-        For single-path cases, always use that path.
-        For multi-path cases, only set if current default is empty or invalid.
+        Empty or stale defaults are repaired to the first configured root.
+        Skips auto-setting when the settings file matches the template
+        (user hasn't customized yet).
         """
-        folder_paths = self.settings.get('folder_paths', {})
+        # Skip auto-setting if the user hasn't customized settings yet (template preserved)
+        if self._preserve_disk_template:
+            return
+
+        folder_paths = self.settings.get("folder_paths", {})
         updated = False
-        # loras
-        loras = folder_paths.get('loras', [])
-        if isinstance(loras, list) and len(loras) == 1:
-            current_lora_root = self.settings.get('default_lora_root')
-            if current_lora_root not in loras:
-                self.settings['default_lora_root'] = loras[0]
-                updated = True
-        # checkpoints
-        checkpoints = folder_paths.get('checkpoints', [])
-        if isinstance(checkpoints, list) and len(checkpoints) == 1:
-            current_checkpoint_root = self.settings.get('default_checkpoint_root')
-            if current_checkpoint_root not in checkpoints:
-                self.settings['default_checkpoint_root'] = checkpoints[0]
-                updated = True
-        # unet (diffusion models) - auto-set if empty or invalid
-        unet_paths = folder_paths.get('unet', [])
-        if isinstance(unet_paths, list) and len(unet_paths) >= 1:
-            current_unet_root = self.settings.get('default_unet_root')
-            # Set to first path if current is empty or not in the valid paths
-            if not current_unet_root or current_unet_root not in unet_paths:
-                self.settings['default_unet_root'] = unet_paths[0]
-                updated = True
-        # embeddings
-        embeddings = folder_paths.get('embeddings', [])
-        if isinstance(embeddings, list) and len(embeddings) == 1:
-            current_embedding_root = self.settings.get('default_embedding_root')
-            if current_embedding_root not in embeddings:
-                self.settings['default_embedding_root'] = embeddings[0]
-                updated = True
+
+        def _check_and_auto_set(key: str, setting_key: str) -> bool:
+            """Repair default roots when empty or no longer present."""
+            current = self.settings.get(setting_key, "")
+            candidates = folder_paths.get(key, [])
+            if not isinstance(candidates, list) or not candidates:
+                return False
+
+            # Filter valid path strings
+            valid_paths = [p for p in candidates if isinstance(p, str) and p.strip()]
+            if not valid_paths:
+                return False
+
+            if current in valid_paths:
+                return False
+
+            self.settings[setting_key] = valid_paths[0]
+            if current:
+                logger.info(
+                    "Repaired stale %s from '%s' to '%s'",
+                    setting_key,
+                    current,
+                    valid_paths[0],
+                )
+            else:
+                logger.info("Auto-set %s to '%s'", setting_key, valid_paths[0])
+            return True
+
+        # Process all model types
+        updated = _check_and_auto_set("loras", "default_lora_root") or updated
+        updated = (
+            _check_and_auto_set("checkpoints", "default_checkpoint_root") or updated
+        )
+        updated = _check_and_auto_set("unet", "default_unet_root") or updated
+        updated = _check_and_auto_set("embeddings", "default_embedding_root") or updated
+
         if updated:
             self._update_active_library_entry(
-                default_lora_root=self.settings.get('default_lora_root'),
-                default_checkpoint_root=self.settings.get('default_checkpoint_root'),
-                default_unet_root=self.settings.get('default_unet_root'),
-                default_embedding_root=self.settings.get('default_embedding_root'),
+                default_lora_root=self.settings.get("default_lora_root"),
+                default_checkpoint_root=self.settings.get("default_checkpoint_root"),
+                default_unet_root=self.settings.get("default_unet_root"),
+                default_embedding_root=self.settings.get("default_embedding_root"),
             )
             if self._bootstrap_reason == "missing":
                 self._needs_initial_save = True
@@ -697,11 +788,11 @@ class SettingsManager:
 
     def _check_environment_variables(self) -> None:
         """Check for environment variables and update settings if needed"""
-        env_api_key = os.environ.get('CIVITAI_API_KEY')
+        env_api_key = os.environ.get("CIVITAI_API_KEY")
         if env_api_key:  # Check if the environment variable exists and is not empty
             logger.info("Found CIVITAI_API_KEY environment variable")
             # Always use the environment variable if it exists
-            self.settings['civitai_api_key'] = env_api_key
+            self.settings["civitai_api_key"] = env_api_key
             self._save_settings()
 
     def _default_settings_actions(self) -> List[Dict[str, Any]]:
@@ -766,7 +857,9 @@ class SettingsManager:
             disk_value = self._original_disk_payload.get(key)
             default_value = defaults.get(key)
             # Compare using JSON serialization for complex objects
-            if json.dumps(disk_value, sort_keys=True, default=str) == json.dumps(default_value, sort_keys=True, default=str):
+            if json.dumps(disk_value, sort_keys=True, default=str) == json.dumps(
+                default_value, sort_keys=True, default=str
+            ):
                 default_value_keys.add(key)
 
         # Only cleanup if there are "many" default keys (indicating a bloated file)
@@ -774,7 +867,7 @@ class SettingsManager:
         if len(default_value_keys) >= DEFAULT_KEYS_CLEANUP_THRESHOLD:
             logger.info(
                 "Cleaning up %d default value(s) from settings.json to keep it minimal",
-                len(default_value_keys)
+                len(default_value_keys),
             )
             self._save_settings()
             # Update original payload to match what we just saved
@@ -784,8 +877,8 @@ class SettingsManager:
         if not self._standalone_mode:
             return
 
-        folder_paths = self.settings.get('folder_paths', {}) or {}
-        monitored_keys = ('loras', 'checkpoints', 'embeddings')
+        folder_paths = self.settings.get("folder_paths", {}) or {}
+        monitored_keys = ("loras", "checkpoints", "embeddings")
 
         has_valid_paths = False
         for key in monitored_keys:
@@ -796,7 +889,10 @@ class SettingsManager:
                 iterator = list(raw_paths)
             except TypeError:
                 continue
-            if any(isinstance(path, str) and path and os.path.exists(path) for path in iterator):
+            if any(
+                isinstance(path, str) and path and os.path.exists(path)
+                for path in iterator
+            ):
                 has_valid_paths = True
                 break
 
@@ -827,13 +923,13 @@ class SettingsManager:
     def _get_default_settings(self) -> Dict[str, Any]:
         """Return default settings"""
         defaults = copy.deepcopy(DEFAULT_SETTINGS)
-        defaults['base_model_path_mappings'] = {}
-        defaults['download_path_templates'] = {}
-        defaults['priority_tags'] = DEFAULT_PRIORITY_TAG_CONFIG.copy()
-        defaults.setdefault('folder_paths', {})
-        defaults.setdefault('extra_folder_paths', {})
-        defaults['auto_organize_exclusions'] = []
-        defaults['metadata_refresh_skip_paths'] = []
+        defaults["base_model_path_mappings"] = {}
+        defaults["download_path_templates"] = {}
+        defaults["priority_tags"] = DEFAULT_PRIORITY_TAG_CONFIG.copy()
+        defaults.setdefault("folder_paths", {})
+        defaults.setdefault("extra_folder_paths", {})
+        defaults["auto_organize_exclusions"] = []
+        defaults["metadata_refresh_skip_paths"] = []
 
         library_name = defaults.get("active_library") or "default"
         default_library = self._build_library_payload(
@@ -843,8 +939,8 @@ class SettingsManager:
             default_checkpoint_root=defaults.get("default_checkpoint_root"),
             default_embedding_root=defaults.get("default_embedding_root"),
         )
-        defaults['libraries'] = {library_name: default_library}
-        defaults['active_library'] = library_name
+        defaults["libraries"] = {library_name: default_library}
+        defaults["active_library"] = library_name
         return defaults
 
     def _normalize_priority_tag_config(self, value: Any) -> Dict[str, str]:
@@ -860,6 +956,13 @@ class SettingsManager:
 
         return normalized
 
+    def normalize_mature_blur_level(self, value: Any) -> str:
+        if isinstance(value, str):
+            normalized = value.strip().upper()
+            if normalized in VALID_MATURE_BLUR_LEVELS:
+                return normalized
+        return "R"
+
     def normalize_auto_organize_exclusions(self, value: Any) -> List[str]:
         if value is None:
             return []
@@ -868,7 +971,9 @@ class SettingsManager:
             candidates: Iterable[str] = (
                 value.replace("\n", ",").replace(";", ",").split(",")
             )
-        elif isinstance(value, Sequence) and not isinstance(value, (bytes, bytearray, str)):
+        elif isinstance(value, Sequence) and not isinstance(
+            value, (bytes, bytearray, str)
+        ):
             candidates = value
         else:
             return []
@@ -914,7 +1019,9 @@ class SettingsManager:
             candidates: Iterable[str] = (
                 value.replace("\n", ",").replace(";", ",").split(",")
             )
-        elif isinstance(value, Sequence) and not isinstance(value, (bytes, bytearray, str)):
+        elif isinstance(value, Sequence) and not isinstance(
+            value, (bytes, bytearray, str)
+        ):
             candidates = value
         else:
             return []
@@ -944,6 +1051,45 @@ class SettingsManager:
             self._save_settings()
         return skip_paths
 
+    def normalize_download_skip_base_models(self, value: Any) -> List[str]:
+        if value is None:
+            return []
+
+        if isinstance(value, str):
+            candidates: Iterable[str] = (
+                value.replace("\n", ",").replace(";", ",").split(",")
+            )
+        elif isinstance(value, Sequence) and not isinstance(
+            value, (bytes, bytearray, str)
+        ):
+            candidates = value
+        else:
+            return []
+
+        base_models: List[str] = []
+        seen = set()
+        for raw in candidates:
+            if not isinstance(raw, str):
+                continue
+            token = raw.strip()
+            if not token or token not in SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS:
+                continue
+            if token in seen:
+                continue
+            seen.add(token)
+            base_models.append(token)
+
+        return base_models
+
+    def get_download_skip_base_models(self) -> List[str]:
+        base_models = self.normalize_download_skip_base_models(
+            self.settings.get("download_skip_base_models")
+        )
+        if base_models != self.settings.get("download_skip_base_models"):
+            self.settings["download_skip_base_models"] = base_models
+            self._save_settings()
+        return base_models
+
     def get_extra_folder_paths(self) -> Dict[str, List[str]]:
         """Get extra folder paths for the active library.
 
@@ -967,6 +1113,74 @@ class SettingsManager:
         active_name = self.get_active_library_name()
         self._validate_folder_paths(active_name, extra_folder_paths)
 
+        active_library = self.get_active_library()
+        active_folder_paths = active_library.get("folder_paths", {})
+        active_lora_paths = active_folder_paths.get("loras", []) or []
+        requested_extra_lora_paths = extra_folder_paths.get("loras", []) or []
+
+        primary_real_paths = set()
+        for path in active_lora_paths:
+            if not isinstance(path, str):
+                continue
+            stripped = path.strip()
+            if not stripped:
+                continue
+            normalized = os.path.normcase(os.path.normpath(stripped))
+            if os.path.exists(stripped):
+                normalized = os.path.normcase(
+                    os.path.normpath(os.path.realpath(stripped))
+                )
+            primary_real_paths.add(normalized)
+
+        primary_symlink_targets = set()
+        for path in active_lora_paths:
+            if not isinstance(path, str):
+                continue
+            stripped = path.strip()
+            if not stripped or not os.path.isdir(stripped):
+                continue
+            try:
+                with os.scandir(stripped) as iterator:
+                    for entry in iterator:
+                        try:
+                            if not entry.is_symlink():
+                                continue
+                            target_path = os.path.realpath(entry.path)
+                            if not os.path.isdir(target_path):
+                                continue
+                            primary_symlink_targets.add(
+                                os.path.normcase(os.path.normpath(target_path))
+                            )
+                        except Exception:
+                            continue
+            except Exception:
+                continue
+
+        overlapping_paths = []
+        for path in requested_extra_lora_paths:
+            if not isinstance(path, str):
+                continue
+            stripped = path.strip()
+            if not stripped:
+                continue
+            normalized = os.path.normcase(os.path.normpath(stripped))
+            if os.path.exists(stripped):
+                normalized = os.path.normcase(
+                    os.path.normpath(os.path.realpath(stripped))
+                )
+            if (
+                normalized in primary_real_paths
+                or normalized in primary_symlink_targets
+            ):
+                overlapping_paths.append(stripped)
+
+        if overlapping_paths:
+            collisions = ", ".join(sorted(set(overlapping_paths)))
+            # Settings writes should reject new conflicting configuration instead of tolerating it.
+            raise ValueError(
+                f"Extra LoRA path(s) {collisions} overlap with the active library's primary LoRA roots"
+            )
+
         normalized_paths = self._normalize_folder_paths(extra_folder_paths)
         self.settings["extra_folder_paths"] = normalized_paths
         self._update_active_library_entry(extra_folder_paths=normalized_paths)
@@ -1012,24 +1226,28 @@ class SettingsManager:
             value = self.normalize_auto_organize_exclusions(value)
         elif key == "metadata_refresh_skip_paths":
             value = self.normalize_metadata_refresh_skip_paths(value)
+        elif key == "download_skip_base_models":
+            value = self.normalize_download_skip_base_models(value)
+        elif key == "mature_blur_level":
+            value = self.normalize_mature_blur_level(value)
         self.settings[key] = value
         portable_switch_pending = False
         if key == "use_portable_settings" and isinstance(value, bool):
             portable_switch_pending = True
             self._prepare_portable_switch(value)
-        if key == 'folder_paths' and isinstance(value, Mapping):
+        if key == "folder_paths" and isinstance(value, Mapping):
             self._update_active_library_entry(folder_paths=value)  # type: ignore[arg-type]
-        elif key == 'extra_folder_paths' and isinstance(value, Mapping):
+        elif key == "extra_folder_paths" and isinstance(value, Mapping):
             self._update_active_library_entry(extra_folder_paths=value)  # type: ignore[arg-type]
-        elif key == 'default_lora_root':
+        elif key == "default_lora_root":
             self._update_active_library_entry(default_lora_root=str(value))
-        elif key == 'default_checkpoint_root':
+        elif key == "default_checkpoint_root":
             self._update_active_library_entry(default_checkpoint_root=str(value))
-        elif key == 'default_unet_root':
+        elif key == "default_unet_root":
             self._update_active_library_entry(default_unet_root=str(value))
-        elif key == 'default_embedding_root':
+        elif key == "default_embedding_root":
             self._update_active_library_entry(default_embedding_root=str(value))
-        elif key == 'model_name_display':
+        elif key == "model_name_display":
             self._notify_model_name_display_change(value)
         self._save_settings()
         if portable_switch_pending:
@@ -1105,10 +1323,9 @@ class SettingsManager:
 
         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)
-        ):
+        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,
@@ -1126,10 +1343,9 @@ class SettingsManager:
 
         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)
-        ):
+        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:
@@ -1155,7 +1371,9 @@ class SettingsManager:
         try:
             os.makedirs(config_dir, exist_ok=True)
         except Exception as exc:
-            logger.warning("Failed to create user config directory %s: %s", config_dir, exc)
+            logger.warning(
+                "Failed to create user config directory %s: %s", config_dir, exc
+            )
 
         return config_dir
 
@@ -1215,7 +1433,9 @@ class SettingsManager:
                 try:
                     asyncio.run(coroutine)
                 except RuntimeError:
-                    logger.debug("Skipping name display update due to missing event loop")
+                    logger.debug(
+                        "Skipping name display update due to missing event loop"
+                    )
                 continue
 
             if loop is not None and target_loop is loop:
@@ -1238,7 +1458,7 @@ class SettingsManager:
         """Save settings to file"""
         try:
             payload = self._serialize_settings_for_disk()
-            with open(self.settings_file, 'w', encoding='utf-8') as f:
+            with open(self.settings_file, "w", encoding="utf-8") as f:
                 json.dump(payload, f, indent=2)
         except Exception as e:
             logger.error(f"Error saving settings: {e}")
@@ -1279,7 +1499,9 @@ class SettingsManager:
                 minimal[key] = copy.deepcopy(value)
             # Complex objects need deep comparison
             elif isinstance(value, (dict, list)) and default_value is not None:
-                if json.dumps(value, sort_keys=True, default=str) != json.dumps(default_value, sort_keys=True, default=str):
+                if json.dumps(value, sort_keys=True, default=str) != json.dumps(
+                    default_value, sort_keys=True, default=str
+                ):
                     minimal[key] = copy.deepcopy(value)
             # Simple values use direct comparison
             elif value != default_value:
@@ -1356,9 +1578,15 @@ class SettingsManager:
         existing = libraries.get(name, {})
 
         payload = self._build_library_payload(
-            folder_paths=folder_paths if folder_paths is not None else existing.get("folder_paths"),
-            extra_folder_paths=extra_folder_paths if extra_folder_paths is not None else existing.get("extra_folder_paths"),
-            default_lora_root=default_lora_root if default_lora_root is not None else existing.get("default_lora_root"),
+            folder_paths=folder_paths
+            if folder_paths is not None
+            else existing.get("folder_paths"),
+            extra_folder_paths=extra_folder_paths
+            if extra_folder_paths is not None
+            else existing.get("extra_folder_paths"),
+            default_lora_root=default_lora_root
+            if default_lora_root is not None
+            else existing.get("default_lora_root"),
             default_checkpoint_root=(
                 default_checkpoint_root
                 if default_checkpoint_root is not None
@@ -1518,7 +1746,9 @@ class SettingsManager:
                 if service and hasattr(service, "on_library_changed"):
                     try:
                         service.on_library_changed()
-                    except Exception as service_exc:  # pragma: no cover - defensive logging
+                    except (
+                        Exception
+                    ) as service_exc:  # pragma: no cover - defensive logging
                         logger.debug(
                             "Service %s failed to handle library change: %s",
                             service_name,
@@ -1529,15 +1759,15 @@ class SettingsManager:
 
     def get_download_path_template(self, model_type: str) -> str:
         """Get download path template for specific model type
-        
+
         Args:
             model_type: The type of model ('lora', 'checkpoint', 'embedding')
-            
+
         Returns:
             Template string for the model type, defaults to '{base_model}/{first_tag}'
         """
-        templates = self.settings.get('download_path_templates', {})
-        
+        templates = self.settings.get("download_path_templates", {})
+
         # Handle edge case where templates might be stored as JSON string
         if isinstance(templates, str):
             try:
@@ -1545,36 +1775,40 @@ class SettingsManager:
                 parsed_templates = json.loads(templates)
                 if isinstance(parsed_templates, dict):
                     # Update settings with parsed dictionary
-                    self.settings['download_path_templates'] = parsed_templates
+                    self.settings["download_path_templates"] = parsed_templates
                     self._save_settings()
                     templates = parsed_templates
-                    logger.info("Successfully parsed download_path_templates from JSON string")
+                    logger.info(
+                        "Successfully parsed download_path_templates from JSON string"
+                    )
                 else:
                     raise ValueError("Parsed JSON is not a dictionary")
             except (json.JSONDecodeError, ValueError) as e:
                 # If parsing fails, set default values
-                logger.warning(f"Failed to parse download_path_templates JSON string: {e}. Setting default values.")
-                default_template = '{base_model}/{first_tag}'
+                logger.warning(
+                    f"Failed to parse download_path_templates JSON string: {e}. Setting default values."
+                )
+                default_template = "{base_model}/{first_tag}"
                 templates = {
-                    'lora': default_template,
-                    'checkpoint': default_template,
-                    'embedding': default_template
+                    "lora": default_template,
+                    "checkpoint": default_template,
+                    "embedding": default_template,
                 }
-                self.settings['download_path_templates'] = templates
+                self.settings["download_path_templates"] = templates
                 self._save_settings()
-        
+
         # Ensure templates is a dictionary
         if not isinstance(templates, dict):
-            default_template = '{base_model}/{first_tag}'
+            default_template = "{base_model}/{first_tag}"
             templates = {
-                'lora': default_template,
-                'checkpoint': default_template,
-                'embedding': default_template
+                "lora": default_template,
+                "checkpoint": default_template,
+                "embedding": default_template,
             }
-            self.settings['download_path_templates'] = templates
+            self.settings["download_path_templates"] = templates
             self._save_settings()
-        
-        return templates.get(model_type, '{base_model}/{first_tag}')
+
+        return templates.get(model_type, "{base_model}/{first_tag}")
 
 
 _SETTINGS_MANAGER: Optional["SettingsManager"] = None

py/utils/constants.py

@@ -110,6 +110,71 @@ DIFFUSION_MODEL_BASE_MODELS = frozenset(
         "Wan Video 2.2 T2V-A14B",
         "Wan Video 2.5 T2V",
         "Wan Video 2.5 I2V",
+        "CogVideoX",
+        "Mochi",
         "Qwen",
     ]
 )
+
+# Supported baseModel values for download exclusion settings.
+# Keep this aligned with static/js/utils/constants.js, excluding the generic "Other" value.
+SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS = frozenset(
+    [
+        "SD 1.4",
+        "SD 1.5",
+        "SD 1.5 LCM",
+        "SD 1.5 Hyper",
+        "SD 2.0",
+        "SD 2.1",
+        "SD 3",
+        "SD 3.5",
+        "SD 3.5 Medium",
+        "SD 3.5 Large",
+        "SD 3.5 Large Turbo",
+        "SDXL 1.0",
+        "SDXL Lightning",
+        "SDXL Hyper",
+        "Flux.1 D",
+        "Flux.1 S",
+        "Flux.1 Krea",
+        "Flux.1 Kontext",
+        "Flux.2 D",
+        "Flux.2 Klein 9B",
+        "Flux.2 Klein 9B-base",
+        "Flux.2 Klein 4B",
+        "Flux.2 Klein 4B-base",
+        "AuraFlow",
+        "Chroma",
+        "PixArt a",
+        "PixArt E",
+        "Hunyuan 1",
+        "Lumina",
+        "Kolors",
+        "NoobAI",
+        "Illustrious",
+        "Pony",
+        "Pony V7",
+        "HiDream",
+        "Qwen",
+        "ZImageTurbo",
+        "ZImageBase",
+        "SVD",
+        "LTXV",
+        "LTXV2",
+        "LTXV 2.3",
+        "CogVideoX",
+        "Mochi",
+        "Wan Video",
+        "Wan Video 1.3B t2v",
+        "Wan Video 14B t2v",
+        "Wan Video 14B i2v 480p",
+        "Wan Video 14B i2v 720p",
+        "Wan Video 2.2 TI2V-5B",
+        "Wan Video 2.2 T2V-A14B",
+        "Wan Video 2.2 I2V-A14B",
+        "Wan Video 2.5 T2V",
+        "Wan Video 2.5 I2V",
+        "Hunyuan Video",
+        "Anima",
+    ]
+)

py/utils/preview_selection.py

@@ -2,11 +2,12 @@
 
 from __future__ import annotations
 
-from typing import Mapping, Optional, Sequence, Tuple
+from typing import Any, Mapping, Optional, Sequence, Tuple
 
 from .constants import NSFW_LEVELS
 
 PreviewMedia = Mapping[str, object]
+VALID_MATURE_BLUR_LEVELS = ("PG13", "R", "X", "XXX")
 
 
 def _extract_nsfw_level(entry: Mapping[str, object]) -> int:
@@ -19,17 +20,36 @@ def _extract_nsfw_level(entry: Mapping[str, object]) -> int:
         return 0
 
 
+def resolve_mature_threshold(settings: Mapping[str, Any] | None) -> int:
+    """Resolve the configured mature blur threshold from settings.
+
+    Allowed values are ``PG13``, ``R``, ``X``, and ``XXX``. Any invalid or
+    missing value falls back to ``R``.
+    """
+
+    if not isinstance(settings, Mapping):
+        return NSFW_LEVELS.get("R", 4)
+
+    raw_level = settings.get("mature_blur_level", "R")
+    normalized = str(raw_level).strip().upper()
+    if normalized not in VALID_MATURE_BLUR_LEVELS:
+        normalized = "R"
+    return NSFW_LEVELS.get(normalized, NSFW_LEVELS.get("R", 4))
+
+
 def select_preview_media(
     images: Sequence[Mapping[str, object]] | None,
     *,
     blur_mature_content: bool,
+    mature_threshold: int | None = None,
 ) -> Tuple[Optional[PreviewMedia], int]:
     """Select the most appropriate preview media entry.
 
     When ``blur_mature_content`` is enabled we first try to return the first media
-    item with an ``nsfwLevel`` lower than :pydata:`NSFW_LEVELS["R"]`. If none are
-    available we return the media entry with the lowest NSFW level. When the
-    setting is disabled we simply return the first entry.
+    item with an ``nsfwLevel`` lower than the configured mature threshold
+    (defaults to :pydata:`NSFW_LEVELS["R"]`). If none are available we return
+    the media entry with the lowest NSFW level. When the setting is disabled we
+    simply return the first entry.
     """
 
     if not images:
@@ -45,7 +65,9 @@ def select_preview_media(
     if not blur_mature_content:
         return selected, selected_level
 
-    safe_threshold = NSFW_LEVELS.get("R", 4)
+    safe_threshold = (
+        mature_threshold if isinstance(mature_threshold, int) else NSFW_LEVELS.get("R", 4)
+    )
     for candidate in candidates:
         level = _extract_nsfw_level(candidate)
         if level < safe_threshold:
@@ -60,4 +82,4 @@ def select_preview_media(
     return selected, selected_level
 
 
-__all__ = ["select_preview_media"]
+__all__ = ["resolve_mature_threshold", "select_preview_media", "VALID_MATURE_BLUR_LEVELS"]

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "1.0.0"
+version = "1.0.1"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

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

@@ -835,7 +835,8 @@
 }
 
 [data-theme="dark"] .creator-info,
-[data-theme="dark"] .civitai-view {
+[data-theme="dark"] .civitai-view,
+[data-theme="dark"] .modal-send-btn {
     background: rgba(255, 255, 255, 0.03);
     border: 1px solid var(--lora-border);
 }
@@ -875,7 +876,8 @@
 
 /* Add hover effect for creator info */
 .creator-info:hover,
-.civitai-view:hover {
+.civitai-view:hover,
+.modal-send-btn:hover {
     background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
     border-color: var(--lora-accent);
     transform: translateY(-1px);
@@ -910,3 +912,42 @@
     align-items: center;
     justify-content: center;
 }
+
+/* Send to ComfyUI Button */
+.modal-send-btn {
+    display: inline-flex;
+    align-items: center;
+    gap: 6px;
+    padding: 6px 12px;
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-sm);
+    color: var(--text-color);
+    cursor: pointer;
+    font-weight: 500;
+    font-size: 0.9em;
+    transition: all 0.2s;
+}
+
+[data-theme="dark"] .modal-send-btn {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+.modal-send-btn:hover {
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+    border-color: var(--lora-accent);
+    transform: translateY(-1px);
+}
+
+.modal-send-btn:active {
+    transform: translateY(0);
+}
+
+.modal-send-btn i {
+    font-size: 14px;
+}
+
+.modal-send-btn span {
+    white-space: nowrap;
+}

static/css/components/modal/_base.css

@@ -151,7 +151,8 @@ body.modal-open {
 [data-theme="dark"] .changelog-section,
 [data-theme="dark"] .update-info,
 [data-theme="dark"] .info-item,
-[data-theme="dark"] .path-preview {
+[data-theme="dark"] .path-preview,
+[data-theme="dark"] #bulkDownloadMissingLorasModal .bulk-download-loras-preview {
     background: rgba(255, 255, 255, 0.03);
     border: 1px solid var(--lora-border);
 }
@@ -349,3 +350,87 @@ button:disabled,
     margin-top: var(--space-1);
     text-align: center;
 }
+
+/* Bulk Download Missing LoRAs Modal */
+#bulkDownloadMissingLorasModal .modal-body {
+    padding: var(--space-3);
+}
+
+#bulkDownloadMissingLorasModal .confirmation-message {
+    color: var(--text-color);
+    margin-bottom: var(--space-3);
+    font-size: 1em;
+    line-height: 1.5;
+}
+
+#bulkDownloadMissingLorasModal .bulk-download-loras-preview {
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-sm);
+    padding: var(--space-3);
+    margin-bottom: var(--space-3);
+}
+
+#bulkDownloadMissingLorasModal .preview-title {
+    font-weight: 600;
+    margin-bottom: var(--space-2);
+    color: var(--text-color);
+    font-size: 0.95em;
+}
+
+#bulkDownloadMissingLorasModal .bulk-download-loras-list {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+}
+
+#bulkDownloadMissingLorasModal .bulk-download-loras-list li {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: var(--space-1) 0;
+    border-bottom: 1px solid var(--border-color);
+    font-size: 0.9em;
+}
+
+#bulkDownloadMissingLorasModal .bulk-download-loras-list li:last-child {
+    border-bottom: none;
+}
+
+#bulkDownloadMissingLorasModal .bulk-download-loras-list li.more-items {
+    font-style: italic;
+    opacity: 0.7;
+    text-align: center;
+    justify-content: center;
+    padding: var(--space-2) 0;
+}
+
+#bulkDownloadMissingLorasModal .lora-name {
+    font-weight: 500;
+    color: var(--text-color);
+    flex: 1;
+}
+
+#bulkDownloadMissingLorasModal .lora-version {
+    font-size: 0.85em;
+    opacity: 0.7;
+    margin-left: var(--space-1);
+    color: var(--text-muted);
+}
+
+#bulkDownloadMissingLorasModal .confirmation-note {
+    display: flex;
+    align-items: flex-start;
+    gap: var(--space-2);
+    padding: var(--space-2);
+    background: rgba(59, 130, 246, 0.1);
+    border-radius: var(--border-radius-sm);
+    font-size: 0.9em;
+    color: var(--text-color);
+}
+
+#bulkDownloadMissingLorasModal .confirmation-note i {
+    color: var(--lora-accent);
+    margin-top: 2px;
+    flex-shrink: 0;
+}

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

@@ -430,6 +430,88 @@
     box-sizing: border-box;
 }
 
+.base-model-skip-toggle {
+    min-width: 220px;
+    justify-content: space-between;
+    gap: 10px;
+}
+
+.base-model-skip-toggle-label {
+    opacity: 0.75;
+    white-space: nowrap;
+}
+
+.base-model-skip-panel {
+    margin-top: var(--space-2);
+    padding: 12px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background-color: var(--lora-surface);
+}
+
+.base-model-skip-toolbar {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    margin-bottom: 10px;
+}
+
+.base-model-skip-search {
+    flex: 1;
+    min-width: 0;
+    padding: 8px 10px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background-color: var(--settings-bg);
+    color: var(--text-color);
+}
+
+.base-model-skip-search:focus {
+    border-color: var(--lora-accent);
+    outline: none;
+    box-shadow: 0 0 0 2px rgba(var(--lora-accent-rgb, 79, 70, 229), 0.1);
+}
+
+.base-model-skip-list {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(180px, 1fr));
+    gap: 8px;
+    max-height: 220px;
+    overflow-y: auto;
+}
+
+.base-model-skip-option {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 8px 10px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background-color: var(--settings-bg);
+    cursor: pointer;
+    transition: border-color 0.15s ease, background-color 0.15s ease;
+}
+
+.base-model-skip-option:hover {
+    border-color: var(--lora-accent);
+    background-color: rgba(var(--lora-accent-rgb, 79, 70, 229), 0.05);
+}
+
+.base-model-skip-option input {
+    margin: 0;
+}
+
+.base-model-skip-option span {
+    font-size: 0.9em;
+    line-height: 1.25;
+}
+
+.base-model-skip-empty {
+    padding: 8px 0 0;
+    font-size: 0.9em;
+    opacity: 0.75;
+}
+
 .priority-tags-input:focus {
     border-color: var(--lora-accent);
     outline: none;

static/css/components/recipe-modal.css

@@ -424,6 +424,7 @@
     display: flex;
     justify-content: space-between;
     align-items: center;
+    gap: 8px;
 }
 
 .param-header label {
@@ -431,7 +432,14 @@
     color: var(--text-color);
 }
 
-.copy-btn {
+.param-actions {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+}
+
+.copy-btn,
+.edit-btn {
     background: none;
     border: none;
     color: var(--text-color);
@@ -442,7 +450,8 @@
     transition: all 0.2s;
 }
 
-.copy-btn:hover {
+.copy-btn:hover,
+.edit-btn:hover {
     opacity: 1;
     background: var(--lora-surface);
 }
@@ -461,6 +470,48 @@
     word-break: break-word;
 }
 
+.param-content.hide {
+    display: none;
+}
+
+.param-content.is-placeholder {
+    color: color-mix(in oklch, var(--text-color), transparent 35%);
+    font-style: italic;
+}
+
+.param-editor {
+    display: none;
+    flex-direction: column;
+    gap: 10px;
+}
+
+.param-editor.active {
+    display: flex;
+}
+
+.param-textarea {
+    width: 100%;
+    max-width: 100%;
+    min-height: 140px;
+    resize: vertical;
+    background: var(--bg-color);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-xs);
+    padding: 10px 12px;
+    font-size: 0.9em;
+    line-height: 1.5;
+    color: var(--text-color);
+    font-family: inherit;
+    box-sizing: border-box;
+    overflow-x: hidden;
+}
+
+.param-editor-hint {
+    font-size: 0.78em;
+    line-height: 1.4;
+    color: color-mix(in oklch, var(--text-color), transparent 35%);
+}
+
 /* Other Parameters */
 .other-params {
     display: flex;
@@ -565,6 +616,26 @@
     color: var(--lora-accent);
 }
 
+.send-recipe-btn {
+    background: none;
+    border: none;
+    color: var(--text-color);
+    opacity: 0.7;
+    cursor: pointer;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.send-recipe-btn:hover {
+    opacity: 1;
+    background: var(--lora-surface);
+    color: var(--lora-accent);
+}
+
 #recipeLorasCount {
     font-size: 0.9em;
     color: var(--text-color);

static/js/api/civitaiBaseModelApi.js

@@ -0,0 +1,164 @@
+/**
+ * API client for Civitai base model management
+ * Handles fetching and refreshing base models from Civitai API
+ */
+
+import { showToast } from '../utils/uiHelpers.js';
+
+const BASE_MODEL_ENDPOINTS = {
+    getModels: '/api/lm/base-models',
+    refresh: '/api/lm/base-models/refresh',
+    categories: '/api/lm/base-models/categories',
+    cacheStatus: '/api/lm/base-models/cache-status',
+};
+
+/**
+ * Civitai Base Model API Client
+ */
+export class CivitaiBaseModelApi {
+    constructor() {
+        this.cache = null;
+        this.cacheTimestamp = null;
+    }
+
+    /**
+     * Get base models (with caching)
+     * @param {boolean} forceRefresh - Force refresh from API
+     * @returns {Promise<Object>} Response with models, source, and counts
+     */
+    async getBaseModels(forceRefresh = false) {
+        try {
+            const url = new URL(BASE_MODEL_ENDPOINTS.getModels, window.location.origin);
+            if (forceRefresh) {
+                url.searchParams.append('refresh', 'true');
+            }
+
+            const response = await fetch(url);
+            if (!response.ok) {
+                throw new Error(`Failed to fetch base models: ${response.statusText}`);
+            }
+
+            const data = await response.json();
+            
+            if (data.success) {
+                this.cache = data.data;
+                this.cacheTimestamp = Date.now();
+                return data.data;
+            } else {
+                throw new Error(data.error || 'Failed to fetch base models');
+            }
+        } catch (error) {
+            console.error('Error fetching base models:', error);
+            showToast('Failed to fetch base models', { message: error.message }, 'error');
+            throw error;
+        }
+    }
+
+    /**
+     * Force refresh base models from Civitai API
+     * @returns {Promise<Object>} Refreshed data
+     */
+    async refreshBaseModels() {
+        try {
+            const response = await fetch(BASE_MODEL_ENDPOINTS.refresh, {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' }
+            });
+
+            if (!response.ok) {
+                throw new Error(`Failed to refresh base models: ${response.statusText}`);
+            }
+
+            const data = await response.json();
+            
+            if (data.success) {
+                this.cache = data.data;
+                this.cacheTimestamp = Date.now();
+                showToast('Base models refreshed successfully', {}, 'success');
+                return data.data;
+            } else {
+                throw new Error(data.error || 'Failed to refresh base models');
+            }
+        } catch (error) {
+            console.error('Error refreshing base models:', error);
+            showToast('Failed to refresh base models', { message: error.message }, 'error');
+            throw error;
+        }
+    }
+
+    /**
+     * Get base model categories
+     * @returns {Promise<Object>} Categories with model lists
+     */
+    async getCategories() {
+        try {
+            const response = await fetch(BASE_MODEL_ENDPOINTS.categories);
+            if (!response.ok) {
+                throw new Error(`Failed to fetch categories: ${response.statusText}`);
+            }
+
+            const data = await response.json();
+            
+            if (data.success) {
+                return data.data;
+            } else {
+                throw new Error(data.error || 'Failed to fetch categories');
+            }
+        } catch (error) {
+            console.error('Error fetching categories:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get cache status
+     * @returns {Promise<Object>} Cache status information
+     */
+    async getCacheStatus() {
+        try {
+            const response = await fetch(BASE_MODEL_ENDPOINTS.cacheStatus);
+            if (!response.ok) {
+                throw new Error(`Failed to fetch cache status: ${response.statusText}`);
+            }
+
+            const data = await response.json();
+            
+            if (data.success) {
+                return data.data;
+            } else {
+                throw new Error(data.error || 'Failed to fetch cache status');
+            }
+        } catch (error) {
+            console.error('Error fetching cache status:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get cached models (if available)
+     * @returns {Object|null} Cached data or null
+     */
+    getCachedModels() {
+        return this.cache;
+    }
+
+    /**
+     * Check if cache is available
+     * @returns {boolean}
+     */
+    hasCache() {
+        return this.cache !== null;
+    }
+
+    /**
+     * Get cache age in milliseconds
+     * @returns {number|null} Age in ms or null if no cache
+     */
+    getCacheAge() {
+        if (!this.cacheTimestamp) return null;
+        return Date.now() - this.cacheTimestamp;
+    }
+}
+
+// Export singleton instance
+export const civitaiBaseModelApi = new CivitaiBaseModelApi();

static/js/api/recipeApi.js

@@ -83,6 +83,9 @@ export async function fetchRecipesPage(page = 1, pageSize = 100) {
         if (pageState.customFilter?.active && pageState.customFilter?.loraHash) {
             params.append('lora_hash', pageState.customFilter.loraHash);
             params.append('bypass_filters', 'true');
+        } else if (pageState.customFilter?.active && pageState.customFilter?.checkpointHash) {
+            params.append('checkpoint_hash', pageState.customFilter.checkpointHash);
+            params.append('bypass_filters', 'true');
         } else {
             // Normal filtering logic
 

static/js/components/ContextMenu/BulkContextMenu.js

@@ -2,6 +2,8 @@ import { BaseContextMenu } from './BaseContextMenu.js';
 import { state } from '../../state/index.js';
 import { bulkManager } from '../../managers/BulkManager.js';
 import { updateElementText, translate } from '../../utils/i18nHelpers.js';
+import { bulkMissingLoraDownloadManager } from '../../managers/BulkMissingLoraDownloadManager.js';
+import { showToast } from '../../utils/uiHelpers.js';
 
 export class BulkContextMenu extends BaseContextMenu {
     constructor() {
@@ -37,6 +39,7 @@ export class BulkContextMenu extends BaseContextMenu {
         const moveAllItem = this.menu.querySelector('[data-action="move-all"]');
         const autoOrganizeItem = this.menu.querySelector('[data-action="auto-organize"]');
         const deleteAllItem = this.menu.querySelector('[data-action="delete-all"]');
+        const downloadMissingLorasItem = this.menu.querySelector('[data-action="download-missing-loras"]');
 
         if (sendToWorkflowAppendItem) {
             sendToWorkflowAppendItem.style.display = config.sendToWorkflow ? 'flex' : 'none';
@@ -71,6 +74,10 @@ export class BulkContextMenu extends BaseContextMenu {
         if (setContentRatingItem) {
             setContentRatingItem.style.display = config.setContentRating ? 'flex' : 'none';
         }
+        if (downloadMissingLorasItem) {
+            // Only show for recipes page
+            downloadMissingLorasItem.style.display = currentModelType === 'recipes' ? 'flex' : 'none';
+        }
 
         const skipMetadataRefreshItem = this.menu.querySelector('[data-action="skip-metadata-refresh"]');
         const resumeMetadataRefreshItem = this.menu.querySelector('[data-action="resume-metadata-refresh"]');
@@ -178,6 +185,9 @@ export class BulkContextMenu extends BaseContextMenu {
             case 'delete-all':
                 bulkManager.showBulkDeleteModal();
                 break;
+            case 'download-missing-loras':
+                this.handleDownloadMissingLoras();
+                break;
             case 'clear':
                 bulkManager.clearSelection();
                 break;
@@ -185,4 +195,39 @@ export class BulkContextMenu extends BaseContextMenu {
                 console.warn(`Unknown bulk action: ${action}`);
         }
     }
+
+    /**
+     * Handle downloading missing LoRAs for selected recipes
+     */
+    async handleDownloadMissingLoras() {
+        if (state.selectedModels.size === 0) {
+            return;
+        }
+
+        // Get selected recipes from the virtual scroller
+        const selectedRecipes = [];
+        state.selectedModels.forEach(filePath => {
+            const card = document.querySelector(`.model-card[data-filepath="${CSS.escape(filePath)}"]`);
+            if (card && card.recipeData) {
+                selectedRecipes.push(card.recipeData);
+            }
+        });
+
+        if (selectedRecipes.length === 0) {
+            // Try to get recipes from virtual scroller state
+            const items = state.virtualScroller?.items || [];
+            items.forEach(recipe => {
+                if (recipe.file_path && state.selectedModels.has(recipe.file_path)) {
+                    selectedRecipes.push(recipe);
+                }
+            });
+        }
+
+        if (selectedRecipes.length === 0) {
+            showToast('toast.recipes.noRecipesSelected', {}, 'warning');
+            return;
+        }
+
+        await bulkMissingLoraDownloadManager.downloadMissingLoras(selectedRecipes);
+    }
 }

static/js/components/ContextMenu/CheckpointContextMenu.js

@@ -4,6 +4,8 @@ import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js'
 import { showDeleteModal, showExcludeModal } from '../../utils/modalUtils.js';
 import { moveManager } from '../../managers/MoveManager.js';
 import { i18n } from '../../i18n/index.js';
+import { sendModelPathToWorkflow } from '../../utils/uiHelpers.js';
+import { MODEL_TYPES } from '../../api/apiConfig.js';
 
 export class CheckpointContextMenu extends BaseContextMenu {
     constructor() {
@@ -60,6 +62,10 @@ export class CheckpointContextMenu extends BaseContextMenu {
                     this.currentCard.querySelector('.fa-copy').click();
                 }
                 break;
+            case 'sendworkflow':
+                // Send checkpoint to workflow (always replace mode)
+                this.sendCheckpointToWorkflow();
+                break;
             case 'refresh-metadata':
                 // Refresh metadata from CivitAI
                 apiClient.refreshSingleModelMetadata(this.currentCard.dataset.filepath);
@@ -79,6 +85,52 @@ export class CheckpointContextMenu extends BaseContextMenu {
                 break;
         }
     }
+
+    async sendCheckpointToWorkflow() {
+        const modelPath = this.currentCard.dataset.filepath;
+        if (!modelPath) {
+            return;
+        }
+
+        const subtype = (this.currentCard.dataset.sub_type || 'checkpoint').toLowerCase();
+        const isDiffusionModel = subtype === 'diffusion_model';
+        const widgetName = isDiffusionModel ? 'unet_name' : 'ckpt_name';
+        const actionTypeText = i18n.t(
+            isDiffusionModel ? 'uiHelpers.nodeSelector.diffusionModel' : 'uiHelpers.nodeSelector.checkpoint',
+            {},
+            isDiffusionModel ? 'Diffusion Model' : 'Checkpoint'
+        );
+        const successMessage = i18n.t(
+            'uiHelpers.workflow.modelUpdated',
+            {},
+            'Model updated in workflow'
+        );
+        const failureMessage = i18n.t(
+            'uiHelpers.workflow.modelFailed',
+            {},
+            'Failed to update model node'
+        );
+        const missingNodesMessage = i18n.t(
+            'uiHelpers.workflow.noMatchingNodes',
+            {},
+            'No compatible nodes available in the current workflow'
+        );
+        const missingTargetMessage = i18n.t(
+            'uiHelpers.workflow.noTargetNodeSelected',
+            {},
+            'No target node selected'
+        );
+
+        await sendModelPathToWorkflow(modelPath, {
+            widgetName,
+            collectionType: MODEL_TYPES.CHECKPOINT,
+            actionTypeText,
+            successMessage,
+            failureMessage,
+            missingNodesMessage,
+            missingTargetMessage,
+        });
+    }
 }
 
 // Mix in shared methods

static/js/components/RecipeCard.js

@@ -6,7 +6,7 @@ import { modalManager } from '../managers/ModalManager.js';
 import { getCurrentPageState } from '../state/index.js';
 import { state } from '../state/index.js';
 import { bulkManager } from '../managers/BulkManager.js';
-import { NSFW_LEVELS, getBaseModelAbbreviation } from '../utils/constants.js';
+import { NSFW_LEVELS, getBaseModelAbbreviation, getMatureBlurThreshold } from '../utils/constants.js';
 
 class RecipeCard {
     constructor(recipe, clickHandler) {
@@ -74,7 +74,8 @@ class RecipeCard {
 
         // NSFW blur logic - similar to LoraCard
         const nsfwLevel = this.recipe.preview_nsfw_level !== undefined ? this.recipe.preview_nsfw_level : 0;
-        const shouldBlur = state.settings.blur_mature_content && nsfwLevel > NSFW_LEVELS.PG13;
+        const matureBlurThreshold = getMatureBlurThreshold(state.settings);
+        const shouldBlur = state.settings.blur_mature_content && nsfwLevel >= matureBlurThreshold;
 
         if (shouldBlur) {
             card.classList.add('nsfw-content');

static/js/components/RecipeModal.js

@@ -1,5 +1,5 @@
 // Recipe Modal Component
-import { showToast, copyToClipboard, sendModelPathToWorkflow, openCivitaiByMetadata } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard, sendLoraToWorkflow, sendModelPathToWorkflow, openCivitaiByMetadata } from '../utils/uiHelpers.js';
 import { translate } from '../utils/i18nHelpers.js';
 import { state } from '../state/index.js';
 import { setSessionItem, removeSessionItem } from '../utils/storageHelpers.js';
@@ -9,11 +9,13 @@ import { MODEL_TYPES } from '../api/apiConfig.js';
 
 class RecipeModal {
     constructor() {
+        this.promptEditorState = {};
         this.init();
     }
 
     init() {
         this.setupCopyButtons();
+        this.setupPromptEditors();
         // Set up tooltip positioning handlers after DOM is ready
         document.addEventListener('DOMContentLoaded', () => {
             this.setupTooltipPositioning();
@@ -87,6 +89,7 @@ class RecipeModal {
     showRecipeDetails(recipe) {
         // Store the full recipe for editing
         this.currentRecipe = recipe;
+        this.resetPromptEditors();
 
         // Set modal title with edit icon
         const modalTitle = document.getElementById('recipeModalTitle');
@@ -300,20 +303,19 @@ class RecipeModal {
         const promptElement = document.getElementById('recipePrompt');
         const negativePromptElement = document.getElementById('recipeNegativePrompt');
         const otherParamsElement = document.getElementById('recipeOtherParams');
+        const promptInput = document.getElementById('recipePromptInput');
+        const negativePromptInput = document.getElementById('recipeNegativePromptInput');
 
         if (recipe.gen_params) {
-            // Set prompt
-            if (promptElement && recipe.gen_params.prompt) {
-                promptElement.textContent = recipe.gen_params.prompt;
-            } else if (promptElement) {
-                promptElement.textContent = 'No prompt information available';
+            this.renderPromptContent(promptElement, recipe.gen_params.prompt, 'No prompt information available');
+            this.renderPromptContent(negativePromptElement, recipe.gen_params.negative_prompt, 'No negative prompt information available');
+
+            if (promptInput) {
+                promptInput.value = recipe.gen_params.prompt || '';
             }
 
-            // Set negative prompt
-            if (negativePromptElement && recipe.gen_params.negative_prompt) {
-                negativePromptElement.textContent = recipe.gen_params.negative_prompt;
-            } else if (negativePromptElement) {
-                negativePromptElement.textContent = 'No negative prompt information available';
+            if (negativePromptInput) {
+                negativePromptInput.value = recipe.gen_params.negative_prompt || '';
             }
 
             // Set other parameters
@@ -343,8 +345,10 @@ class RecipeModal {
             }
         } else {
             // No generation parameters available
-            if (promptElement) promptElement.textContent = 'No prompt information available';
-            if (negativePromptElement) promptElement.textContent = 'No negative prompt information available';
+            this.renderPromptContent(promptElement, '', 'No prompt information available');
+            this.renderPromptContent(negativePromptElement, '', 'No negative prompt information available');
+            if (promptInput) promptInput.value = '';
+            if (negativePromptInput) negativePromptInput.value = '';
             if (otherParamsElement) otherParamsElement.innerHTML = '<div class="no-params">No parameters available</div>';
         }
 
@@ -711,16 +715,202 @@ class RecipeModal {
         }
     }
 
+    setupPromptEditors() {
+        const promptConfigs = [
+            {
+                editButtonId: 'editPromptBtn',
+                contentId: 'recipePrompt',
+                editorId: 'recipePromptEditor',
+                inputId: 'recipePromptInput',
+                field: 'prompt',
+                placeholder: 'No prompt information available',
+                successKey: 'toast.recipes.promptUpdated',
+                successFallback: 'Prompt updated successfully',
+            },
+            {
+                editButtonId: 'editNegativePromptBtn',
+                contentId: 'recipeNegativePrompt',
+                editorId: 'recipeNegativePromptEditor',
+                inputId: 'recipeNegativePromptInput',
+                field: 'negative_prompt',
+                placeholder: 'No negative prompt information available',
+                successKey: 'toast.recipes.negativePromptUpdated',
+                successFallback: 'Negative prompt updated successfully',
+            }
+        ];
+
+        promptConfigs.forEach((config) => {
+            const editButton = document.getElementById(config.editButtonId);
+            const input = document.getElementById(config.inputId);
+
+            if (editButton) {
+                editButton.addEventListener('click', () => this.showPromptEditor(config));
+            }
+
+            if (input) {
+                input.addEventListener('keydown', (event) => {
+                    if (event.key === 'Escape') {
+                        event.preventDefault();
+                        event.stopPropagation();
+                        this.cancelPromptEdit(config);
+                        return;
+                    }
+
+                    if (event.key === 'Enter' && !event.shiftKey) {
+                        event.preventDefault();
+                        event.stopPropagation();
+                        this.promptEditorState[config.field] = {
+                            ...(this.promptEditorState[config.field] || {}),
+                            skipBlurSave: true,
+                        };
+                        this.savePromptEdit(config);
+                    }
+                });
+                input.addEventListener('blur', () => {
+                    const promptState = this.promptEditorState[config.field] || {};
+                    if (promptState.skipBlurSave) {
+                        this.promptEditorState[config.field] = {
+                            ...promptState,
+                            skipBlurSave: false,
+                        };
+                        return;
+                    }
+
+                    this.savePromptEdit(config);
+                });
+            }
+        });
+    }
+
+    renderPromptContent(element, value, placeholder) {
+        if (!element) {
+            return;
+        }
+
+        const text = value || '';
+        if (text) {
+            element.textContent = text;
+            element.classList.remove('is-placeholder');
+        } else {
+            element.textContent = placeholder;
+            element.classList.add('is-placeholder');
+        }
+    }
+
+    resetPromptEditors() {
+        this.hidePromptEditor({ contentId: 'recipePrompt', editorId: 'recipePromptEditor' });
+        this.hidePromptEditor({ contentId: 'recipeNegativePrompt', editorId: 'recipeNegativePromptEditor' });
+    }
+
+    showPromptEditor(config) {
+        const content = document.getElementById(config.contentId);
+        const editor = document.getElementById(config.editorId);
+        const input = document.getElementById(config.inputId);
+
+        if (!content || !editor || !input) {
+            return;
+        }
+
+        const currentValue = this.currentRecipe?.gen_params?.[config.field] || '';
+        input.value = currentValue;
+        this.promptEditorState[config.field] = {
+            initialValue: currentValue,
+            skipBlurSave: false,
+            isSaving: false,
+        };
+        content.classList.add('hide');
+        editor.classList.add('active');
+        input.focus();
+        input.setSelectionRange(input.value.length, input.value.length);
+    }
+
+    async savePromptEdit(config) {
+        const content = document.getElementById(config.contentId);
+        const editor = document.getElementById(config.editorId);
+        const input = document.getElementById(config.inputId);
+
+        if (!content || !editor || !input || !this.currentRecipe) {
+            return;
+        }
+
+        const promptState = this.promptEditorState[config.field] || {};
+        if (promptState.isSaving) {
+            return;
+        }
+
+        const currentGenParams = this.currentRecipe.gen_params || {};
+        const nextValue = input.value.trim() === '' ? '' : input.value;
+        const currentValue = currentGenParams[config.field] || '';
+
+        if (nextValue === currentValue) {
+            this.hidePromptEditor(config);
+            return;
+        }
+
+        const nextGenParams = {
+            ...currentGenParams,
+            [config.field]: nextValue,
+        };
+
+        try {
+            this.promptEditorState[config.field] = {
+                ...promptState,
+                isSaving: true,
+            };
+            await updateRecipeMetadata(this.filePath, { gen_params: nextGenParams });
+            this.currentRecipe.gen_params = nextGenParams;
+            this.renderPromptContent(content, nextValue, config.placeholder);
+            showToast(config.successKey, {}, 'success', config.successFallback);
+        } catch (error) {
+            this.renderPromptContent(content, currentValue, config.placeholder);
+            input.value = currentValue;
+        } finally {
+            this.hidePromptEditor(config);
+        }
+    }
+
+    cancelPromptEdit(config) {
+        const input = document.getElementById(config.inputId);
+        if (input) {
+            const initialValue = this.promptEditorState[config.field]?.initialValue;
+            input.value = initialValue ?? (this.currentRecipe?.gen_params?.[config.field] || '');
+        }
+
+        this.hidePromptEditor(config);
+    }
+
+    hidePromptEditor(config) {
+        const content = document.getElementById(config.contentId);
+        const editor = document.getElementById(config.editorId);
+
+        if (content) {
+            content.classList.remove('hide');
+        }
+
+        if (editor) {
+            editor.classList.remove('active');
+        }
+
+        delete this.promptEditorState[config.field];
+    }
+
     // Setup source URL handlers
     setupSourceUrlHandlers() {
         const sourceUrlContainer = document.querySelector('.source-url-container');
         const sourceUrlEditor = document.querySelector('.source-url-editor');
+        if (!sourceUrlContainer || !sourceUrlEditor) {
+            return;
+        }
         const sourceUrlText = sourceUrlContainer.querySelector('.source-url-text');
         const sourceUrlEditBtn = sourceUrlContainer.querySelector('.source-url-edit-btn');
         const sourceUrlCancelBtn = sourceUrlEditor.querySelector('.source-url-cancel-btn');
         const sourceUrlSaveBtn = sourceUrlEditor.querySelector('.source-url-save-btn');
         const sourceUrlInput = sourceUrlEditor.querySelector('.source-url-input');
 
+        if (!sourceUrlText || !sourceUrlEditBtn || !sourceUrlCancelBtn || !sourceUrlSaveBtn || !sourceUrlInput) {
+            return;
+        }
+
         // Show editor on edit button click
         sourceUrlEditBtn.addEventListener('click', () => {
             sourceUrlContainer.classList.add('hide');
@@ -778,17 +968,18 @@ class RecipeModal {
         const copyPromptBtn = document.getElementById('copyPromptBtn');
         const copyNegativePromptBtn = document.getElementById('copyNegativePromptBtn');
         const copyRecipeSyntaxBtn = document.getElementById('copyRecipeSyntaxBtn');
+        const sendRecipeBtn = document.getElementById('sendRecipeBtn');
 
         if (copyPromptBtn) {
             copyPromptBtn.addEventListener('click', () => {
-                const promptText = document.getElementById('recipePrompt').textContent;
+                const promptText = this.currentRecipe?.gen_params?.prompt || '';
                 this.copyToClipboard(promptText, 'Prompt copied to clipboard');
             });
         }
 
         if (copyNegativePromptBtn) {
             copyNegativePromptBtn.addEventListener('click', () => {
-                const negativePromptText = document.getElementById('recipeNegativePrompt').textContent;
+                const negativePromptText = this.currentRecipe?.gen_params?.negative_prompt || '';
                 this.copyToClipboard(negativePromptText, 'Negative prompt copied to clipboard');
             });
         }
@@ -799,6 +990,13 @@ class RecipeModal {
                 this.fetchAndCopyRecipeSyntax();
             });
         }
+
+        if (sendRecipeBtn) {
+            sendRecipeBtn.addEventListener('click', () => {
+                // Send recipe to ComfyUI workflow
+                this.sendRecipeToWorkflow();
+            });
+        }
     }
 
     // Fetch recipe syntax from backend and copy to clipboard
@@ -835,6 +1033,35 @@ class RecipeModal {
         copyToClipboard(text, successMessage);
     }
 
+    // Send recipe to ComfyUI workflow
+    async sendRecipeToWorkflow() {
+        if (!this.recipeId) {
+            showToast('toast.recipes.noRecipeId', {}, 'error');
+            return;
+        }
+
+        try {
+            // Fetch recipe syntax from backend
+            const response = await fetch(`/api/lm/recipe/${this.recipeId}/syntax`);
+
+            if (!response.ok) {
+                throw new Error(`Failed to get recipe syntax: ${response.statusText}`);
+            }
+
+            const data = await response.json();
+
+            if (data.success && data.syntax) {
+                // Send the recipe syntax to ComfyUI workflow
+                await sendLoraToWorkflow(data.syntax, false, 'recipe');
+            } else {
+                throw new Error(data.error || 'No syntax returned from server');
+            }
+        } catch (error) {
+            console.error('Error sending recipe to workflow:', error);
+            showToast('toast.recipes.sendToWorkflowFailed', { message: error.message }, 'error');
+        }
+    }
+
     // Add new method to handle downloading missing LoRAs
     async showDownloadMissingLorasModal() {
         console.log("currentRecipe", this.currentRecipe);
@@ -1188,14 +1415,14 @@ class RecipeModal {
             isDiffusionModel ? 'Diffusion Model' : 'Checkpoint'
         );
         const successMessage = translate(
-            isDiffusionModel ? 'uiHelpers.workflow.diffusionModelUpdated' : 'uiHelpers.workflow.checkpointUpdated',
+            'uiHelpers.workflow.modelUpdated',
             {},
-            isDiffusionModel ? 'Diffusion model updated in workflow' : 'Checkpoint updated in workflow'
+            'Model updated in workflow'
         );
         const failureMessage = translate(
-            isDiffusionModel ? 'uiHelpers.workflow.diffusionModelFailed' : 'uiHelpers.workflow.checkpointFailed',
+            'uiHelpers.workflow.modelFailed',
             {},
-            isDiffusionModel ? 'Failed to update diffusion model node' : 'Failed to update checkpoint node'
+            'Failed to update model node'
         );
         const missingNodesMessage = translate(
             'uiHelpers.workflow.noMatchingNodes',
@@ -1259,7 +1486,7 @@ class RecipeModal {
             const versionId = checkpoint.id || checkpoint.modelVersionId;
             const modelName = checkpoint.name || checkpoint.modelName || checkpoint.file_name;
 
-            if (modelId || modelName) {
+            if (modelId || versionId || modelName) {
                 openCivitaiByMetadata(modelId, versionId, modelName);
                 return;
             }
@@ -1299,7 +1526,6 @@ class RecipeModal {
 
     // New method to navigate to the LoRAs page
     navigateToLorasPage(specificLoraIndex = null) {
-        debugger;
         // Close the current modal
         modalManager.closeModal('recipeModal');
 
@@ -1318,7 +1544,7 @@ class RecipeModal {
                 const versionId = lora.id || lora.modelVersionId;
                 const modelName = lora.modelName || lora.name || lora.file_name;
 
-                if (modelId || modelName) {
+                if (modelId || versionId || modelName) {
                     openCivitaiByMetadata(modelId, versionId, modelName);
                     return;
                 }

static/js/components/shared/ModelCard.js

@@ -4,7 +4,7 @@ import { showModelModal } from './ModelModal.js';
 import { toggleShowcase } from './showcase/ShowcaseView.js';
 import { bulkManager } from '../../managers/BulkManager.js';
 import { modalManager } from '../../managers/ModalManager.js';
-import { NSFW_LEVELS, getBaseModelAbbreviation, getSubTypeAbbreviation, MODEL_SUBTYPE_DISPLAY_NAMES } from '../../utils/constants.js';
+import { NSFW_LEVELS, getBaseModelAbbreviation, getSubTypeAbbreviation, getMatureBlurThreshold, MODEL_SUBTYPE_DISPLAY_NAMES } from '../../utils/constants.js';
 import { MODEL_TYPES } from '../../api/apiConfig.js';
 import { getModelApiClient } from '../../api/modelApiFactory.js';
 import { showDeleteModal } from '../../utils/modalUtils.js';
@@ -185,14 +185,14 @@ function handleSendToWorkflow(card, replaceMode, modelType) {
             isDiffusionModel ? 'Diffusion Model' : 'Checkpoint'
         );
         const successMessage = translate(
-            isDiffusionModel ? 'uiHelpers.workflow.diffusionModelUpdated' : 'uiHelpers.workflow.checkpointUpdated',
+            'uiHelpers.workflow.modelUpdated',
             {},
-            isDiffusionModel ? 'Diffusion model updated in workflow' : 'Checkpoint updated in workflow'
+            'Model updated in workflow'
         );
         const failureMessage = translate(
-            isDiffusionModel ? 'uiHelpers.workflow.diffusionModelFailed' : 'uiHelpers.workflow.checkpointFailed',
+            'uiHelpers.workflow.modelFailed',
             {},
-            isDiffusionModel ? 'Failed to update diffusion model node' : 'Failed to update checkpoint node'
+            'Failed to update model node'
         );
         const missingNodesMessage = translate(
             'uiHelpers.workflow.noMatchingNodes',
@@ -478,7 +478,8 @@ export function createModelCard(model, modelType) {
     card.dataset.nsfwLevel = nsfwLevel;
 
     // Determine if the preview should be blurred based on NSFW level and user settings
-    const shouldBlur = state.settings.blur_mature_content && nsfwLevel > NSFW_LEVELS.PG13;
+    const matureBlurThreshold = getMatureBlurThreshold(state.settings);
+    const shouldBlur = state.settings.blur_mature_content && nsfwLevel >= matureBlurThreshold;
     if (shouldBlur) {
         card.classList.add('nsfw-content');
     }

static/js/components/shared/ModelModal.js

@@ -1,5 +1,6 @@
-import { showToast, openCivitai } from '../../utils/uiHelpers.js';
+import { showToast, openCivitai, sendLoraToWorkflow, sendModelPathToWorkflow, buildLoraSyntax } from '../../utils/uiHelpers.js';
 import { modalManager } from '../../managers/ModalManager.js';
+import { MODEL_TYPES } from '../../api/apiConfig.js';
 import {
     toggleShowcase,
     setupShowcaseScroll,
@@ -18,7 +19,7 @@ import { renderCompactTags, setupTagTooltip, formatFileSize, escapeAttribute, es
 import { renderTriggerWords, setupTriggerWordsEditMode } from './TriggerWords.js';
 import { parsePresets, renderPresetTags } from './PresetTags.js';
 import { initVersionsTab } from './ModelVersionsTab.js';
-import { loadRecipesForLora } from './RecipeTab.js';
+import { loadRecipesForModel } from './RecipeTab.js';
 import { translate } from '../../utils/i18nHelpers.js';
 import { state } from '../../state/index.js';
 
@@ -294,6 +295,17 @@ export async function showModelModal(model, modelType) {
         ].join('\n')
         : '';
     const headerActionItems = [];
+    
+    // Add send to ComfyUI button for all model types
+    const sendToWorkflowTitle = translate('modals.model.actions.sendToWorkflow', {}, 'Send to ComfyUI');
+    const sendToWorkflowButton = `
+        <button class="modal-send-btn" data-action="send-to-workflow" data-model-type="${modelType}" title="${sendToWorkflowTitle}">
+            <i class="fas fa-paper-plane"></i>
+            <span>${translate('modals.model.actions.sendToWorkflowText', {}, 'Send to ComfyUI')}</span>
+        </button>
+    `.trim();
+    headerActionItems.push(indentMarkup(sendToWorkflowButton, 20));
+    
     if (creatorActionsMarkup) {
         headerActionItems.push(creatorActionsMarkup);
     }
@@ -343,7 +355,9 @@ export async function showModelModal(model, modelType) {
                 ${versionsTabBadge}
             </button>`.trim();
 
-    const tabsContent = modelType === 'loras' ?
+    const supportsRecipesTab = modelType === 'loras' || modelType === 'checkpoints';
+
+    const tabsContent = supportsRecipesTab ?
         `<button class="tab-btn active" data-tab="showcase">${examplesText}</button>
             <button class="tab-btn" data-tab="description">${descriptionText}</button>
             ${versionsTabButton}
@@ -373,7 +387,7 @@ export async function showModelModal(model, modelType) {
                     </button>
                 </div>`.trim();
 
-    const tabPanesContent = modelType === 'loras' ?
+    const tabPanesContent = supportsRecipesTab ?
         `<div id="showcase-tab" class="tab-pane active">
             <div class="example-images-loading">
                 <i class="fas fa-spinner fa-spin"></i> ${loadingExampleImagesText}
@@ -615,6 +629,14 @@ export async function showModelModal(model, modelType) {
     const activeModalElement = document.getElementById(modalId);
     if (activeModalElement) {
         activeModalElement.dataset.filePath = modelWithFullData.file_path || '';
+        // Store usage_tips for LoRA models
+        if (modelType === 'loras' && modelWithFullData.usage_tips) {
+            activeModalElement.dataset.usageTips = modelWithFullData.usage_tips;
+        }
+        // Store sub_type for checkpoint models
+        if (modelType === 'checkpoints' && modelWithFullData.sub_type) {
+            activeModalElement.dataset.subType = modelWithFullData.sub_type;
+        }
     }
     updateVersionsTabBadge(updateAvailabilityState.hasUpdateAvailable);
     const versionsTabController = initVersionsTab({
@@ -644,14 +666,23 @@ export async function showModelModal(model, modelType) {
     setupNavigationShortcuts(modelType);
     updateNavigationControls();
 
-    // LoRA specific setup
+    // Model-specific setup
     if (modelType === 'loras' || modelType === 'embeddings') {
         setupTriggerWordsEditMode();
+    }
 
-        if (modelType == 'loras') {
-            // Load recipes for this LoRA
-            loadRecipesForLora(modelWithFullData.model_name, modelWithFullData.sha256);
-        }
+    if (modelType === 'loras') {
+        loadRecipesForModel({
+            modelKind: 'lora',
+            displayName: modelWithFullData.model_name,
+            sha256: modelWithFullData.sha256,
+        });
+    } else if (modelType === 'checkpoints') {
+        loadRecipesForModel({
+            modelKind: 'checkpoint',
+            displayName: modelWithFullData.model_name,
+            sha256: modelWithFullData.sha256,
+        });
     }
 
     // Load example images asynchronously - merge regular and custom images
@@ -747,6 +778,9 @@ function setupEventHandlers(filePath, modelType) {
             case 'nav-next':
                 handleDirectionalNavigation('next', modelType);
                 break;
+            case 'send-to-workflow':
+                handleSendToWorkflow(target, modelType);
+                break;
         }
     }
 
@@ -1026,6 +1060,70 @@ async function openFileLocation(filePath) {
     }
 }
 
+async function handleSendToWorkflow(target, modelType) {
+    const filePath = getModalFilePath();
+    if (!filePath) {
+        showToast('modals.model.sendToWorkflow.noFilePath', {}, 'error');
+        return;
+    }
+
+    // Get the current model data from the modal
+    const modalElement = document.getElementById('modelModal');
+    const currentFileName = modalElement?.querySelector('#file-name')?.textContent || '';
+    
+    if (modelType === 'loras') {
+        // For LoRA: Build syntax from usage tips and send
+        const usageTipsData = modalElement?.dataset?.usageTips;
+        const usageTips = usageTipsData ? JSON.parse(usageTipsData) : {};
+        const loraSyntax = buildLoraSyntax(currentFileName, usageTips);
+        await sendLoraToWorkflow(loraSyntax, false, 'lora');
+    } else if (modelType === 'checkpoints') {
+        // For Checkpoint: Send model path
+        const subtype = (modalElement?.dataset?.subType || 'checkpoint').toLowerCase();
+        const isDiffusionModel = subtype === 'diffusion_model';
+        const widgetName = isDiffusionModel ? 'unet_name' : 'ckpt_name';
+        const actionTypeText = translate(
+            isDiffusionModel ? 'uiHelpers.nodeSelector.diffusionModel' : 'uiHelpers.nodeSelector.checkpoint',
+            {},
+            isDiffusionModel ? 'Diffusion Model' : 'Checkpoint'
+        );
+        const successMessage = translate(
+            'uiHelpers.workflow.modelUpdated',
+            {},
+            'Model updated in workflow'
+        );
+        const failureMessage = translate(
+            'uiHelpers.workflow.modelFailed',
+            {},
+            'Failed to update model node'
+        );
+        const missingNodesMessage = translate(
+            'uiHelpers.workflow.noMatchingNodes',
+            {},
+            'No compatible nodes available in the current workflow'
+        );
+        const missingTargetMessage = translate(
+            'uiHelpers.workflow.noTargetNodeSelected',
+            {},
+            'No target node selected'
+        );
+
+        await sendModelPathToWorkflow(filePath, {
+            widgetName,
+            collectionType: MODEL_TYPES.CHECKPOINT,
+            actionTypeText,
+            successMessage,
+            failureMessage,
+            missingNodesMessage,
+            missingTargetMessage,
+        });
+    } else if (modelType === 'embeddings') {
+        // For Embedding: Send as LoRA syntax (embedding name only)
+        const embeddingSyntax = `<embed:${currentFileName}:1>`;
+        await sendLoraToWorkflow(embeddingSyntax, false, 'embedding');
+    }
+}
+
 // Export the model modal API
 const modelModal = {
     show: showModelModal,

static/js/components/shared/RecipeTab.js

@@ -1,38 +1,47 @@
 /**
- * RecipeTab - Handles the recipes tab in model modals (LoRA specific functionality)
- * Moved to shared directory for consistency
+ * RecipeTab - Handles the recipes tab in model modals.
  */
 import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { setSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
 
 /**
- * Loads recipes that use the specified Lora and renders them in the tab
- * @param {string} loraName - The display name of the Lora
- * @param {string} sha256 - The SHA256 hash of the Lora
+ * Loads recipes that use the specified model and renders them in the tab.
+ * @param {Object} options
+ * @param {'lora'|'checkpoint'} options.modelKind - Model kind for copy and endpoint selection
+ * @param {string} options.displayName - The display name of the model
+ * @param {string} options.sha256 - The SHA256 hash of the model
  */
-export function loadRecipesForLora(loraName, sha256) {
+export function loadRecipesForModel({ modelKind, displayName, sha256 }) {
     const recipeTab = document.getElementById('recipes-tab');
     if (!recipeTab) return;
-    
+
+    const normalizedHash = sha256?.toLowerCase?.() || '';
+    const modelLabel = getModelLabel(modelKind);
+
     // Show loading state
     recipeTab.innerHTML = `
         <div class="recipes-loading">
             <i class="fas fa-spinner fa-spin"></i> Loading recipes...
         </div>
     `;
-    
-    // Fetch recipes that use this Lora by hash
-    fetch(`/api/lm/recipes/for-lora?hash=${encodeURIComponent(sha256.toLowerCase())}`)
+
+    // Fetch recipes that use this model by hash
+    fetch(`${getRecipesEndpoint(modelKind)}?hash=${encodeURIComponent(normalizedHash)}`)
         .then(response => response.json())
         .then(data => {
             if (!data.success) {
                 throw new Error(data.error || 'Failed to load recipes');
             }
-            
-            renderRecipes(recipeTab, data.recipes, loraName, sha256);
+
+            renderRecipes(recipeTab, data.recipes, {
+                modelKind,
+                displayName,
+                modelHash: normalizedHash,
+                modelLabel,
+            });
         })
         .catch(error => {
-            console.error('Error loading recipes for Lora:', error);
+            console.error(`Error loading recipes for ${modelLabel}:`, error);
             recipeTab.innerHTML = `
                 <div class="recipes-error">
                     <i class="fas fa-exclamation-circle"></i>
@@ -46,18 +55,24 @@ export function loadRecipesForLora(loraName, sha256) {
  * Renders the recipe cards in the tab
  * @param {HTMLElement} tabElement - The tab element to render into
  * @param {Array} recipes - Array of recipe objects
- * @param {string} loraName - The display name of the Lora
- * @param {string} loraHash - The hash of the Lora
+ * @param {Object} options - Render options
  */
-function renderRecipes(tabElement, recipes, loraName, loraHash) {
+function renderRecipes(tabElement, recipes, options) {
+    const {
+        modelKind,
+        displayName,
+        modelHash,
+        modelLabel,
+    } = options;
+
     if (!recipes || recipes.length === 0) {
         tabElement.innerHTML = `
             <div class="recipes-empty">
                 <i class="fas fa-book-open"></i>
-                <p>No recipes found that use this Lora.</p>
+                <p>No recipes found that use this ${modelLabel}.</p>
             </div>
         `;
-        
+
         return;
     }
 
@@ -73,13 +88,13 @@ function renderRecipes(tabElement, recipes, loraName, loraHash) {
     headerText.appendChild(eyebrow);
 
     const title = document.createElement('h3');
-    title.textContent = `${recipes.length} recipe${recipes.length > 1 ? 's' : ''} using this Lora`;
+    title.textContent = `${recipes.length} recipe${recipes.length > 1 ? 's' : ''} using this ${modelLabel}`;
     headerText.appendChild(title);
 
     const description = document.createElement('p');
     description.className = 'recipes-header__description';
-    description.textContent = loraName ?
-        `Discover workflows crafted for ${loraName}.` :
+    description.textContent = displayName ?
+        `Discover workflows crafted for ${displayName}.` :
         'Discover workflows crafted for this model.';
     headerText.appendChild(description);
 
@@ -101,7 +116,11 @@ function renderRecipes(tabElement, recipes, loraName, loraHash) {
     headerElement.appendChild(viewAllButton);
 
     viewAllButton.addEventListener('click', () => {
-        navigateToRecipesPage(loraName, loraHash);
+        navigateToRecipesPage({
+            modelKind,
+            displayName,
+            modelHash,
+        });
     });
 
     const cardGrid = document.createElement('div');
@@ -280,26 +299,32 @@ function copyRecipeSyntax(recipeId) {
 }
 
 /**
- * Navigates to the recipes page with filter for the current Lora
- * @param {string} loraName - The Lora display name to filter by
- * @param {string} loraHash - The hash of the Lora to filter by
- * @param {boolean} createNew - Whether to open the create recipe dialog
+ * Navigates to the recipes page with filter for the current model
+ * @param {Object} options - Navigation options
  */
-function navigateToRecipesPage(loraName, loraHash) {
+function navigateToRecipesPage({ modelKind, displayName, modelHash }) {
     // Close the current modal
     if (window.modalManager) {
         modalManager.closeModal('modelModal');
     }
-    
+
     // Clear any previous filters first
     removeSessionItem('lora_to_recipe_filterLoraName');
     removeSessionItem('lora_to_recipe_filterLoraHash');
+    removeSessionItem('checkpoint_to_recipe_filterCheckpointName');
+    removeSessionItem('checkpoint_to_recipe_filterCheckpointHash');
     removeSessionItem('viewRecipeId');
-    
-    // Store the LoRA name and hash filter in sessionStorage
-    setSessionItem('lora_to_recipe_filterLoraName', loraName);
-    setSessionItem('lora_to_recipe_filterLoraHash', loraHash);
-    
+
+    if (modelKind === 'checkpoint') {
+        // Store the checkpoint name and hash filter in sessionStorage
+        setSessionItem('checkpoint_to_recipe_filterCheckpointName', displayName);
+        setSessionItem('checkpoint_to_recipe_filterCheckpointHash', modelHash);
+    } else {
+        // Store the LoRA name and hash filter in sessionStorage
+        setSessionItem('lora_to_recipe_filterLoraName', displayName);
+        setSessionItem('lora_to_recipe_filterLoraHash', modelHash);
+    }
+
     // Directly navigate to recipes page
     window.location.href = '/loras/recipes';
 }
@@ -321,7 +346,18 @@ function navigateToRecipeDetails(recipeId) {
     
     // Store the recipe ID in sessionStorage to load on recipes page
     setSessionItem('viewRecipeId', recipeId);
-    
+
     // Directly navigate to recipes page
     window.location.href = '/loras/recipes';
 }
+
+function getRecipesEndpoint(modelKind) {
+    if (modelKind === 'checkpoint') {
+        return '/api/lm/recipes/for-checkpoint';
+    }
+    return '/api/lm/recipes/for-lora';
+}
+
+function getModelLabel(modelKind) {
+    return modelKind === 'checkpoint' ? 'checkpoint' : 'LoRA';
+}

static/js/components/shared/showcase/MediaUtils.js

@@ -6,7 +6,7 @@
 import { showToast, copyToClipboard, getNSFWLevelName } from '../../../utils/uiHelpers.js';
 import { state } from '../../../state/index.js';
 import { getModelApiClient } from '../../../api/modelApiFactory.js';
-import { NSFW_LEVELS } from '../../../utils/constants.js';
+import { NSFW_LEVELS, getMatureBlurThreshold } from '../../../utils/constants.js';
 import { getNsfwLevelSelector } from '../NsfwLevelSelector.js';
 
 /**
@@ -607,7 +607,8 @@ function applyNsfwLevelChange(mediaWrapper, nsfwLevel) {
     }
     mediaWrapper.dataset.nsfwLevel = String(nsfwLevel);
 
-    const shouldBlur = state.settings.blur_mature_content && nsfwLevel > NSFW_LEVELS.PG13;
+    const matureBlurThreshold = getMatureBlurThreshold(state.settings);
+    const shouldBlur = state.settings.blur_mature_content && nsfwLevel >= matureBlurThreshold;
     let overlay = mediaWrapper.querySelector('.nsfw-overlay');
     let toggleBtn = mediaWrapper.querySelector('.toggle-blur-btn');
 

static/js/components/shared/showcase/ShowcaseView.js

@@ -6,7 +6,7 @@ import { showToast } from '../../../utils/uiHelpers.js';
 import { state } from '../../../state/index.js';
 import { modalManager } from '../../../managers/ModalManager.js';
 import { translate } from '../../../utils/i18nHelpers.js';
-import { NSFW_LEVELS } from '../../../utils/constants.js';
+import { NSFW_LEVELS, getMatureBlurThreshold } from '../../../utils/constants.js';
 import { 
     initLazyLoading,
     initNsfwBlurHandlers, 
@@ -184,7 +184,8 @@ function renderMediaItem(img, index, exampleFiles) {
     
     // Check if media should be blurred
     const nsfwLevel = img.nsfwLevel !== undefined ? img.nsfwLevel : 0;
-    const shouldBlur = state.settings.blur_mature_content && nsfwLevel > NSFW_LEVELS.PG13;
+    const matureBlurThreshold = getMatureBlurThreshold(state.settings);
+    const shouldBlur = state.settings.blur_mature_content && nsfwLevel >= matureBlurThreshold;
     
     // Determine NSFW warning text based on level
     let nsfwText = "Mature Content";

static/js/core.js

@@ -17,6 +17,8 @@ import { onboardingManager } from './managers/OnboardingManager.js';
 import { BulkContextMenu } from './components/ContextMenu/BulkContextMenu.js';
 import { createPageContextMenu, createGlobalContextMenu } from './components/ContextMenu/index.js';
 import { initializeEventManagement } from './utils/eventManagementInit.js';
+import { civitaiBaseModelApi } from './api/civitaiBaseModelApi.js';
+import { setDynamicBaseModels } from './utils/constants.js';
 
 // Core application class
 export class AppCore {
@@ -42,6 +44,10 @@ export class AppCore {
         await settingsManager.waitForInitialization();
         console.log('AppCore: Settings initialized');
         
+        // Initialize dynamic base models (async, non-blocking)
+        console.log('AppCore: Initializing dynamic base models...');
+        this.initializeDynamicBaseModels();
+        
         // Initialize managers
         state.loadingManager = new LoadingManager();
         modalManager.initialize();
@@ -116,6 +122,21 @@ export class AppCore {
             window.globalContextMenuInstance = createGlobalContextMenu();
         }
     }
+    
+    // Initialize dynamic base models from Civitai API
+    // This is non-blocking - runs in background
+    async initializeDynamicBaseModels() {
+        try {
+            const result = await civitaiBaseModelApi.getBaseModels();
+            if (result && result.models) {
+                setDynamicBaseModels(result.models, result.last_updated);
+                console.log(`AppCore: Loaded ${result.merged_count} base models (${result.hardcoded_count} hardcoded + ${result.remote_count} remote)`);
+            }
+        } catch (error) {
+            console.warn('AppCore: Failed to load dynamic base models:', error);
+            // Non-critical error - app continues with hardcoded models
+        }
+    }
 }
 
 // Create and export a singleton instance

static/js/managers/BulkMissingLoraDownloadManager.js

@@ -0,0 +1,357 @@
+import { showToast } from '../utils/uiHelpers.js';
+import { translate } from '../utils/i18nHelpers.js';
+import { getModelApiClient } from '../api/modelApiFactory.js';
+import { MODEL_TYPES } from '../api/apiConfig.js';
+import { state } from '../state/index.js';
+import { modalManager } from './ModalManager.js';
+
+/**
+ * Manager for downloading missing LoRAs for selected recipes in bulk
+ */
+export class BulkMissingLoraDownloadManager {
+    constructor() {
+        this.loraApiClient = getModelApiClient(MODEL_TYPES.LORA);
+        this.pendingLoras = [];
+        this.pendingRecipes = [];
+    }
+
+    /**
+     * Collect missing LoRAs from selected recipes with deduplication
+     * @param {Array} selectedRecipes - Array of selected recipe objects
+     * @returns {Object} - Object containing unique missing LoRAs and statistics
+     */
+    collectMissingLoras(selectedRecipes) {
+        const uniqueLoras = new Map(); // key: hash or modelVersionId, value: lora object
+        const missingLorasByRecipe = new Map();
+        let totalMissingCount = 0;
+
+        selectedRecipes.forEach(recipe => {
+            const missingLoras = [];
+            
+            if (recipe.loras && Array.isArray(recipe.loras)) {
+                recipe.loras.forEach(lora => {
+                    // Only include LoRAs not in library and not deleted
+                    if (!lora.inLibrary && !lora.isDeleted) {
+                        const uniqueKey = lora.hash || lora.id || lora.modelVersionId;
+                        
+                        if (uniqueKey && !uniqueLoras.has(uniqueKey)) {
+                            // Store the LoRA info
+                            uniqueLoras.set(uniqueKey, {
+                                ...lora,
+                                modelId: lora.modelId || lora.model_id,
+                                id: lora.id || lora.modelVersionId,
+                            });
+                        }
+                        
+                        missingLoras.push(lora);
+                        totalMissingCount++;
+                    }
+                });
+            }
+            
+            if (missingLoras.length > 0) {
+                missingLorasByRecipe.set(recipe.id || recipe.file_path, {
+                    recipe,
+                    missingLoras
+                });
+            }
+        });
+
+        return {
+            uniqueLoras: Array.from(uniqueLoras.values()),
+            uniqueCount: uniqueLoras.size,
+            totalMissingCount,
+            missingLorasByRecipe
+        };
+    }
+
+    /**
+     * Show confirmation modal for downloading missing LoRAs
+     * @param {Object} stats - Statistics about missing LoRAs
+     * @returns {Promise<boolean>} - Whether user confirmed
+     */
+    async showConfirmationModal(stats) {
+        const { uniqueCount, totalMissingCount, uniqueLoras } = stats;
+
+        if (uniqueCount === 0) {
+            showToast('toast.recipes.noMissingLoras', {}, 'info');
+            return false;
+        }
+
+        // Store pending data for confirmation
+        this.pendingLoras = uniqueLoras;
+
+        // Update modal content
+        const messageEl = document.getElementById('bulkDownloadMissingLorasMessage');
+        const listEl = document.getElementById('bulkDownloadMissingLorasList');
+        const confirmBtn = document.getElementById('bulkDownloadMissingLorasConfirmBtn');
+
+        if (messageEl) {
+            messageEl.textContent = translate('modals.bulkDownloadMissingLoras.message', { 
+                uniqueCount, 
+                totalCount: totalMissingCount 
+            }, `Found ${uniqueCount} unique missing LoRAs (from ${totalMissingCount} total across selected recipes).`);
+        }
+
+        if (listEl) {
+            listEl.innerHTML = uniqueLoras.slice(0, 10).map(lora => `
+                <li>
+                    <span class="lora-name">${lora.name || lora.file_name || 'Unknown'}</span>
+                    ${lora.version ? `<span class="lora-version">${lora.version}</span>` : ''}
+                </li>
+            `).join('') + 
+            (uniqueLoras.length > 10 ? `
+                <li class="more-items">${translate('modals.bulkDownloadMissingLoras.moreItems', { count: uniqueLoras.length - 10 }, `...and ${uniqueLoras.length - 10} more`)}</li>
+            ` : '');
+        }
+
+        if (confirmBtn) {
+            confirmBtn.innerHTML = `
+                <i class="fas fa-download"></i>
+                ${translate('modals.bulkDownloadMissingLoras.downloadButton', { count: uniqueCount }, `Download ${uniqueCount} LoRA(s)`)}
+            `;
+        }
+
+        // Show modal
+        modalManager.showModal('bulkDownloadMissingLorasModal');
+        
+        // Return a promise that will be resolved when user confirms or cancels
+        return new Promise((resolve) => {
+            this.confirmResolve = resolve;
+        });
+    }
+
+    /**
+     * Called when user confirms download in modal
+     */
+    async confirmDownload() {
+        modalManager.closeModal('bulkDownloadMissingLorasModal');
+        
+        if (this.confirmResolve) {
+            this.confirmResolve(true);
+            this.confirmResolve = null;
+        }
+
+        // Execute download
+        await this.executeDownload(this.pendingLoras);
+        this.pendingLoras = [];
+    }
+
+    /**
+     * Download missing LoRAs for selected recipes
+     * @param {Array} selectedRecipes - Array of selected recipe objects
+     */
+    async downloadMissingLoras(selectedRecipes) {
+        if (!selectedRecipes || selectedRecipes.length === 0) {
+            showToast('toast.recipes.noRecipesSelected', {}, 'warning');
+            return;
+        }
+
+        // Store selected recipes
+        this.pendingRecipes = selectedRecipes;
+
+        // Collect missing LoRAs with deduplication
+        const stats = this.collectMissingLoras(selectedRecipes);
+        
+        if (stats.uniqueCount === 0) {
+            showToast('toast.recipes.noMissingLorasInSelection', {}, 'info');
+            return;
+        }
+
+        // Show confirmation modal
+        const confirmed = await this.showConfirmationModal(stats);
+        if (!confirmed) {
+            return;
+        }
+    }
+
+    /**
+     * Execute the download process
+     * @param {Array} lorasToDownload - Array of unique LoRAs to download
+     */
+    async executeDownload(lorasToDownload) {
+        const totalLoras = lorasToDownload.length;
+        
+        // Get LoRA root directory
+        const loraRoot = await this.getLoraRoot();
+        if (!loraRoot) {
+            showToast('toast.recipes.noLoraRootConfigured', {}, 'error');
+            return;
+        }
+
+        // Generate batch download ID
+        const batchDownloadId = Date.now().toString();
+        
+        // Use default paths
+        const useDefaultPaths = true;
+
+        // Set up WebSocket for progress updates
+        const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
+        const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/download-progress?id=${batchDownloadId}`);
+
+        // Show download progress UI
+        const loadingManager = state.loadingManager;
+        const updateProgress = loadingManager.showDownloadProgress(totalLoras);
+
+        let completedDownloads = 0;
+        let failedDownloads = 0;
+        let currentLoraProgress = 0;
+
+        // Set up WebSocket message handler
+        ws.onmessage = (event) => {
+            const data = JSON.parse(event.data);
+
+            // Handle download ID confirmation
+            if (data.type === 'download_id') {
+                console.log(`Connected to batch download progress with ID: ${data.download_id}`);
+                return;
+            }
+
+            // Process progress updates
+            if (data.status === 'progress' && data.download_id && data.download_id.startsWith(batchDownloadId)) {
+                currentLoraProgress = data.progress;
+                
+                const currentLora = lorasToDownload[completedDownloads + failedDownloads];
+                const loraName = currentLora ? (currentLora.name || currentLora.file_name || 'Unknown') : '';
+
+                const metrics = {
+                    bytesDownloaded: data.bytes_downloaded,
+                    totalBytes: data.total_bytes,
+                    bytesPerSecond: data.bytes_per_second
+                };
+
+                updateProgress(currentLoraProgress, completedDownloads, loraName, metrics);
+
+                // Update status message
+                if (currentLoraProgress < 3) {
+                    loadingManager.setStatus(
+                        translate('recipes.controls.import.startingDownload', 
+                            { current: completedDownloads + failedDownloads + 1, total: totalLoras },
+                            `Starting download for LoRA ${completedDownloads + failedDownloads + 1}/${totalLoras}`
+                        )
+                    );
+                } else if (currentLoraProgress > 3 && currentLoraProgress < 100) {
+                    loadingManager.setStatus(
+                        translate('recipes.controls.import.downloadingLoras', {}, `Downloading LoRAs...`)
+                    );
+                }
+            }
+        };
+
+        // Wait for WebSocket to connect
+        await new Promise((resolve, reject) => {
+            ws.onopen = resolve;
+            ws.onerror = (error) => {
+                console.error('WebSocket error:', error);
+                reject(error);
+            };
+        });
+
+        // Download each LoRA sequentially
+        for (let i = 0; i < lorasToDownload.length; i++) {
+            const lora = lorasToDownload[i];
+            
+            currentLoraProgress = 0;
+            
+            loadingManager.setStatus(
+                translate('recipes.controls.import.startingDownload', 
+                    { current: i + 1, total: totalLoras },
+                    `Starting download for LoRA ${i + 1}/${totalLoras}`
+                )
+            );
+            updateProgress(0, completedDownloads, lora.name || lora.file_name || 'Unknown');
+
+            try {
+                const modelId = lora.modelId || lora.model_id;
+                const versionId = lora.id || lora.modelVersionId;
+
+                if (!modelId && !versionId) {
+                    console.warn(`Skipping LoRA without model/version ID:`, lora);
+                    failedDownloads++;
+                    continue;
+                }
+
+                const response = await this.loraApiClient.downloadModel(
+                    modelId,
+                    versionId,
+                    loraRoot,
+                    '', // Empty relative path, use default paths
+                    useDefaultPaths,
+                    batchDownloadId
+                );
+
+                if (!response.success) {
+                    console.error(`Failed to download LoRA ${lora.name || lora.file_name}: ${response.error}`);
+                    failedDownloads++;
+                } else {
+                    completedDownloads++;
+                    updateProgress(100, completedDownloads, '');
+                }
+            } catch (error) {
+                console.error(`Error downloading LoRA ${lora.name || lora.file_name}:`, error);
+                failedDownloads++;
+            }
+        }
+
+        // Close WebSocket
+        ws.close();
+
+        // Hide loading UI
+        loadingManager.hide();
+
+        // Show completion message
+        if (failedDownloads === 0) {
+            showToast('toast.loras.allDownloadSuccessful', { count: completedDownloads }, 'success');
+        } else {
+            showToast('toast.loras.downloadPartialSuccess', {
+                completed: completedDownloads,
+                total: totalLoras
+            }, 'warning');
+        }
+
+        // Refresh the recipes list to update LoRA status
+        if (window.recipeManager) {
+            window.recipeManager.loadRecipes();
+        }
+    }
+
+    /**
+     * Get LoRA root directory from API
+     * @returns {Promise<string|null>} - LoRA root directory or null
+     */
+    async getLoraRoot() {
+        try {
+            // Fetch available LoRA roots from API
+            const rootsData = await this.loraApiClient.fetchModelRoots();
+            
+            if (!rootsData || !rootsData.roots || rootsData.roots.length === 0) {
+                console.error('No LoRA roots available');
+                return null;
+            }
+
+            // Try to get default root from settings
+            const defaultRootKey = 'default_lora_root';
+            const defaultRoot = state.global?.settings?.[defaultRootKey];
+            
+            // If default root is set and exists in available roots, use it
+            if (defaultRoot && rootsData.roots.includes(defaultRoot)) {
+                return defaultRoot;
+            }
+            
+            // Otherwise, return the first available root
+            return rootsData.roots[0];
+            
+        } catch (error) {
+            console.error('Error getting LoRA root:', error);
+            return null;
+        }
+    }
+}
+
+// Export singleton instance
+export const bulkMissingLoraDownloadManager = new BulkMissingLoraDownloadManager();
+
+// Make available globally for HTML onclick handlers
+if (typeof window !== 'undefined') {
+    window.bulkMissingLoraDownloadManager = bulkMissingLoraDownloadManager;
+}

static/js/managers/DownloadManager.js

@@ -492,7 +492,7 @@ export class DownloadManager {
                 console.error('WebSocket error:', error);
             };
 
-            await this.apiClient.downloadModel(
+            const response = await this.apiClient.downloadModel(
                 modelId,
                 versionId,
                 modelRoot,
@@ -502,6 +502,16 @@ export class DownloadManager {
                 source
             );
 
+            if (response?.skipped) {
+                this.loadingManager.setStatus(translate('modals.download.status.finalizing'));
+                updateProgress(100, 0, displayName);
+                showToast('toast.loras.downloadSkippedByBaseModel', { baseModel: response.base_model || 'Unknown' }, 'warning');
+                if (closeModal) {
+                    modalManager.closeModal('downloadModal');
+                }
+                return true;
+            }
+
             showToast('toast.loras.downloadCompleted', {}, 'success');
 
             if (closeModal) {

static/js/managers/ModalManager.js

@@ -291,6 +291,19 @@ export class ModalManager {
             });
         }
 
+        // Register bulkDownloadMissingLorasModal
+        const bulkDownloadMissingLorasModal = document.getElementById('bulkDownloadMissingLorasModal');
+        if (bulkDownloadMissingLorasModal) {
+            this.registerModal('bulkDownloadMissingLorasModal', {
+                element: bulkDownloadMissingLorasModal,
+                onClose: () => {
+                    this.getModal('bulkDownloadMissingLorasModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');
+                },
+                closeOnOutsideClick: true
+            });
+        }
+
         document.addEventListener('keydown', this.boundHandleEscape);
         this.initialized = true;
     }

static/js/managers/SettingsManager.js

@@ -2,7 +2,14 @@ import { modalManager } from './ModalManager.js';
 import { showToast } from '../utils/uiHelpers.js';
 import { state, createDefaultSettings } from '../state/index.js';
 import { resetAndReload } from '../api/modelApiFactory.js';
-import { DOWNLOAD_PATH_TEMPLATES, MAPPABLE_BASE_MODELS, PATH_TEMPLATE_PLACEHOLDERS, DEFAULT_PATH_TEMPLATES, DEFAULT_PRIORITY_TAG_CONFIG } from '../utils/constants.js';
+import { 
+    DOWNLOAD_PATH_TEMPLATES, 
+    MAPPABLE_BASE_MODELS, 
+    PATH_TEMPLATE_PLACEHOLDERS, 
+    DEFAULT_PATH_TEMPLATES, 
+    DEFAULT_PRIORITY_TAG_CONFIG,
+    getMappableBaseModelsDynamic
+} from '../utils/constants.js';
 import { translate } from '../utils/i18nHelpers.js';
 import { i18n } from '../i18n/index.js';
 import { configureModelCardVideo } from '../components/shared/ModelCard.js';
@@ -10,6 +17,8 @@ import { validatePriorityTagString, getPriorityTagSuggestionsMap, invalidatePrio
 import { bannerService } from './BannerService.js';
 import { sidebarManager } from '../components/SidebarManager.js';
 
+const VALID_MATURE_BLUR_LEVELS = new Set(['PG13', 'R', 'X', 'XXX']);
+
 export class SettingsManager {
     constructor() {
         this.initialized = false;
@@ -137,11 +146,29 @@ export class SettingsManager {
             backendSettings?.metadata_refresh_skip_paths ?? defaults.metadata_refresh_skip_paths
         );
 
+        merged.download_skip_base_models = this.normalizeDownloadSkipBaseModels(
+            backendSettings?.download_skip_base_models ?? defaults.download_skip_base_models
+        );
+
+        merged.mature_blur_level = this.normalizeMatureBlurLevel(
+            backendSettings?.mature_blur_level ?? defaults.mature_blur_level
+        );
+
         Object.keys(merged).forEach(key => this.backendSettingKeys.add(key));
 
         return merged;
     }
 
+    normalizeMatureBlurLevel(value) {
+        if (typeof value === 'string') {
+            const normalized = value.trim().toUpperCase();
+            if (VALID_MATURE_BLUR_LEVELS.has(normalized)) {
+                return normalized;
+            }
+        }
+        return 'R';
+    }
+
     normalizePatternList(value) {
         if (Array.isArray(value)) {
             const sanitized = value
@@ -163,6 +190,17 @@ export class SettingsManager {
         return [];
     }
 
+    getAvailableDownloadSkipBaseModels() {
+        // Use dynamic base models if available, fallback to hardcoded
+        const models = getMappableBaseModelsDynamic();
+        return models.filter(model => model !== 'Other');
+    }
+
+    normalizeDownloadSkipBaseModels(value) {
+        const allowed = new Set(this.getAvailableDownloadSkipBaseModels());
+        return this.normalizePatternList(value).filter(model => allowed.has(model));
+    }
+
     registerStartupMessages(messages = []) {
         if (!Array.isArray(messages) || messages.length === 0) {
             return;
@@ -363,6 +401,36 @@ export class SettingsManager {
             });
         }
 
+        const downloadSkipBaseModelsContainer = document.getElementById('downloadSkipBaseModelsContainer');
+        if (downloadSkipBaseModelsContainer) {
+            downloadSkipBaseModelsContainer.addEventListener('change', (event) => {
+                if (event.target instanceof HTMLInputElement && event.target.name === 'downloadSkipBaseModel') {
+                    this.saveDownloadSkipBaseModels();
+                }
+            });
+        }
+
+        const downloadSkipBaseModelsToggle = document.getElementById('downloadSkipBaseModelsToggle');
+        if (downloadSkipBaseModelsToggle) {
+            downloadSkipBaseModelsToggle.addEventListener('click', () => {
+                this.toggleDownloadSkipBaseModelsPanel();
+            });
+        }
+
+        const downloadSkipBaseModelsSearch = document.getElementById('downloadSkipBaseModelsSearch');
+        if (downloadSkipBaseModelsSearch) {
+            downloadSkipBaseModelsSearch.addEventListener('input', () => {
+                this.renderDownloadSkipBaseModels();
+            });
+        }
+
+        const downloadSkipBaseModelsClear = document.getElementById('downloadSkipBaseModelsClear');
+        if (downloadSkipBaseModelsClear) {
+            downloadSkipBaseModelsClear.addEventListener('click', () => {
+                this.clearDownloadSkipBaseModels();
+            });
+        }
+
         this.setupPriorityTagInputs();
         this.initializeNavigation();
         this.initializeSearch();
@@ -682,6 +750,13 @@ export class SettingsManager {
             showOnlySFWCheckbox.checked = state.global.settings.show_only_sfw ?? false;
         }
 
+        const matureBlurLevelSelect = document.getElementById('matureBlurLevel');
+        if (matureBlurLevelSelect) {
+            matureBlurLevelSelect.value = this.normalizeMatureBlurLevel(
+                state.global.settings.mature_blur_level
+            );
+        }
+
         const usePortableCheckbox = document.getElementById('usePortableSettings');
         if (usePortableCheckbox) {
             usePortableCheckbox.checked = !!state.global.settings.use_portable_settings;
@@ -707,6 +782,13 @@ export class SettingsManager {
             metadataRefreshSkipPathsError.textContent = '';
         }
 
+        this.renderDownloadSkipBaseModels();
+        const downloadSkipBaseModelsError = document.getElementById('downloadSkipBaseModelsError');
+        if (downloadSkipBaseModelsError) {
+            downloadSkipBaseModelsError.textContent = '';
+        }
+        this.setDownloadSkipBaseModelsPanelOpen(false);
+
         // Set video autoplay on hover setting
         const autoplayOnHoverCheckbox = document.getElementById('autoplayOnHover');
         if (autoplayOnHoverCheckbox) {
@@ -1164,10 +1246,7 @@ export class SettingsManager {
                 throw new Error('No LoRA roots found');
             }
 
-            // Clear existing options except the first one (No Default)
-            const noDefaultOption = defaultLoraRootSelect.querySelector('option[value=""]');
             defaultLoraRootSelect.innerHTML = '';
-            defaultLoraRootSelect.appendChild(noDefaultOption);
 
             // Add options for each root
             data.roots.forEach(root => {
@@ -1177,9 +1256,8 @@ export class SettingsManager {
                 defaultLoraRootSelect.appendChild(option);
             });
 
-            // Set selected value from settings
             const defaultRoot = state.global.settings.default_lora_root || '';
-            defaultLoraRootSelect.value = defaultRoot;
+            defaultLoraRootSelect.value = data.roots.includes(defaultRoot) ? defaultRoot : data.roots[0];
 
         } catch (error) {
             console.error('Error loading LoRA roots:', error);
@@ -1203,10 +1281,7 @@ export class SettingsManager {
                 throw new Error('No checkpoint roots found');
             }
 
-            // Clear existing options except first one (No Default)
-            const noDefaultOption = defaultCheckpointRootSelect.querySelector('option[value=""]');
             defaultCheckpointRootSelect.innerHTML = '';
-            defaultCheckpointRootSelect.appendChild(noDefaultOption);
 
             // Add options for each root
             data.roots.forEach(root => {
@@ -1216,9 +1291,8 @@ export class SettingsManager {
                 defaultCheckpointRootSelect.appendChild(option);
             });
 
-            // Set selected value from settings
             const defaultRoot = state.global.settings.default_checkpoint_root || '';
-            defaultCheckpointRootSelect.value = defaultRoot;
+            defaultCheckpointRootSelect.value = data.roots.includes(defaultRoot) ? defaultRoot : data.roots[0];
 
         } catch (error) {
             console.error('Error loading checkpoint roots:', error);
@@ -1242,10 +1316,7 @@ export class SettingsManager {
                 throw new Error('No diffusion model roots found');
             }
 
-            // Clear existing options except first one (No Default)
-            const noDefaultOption = defaultUnetRootSelect.querySelector('option[value=""]');
             defaultUnetRootSelect.innerHTML = '';
-            defaultUnetRootSelect.appendChild(noDefaultOption);
 
             // Add options for each root
             data.roots.forEach(root => {
@@ -1255,9 +1326,8 @@ export class SettingsManager {
                 defaultUnetRootSelect.appendChild(option);
             });
 
-            // Set selected value from settings
             const defaultRoot = state.global.settings.default_unet_root || '';
-            defaultUnetRootSelect.value = defaultRoot;
+            defaultUnetRootSelect.value = data.roots.includes(defaultRoot) ? defaultRoot : data.roots[0];
 
         } catch (error) {
             console.error('Error loading diffusion model roots:', error);
@@ -1281,10 +1351,7 @@ export class SettingsManager {
                 throw new Error('No embedding roots found');
             }
 
-            // Clear existing options except first one (No Default)
-            const noDefaultOption = defaultEmbeddingRootSelect.querySelector('option[value=""]');
             defaultEmbeddingRootSelect.innerHTML = '';
-            defaultEmbeddingRootSelect.appendChild(noDefaultOption);
 
             // Add options for each root
             data.roots.forEach(root => {
@@ -1294,9 +1361,8 @@ export class SettingsManager {
                 defaultEmbeddingRootSelect.appendChild(option);
             });
 
-            // Set selected value from settings
             const defaultRoot = state.global.settings.default_embedding_root || '';
-            defaultEmbeddingRootSelect.value = defaultRoot;
+            defaultEmbeddingRootSelect.value = data.roots.includes(defaultRoot) ? defaultRoot : data.roots[0];
 
         } catch (error) {
             console.error('Error loading embedding roots:', error);
@@ -1395,7 +1461,7 @@ export class SettingsManager {
         try {
             // Save to backend - this triggers path validation
             await this.saveSetting('extra_folder_paths', extraFolderPaths);
-            showToast('toast.settings.settingsUpdated', { setting: 'Extra Folder Paths' }, 'success');
+            showToast('settings.extraFolderPaths.saveSuccess', {}, 'success');
 
             // Add empty row if no valid paths exist for the changed type
             const container = document.getElementById(`extraFolderPaths-${changedModelType}`);
@@ -1444,7 +1510,7 @@ export class SettingsManager {
         const row = document.createElement('div');
         row.className = 'mapping-row';
 
-        const availableModels = MAPPABLE_BASE_MODELS.filter(model => {
+        const availableModels = getMappableBaseModelsDynamic().filter(model => {
             const existingMappings = state.global.settings.base_model_path_mappings || {};
             return !existingMappings.hasOwnProperty(model) || model === baseModel;
         });
@@ -1546,7 +1612,7 @@ export class SettingsManager {
             const currentValue = select.value;
 
             // Get available models (not already mapped, except current)
-            const availableModels = MAPPABLE_BASE_MODELS.filter(model =>
+            const availableModels = getMappableBaseModelsDynamic().filter(model =>
                 !existingMappings.hasOwnProperty(model) || model === currentValue
             );
 
@@ -1811,7 +1877,9 @@ export class SettingsManager {
         const element = document.getElementById(elementId);
         if (!element) return;
 
-        const value = element.value;
+        const value = settingKey === 'mature_blur_level'
+            ? this.normalizeMatureBlurLevel(element.value)
+            : element.value;
 
         try {
             // Update frontend state with mapped keys
@@ -1834,7 +1902,12 @@ export class SettingsManager {
 
             showToast('toast.settings.settingsUpdated', { setting: settingKey.replace(/_/g, ' ') }, 'success');
 
-            if (settingKey === 'model_name_display' || settingKey === 'model_card_footer_action' || settingKey === 'update_flag_strategy') {
+            if (
+                settingKey === 'model_name_display'
+                || settingKey === 'model_card_footer_action'
+                || settingKey === 'update_flag_strategy'
+                || settingKey === 'mature_blur_level'
+            ) {
                 this.reloadContent();
             }
         } catch (error) {
@@ -2140,6 +2213,190 @@ export class SettingsManager {
         }
     }
 
+    renderDownloadSkipBaseModels() {
+        const container = document.getElementById('downloadSkipBaseModelsContainer');
+        const searchInput = document.getElementById('downloadSkipBaseModelsSearch');
+        const emptyState = document.getElementById('downloadSkipBaseModelsEmpty');
+        if (!container) {
+            return;
+        }
+
+        const selectedValues = this.normalizeDownloadSkipBaseModels(
+            state.global.settings.download_skip_base_models
+        );
+        const selected = new Set(selectedValues);
+        const options = this.getAvailableDownloadSkipBaseModels();
+        const query = (searchInput?.value || '').trim().toLowerCase();
+        const filteredOptions = query
+            ? options.filter((baseModel) => baseModel.toLowerCase().includes(query))
+            : options;
+
+        container.innerHTML = filteredOptions.map((baseModel) => `
+            <label class="base-model-skip-option">
+                <input
+                    type="checkbox"
+                    name="downloadSkipBaseModel"
+                    value="${baseModel}"
+                    ${selected.has(baseModel) ? 'checked' : ''}
+                >
+                <span>${baseModel}</span>
+            </label>
+        `).join('');
+
+        if (emptyState) {
+            emptyState.hidden = filteredOptions.length > 0;
+        }
+
+        this.renderDownloadSkipBaseModelsSummary(selectedValues);
+    }
+
+    renderDownloadSkipBaseModelsSummary(selectedValues = null) {
+        const summaryElement = document.getElementById('downloadSkipBaseModelsSummary');
+        if (!summaryElement) {
+            return;
+        }
+
+        const values = Array.isArray(selectedValues)
+            ? selectedValues
+            : this.normalizeDownloadSkipBaseModels(state.global.settings.download_skip_base_models);
+
+        if (values.length === 0) {
+            summaryElement.textContent = translate(
+                'settings.downloadSkipBaseModels.summary.none',
+                {},
+                'None selected'
+            );
+            return;
+        }
+
+        if (values.length <= 2) {
+            summaryElement.textContent = values.join(', ');
+            return;
+        }
+
+        summaryElement.textContent = translate(
+            'settings.downloadSkipBaseModels.summary.count',
+            { count: values.length },
+            `${values.length} selected`
+        );
+    }
+
+    setDownloadSkipBaseModelsPanelOpen(isOpen) {
+        const panel = document.getElementById('downloadSkipBaseModelsPanel');
+        const toggle = document.getElementById('downloadSkipBaseModelsToggle');
+        const toggleLabel = toggle?.querySelector('.base-model-skip-toggle-label');
+        if (!panel || !toggle) {
+            return;
+        }
+
+        panel.hidden = !isOpen;
+        toggle.setAttribute('aria-expanded', isOpen ? 'true' : 'false');
+        if (toggleLabel) {
+            toggleLabel.textContent = isOpen
+                ? translate('settings.downloadSkipBaseModels.actions.collapse', {}, 'Collapse')
+                : translate('settings.downloadSkipBaseModels.actions.edit', {}, 'Edit');
+        }
+
+        if (isOpen) {
+            const searchInput = document.getElementById('downloadSkipBaseModelsSearch');
+            searchInput?.focus();
+        }
+    }
+
+    toggleDownloadSkipBaseModelsPanel() {
+        const panel = document.getElementById('downloadSkipBaseModelsPanel');
+        if (!panel) {
+            return;
+        }
+        this.setDownloadSkipBaseModelsPanelOpen(panel.hidden);
+    }
+
+    async saveDownloadSkipBaseModels() {
+        const container = document.getElementById('downloadSkipBaseModelsContainer');
+        const errorElement = document.getElementById('downloadSkipBaseModelsError');
+        if (!container) return;
+
+        const selected = Array.from(
+            container.querySelectorAll('input[name="downloadSkipBaseModel"]:checked')
+        ).map((input) => input.value);
+        const normalized = this.normalizeDownloadSkipBaseModels(selected);
+        const current = this.normalizeDownloadSkipBaseModels(state.global.settings.download_skip_base_models);
+
+        if (normalized.join('|') === current.join('|')) {
+            if (errorElement) {
+                errorElement.textContent = '';
+            }
+            return;
+        }
+
+        try {
+            if (errorElement) {
+                errorElement.textContent = '';
+            }
+
+            await this.saveSetting('download_skip_base_models', normalized);
+            this.renderDownloadSkipBaseModels();
+
+            showToast(
+                'toast.settings.settingsUpdated',
+                { setting: translate('settings.downloadSkipBaseModels.label') },
+                'success'
+            );
+        } catch (error) {
+            console.error('Failed to save download skip base models:', error);
+            if (errorElement) {
+                errorElement.textContent = translate(
+                    'settings.downloadSkipBaseModels.validation.saveFailed',
+                    { message: error.message },
+                    `Unable to save excluded base models: ${error.message}`
+                );
+            }
+            showToast('toast.settings.settingSaveFailed', { message: error.message }, 'error');
+        }
+    }
+
+    async clearDownloadSkipBaseModels() {
+        const searchInput = document.getElementById('downloadSkipBaseModelsSearch');
+        if (searchInput) {
+            searchInput.value = '';
+        }
+
+        const current = this.normalizeDownloadSkipBaseModels(
+            state.global.settings.download_skip_base_models
+        );
+        if (current.length === 0) {
+            this.renderDownloadSkipBaseModels();
+            return;
+        }
+
+        try {
+            const errorElement = document.getElementById('downloadSkipBaseModelsError');
+            if (errorElement) {
+                errorElement.textContent = '';
+            }
+
+            await this.saveSetting('download_skip_base_models', []);
+            this.renderDownloadSkipBaseModels();
+
+            showToast(
+                'toast.settings.settingsUpdated',
+                { setting: translate('settings.downloadSkipBaseModels.label') },
+                'success'
+            );
+        } catch (error) {
+            const errorElement = document.getElementById('downloadSkipBaseModelsError');
+            console.error('Failed to clear download skip base models:', error);
+            if (errorElement) {
+                errorElement.textContent = translate(
+                    'settings.downloadSkipBaseModels.validation.saveFailed',
+                    { message: error.message },
+                    `Unable to save excluded base models: ${error.message}`
+                );
+            }
+            showToast('toast.settings.settingSaveFailed', { message: error.message }, 'error');
+        }
+    }
+
     async saveMetadataRefreshSkipPaths() {
         const input = document.getElementById('metadataRefreshSkipPaths');
         const errorElement = document.getElementById('metadataRefreshSkipPathsError');

static/js/managers/import/RecipeDataManager.js

@@ -6,8 +6,31 @@ export class RecipeDataManager {
         this.importManager = importManager;
     }
 
+    setupTagInputEnterHandler() {
+        const tagInput = document.getElementById('tagInput');
+        if (!tagInput || tagInput.hasEnterAddTagHandler) {
+            return;
+        }
+
+        tagInput.addEventListener('keydown', (event) => {
+            if (event.key !== 'Enter') {
+                return;
+            }
+
+            if (event.isComposing || event.keyCode === 229) {
+                return;
+            }
+
+            event.preventDefault();
+            this.addTag();
+        });
+
+        tagInput.hasEnterAddTagHandler = true;
+    }
+
     showRecipeDetailsStep() {
         this.importManager.stepManager.showStep('detailsStep');
+        this.setupTagInputEnterHandler();
 
         // Set default recipe name from prompt or image filename
         const recipeName = document.getElementById('recipeName');

static/js/recipes.js

@@ -66,6 +66,8 @@ class RecipeManager {
             active: false,
             loraName: null,
             loraHash: null,
+            checkpointName: null,
+            checkpointHash: null,
             recipeId: null
         };
     }
@@ -127,16 +129,20 @@ class RecipeManager {
         // Check for Lora filter
         const filterLoraName = getSessionItem('lora_to_recipe_filterLoraName');
         const filterLoraHash = getSessionItem('lora_to_recipe_filterLoraHash');
+        const filterCheckpointName = getSessionItem('checkpoint_to_recipe_filterCheckpointName');
+        const filterCheckpointHash = getSessionItem('checkpoint_to_recipe_filterCheckpointHash');
 
         // Check for specific recipe ID
         const viewRecipeId = getSessionItem('viewRecipeId');
 
         // Set custom filter if any parameter is present
-        if (filterLoraName || filterLoraHash || viewRecipeId) {
+        if (filterLoraName || filterLoraHash || filterCheckpointName || filterCheckpointHash || viewRecipeId) {
             this.pageState.customFilter = {
                 active: true,
                 loraName: filterLoraName,
                 loraHash: filterLoraHash,
+                checkpointName: filterCheckpointName,
+                checkpointHash: filterCheckpointHash,
                 recipeId: viewRecipeId
             };
 
@@ -164,6 +170,13 @@ class RecipeManager {
                 loraName;
 
             filterText = `<span>Recipes using: <span class="lora-name">${displayName}</span></span>`;
+        } else if (this.pageState.customFilter.checkpointName) {
+            const checkpointName = this.pageState.customFilter.checkpointName;
+            const displayName = checkpointName.length > 25 ?
+                checkpointName.substring(0, 22) + '...' :
+                checkpointName;
+
+            filterText = `<span>Recipes using checkpoint: <span class="lora-name">${displayName}</span></span>`;
         } else {
             filterText = 'Filtered recipes';
         }
@@ -173,6 +186,10 @@ class RecipeManager {
         // Add title attribute to show the lora name as a tooltip
         if (this.pageState.customFilter.loraName) {
             textElement.setAttribute('title', this.pageState.customFilter.loraName);
+        } else if (this.pageState.customFilter.checkpointName) {
+            textElement.setAttribute('title', this.pageState.customFilter.checkpointName);
+        } else {
+            textElement.removeAttribute('title');
         }
         indicator.classList.remove('hidden');
 
@@ -199,6 +216,8 @@ class RecipeManager {
             active: false,
             loraName: null,
             loraHash: null,
+            checkpointName: null,
+            checkpointHash: null,
             recipeId: null
         };
 
@@ -211,6 +230,8 @@ class RecipeManager {
         // Clear any session storage items
         removeSessionItem('lora_to_recipe_filterLoraName');
         removeSessionItem('lora_to_recipe_filterLoraHash');
+        removeSessionItem('checkpoint_to_recipe_filterCheckpointName');
+        removeSessionItem('checkpoint_to_recipe_filterCheckpointHash');
         removeSessionItem('viewRecipeId');
 
         // Reset and refresh the virtual scroller

static/js/state/index.js

@@ -24,6 +24,7 @@ const DEFAULT_SETTINGS_BASE = Object.freeze({
     optimize_example_images: true,
     auto_download_example_images: false,
     blur_mature_content: true,
+    mature_blur_level: 'R',
     autoplay_on_hover: false,
     display_density: 'default',
     card_info_display: 'always',
@@ -37,6 +38,7 @@ const DEFAULT_SETTINGS_BASE = Object.freeze({
     hide_early_access_updates: false,
     auto_organize_exclusions: [],
     metadata_refresh_skip_paths: [],
+    download_skip_base_models: [],
 });
 
 export function createDefaultSettings() {

static/js/utils/constants.js

@@ -50,6 +50,9 @@ export const BASE_MODELS = {
     SVD: "SVD",
     LTXV: "LTXV",
     LTXV2: "LTXV2",
+    LTXV_2_3: "LTXV 2.3",
+    COGVIDE_X: "CogVideoX",
+    MOCHI: "Mochi",
     WAN_VIDEO: "Wan Video",
     WAN_VIDEO_1_3B_T2V: "Wan Video 1.3B t2v",
     WAN_VIDEO_14B_T2V: "Wan Video 14B t2v",
@@ -58,7 +61,12 @@ export const BASE_MODELS = {
     WAN_VIDEO_2_2_TI2V_5B: "Wan Video 2.2 TI2V-5B",
     WAN_VIDEO_2_2_T2V_A14B: "Wan Video 2.2 T2V-A14B",
     WAN_VIDEO_2_2_I2V_A14B: "Wan Video 2.2 I2V-A14B",
+    WAN_VIDEO_2_5_T2V: "Wan Video 2.5 T2V",
+    WAN_VIDEO_2_5_I2V: "Wan Video 2.5 I2V",
     HUNYUAN_VIDEO: "Hunyuan Video",
+    // Other models
+    ANIMA: "Anima",
+    PONY_V7: "Pony V7",
     // Default
     UNKNOWN: "Other"
 };
@@ -151,6 +159,9 @@ export const BASE_MODEL_ABBREVIATIONS = {
     [BASE_MODELS.SVD]: 'SVD',
     [BASE_MODELS.LTXV]: 'LTXV',
     [BASE_MODELS.LTXV2]: 'LTV2',
+    [BASE_MODELS.LTXV_2_3]: 'LTX',
+    [BASE_MODELS.COGVIDE_X]: 'CVX',
+    [BASE_MODELS.MOCHI]: 'MCHI',
     [BASE_MODELS.WAN_VIDEO]: 'WAN',
     [BASE_MODELS.WAN_VIDEO_1_3B_T2V]: 'WAN',
     [BASE_MODELS.WAN_VIDEO_14B_T2V]: 'WAN',
@@ -159,8 +170,28 @@ export const BASE_MODEL_ABBREVIATIONS = {
     [BASE_MODELS.WAN_VIDEO_2_2_TI2V_5B]: 'WAN',
     [BASE_MODELS.WAN_VIDEO_2_2_T2V_A14B]: 'WAN',
     [BASE_MODELS.WAN_VIDEO_2_2_I2V_A14B]: 'WAN',
+    [BASE_MODELS.WAN_VIDEO_2_5_T2V]: 'WAN',
+    [BASE_MODELS.WAN_VIDEO_2_5_I2V]: 'WAN',
     [BASE_MODELS.HUNYUAN_VIDEO]: 'HYV',
 
+    // Other diffusion models
+    [BASE_MODELS.AURAFLOW]: 'AF',
+    [BASE_MODELS.CHROMA]: 'CHR',
+    [BASE_MODELS.PIXART_A]: 'PXA',
+    [BASE_MODELS.PIXART_E]: 'PXE',
+    [BASE_MODELS.HUNYUAN_1]: 'HY',
+    [BASE_MODELS.LUMINA]: 'L',
+    [BASE_MODELS.KOLORS]: 'KLR',
+    [BASE_MODELS.NOOBAI]: 'NAI',
+    [BASE_MODELS.ILLUSTRIOUS]: 'IL',
+    [BASE_MODELS.PONY]: 'PONY',
+    [BASE_MODELS.PONY_V7]: 'PNY7',
+    [BASE_MODELS.HIDREAM]: 'HID',
+    [BASE_MODELS.QWEN]: 'QWEN',
+    [BASE_MODELS.ZIMAGE_TURBO]: 'ZIT',
+    [BASE_MODELS.ZIMAGE_BASE]: 'ZIB',
+    [BASE_MODELS.ANIMA]: 'ANI',
+
     // Default
     [BASE_MODELS.UNKNOWN]: 'OTH'
 };
@@ -309,6 +340,15 @@ export const NSFW_LEVELS = {
     BLOCKED: 32
 };
 
+export const VALID_MATURE_BLUR_LEVELS = ['PG13', 'R', 'X', 'XXX'];
+
+export function getMatureBlurThreshold(settings = {}) {
+    const rawValue = settings?.mature_blur_level;
+    const normalizedValue = typeof rawValue === 'string' ? rawValue.trim().toUpperCase() : '';
+    const levelName = VALID_MATURE_BLUR_LEVELS.includes(normalizedValue) ? normalizedValue : 'R';
+    return NSFW_LEVELS[levelName] ?? NSFW_LEVELS.R;
+}
+
 // Node type constants
 export const NODE_TYPES = {
     LORA_LOADER: 1,
@@ -340,18 +380,20 @@ export const BASE_MODEL_CATEGORIES = {
     'Stable Diffusion 3.x': [BASE_MODELS.SD_3, BASE_MODELS.SD_3_5, BASE_MODELS.SD_3_5_MEDIUM, BASE_MODELS.SD_3_5_LARGE, BASE_MODELS.SD_3_5_LARGE_TURBO],
     'SDXL': [BASE_MODELS.SDXL, BASE_MODELS.SDXL_LIGHTNING, BASE_MODELS.SDXL_HYPER],
     'Video Models': [
-        BASE_MODELS.SVD, BASE_MODELS.LTXV, BASE_MODELS.LTXV2, BASE_MODELS.HUNYUAN_VIDEO, BASE_MODELS.WAN_VIDEO, 
-        BASE_MODELS.WAN_VIDEO_1_3B_T2V, BASE_MODELS.WAN_VIDEO_14B_T2V,
+        BASE_MODELS.SVD, BASE_MODELS.LTXV, BASE_MODELS.LTXV2, BASE_MODELS.LTXV_2_3, 
+        BASE_MODELS.COGVIDE_X, BASE_MODELS.MOCHI, BASE_MODELS.HUNYUAN_VIDEO,
+        BASE_MODELS.WAN_VIDEO, BASE_MODELS.WAN_VIDEO_1_3B_T2V, BASE_MODELS.WAN_VIDEO_14B_T2V,
         BASE_MODELS.WAN_VIDEO_14B_I2V_480P, BASE_MODELS.WAN_VIDEO_14B_I2V_720P,
         BASE_MODELS.WAN_VIDEO_2_2_TI2V_5B, BASE_MODELS.WAN_VIDEO_2_2_T2V_A14B,
-        BASE_MODELS.WAN_VIDEO_2_2_I2V_A14B
+        BASE_MODELS.WAN_VIDEO_2_2_I2V_A14B, BASE_MODELS.WAN_VIDEO_2_5_T2V,
+        BASE_MODELS.WAN_VIDEO_2_5_I2V
     ],
     'Flux Models': [BASE_MODELS.FLUX_1_D, BASE_MODELS.FLUX_1_S, BASE_MODELS.FLUX_1_KONTEXT, BASE_MODELS.FLUX_1_KREA, BASE_MODELS.FLUX_2_D, BASE_MODELS.FLUX_2_KLEIN_9B, BASE_MODELS.FLUX_2_KLEIN_9B_BASE, BASE_MODELS.FLUX_2_KLEIN_4B, BASE_MODELS.FLUX_2_KLEIN_4B_BASE],
     'Other Models': [
-        BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.HIDREAM,
+        BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.PONY_V7, BASE_MODELS.HIDREAM,
         BASE_MODELS.QWEN, BASE_MODELS.AURAFLOW, BASE_MODELS.CHROMA, BASE_MODELS.ZIMAGE_TURBO, BASE_MODELS.ZIMAGE_BASE,
         BASE_MODELS.PIXART_A, BASE_MODELS.PIXART_E, BASE_MODELS.HUNYUAN_1,
-        BASE_MODELS.LUMINA, BASE_MODELS.KOLORS, BASE_MODELS.NOOBAI,
+        BASE_MODELS.LUMINA, BASE_MODELS.KOLORS, BASE_MODELS.NOOBAI, BASE_MODELS.ANIMA,
         BASE_MODELS.UNKNOWN
     ]
 };
@@ -369,3 +411,94 @@ export const DEFAULT_PRIORITY_TAG_CONFIG = {
     checkpoint: DEFAULT_PRIORITY_TAG_ENTRIES.join(', '),
     embedding: DEFAULT_PRIORITY_TAG_ENTRIES.join(', ')
 };
+
+// ============================================================================
+// Dynamic Base Model Support
+// ============================================================================
+
+/**
+ * Dynamic base model cache
+ * Stores models fetched from Civitai API
+ */
+let dynamicBaseModels = null;
+let dynamicBaseModelsTimestamp = null;
+const CACHE_TTL_MS = 7 * 24 * 60 * 60 * 1000; // 7 days
+
+/**
+ * Set dynamic base models (called after fetching from API)
+ * @param {Array} models - Array of base model names
+ * @param {string} timestamp - ISO timestamp of fetch
+ */
+export function setDynamicBaseModels(models, timestamp) {
+    dynamicBaseModels = models;
+    dynamicBaseModelsTimestamp = timestamp;
+}
+
+/**
+ * Get dynamic base models
+ * @returns {Object|null} { models, timestamp } or null if not set
+ */
+export function getDynamicBaseModels() {
+    if (!dynamicBaseModels) return null;
+    
+    // Check if cache is expired
+    if (dynamicBaseModelsTimestamp) {
+        const age = Date.now() - new Date(dynamicBaseModelsTimestamp).getTime();
+        if (age > CACHE_TTL_MS) {
+            dynamicBaseModels = null;
+            dynamicBaseModelsTimestamp = null;
+            return null;
+        }
+    }
+    
+    return {
+        models: dynamicBaseModels,
+        timestamp: dynamicBaseModelsTimestamp
+    };
+}
+
+/**
+ * Get merged base models (hardcoded + dynamic)
+ * Returns unique sorted list of all available base models
+ * @returns {Array} Sorted array of base model names
+ */
+export function getMergedBaseModels() {
+    const hardcoded = Object.values(BASE_MODELS);
+    const dynamic = getDynamicBaseModels();
+    
+    if (!dynamic || !dynamic.models) {
+        return hardcoded.sort();
+    }
+    
+    // Merge and deduplicate
+    const merged = new Set([...hardcoded, ...dynamic.models]);
+    return Array.from(merged).sort();
+}
+
+/**
+ * Get mappable base models (for UI selection)
+ * Excludes 'Other' value
+ * @returns {Array} Sorted array of base model names (excluding 'Other')
+ */
+export function getMappableBaseModelsDynamic() {
+    const merged = getMergedBaseModels();
+    return merged.filter(model => model !== 'Other');
+}
+
+/**
+ * Clear dynamic base models cache
+ */
+export function clearDynamicBaseModels() {
+    dynamicBaseModels = null;
+    dynamicBaseModelsTimestamp = null;
+}
+
+/**
+ * Check if dynamic base models cache is valid
+ * @returns {boolean}
+ */
+export function isDynamicBaseModelsCacheValid() {
+    if (!dynamicBaseModels || !dynamicBaseModelsTimestamp) return false;
+    const age = Date.now() - new Date(dynamicBaseModelsTimestamp).getTime();
+    return age <= CACHE_TTL_MS;
+}

static/js/utils/uiHelpers.js

@@ -184,14 +184,13 @@ function filterByFolder(folderPath) {
 }
 
 export function openCivitaiByMetadata(civitaiId, versionId, modelName = null) {
-  if (civitaiId) {
-    let url = `https://civitai.com/models/${civitaiId}`;
-    if (versionId) {
-      url += `?modelVersionId=${versionId}`;
-    }
-    window.open(url, '_blank');
+  if (versionId) {
+    // Use model-versions endpoint which auto-redirects to correct model page
+    window.open(`https://civitai.com/model-versions/${versionId}`, '_blank');
+  } else if (civitaiId) {
+    window.open(`https://civitai.com/models/${civitaiId}`, '_blank');
   } else if (modelName) {
-    // 如果没有ID，尝试使用名称搜索
+    // Fallback: search by name
     window.open(`https://civitai.com/models?query=${encodeURIComponent(modelName)}`, '_blank');
   }
 }

templates/checkpoints.html

@@ -13,6 +13,7 @@
     <div class="context-menu-item" data-action="refresh-metadata"><i class="fas fa-sync"></i> {{ t('loras.contextMenu.refreshMetadata') }}</div>
     <div class="context-menu-item" data-action="relink-civitai"><i class="fas fa-link"></i> {{ t('loras.contextMenu.relinkCivitai') }}</div>
     <div class="context-menu-item" data-action="copyname"><i class="fas fa-copy"></i> {{ t('loras.contextMenu.copyFilename') }}</div>
+    <div class="context-menu-item" data-action="sendworkflow"><i class="fas fa-paper-plane"></i> {{ t('checkpoints.contextMenu.sendToWorkflow') }}</div>
     <div class="context-menu-item" data-action="preview"><i class="fas fa-folder-open"></i> {{ t('loras.contextMenu.openExamples') }}</div>
     <div class="context-menu-item" data-action="download-examples"><i class="fas fa-download"></i> {{ t('loras.contextMenu.downloadExamples') }}</div>
     <div class="context-menu-item" data-action="replace-preview"><i class="fas fa-image"></i> {{ t('loras.contextMenu.replacePreview') }}</div>

templates/components/context_menu.html

@@ -87,6 +87,9 @@
         <i class="fas fa-redo"></i> <span>{{ t('loras.bulkOperations.resumeMetadataRefresh') }}</span>
     </div>
     <div class="context-menu-separator"></div>
+    <div class="context-menu-item" data-action="download-missing-loras">
+        <i class="fas fa-download"></i> <span>{{ t('loras.bulkOperations.downloadMissingLoras') }}</span>
+    </div>
     <div class="context-menu-item" data-action="move-all">
         <i class="fas fa-folder-open"></i> <span>{{ t('loras.bulkOperations.moveAll') }}</span>
     </div>

templates/components/modals/confirm_modals.html

@@ -80,4 +80,32 @@
             <button class="primary-btn" data-action="confirm-check-updates">{{ t('modals.checkUpdates.action') }}</button>
         </div>
     </div>
+</div>
+
+<!-- Bulk Download Missing LoRAs Confirmation Modal -->
+<div id="bulkDownloadMissingLorasModal" class="modal">
+    <div class="modal-content">
+        <div class="modal-header">
+            <h2>{{ t('modals.bulkDownloadMissingLoras.title') }}</h2>
+            <span class="close" onclick="modalManager.closeModal('bulkDownloadMissingLorasModal')">&times;</span>
+        </div>
+        <div class="modal-body">
+            <p class="confirmation-message" id="bulkDownloadMissingLorasMessage"></p>
+            <div class="bulk-download-loras-preview" id="bulkDownloadMissingLorasPreview">
+                <p class="preview-title">{{ t('modals.bulkDownloadMissingLoras.previewTitle') }}</p>
+                <ul class="bulk-download-loras-list" id="bulkDownloadMissingLorasList"></ul>
+            </div>
+            <p class="confirmation-note">
+                <i class="fas fa-info-circle"></i>
+                {{ t('modals.bulkDownloadMissingLoras.note') }}
+            </p>
+        </div>
+        <div class="modal-actions">
+            <button class="secondary-btn" onclick="modalManager.closeModal('bulkDownloadMissingLorasModal')">{{ t('common.actions.cancel') }}</button>
+            <button class="primary-btn" id="bulkDownloadMissingLorasConfirmBtn" onclick="bulkMissingLoraDownloadManager.confirmDownload()">
+                <i class="fas fa-download"></i>
+                {{ t('modals.bulkDownloadMissingLoras.downloadButton') }}
+            </button>
+        </div>
+    </div>
 </div>

templates/components/modals/settings_modal.html

@@ -281,6 +281,26 @@
                                     </div>
                                 </div>
                             </div>
+
+                            <div class="setting-item">
+                                <div class="setting-row">
+                                    <div class="setting-info">
+                                        <label for="matureBlurLevel">
+                                        {{ t('settings.contentFiltering.matureBlurThreshold') }}
+                                        <i class="fas fa-info-circle info-icon" data-tooltip="{{ t('settings.contentFiltering.matureBlurThresholdHelp') }}"></i>
+                                    </label>
+                                    </div>
+                                    <div class="setting-control select-control">
+                                        <select id="matureBlurLevel"
+                                                onchange="settingsManager.saveSelectSetting('matureBlurLevel', 'mature_blur_level')">
+                                            <option value="PG13">{{ t('settings.contentFiltering.matureBlurThresholdOptions.pg13') }}</option>
+                                            <option value="R">{{ t('settings.contentFiltering.matureBlurThresholdOptions.r') }}</option>
+                                            <option value="X">{{ t('settings.contentFiltering.matureBlurThresholdOptions.x') }}</option>
+                                            <option value="XXX">{{ t('settings.contentFiltering.matureBlurThresholdOptions.xxx') }}</option>
+                                        </select>
+                                    </div>
+                                </div>
+                            </div>
                         </div>
                         
                         <!-- Video Settings -->
@@ -464,9 +484,7 @@
                                     </label>
                                     </div>
                                     <div class="setting-control select-control">
-                                        <select id="defaultLoraRoot" onchange="settingsManager.saveSelectSetting('defaultLoraRoot', 'default_lora_root')">
-                                            <option value="">{{ t('settings.folderSettings.noDefault') }}</option>
-                                        </select>
+                                        <select id="defaultLoraRoot" onchange="settingsManager.saveSelectSetting('defaultLoraRoot', 'default_lora_root')"></select>
                                     </div>
                                 </div>
                             </div>
@@ -480,9 +498,7 @@
                                     </label>
                                     </div>
                                     <div class="setting-control select-control">
-                                        <select id="defaultCheckpointRoot" onchange="settingsManager.saveSelectSetting('defaultCheckpointRoot', 'default_checkpoint_root')">
-                                            <option value="">{{ t('settings.folderSettings.noDefault') }}</option>
-                                        </select>
+                                        <select id="defaultCheckpointRoot" onchange="settingsManager.saveSelectSetting('defaultCheckpointRoot', 'default_checkpoint_root')"></select>
                                     </div>
                                 </div>
                             </div>
@@ -496,9 +512,7 @@
                                     </label>
                                     </div>
                                     <div class="setting-control select-control">
-                                        <select id="defaultUnetRoot" onchange="settingsManager.saveSelectSetting('defaultUnetRoot', 'default_unet_root')">
-                                            <option value="">{{ t('settings.folderSettings.noDefault') }}</option>
-                                        </select>
+                                        <select id="defaultUnetRoot" onchange="settingsManager.saveSelectSetting('defaultUnetRoot', 'default_unet_root')"></select>
                                     </div>
                                 </div>
                             </div>
@@ -512,9 +526,7 @@
                                     </label>
                                     </div>
                                     <div class="setting-control select-control">
-                                        <select id="defaultEmbeddingRoot" onchange="settingsManager.saveSelectSetting('defaultEmbeddingRoot', 'default_embedding_root')">
-                                            <option value="">{{ t('settings.folderSettings.noDefault') }}</option>
-                                        </select>
+                                        <select id="defaultEmbeddingRoot" onchange="settingsManager.saveSelectSetting('defaultEmbeddingRoot', 'default_embedding_root')"></select>
                                     </div>
                                 </div>
                             </div>
@@ -525,7 +537,7 @@
                             <div class="settings-subsection-header">
                                 <h4>
                                     {{ t('settings.extraFolderPaths.title') }}
-                                    <i class="fas fa-info-circle info-icon" data-tooltip="{{ t('settings.extraFolderPaths.help') }}"></i>
+                                    <i class="fas fa-sync-alt restart-required-icon" title="{{ t('settings.extraFolderPaths.restartRequired') }}"></i>
                                 </h4>
                             </div>
                             <div class="setting-item">
@@ -723,6 +735,46 @@
                                 </div>
                             </div>
 
+                            <div class="setting-item">
+                                <div class="setting-row">
+                                    <div class="setting-info">
+                                        <label for="downloadSkipBaseModelsToggle">
+                                            {{ t('settings.downloadSkipBaseModels.label') }}
+                                            <i class="fas fa-info-circle info-icon" data-tooltip="{{ t('settings.downloadSkipBaseModels.help') }}"></i>
+                                        </label>
+                                    </div>
+                                    <div class="setting-control">
+                                        <button
+                                            type="button"
+                                            id="downloadSkipBaseModelsToggle"
+                                            class="secondary-btn base-model-skip-toggle"
+                                            aria-expanded="false"
+                                        >
+                                            <span id="downloadSkipBaseModelsSummary">{{ t('settings.downloadSkipBaseModels.summary.none') }}</span>
+                                            <span class="base-model-skip-toggle-label">{{ t('settings.downloadSkipBaseModels.actions.edit') }}</span>
+                                        </button>
+                                    </div>
+                                </div>
+                                <div id="downloadSkipBaseModelsPanel" class="base-model-skip-panel" hidden>
+                                    <div class="base-model-skip-toolbar">
+                                        <input
+                                            type="text"
+                                            id="downloadSkipBaseModelsSearch"
+                                            class="base-model-skip-search"
+                                            placeholder="{{ t('settings.downloadSkipBaseModels.searchPlaceholder') }}"
+                                        />
+                                        <button type="button" class="text-btn base-model-skip-clear" id="downloadSkipBaseModelsClear">
+                                            {{ t('settings.downloadSkipBaseModels.actions.clear') }}
+                                        </button>
+                                    </div>
+                                    <div id="downloadSkipBaseModelsContainer" class="base-model-skip-list"></div>
+                                    <div id="downloadSkipBaseModelsEmpty" class="base-model-skip-empty" hidden>
+                                        {{ t('settings.downloadSkipBaseModels.empty') }}
+                                    </div>
+                                </div>
+                                <div class="settings-input-error-message" id="downloadSkipBaseModelsError"></div>
+                            </div>
+
                             <!-- Priority Tags -->
                             <div class="setting-item priority-tags-item">
                                 <div class="setting-row priority-tags-header-row">

templates/components/recipe_modal.html

@@ -29,22 +29,52 @@
                         <div class="param-group info-item">
                             <div class="param-header">
                                 <label>Prompt</label>
-                                <button class="copy-btn" id="copyPromptBtn" title="Copy Prompt">
-                                    <i class="fas fa-copy"></i>
-                                </button>
+                                <div class="param-actions">
+                                    <button class="copy-btn" id="copyPromptBtn" title="Copy Prompt">
+                                        <i class="fas fa-copy"></i>
+                                    </button>
+                                    <button class="edit-btn" id="editPromptBtn" title="Edit Prompt">
+                                        <i class="fas fa-pencil-alt"></i>
+                                    </button>
+                                </div>
                             </div>
                             <div class="param-content" id="recipePrompt"></div>
+                            <div class="param-editor" id="recipePromptEditor">
+                                <textarea
+                                    class="param-textarea"
+                                    id="recipePromptInput"
+                                    placeholder="Enter prompt"
+                                ></textarea>
+                                <div class="param-editor-hint">
+                                    {{ t('toast.recipes.promptEditorHint') }}
+                                </div>
+                            </div>
                         </div>
                         
                         <!-- Negative Prompt -->
                         <div class="param-group info-item">
                             <div class="param-header">
                                 <label>Negative Prompt</label>
-                                <button class="copy-btn" id="copyNegativePromptBtn" title="Copy Negative Prompt">
-                                    <i class="fas fa-copy"></i>
-                                </button>
+                                <div class="param-actions">
+                                    <button class="copy-btn" id="copyNegativePromptBtn" title="Copy Negative Prompt">
+                                        <i class="fas fa-copy"></i>
+                                    </button>
+                                    <button class="edit-btn" id="editNegativePromptBtn" title="Edit Negative Prompt">
+                                        <i class="fas fa-pencil-alt"></i>
+                                    </button>
+                                </div>
                             </div>
                             <div class="param-content" id="recipeNegativePrompt"></div>
+                            <div class="param-editor" id="recipeNegativePromptEditor">
+                                <textarea
+                                    class="param-textarea"
+                                    id="recipeNegativePromptInput"
+                                    placeholder="Enter negative prompt"
+                                ></textarea>
+                                <div class="param-editor-hint">
+                                    {{ t('toast.recipes.promptEditorHint') }}
+                                </div>
+                            </div>
                         </div>
                         
                         <!-- Other Parameters -->
@@ -65,6 +95,9 @@
                     <button class="copy-btn" id="copyRecipeSyntaxBtn" title="Copy Recipe Syntax">
                         <i class="fas fa-copy"></i>
                     </button>
+                    <button class="action-btn send-recipe-btn" id="sendRecipeBtn" title="Send Recipe to ComfyUI">
+                        <i class="fas fa-paper-plane"></i>
+                    </button>
                 </div>
             </div>
             <div class="recipe-resources-list">

tests/config/test_config_save_paths.py

@@ -131,6 +131,102 @@ def test_save_paths_logs_warning_when_upsert_fails(
     assert "Failed to save folder paths: boom" in caplog.text
 
 
+def test_save_paths_repairs_empty_default_roots(monkeypatch: pytest.MonkeyPatch, tmp_path):
+    folder_paths = _setup_config_environment(monkeypatch, tmp_path)
+
+    class FakeSettingsService:
+        def get_libraries(self):
+            return {
+                "comfyui": {
+                    "folder_paths": {key: list(value) for key, value in folder_paths.items()},
+                    "default_lora_root": "",
+                    "default_checkpoint_root": "",
+                    "default_embedding_root": "",
+                }
+            }
+
+        def rename_library(self, *_):
+            raise AssertionError("rename_library should not be invoked")
+
+        def upsert_library(self, name: str, **payload):
+            self.name = name
+            self.payload = payload
+
+    fake_settings = FakeSettingsService()
+    monkeypatch.setattr(settings_manager_module, "settings", fake_settings)
+
+    config_module.Config()
+
+    assert fake_settings.name == "comfyui"
+    assert fake_settings.payload["default_lora_root"] == folder_paths["loras"][0].replace("\\", "/")
+    assert fake_settings.payload["default_checkpoint_root"] == folder_paths["checkpoints"][0].replace("\\", "/")
+    assert fake_settings.payload["default_embedding_root"] == folder_paths["embeddings"][0].replace("\\", "/")
+
+
+def test_save_paths_repairs_stale_default_roots(monkeypatch: pytest.MonkeyPatch, tmp_path):
+    folder_paths = _setup_config_environment(monkeypatch, tmp_path)
+
+    class FakeSettingsService:
+        def get_libraries(self):
+            return {
+                "comfyui": {
+                    "folder_paths": {key: list(value) for key, value in folder_paths.items()},
+                    "default_lora_root": "/stale/loras",
+                    "default_checkpoint_root": "/stale/checkpoints",
+                    "default_embedding_root": "/stale/embeddings",
+                }
+            }
+
+        def rename_library(self, *_):
+            raise AssertionError("rename_library should not be invoked")
+
+        def upsert_library(self, name: str, **payload):
+            self.name = name
+            self.payload = payload
+
+    fake_settings = FakeSettingsService()
+    monkeypatch.setattr(settings_manager_module, "settings", fake_settings)
+
+    config_module.Config()
+
+    assert fake_settings.name == "comfyui"
+    assert fake_settings.payload["default_lora_root"] == folder_paths["loras"][0].replace("\\", "/")
+    assert fake_settings.payload["default_checkpoint_root"] == folder_paths["checkpoints"][0].replace("\\", "/")
+    assert fake_settings.payload["default_embedding_root"] == folder_paths["embeddings"][0].replace("\\", "/")
+
+
+def test_save_paths_keeps_valid_default_roots(monkeypatch: pytest.MonkeyPatch, tmp_path):
+    folder_paths = _setup_config_environment(monkeypatch, tmp_path)
+
+    class FakeSettingsService:
+        def get_libraries(self):
+            return {
+                "comfyui": {
+                    "folder_paths": {key: list(value) for key, value in folder_paths.items()},
+                    "default_lora_root": folder_paths["loras"][0],
+                    "default_checkpoint_root": folder_paths["checkpoints"][0],
+                    "default_embedding_root": folder_paths["embeddings"][0],
+                }
+            }
+
+        def rename_library(self, *_):
+            raise AssertionError("rename_library should not be invoked")
+
+        def upsert_library(self, name: str, **payload):
+            self.name = name
+            self.payload = payload
+
+    fake_settings = FakeSettingsService()
+    monkeypatch.setattr(settings_manager_module, "settings", fake_settings)
+
+    config_module.Config()
+
+    assert fake_settings.name == "comfyui"
+    assert fake_settings.payload["default_lora_root"] == folder_paths["loras"][0].replace("\\", "/")
+    assert fake_settings.payload["default_checkpoint_root"] == folder_paths["checkpoints"][0].replace("\\", "/")
+    assert fake_settings.payload["default_embedding_root"] == folder_paths["embeddings"][0].replace("\\", "/")
+
+
 def test_save_paths_removes_template_default_library(monkeypatch, tmp_path):
     folder_paths = _setup_config_environment(monkeypatch, tmp_path)
 
@@ -322,3 +418,182 @@ def test_extra_paths_deduplication(monkeypatch, tmp_path):
 
     assert config_instance.loras_roots == [str(loras_dir)]
     assert config_instance.extra_loras_roots == [str(extra_loras_dir)]
+
+
+def test_apply_library_settings_ignores_extra_lora_path_overlapping_primary_symlink(
+    monkeypatch, tmp_path, caplog
+):
+    """Extra LoRA paths should be ignored when they resolve to the same target as a primary root."""
+    real_loras_dir = tmp_path / "loras_real"
+    real_loras_dir.mkdir()
+    loras_link = tmp_path / "loras_link"
+    loras_link.symlink_to(real_loras_dir, target_is_directory=True)
+
+    config_instance = config_module.Config()
+
+    library_config = {
+        "folder_paths": {
+            "loras": [str(loras_link)],
+            "checkpoints": [],
+            "unet": [],
+            "embeddings": [],
+        },
+        "extra_folder_paths": {
+            "loras": [str(real_loras_dir)],
+            "checkpoints": [],
+            "unet": [],
+            "embeddings": [],
+        },
+    }
+
+    with caplog.at_level("WARNING", logger=config_module.logger.name):
+        config_instance.apply_library_settings(library_config)
+
+    assert config_instance.loras_roots == [str(loras_link)]
+    assert config_instance.extra_loras_roots == []
+
+    warning_messages = [
+        record.message
+        for record in caplog.records
+        if record.levelname == "WARNING"
+        and "same lora folder" in record.message.lower()
+    ]
+    assert len(warning_messages) == 1
+    assert "comfyui model paths" in warning_messages[0].lower()
+    assert "extra folder paths" in warning_messages[0].lower()
+    assert "duplicate items" in warning_messages[0].lower()
+
+
+def test_apply_library_settings_detects_overlap_case_insensitively(
+    monkeypatch, tmp_path, caplog
+):
+    """Overlap detection should use case-insensitive comparison on Windows-like paths."""
+    real_loras_dir = tmp_path / "loras_real"
+    real_loras_dir.mkdir()
+    loras_link = tmp_path / "loras_link"
+    loras_link.symlink_to(real_loras_dir, target_is_directory=True)
+
+    original_exists = config_module.os.path.exists
+    original_realpath = config_module.os.path.realpath
+    original_normcase = config_module.os.path.normcase
+
+    def fake_exists(path):
+        if isinstance(path, str) and path.lower() in {
+            str(loras_link).lower(),
+            str(real_loras_dir).lower(),
+            str(loras_link).upper().lower(),
+            str(real_loras_dir).upper().lower(),
+        }:
+            return True
+        return original_exists(path)
+
+    def fake_realpath(path, *args, **kwargs):
+        if isinstance(path, str):
+            lowered = path.lower()
+            if lowered == str(loras_link).lower():
+                return str(real_loras_dir)
+            if lowered == str(real_loras_dir).lower():
+                return str(real_loras_dir)
+        return original_realpath(path, *args, **kwargs)
+
+    monkeypatch.setattr(config_module.os.path, "exists", fake_exists)
+    monkeypatch.setattr(config_module.os.path, "realpath", fake_realpath)
+    monkeypatch.setattr(
+        config_module.os.path,
+        "normcase",
+        lambda value: original_normcase(value).lower(),
+    )
+
+    config_instance = config_module.Config()
+    primary_path = str(loras_link).replace("loras_link", "LORAS_LINK")
+    extra_path = str(real_loras_dir).replace("loras_real", "loras_real")
+
+    library_config = {
+        "folder_paths": {
+            "loras": [primary_path],
+            "checkpoints": [],
+            "unet": [],
+            "embeddings": [],
+        },
+        "extra_folder_paths": {
+            "loras": [extra_path.upper()],
+            "checkpoints": [],
+            "unet": [],
+            "embeddings": [],
+        },
+    }
+
+    with caplog.at_level("WARNING", logger=config_module.logger.name):
+        config_instance.apply_library_settings(library_config)
+
+    assert config_instance.loras_roots == [primary_path]
+    assert config_instance.extra_loras_roots == []
+    assert any("same lora folder" in record.message.lower() for record in caplog.records)
+
+
+def test_apply_library_settings_ignores_missing_extra_lora_paths(monkeypatch, tmp_path, caplog):
+    """Missing extra paths should be ignored without overlap warnings."""
+    loras_dir = tmp_path / "loras"
+    loras_dir.mkdir()
+    missing_extra = tmp_path / "missing_loras"
+
+    config_instance = config_module.Config()
+    library_config = {
+        "folder_paths": {
+            "loras": [str(loras_dir)],
+            "checkpoints": [],
+            "unet": [],
+            "embeddings": [],
+        },
+        "extra_folder_paths": {
+            "loras": [str(missing_extra)],
+            "checkpoints": [],
+            "unet": [],
+            "embeddings": [],
+        },
+    }
+
+    with caplog.at_level("WARNING", logger=config_module.logger.name):
+        config_instance.apply_library_settings(library_config)
+
+    assert config_instance.loras_roots == [str(loras_dir)]
+    assert config_instance.extra_loras_roots == []
+    assert not any("same lora folder" in record.message.lower() for record in caplog.records)
+
+
+def test_apply_library_settings_ignores_extra_lora_path_overlapping_primary_root_symlink(
+    tmp_path, caplog
+):
+    """Extra LoRA paths should be ignored when already reachable via a first-level symlink under the primary root."""
+    loras_dir = tmp_path / "loras"
+    loras_dir.mkdir()
+    external_dir = tmp_path / "external_loras"
+    external_dir.mkdir()
+    link_dir = loras_dir / "link"
+    link_dir.symlink_to(external_dir, target_is_directory=True)
+
+    config_instance = config_module.Config()
+    library_config = {
+        "folder_paths": {
+            "loras": [str(loras_dir)],
+            "checkpoints": [],
+            "unet": [],
+            "embeddings": [],
+        },
+        "extra_folder_paths": {
+            "loras": [str(external_dir)],
+            "checkpoints": [],
+            "unet": [],
+            "embeddings": [],
+        },
+    }
+
+    with caplog.at_level("WARNING", logger=config_module.logger.name):
+        config_instance.apply_library_settings(library_config)
+
+    assert config_instance.loras_roots == [str(loras_dir)]
+    assert config_instance.extra_loras_roots == []
+    assert any(
+        "same lora folder" in record.message.lower()
+        for record in caplog.records
+    )

tests/frontend/components/autocomplete.behavior.test.js

@@ -15,6 +15,8 @@ const {
 }));
 
 const fetchApiMock = vi.fn();
+const settingGetMock = vi.fn();
+const settingSetMock = vi.fn();
 const caretHelperInstance = {
   getBeforeCursor: vi.fn(() => ''),
   getCursorOffset: vi.fn(() => ({ left: 0, top: 0 })),
@@ -37,6 +39,12 @@ vi.mock(APP_MODULE, () => ({
     canvas: {
       ds: { scale: 1 },
     },
+    extensionManager: {
+      setting: {
+        get: settingGetMock,
+        set: settingSetMock,
+      },
+    },
     registerExtension: vi.fn(),
   },
 }));
@@ -55,6 +63,23 @@ describe('AutoComplete widget interactions', () => {
     document.head.querySelectorAll('style').forEach((styleEl) => styleEl.remove());
     Element.prototype.scrollIntoView = vi.fn();
     fetchApiMock.mockReset();
+    settingGetMock.mockReset();
+    settingSetMock.mockReset();
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.autocomplete_append_comma') {
+        return true;
+      }
+      if (key === 'loramanager.autocomplete_accept_key') {
+        return 'both';
+      }
+      if (key === 'loramanager.prompt_tag_autocomplete') {
+        return true;
+      }
+      if (key === 'loramanager.tag_space_replacement') {
+        return false;
+      }
+      return undefined;
+    });
     caretHelperInstance.getBeforeCursor.mockReset();
     caretHelperInstance.getCursorOffset.mockReset();
     caretHelperInstance.getBeforeCursor.mockReturnValue('');
@@ -82,7 +107,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'loras', { debounceDelay: 0, showPreview: false });
+    const autoComplete = new AutoComplete(input,'loras', { debounceDelay: 0, showPreview: false });
 
     input.value = 'example';
     input.dispatchEvent(new Event('input', { bubbles: true }));
@@ -125,25 +150,251 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'loras', { debounceDelay: 0, showPreview: false });
+    const autoComplete = new AutoComplete(input,'loras', { debounceDelay: 0, showPreview: false });
 
     await autoComplete.insertSelection('models/example.safetensors');
 
     expect(fetchApiMock).toHaveBeenCalledWith(
       '/lm/loras/usage-tips-by-path?relative_path=models%2Fexample.safetensors',
     );
-    expect(input.value).toContain('<lora:example:1.5:0.9>, ');
+    expect(input.value).toContain('<lora:example:1.5:0.9>,');
     expect(autoComplete.dropdown.style.display).toBe('none');
     expect(input.focus).toHaveBeenCalled();
     expect(input.setSelectionRange).toHaveBeenCalled();
   });
 
+  it('accepts the selected suggestion with Tab', async () => {
+    caretHelperInstance.getBeforeCursor.mockReturnValue('example');
+
+    const input = document.createElement('textarea');
+    input.value = 'example';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'custom_words', { showPreview: false });
+
+    autoComplete.items = ['example_completion'];
+    autoComplete.selectedIndex = 0;
+    autoComplete.isVisible = true;
+    const insertSelectionSpy = vi.spyOn(autoComplete,'insertSelection').mockResolvedValue();
+
+    const tabEvent = new KeyboardEvent('keydown', { key: 'Tab', bubbles: true, cancelable: true });
+    input.dispatchEvent(tabEvent);
+
+    expect(tabEvent.defaultPrevented).toBe(true);
+    expect(insertSelectionSpy).toHaveBeenCalledWith('example_completion');
+  });
+
+  it('accepts the selected suggestion with Enter', async () => {
+    caretHelperInstance.getBeforeCursor.mockReturnValue('example');
+
+    const input = document.createElement('textarea');
+    input.value = 'example';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'custom_words', { showPreview: false });
+
+    autoComplete.items = ['example_completion'];
+    autoComplete.selectedIndex = 0;
+    autoComplete.isVisible = true;
+    const insertSelectionSpy = vi.spyOn(autoComplete,'insertSelection').mockResolvedValue();
+
+    const enterEvent = new KeyboardEvent('keydown', { key: 'Enter', bubbles: true, cancelable: true });
+    input.dispatchEvent(enterEvent);
+
+    expect(enterEvent.defaultPrevented).toBe(true);
+    expect(insertSelectionSpy).toHaveBeenCalledWith('example_completion');
+  });
+
+  it('prefers the latest best match when Tab is pressed before debounced suggestions fully refresh', async () => {
+    caretHelperInstance.getBeforeCursor.mockReturnValue('loop');
+
+    const input = document.createElement('textarea');
+    input.value = 'loop';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'prompt', { showPreview: false, minChars: 1 });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.items = [
+      { tag_name: 'looking_to_the_side', category: 0, post_count: 1000 },
+      { tag_name: 'loop', category: 0, post_count: 500 },
+    ];
+    autoComplete.currentSearchTerm = 'loo';
+    autoComplete.selectedIndex = 0;
+    autoComplete.isVisible = true;
+    const insertSelectionSpy = vi.spyOn(autoComplete,'insertSelection').mockResolvedValue();
+
+    const tabEvent = new KeyboardEvent('keydown', { key: 'Tab', bubbles: true, cancelable: true });
+    input.dispatchEvent(tabEvent);
+
+    expect(tabEvent.defaultPrevented).toBe(true);
+    expect(autoComplete.selectedIndex).toBe(1);
+    expect(insertSelectionSpy).toHaveBeenCalledWith('loop');
+  });
+
+  it('accepts the first available suggestion with Tab even if delayed auto-selection has not happened yet', async () => {
+    caretHelperInstance.getBeforeCursor.mockReturnValue('loop');
+
+    const input = document.createElement('textarea');
+    input.value = 'loop';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'custom_words', { showPreview: false });
+
+    autoComplete.items = ['loop'];
+    autoComplete.selectedIndex = -1;
+    autoComplete.isVisible = true;
+    const insertSelectionSpy = vi.spyOn(autoComplete,'insertSelection').mockResolvedValue();
+
+    const tabEvent = new KeyboardEvent('keydown', { key: 'Tab', bubbles: true, cancelable: true });
+    input.dispatchEvent(tabEvent);
+
+    expect(tabEvent.defaultPrevented).toBe(true);
+    expect(autoComplete.selectedIndex).toBe(0);
+    expect(insertSelectionSpy).toHaveBeenCalledWith('loop');
+  });
+
+  it('only accepts with Tab when autocomplete accept key is set to tab_only', async () => {
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.autocomplete_append_comma') {
+        return true;
+      }
+      if (key === 'loramanager.autocomplete_accept_key') {
+        return 'tab_only';
+      }
+      if (key === 'loramanager.prompt_tag_autocomplete') {
+        return true;
+      }
+      if (key === 'loramanager.tag_space_replacement') {
+        return false;
+      }
+      return undefined;
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('example');
+
+    const input = document.createElement('textarea');
+    input.value = 'example';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'custom_words', { showPreview: false });
+
+    autoComplete.items = ['example_completion'];
+    autoComplete.selectedIndex = 0;
+    autoComplete.isVisible = true;
+    const insertSelectionSpy = vi.spyOn(autoComplete,'insertSelection').mockResolvedValue();
+
+    const enterEvent = new KeyboardEvent('keydown', { key: 'Enter', bubbles: true, cancelable: true });
+    input.dispatchEvent(enterEvent);
+
+    expect(enterEvent.defaultPrevented).toBe(false);
+    expect(insertSelectionSpy).not.toHaveBeenCalled();
+
+    const tabEvent = new KeyboardEvent('keydown', { key: 'Tab', bubbles: true, cancelable: true });
+    input.dispatchEvent(tabEvent);
+
+    expect(tabEvent.defaultPrevented).toBe(true);
+    expect(insertSelectionSpy).toHaveBeenCalledWith('example_completion');
+  });
+
+  it('only accepts with Enter when autocomplete accept key is set to enter_only', async () => {
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.autocomplete_append_comma') {
+        return true;
+      }
+      if (key === 'loramanager.autocomplete_accept_key') {
+        return 'enter_only';
+      }
+      if (key === 'loramanager.prompt_tag_autocomplete') {
+        return true;
+      }
+      if (key === 'loramanager.tag_space_replacement') {
+        return false;
+      }
+      return undefined;
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('example');
+
+    const input = document.createElement('textarea');
+    input.value = 'example';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'custom_words', { showPreview: false });
+
+    autoComplete.items = ['example_completion'];
+    autoComplete.selectedIndex = 0;
+    autoComplete.isVisible = true;
+    const insertSelectionSpy = vi.spyOn(autoComplete,'insertSelection').mockResolvedValue();
+
+    const tabEvent = new KeyboardEvent('keydown', { key: 'Tab', bubbles: true, cancelable: true });
+    input.dispatchEvent(tabEvent);
+
+    expect(tabEvent.defaultPrevented).toBe(false);
+    expect(insertSelectionSpy).not.toHaveBeenCalled();
+
+    const enterEvent = new KeyboardEvent('keydown', { key: 'Enter', bubbles: true, cancelable: true });
+    input.dispatchEvent(enterEvent);
+
+    expect(enterEvent.defaultPrevented).toBe(true);
+    expect(insertSelectionSpy).toHaveBeenCalledWith('example_completion');
+  });
+
+  it('does not intercept Tab when the dropdown is not visible', async () => {
+    caretHelperInstance.getBeforeCursor.mockReturnValue('example');
+
+    const input = document.createElement('textarea');
+    input.value = 'example';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'custom_words', { showPreview: false });
+
+    autoComplete.items = ['example_completion'];
+    autoComplete.selectedIndex = 0;
+    autoComplete.isVisible = false;
+    const insertSelectionSpy = vi.spyOn(autoComplete,'insertSelection').mockResolvedValue();
+
+    const tabEvent = new KeyboardEvent('keydown', { key: 'Tab', bubbles: true, cancelable: true });
+    input.dispatchEvent(tabEvent);
+
+    expect(tabEvent.defaultPrevented).toBe(false);
+    expect(insertSelectionSpy).not.toHaveBeenCalled();
+  });
+
   it('highlights multiple include tokens while ignoring excluded ones', async () => {
     const input = document.createElement('textarea');
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'loras', { showPreview: false });
+    const autoComplete = new AutoComplete(input,'loras', { showPreview: false });
 
     const highlighted = autoComplete.highlightMatch(
       'models/flux/beta-detail.safetensors',
@@ -160,7 +411,7 @@ describe('AutoComplete widget interactions', () => {
   it('handles arrow key navigation with virtual scrolling', async () => {
     vi.useFakeTimers();
 
-    const mockItems = Array.from({ length: 50 }, (_, i) => `model_${i.toString().padStart(2, '0')}.safetensors`);
+    const mockItems = Array.from({ length: 50 }, (_, i) => `model_${i.toString().padStart(2,'0')}.safetensors`);
 
     fetchApiMock.mockResolvedValue({
       json: () => Promise.resolve({ success: true, relative_paths: mockItems }),
@@ -173,7 +424,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'loras', {
+    const autoComplete = new AutoComplete(input,'loras', {
       debounceDelay: 0,
       showPreview: false,
       enableVirtualScroll: true,
@@ -216,7 +467,7 @@ describe('AutoComplete widget interactions', () => {
   it('maintains selection when scrolling to invisible items', async () => {
     vi.useFakeTimers();
 
-    const mockItems = Array.from({ length: 100 }, (_, i) => `item_${i.toString().padStart(3, '0')}.safetensors`);
+    const mockItems = Array.from({ length: 100 }, (_, i) => `item_${i.toString().padStart(3,'0')}.safetensors`);
 
     fetchApiMock.mockResolvedValue({
       json: () => Promise.resolve({ success: true, relative_paths: mockItems }),
@@ -231,7 +482,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'loras', {
+    const autoComplete = new AutoComplete(input,'loras', {
       debounceDelay: 0,
       showPreview: false,
       enableVirtualScroll: true,
@@ -289,7 +540,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -302,7 +553,7 @@ describe('AutoComplete widget interactions', () => {
 
     await autoComplete.insertSelection('looking_to_the_side');
 
-    expect(input.value).toBe('looking_to_the_side, ');
+    expect(input.value).toBe('looking_to_the_side,');
     expect(autoComplete.dropdown.style.display).toBe('none');
     expect(input.focus).toHaveBeenCalled();
   });
@@ -328,7 +579,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -342,7 +593,7 @@ describe('AutoComplete widget interactions', () => {
 
     await autoComplete.insertSelection('1girl');
 
-    expect(input.value).toBe('hello 1girl, ');
+    expect(input.value).toBe('hello 1girl,');
   });
 
   it('replaces entire phrase for underscore tag match (e.g., "blue hair" -> "blue_hair")', async () => {
@@ -366,7 +617,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -380,7 +631,7 @@ describe('AutoComplete widget interactions', () => {
 
     await autoComplete.insertSelection('blue_hair');
 
-    expect(input.value).toBe('blue_hair, ');
+    expect(input.value).toBe('blue_hair,');
   });
 
   it('handles multi-word phrase with preceding text correctly', async () => {
@@ -403,7 +654,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -417,7 +668,7 @@ describe('AutoComplete widget interactions', () => {
 
     await autoComplete.insertSelection('looking_to_the_side');
 
-    expect(input.value).toBe('1girl, looking_to_the_side, ');
+    expect(input.value).toBe('1girl, looking_to_the_side,');
   });
 
   it('replaces entire command and search term when using command mode with multi-word phrase', async () => {
@@ -442,7 +693,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -458,7 +709,7 @@ describe('AutoComplete widget interactions', () => {
     await autoComplete.insertSelection('looking_to_the_side');
 
     // Command part should be replaced along with search term
-    expect(input.value).toBe('looking_to_the_side, ');
+    expect(input.value).toBe('looking_to_the_side,');
   });
 
   it('replaces only last token when multi-word query does not exactly match selected tag', async () => {
@@ -483,7 +734,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -498,7 +749,7 @@ describe('AutoComplete widget interactions', () => {
     await autoComplete.insertSelection('blue_hair');
 
     // Only "blue" should be replaced, not the entire phrase
-    expect(input.value).toBe('looking to the blue_hair, ');
+    expect(input.value).toBe('looking to the blue_hair,');
   });
 
   it('handles multiple consecutive spaces in multi-word phrase correctly', async () => {
@@ -522,7 +773,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -537,7 +788,7 @@ describe('AutoComplete widget interactions', () => {
     await autoComplete.insertSelection('looking_to_the_side');
 
     // Multiple spaces should be normalized to single underscores for matching
-    expect(input.value).toBe('looking_to_the_side, ');
+    expect(input.value).toBe('looking_to_the_side,');
   });
 
   it('handles command mode with partial match replacing only last token', async () => {
@@ -561,7 +812,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -577,7 +828,7 @@ describe('AutoComplete widget interactions', () => {
     await autoComplete.insertSelection('blue_hair');
 
     // In command mode, the entire command + search term should be replaced
-    expect(input.value).toBe('blue_hair, ');
+    expect(input.value).toBe('blue_hair,');
   });
 
   it('replaces entire phrase when selected tag starts with underscore version of search term (prefix match)', async () => {
@@ -601,7 +852,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -616,7 +867,7 @@ describe('AutoComplete widget interactions', () => {
     await autoComplete.insertSelection('looking_to_the_side');
 
     // Entire phrase should be replaced with selected tag (with underscores)
-    expect(input.value).toBe('looking_to_the_side, ');
+    expect(input.value).toBe('looking_to_the_side,');
   });
 
   it('inserts tag with underscores regardless of space replacement setting', async () => {
@@ -639,7 +890,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -653,7 +904,287 @@ describe('AutoComplete widget interactions', () => {
     await autoComplete.insertSelection('blue_hair');
 
     // Tag should be inserted with underscores, not spaces
-    expect(input.value).toBe('blue_hair, ');
+    expect(input.value).toBe('blue_hair,');
+  });
+
+  it('omits the trailing comma when the append comma setting is disabled', async () => {
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.autocomplete_append_comma') {
+        return false;
+      }
+      if (key === 'loramanager.prompt_tag_autocomplete') {
+        return true;
+      }
+      if (key === 'loramanager.tag_space_replacement') {
+        return false;
+      }
+      return undefined;
+    });
+
+    const mockTags = [
+      { tag_name: 'blue_hair', category: 0, post_count: 45000 },
+    ];
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('blue hair');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = 'blue hair';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = 'blue hair';
+
+    await autoComplete.insertSelection('blue_hair');
+
+    expect(input.value).toBe('blue_hair ');
+  });
+
+  it('uses persisted autocomplete metadata as the next search start when comma append is disabled', async () => {
+    vi.useFakeTimers();
+
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.autocomplete_append_comma') {
+        return false;
+      }
+      if (key === 'loramanager.prompt_tag_autocomplete') {
+        return true;
+      }
+      if (key === 'loramanager.tag_space_replacement') {
+        return false;
+      }
+      return undefined;
+    });
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: [{ tag_name: 'cat_ears', category: 0, post_count: 1234 }] }),
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('1girl cat');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = '1girl cat';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    input._autocompleteMetadataWidget = {
+      value: {
+        version: 1,
+        textWidgetName: 'text',
+        lastAccepted: {
+          start: 0,
+          end: 6,
+          insertedText: '1girl ',
+          textSnapshot: '1girl ',
+        },
+      },
+    };
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    expect(autoComplete.getSearchTerm(input.value)).toBe('cat');
+
+    input.dispatchEvent(new Event('input', { bubbles: true }));
+    await vi.runAllTimersAsync();
+    await Promise.resolve();
+
+    expect(fetchApiMock).toHaveBeenCalledWith('/lm/custom-words/search?enriched=true&search=cat&limit=100');
+  });
+
+  it('invalidates stale autocomplete metadata and falls back to delimiter-based matching', async () => {
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.autocomplete_append_comma') {
+        return false;
+      }
+      if (key === 'loramanager.prompt_tag_autocomplete') {
+        return true;
+      }
+      if (key === 'loramanager.tag_space_replacement') {
+        return false;
+      }
+      return undefined;
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('1boy cat');
+
+    const metadataWidget = {
+      value: {
+        version: 1,
+        textWidgetName: 'text',
+        lastAccepted: {
+          start: 0,
+          end: 6,
+          insertedText: '1girl ',
+          textSnapshot: '1girl ',
+        },
+      },
+    };
+
+    const input = document.createElement('textarea');
+    input.value = '1boy cat';
+    input.selectionStart = input.value.length;
+    input._autocompleteMetadataWidget = metadataWidget;
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    expect(autoComplete.getSearchTerm(input.value)).toBe('1boy cat');
+    expect(metadataWidget.value.lastAccepted).toBeUndefined();
+  });
+
+  it('does not duplicate the first character when accepting a suggestion after a trailing space', async () => {
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.autocomplete_append_comma') {
+        return false;
+      }
+      if (key === 'loramanager.prompt_tag_autocomplete') {
+        return true;
+      }
+      if (key === 'loramanager.tag_space_replacement') {
+        return false;
+      }
+      return undefined;
+    });
+
+    const mockTags = [
+      { tag_name: '1girl', category: 4, post_count: 500000 },
+    ];
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('1girl ');
+
+    const input = document.createElement('textarea');
+    input.value = '1girl ';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+
+    await autoComplete.insertSelection('1girl');
+
+    expect(input.value).toBe('1girl ');
+  });
+
+  it('treats a newline as a hard boundary after dismissing autocomplete', async () => {
+    vi.useFakeTimers();
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: [{ tag_name: '1girl', category: 4, post_count: 500000 }] }),
+    });
+
+    const input = document.createElement('textarea');
+    input.value = '1gi\n';
+    input.selectionStart = input.value.length;
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('1gi');
+    autoComplete.handleInput('1gi');
+    await vi.runAllTimersAsync();
+    await Promise.resolve();
+    expect(fetchApiMock).toHaveBeenCalled();
+
+    fetchApiMock.mockClear();
+    autoComplete.hide();
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('1gi\n');
+    input.dispatchEvent(new Event('input', { bubbles: true }));
+    await vi.runAllTimersAsync();
+    await Promise.resolve();
+
+    expect(autoComplete.getSearchTerm(input.value)).toBe('');
+    expect(fetchApiMock).not.toHaveBeenCalled();
+    expect(autoComplete.isVisible).toBe(false);
+  });
+
+  it('omits the trailing comma for LoRA insertions when the setting is disabled', async () => {
+    settingGetMock.mockImplementation((key) => {
+      if (key === 'loramanager.autocomplete_append_comma') {
+        return false;
+      }
+      if (key === 'loramanager.prompt_tag_autocomplete') {
+        return true;
+      }
+      if (key === 'loramanager.tag_space_replacement') {
+        return false;
+      }
+      return undefined;
+    });
+
+    fetchApiMock.mockImplementation((url) => {
+      if (url.includes('usage-tips-by-path')) {
+        return Promise.resolve({
+          ok: true,
+          json: () => Promise.resolve({
+            success: true,
+            usage_tips: JSON.stringify({ strength: '1.2' }),
+          }),
+        });
+      }
+
+      return Promise.resolve({
+        json: () => Promise.resolve({ success: true, relative_paths: ['models/example.safetensors'] }),
+      });
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('alpha, example');
+
+    const input = document.createElement('textarea');
+    input.value = 'alpha, example';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input,'loras', { debounceDelay: 0, showPreview: false });
+
+    await autoComplete.insertSelection('models/example.safetensors');
+
+    expect(input.value).toContain('<lora:example:1.2>');
+    expect(input.value).not.toContain('<lora:example:1.2>,');
   });
 
   it('replaces entire phrase when selected tag ends with underscore version of search term (suffix match)', async () => {
@@ -677,7 +1208,7 @@ describe('AutoComplete widget interactions', () => {
     document.body.append(input);
 
     const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
-    const autoComplete = new AutoComplete(input, 'prompt', {
+    const autoComplete = new AutoComplete(input,'prompt', {
       debounceDelay: 0,
       showPreview: false,
       minChars: 1,
@@ -692,6 +1223,6 @@ describe('AutoComplete widget interactions', () => {
     await autoComplete.insertSelection('looking_to_the_side');
 
     // Entire phrase should be replaced with selected tag
-    expect(input.value).toBe('looking_to_the_side, ');
+    expect(input.value).toBe('looking_to_the_side,');
   });
 });

tests/frontend/components/contextMenu.interactions.test.js

@@ -245,16 +245,28 @@ describe('Interaction-level regression coverage', () => {
                   <div class="param-group info-item">
                     <div class="param-header">
                       <label>Prompt</label>
-                      <button class="copy-btn" id="copyPromptBtn" title="Copy Prompt"><i class="fas fa-copy"></i></button>
+                      <div class="param-actions">
+                        <button class="copy-btn" id="copyPromptBtn" title="Copy Prompt"><i class="fas fa-copy"></i></button>
+                        <button class="edit-btn" id="editPromptBtn" title="Edit Prompt"><i class="fas fa-pencil-alt"></i></button>
+                      </div>
                     </div>
                     <div class="param-content" id="recipePrompt"></div>
+                    <div class="param-editor" id="recipePromptEditor">
+                      <textarea class="param-textarea" id="recipePromptInput"></textarea>
+                    </div>
                   </div>
                   <div class="param-group info-item">
                     <div class="param-header">
                       <label>Negative Prompt</label>
-                      <button class="copy-btn" id="copyNegativePromptBtn" title="Copy Negative Prompt"><i class="fas fa-copy"></i></button>
+                      <div class="param-actions">
+                        <button class="copy-btn" id="copyNegativePromptBtn" title="Copy Negative Prompt"><i class="fas fa-copy"></i></button>
+                        <button class="edit-btn" id="editNegativePromptBtn" title="Edit Negative Prompt"><i class="fas fa-pencil-alt"></i></button>
+                      </div>
                     </div>
                     <div class="param-content" id="recipeNegativePrompt"></div>
+                    <div class="param-editor" id="recipeNegativePromptEditor">
+                      <textarea class="param-textarea" id="recipeNegativePromptInput"></textarea>
+                    </div>
                   </div>
                   <div class="other-params" id="recipeOtherParams"></div>
                 </div>
@@ -324,6 +336,208 @@ describe('Interaction-level regression coverage', () => {
     expect(recipeModal.currentRecipe.title).toBe('Updated Title');
   });
 
+  it('saves prompt edits on Enter while preserving Shift+Enter for new lines', async () => {
+    document.body.innerHTML = `
+      <div id="recipeModal" class="modal">
+        <div class="modal-content">
+          <header class="recipe-modal-header">
+            <h2 id="recipeModalTitle">Recipe Details</h2>
+            <div class="recipe-tags-container">
+              <div class="recipe-tags-compact" id="recipeTagsCompact"></div>
+              <div class="recipe-tags-tooltip" id="recipeTagsTooltip">
+                <div class="tooltip-content" id="recipeTagsTooltipContent"></div>
+              </div>
+            </div>
+          </header>
+          <div class="modal-body">
+            <div class="recipe-top-section">
+              <div class="recipe-preview-container" id="recipePreviewContainer">
+                <img id="recipeModalImage" src="" alt="Recipe Preview" class="recipe-preview-media">
+              </div>
+              <div class="info-section recipe-gen-params">
+                <div class="gen-params-container">
+                  <div class="param-group info-item">
+                    <div class="param-header">
+                      <label>Prompt</label>
+                      <div class="param-actions">
+                        <button class="copy-btn" id="copyPromptBtn" title="Copy Prompt"><i class="fas fa-copy"></i></button>
+                        <button class="edit-btn" id="editPromptBtn" title="Edit Prompt"><i class="fas fa-pencil-alt"></i></button>
+                      </div>
+                    </div>
+                    <div class="param-content" id="recipePrompt"></div>
+                    <div class="param-editor" id="recipePromptEditor">
+                      <textarea class="param-textarea" id="recipePromptInput"></textarea>
+                    </div>
+                  </div>
+                  <div class="param-group info-item">
+                    <div class="param-header">
+                      <label>Negative Prompt</label>
+                      <div class="param-actions">
+                        <button class="copy-btn" id="copyNegativePromptBtn" title="Copy Negative Prompt"><i class="fas fa-copy"></i></button>
+                        <button class="edit-btn" id="editNegativePromptBtn" title="Edit Negative Prompt"><i class="fas fa-pencil-alt"></i></button>
+                      </div>
+                    </div>
+                    <div class="param-content" id="recipeNegativePrompt"></div>
+                    <div class="param-editor" id="recipeNegativePromptEditor">
+                      <textarea class="param-textarea" id="recipeNegativePromptInput"></textarea>
+                    </div>
+                  </div>
+                  <div class="other-params" id="recipeOtherParams"></div>
+                </div>
+              </div>
+            </div>
+            <div class="info-section recipe-bottom-section">
+              <div class="recipe-section-header">
+                <h3>Resources</h3>
+                <div class="recipe-section-actions">
+                  <span id="recipeLorasCount"><i class="fas fa-layer-group"></i> 0 LoRAs</span>
+                </div>
+              </div>
+              <div class="recipe-loras-list" id="recipeLorasList"></div>
+            </div>
+          </div>
+        </div>
+      </div>
+    `;
+
+    const { RecipeModal } = await import('../../../static/js/components/RecipeModal.js');
+    const recipeModal = new RecipeModal();
+
+    recipeModal.showRecipeDetails({
+      id: 'recipe-2',
+      file_path: '/recipes/prompt.json',
+      title: 'Prompt Recipe',
+      tags: [],
+      file_url: '',
+      preview_url: '',
+      source_path: '',
+      gen_params: {
+        prompt: 'old prompt',
+        negative_prompt: 'keep negative',
+        steps: 30,
+        cfg_scale: 7,
+      },
+      loras: [],
+    });
+
+    document.getElementById('editPromptBtn').click();
+    const textarea = document.getElementById('recipePromptInput');
+    textarea.value = 'new prompt text';
+    textarea.dispatchEvent(new KeyboardEvent('keydown', { key: 'Enter', shiftKey: true, bubbles: true }));
+    await flushAsyncTasks();
+
+    expect(updateRecipeMetadataMock).not.toHaveBeenCalled();
+
+    textarea.dispatchEvent(new KeyboardEvent('keydown', { key: 'Enter', bubbles: true }));
+    await updateRecipeMetadataMock.mock.results[0].value;
+    await flushAsyncTasks();
+
+    expect(updateRecipeMetadataMock).toHaveBeenCalledWith('/recipes/prompt.json', {
+      gen_params: {
+        prompt: 'new prompt text',
+        negative_prompt: 'keep negative',
+        steps: 30,
+        cfg_scale: 7,
+      },
+    });
+    expect(document.getElementById('recipePrompt').textContent).toBe('new prompt text');
+    expect(recipeModal.currentRecipe.gen_params.prompt).toBe('new prompt text');
+  });
+
+  it('cancels negative prompt edits on Escape without saving', async () => {
+    document.body.innerHTML = `
+      <div id="recipeModal" class="modal">
+        <div class="modal-content">
+          <header class="recipe-modal-header">
+            <h2 id="recipeModalTitle">Recipe Details</h2>
+            <div class="recipe-tags-container">
+              <div class="recipe-tags-compact" id="recipeTagsCompact"></div>
+              <div class="recipe-tags-tooltip" id="recipeTagsTooltip">
+                <div class="tooltip-content" id="recipeTagsTooltipContent"></div>
+              </div>
+            </div>
+          </header>
+          <div class="modal-body">
+            <div class="recipe-top-section">
+              <div class="recipe-preview-container" id="recipePreviewContainer">
+                <img id="recipeModalImage" src="" alt="Recipe Preview" class="recipe-preview-media">
+              </div>
+              <div class="info-section recipe-gen-params">
+                <div class="gen-params-container">
+                  <div class="param-group info-item">
+                    <div class="param-header">
+                      <label>Prompt</label>
+                      <div class="param-actions">
+                        <button class="copy-btn" id="copyPromptBtn" title="Copy Prompt"><i class="fas fa-copy"></i></button>
+                        <button class="edit-btn" id="editPromptBtn" title="Edit Prompt"><i class="fas fa-pencil-alt"></i></button>
+                      </div>
+                    </div>
+                    <div class="param-content" id="recipePrompt"></div>
+                    <div class="param-editor" id="recipePromptEditor">
+                      <textarea class="param-textarea" id="recipePromptInput"></textarea>
+                    </div>
+                  </div>
+                  <div class="param-group info-item">
+                    <div class="param-header">
+                      <label>Negative Prompt</label>
+                      <div class="param-actions">
+                        <button class="copy-btn" id="copyNegativePromptBtn" title="Copy Negative Prompt"><i class="fas fa-copy"></i></button>
+                        <button class="edit-btn" id="editNegativePromptBtn" title="Edit Negative Prompt"><i class="fas fa-pencil-alt"></i></button>
+                      </div>
+                    </div>
+                    <div class="param-content" id="recipeNegativePrompt"></div>
+                    <div class="param-editor" id="recipeNegativePromptEditor">
+                      <textarea class="param-textarea" id="recipeNegativePromptInput"></textarea>
+                    </div>
+                  </div>
+                  <div class="other-params" id="recipeOtherParams"></div>
+                </div>
+              </div>
+            </div>
+            <div class="info-section recipe-bottom-section">
+              <div class="recipe-section-header">
+                <h3>Resources</h3>
+                <div class="recipe-section-actions">
+                  <span id="recipeLorasCount"><i class="fas fa-layer-group"></i> 0 LoRAs</span>
+                </div>
+              </div>
+              <div class="recipe-loras-list" id="recipeLorasList"></div>
+            </div>
+          </div>
+        </div>
+      </div>
+    `;
+
+    const { RecipeModal } = await import('../../../static/js/components/RecipeModal.js');
+    const recipeModal = new RecipeModal();
+
+    recipeModal.showRecipeDetails({
+      id: 'recipe-3',
+      file_path: '/recipes/negative.json',
+      title: 'Negative Recipe',
+      tags: [],
+      file_url: '',
+      preview_url: '',
+      source_path: '',
+      gen_params: {
+        prompt: '',
+        negative_prompt: 'existing negative',
+        steps: 20,
+      },
+      loras: [],
+    });
+
+    document.getElementById('editNegativePromptBtn').click();
+    const textarea = document.getElementById('recipeNegativePromptInput');
+    textarea.value = 'changed negative';
+    textarea.dispatchEvent(new KeyboardEvent('keydown', { key: 'Escape', bubbles: true }));
+
+    expect(updateRecipeMetadataMock).not.toHaveBeenCalled();
+    expect(modalManagerMock.closeModal).not.toHaveBeenCalled();
+    expect(document.getElementById('recipeNegativePrompt').textContent).toBe('existing negative');
+    expect(document.getElementById('recipeNegativePromptEditor').classList.contains('active')).toBe(false);
+  });
+
   it('processes global context menu actions for downloads and cleanup', async () => {
     document.body.innerHTML = `
       <div id="globalContextMenu" class="context-menu">

tests/frontend/components/loraLoader.triggerWords.test.js

@@ -37,6 +37,13 @@ const updateConnectedTriggerWords = vi.fn();
 const mergeLoras = vi.fn();
 const getAllGraphNodes = vi.fn();
 const getNodeFromGraph = vi.fn();
+const getWidgetByName = vi.fn((node, name) =>
+  node?.widgets?.find((widget) => widget?.name === name) ?? null
+);
+const getWidgetSerializedValue = vi.fn((node, name) => {
+  const index = node?.widgets?.findIndex((widget) => widget?.name === name) ?? -1;
+  return index >= 0 ? node.widgets_values?.[index] : undefined;
+});
 
 vi.mock(UTILS_MODULE, () => ({
   collectActiveLorasFromChain,
@@ -47,6 +54,8 @@ vi.mock(UTILS_MODULE, () => ({
   },
   getAllGraphNodes,
   getNodeFromGraph,
+  getWidgetByName,
+  getWidgetSerializedValue,
   LORA_PATTERN: /<lora:([^:]+):([-\d.]+)(?::([-\d.]+))?>/g,
 }));
 
@@ -71,6 +80,9 @@ describe("Lora Loader trigger word updates", () => {
     mergeLoras.mockClear();
     mergeLoras.mockImplementation(() => [{ name: "Alpha", active: true }]);
 
+    getWidgetByName.mockClear();
+    getWidgetSerializedValue.mockClear();
+
     addLorasWidget.mockClear();
     addLorasWidget.mockImplementation((_node, _name, _opts, callback) => ({
       widget: { value: [], callback },
@@ -89,14 +101,21 @@ describe("Lora Loader trigger word updates", () => {
 
     // Create mock widget (AUTOCOMPLETE_TEXT_LORAS type created by Vue widgets)
     const inputWidget = {
+      name: "text",
       value: "",
       options: {},
       callback: null, // Will be set by onNodeCreated
     };
 
+    const metadataWidget = {
+      name: "__autocomplete_metadata_text",
+      value: { version: 1, textWidgetName: "text" },
+      options: {},
+    };
+
     const node = {
       comfyClass: "Lora Loader (LoraManager)",
-      widgets: [inputWidget],
+      widgets: [metadataWidget, inputWidget],
       addInput: vi.fn(),
       graph: {},
     };
@@ -106,6 +125,7 @@ describe("Lora Loader trigger word updates", () => {
     // The widget is now the AUTOCOMPLETE_TEXT_LORAS type, created automatically by Vue widgets
     expect(node.inputWidget).toBe(inputWidget);
     expect(node.lorasWidget).toBeDefined();
+    expect(getWidgetByName).toHaveBeenCalledWith(node, "text");
 
     // The callback should have been set up by onNodeCreated
     const inputCallback = inputWidget.callback;

tests/frontend/components/modelMetadata.renamePath.test.js

@@ -82,24 +82,35 @@ vi.mock(MODEL_VERSIONS_MODULE, () => ({
 }));
 
 vi.mock(RECIPE_TAB_MODULE, () => ({
-  loadRecipesForLora: vi.fn(),
+  loadRecipesForModel: vi.fn(),
 }));
 
 vi.mock(I18N_HELPERS_MODULE, () => ({
   translate: vi.fn((_, __, fallback) => fallback || ''),
 }));
 
+vi.mock('../../../static/js/api/apiConfig.js', () => ({
+  MODEL_TYPES: {
+    LORA: 'loras',
+    CHECKPOINT: 'checkpoints',
+    EMBEDDING: 'embeddings'
+  }
+}));
+
 vi.mock(API_FACTORY, () => ({
   getModelApiClient: vi.fn(),
 }));
 
 describe('Model metadata interactions keep file path in sync', () => {
   let getModelApiClient;
+  let loadRecipesForModel;
 
   beforeEach(async () => {
     document.body.innerHTML = '';
     ({ getModelApiClient } = await import(API_FACTORY));
+    ({ loadRecipesForModel } = await import(RECIPE_TAB_MODULE));
     getModelApiClient.mockReset();
+    loadRecipesForModel.mockReset();
   });
 
   afterEach(() => {
@@ -198,4 +209,33 @@ describe('Model metadata interactions keep file path in sync', () => {
       expect(saveModelMetadata).toHaveBeenCalledWith('models/Qwen.testing.safetensors', { notes: 'Updated notes' });
     });
   });
+
+  it('shows recipes tab for checkpoint modals and loads linked recipes by hash', async () => {
+    const fetchModelMetadata = vi.fn().mockResolvedValue(null);
+
+    getModelApiClient.mockReturnValue({
+      fetchModelMetadata,
+      saveModelMetadata: vi.fn(),
+    });
+
+    const { showModelModal } = await import(MODAL_MODULE);
+
+    await showModelModal(
+      {
+        model_name: 'Flux Base',
+        file_path: 'models/checkpoints/flux-base.safetensors',
+        file_name: 'flux-base.safetensors',
+        sha256: 'ABC123',
+        civitai: {},
+      },
+      'checkpoints',
+    );
+
+    expect(document.querySelector('.tab-btn[data-tab="recipes"]')).not.toBeNull();
+    expect(loadRecipesForModel).toHaveBeenCalledWith({
+      modelKind: 'checkpoint',
+      displayName: 'Flux Base',
+      sha256: 'ABC123',
+    });
+  });
 });

tests/frontend/components/modelModal.licenseIcons.test.js

@@ -80,13 +80,21 @@ vi.mock(MODEL_VERSIONS_MODULE, () => ({
 }));
 
 vi.mock(RECIPE_TAB_MODULE, () => ({
-  loadRecipesForLora: vi.fn(),
+  loadRecipesForModel: vi.fn(),
 }));
 
 vi.mock(I18N_HELPERS_MODULE, () => ({
   translate: vi.fn((_, __, fallback) => fallback || ''),
 }));
 
+vi.mock('../../../static/js/api/apiConfig.js', () => ({
+  MODEL_TYPES: {
+    LORA: 'loras',
+    CHECKPOINT: 'checkpoints',
+    EMBEDDING: 'embeddings'
+  }
+}));
+
 vi.mock(API_FACTORY, () => ({
   getModelApiClient: vi.fn(),
 }));

tests/frontend/components/nodeModeChange.test.js

@@ -50,6 +50,13 @@ const getAllGraphNodes = vi.fn();
 const getNodeFromGraph = vi.fn();
 const getNodeKey = vi.fn();
 const getLinkFromGraph = vi.fn();
+const getWidgetByName = vi.fn((node, name) =>
+  node?.widgets?.find((widget) => widget?.name === name) ?? null
+);
+const getWidgetSerializedValue = vi.fn((node, name) => {
+  const index = node?.widgets?.findIndex((widget) => widget?.name === name) ?? -1;
+  return index >= 0 ? node.widgets_values?.[index] : undefined;
+});
 const chainCallback = vi.fn((proto, property, callback) => {
   proto[property] = callback;
 });
@@ -68,6 +75,8 @@ vi.mock(UTILS_MODULE, async (importOriginal) => {
     getNodeFromGraph,
     getNodeKey,
     getLinkFromGraph,
+    getWidgetByName,
+    getWidgetSerializedValue,
   };
 });
 
@@ -98,6 +107,9 @@ describe("Node mode change handling", () => {
     mergeLoras.mockClear();
     mergeLoras.mockImplementation(() => [{ name: "Alpha", active: true }]);
 
+    getWidgetByName.mockClear();
+    getWidgetSerializedValue.mockClear();
+
     addLorasWidget.mockClear();
     addLorasWidget.mockImplementation((_node, _name, _opts, callback) => ({
       widget: { value: [], callback },
@@ -119,8 +131,13 @@ describe("Node mode change handling", () => {
 
       await extension.beforeRegisterNodeDef(nodeType, nodeData, {});
 
-      // Create widgets with proper structure for lora_stacker.js
-      // Widget at index 0 is the AUTOCOMPLETE_TEXT_LORAS widget (created by Vue widgets)
+      // Include a hidden metadata widget ahead of the actual text widget to match runtime ordering.
+      const metadataWidget = {
+        name: "__autocomplete_metadata_text",
+        value: { version: 1, textWidgetName: "text" },
+        options: {},
+      };
+
       const inputWidget = {
         name: "text",
         value: "",
@@ -139,7 +156,7 @@ describe("Node mode change handling", () => {
 
       node = {
         comfyClass: "Lora Stacker (LoraManager)",
-        widgets: [inputWidget, lorasWidget],
+        widgets: [metadataWidget, inputWidget, lorasWidget],
         lorasWidget,
         addInput: vi.fn(),
         mode: 0, // Initial mode
@@ -189,11 +206,18 @@ describe("Node mode change handling", () => {
       const nodeType = { comfyClass: "Lora Loader (LoraManager)", prototype: {} };
       await extension.beforeRegisterNodeDef(nodeType, {}, {});
 
-      // Widget at index 0 is the AUTOCOMPLETE_TEXT_LORAS widget (created by Vue widgets)
+      const metadataWidget = {
+        name: "__autocomplete_metadata_text",
+        value: { version: 1, textWidgetName: "text" },
+        options: {},
+      };
+
       node = {
         comfyClass: "Lora Loader (LoraManager)",
         widgets: [
+          metadataWidget,
           {
+            name: "text",
             value: "",
             options: {},
             callback: null, // Will be set by onNodeCreated

tests/frontend/managers/RecipeDataManager.tagInput.test.js

@@ -0,0 +1,106 @@
+import { beforeEach, describe, expect, it, vi } from 'vitest';
+
+vi.mock('../../../static/js/utils/uiHelpers.js', () => ({
+  showToast: vi.fn(),
+}));
+
+vi.mock('../../../static/js/utils/i18nHelpers.js', () => ({
+  translate: (_key, _params, fallback) => fallback ?? '',
+}));
+
+describe('RecipeDataManager tag input Enter behavior', () => {
+  beforeEach(() => {
+    vi.resetModules();
+    document.body.innerHTML = `
+      <input id="tagInput" type="text" />
+      <div id="tagsContainer"></div>
+    `;
+  });
+
+  it('adds a tag when pressing Enter in tag input', async () => {
+    const { RecipeDataManager } = await import('../../../static/js/managers/import/RecipeDataManager.js');
+    const importManager = {
+      recipeTags: [],
+      stepManager: { showStep: vi.fn() },
+    };
+    const manager = new RecipeDataManager(importManager);
+
+    manager.setupTagInputEnterHandler();
+
+    const tagInput = document.getElementById('tagInput');
+    tagInput.value = 'portrait';
+    tagInput.dispatchEvent(new KeyboardEvent('keydown', { key: 'Enter', bubbles: true }));
+
+    expect(importManager.recipeTags).toEqual(['portrait']);
+    expect(tagInput.value).toBe('');
+    expect(document.getElementById('tagsContainer').textContent).toContain('portrait');
+  });
+
+  it('does not register duplicate Enter handlers when setup runs multiple times', async () => {
+    const { RecipeDataManager } = await import('../../../static/js/managers/import/RecipeDataManager.js');
+    const importManager = {
+      recipeTags: [],
+      stepManager: { showStep: vi.fn() },
+    };
+    const manager = new RecipeDataManager(importManager);
+
+    manager.setupTagInputEnterHandler();
+    manager.setupTagInputEnterHandler();
+
+    const tagInput = document.getElementById('tagInput');
+    tagInput.value = 'anime';
+    tagInput.dispatchEvent(new KeyboardEvent('keydown', { key: 'Enter', bubbles: true }));
+
+    expect(importManager.recipeTags).toEqual(['anime']);
+  });
+
+  it('ignores Enter while IME composition is active', async () => {
+    const { RecipeDataManager } = await import('../../../static/js/managers/import/RecipeDataManager.js');
+    const importManager = {
+      recipeTags: [],
+      stepManager: { showStep: vi.fn() },
+    };
+    const manager = new RecipeDataManager(importManager);
+
+    manager.setupTagInputEnterHandler();
+
+    const tagInput = document.getElementById('tagInput');
+    tagInput.value = '未確定';
+    const event = new KeyboardEvent('keydown', {
+      key: 'Enter',
+      bubbles: true,
+      cancelable: true,
+    });
+    Object.defineProperty(event, 'isComposing', { value: true });
+    tagInput.dispatchEvent(event);
+
+    expect(importManager.recipeTags).toEqual([]);
+    expect(tagInput.value).toBe('未確定');
+    expect(event.defaultPrevented).toBe(false);
+  });
+
+  it('ignores keyCode 229 fallback during composition', async () => {
+    const { RecipeDataManager } = await import('../../../static/js/managers/import/RecipeDataManager.js');
+    const importManager = {
+      recipeTags: [],
+      stepManager: { showStep: vi.fn() },
+    };
+    const manager = new RecipeDataManager(importManager);
+
+    manager.setupTagInputEnterHandler();
+
+    const tagInput = document.getElementById('tagInput');
+    tagInput.value = '候補';
+    const event = new KeyboardEvent('keydown', {
+      key: 'Enter',
+      bubbles: true,
+      cancelable: true,
+    });
+    Object.defineProperty(event, 'keyCode', { value: 229 });
+    tagInput.dispatchEvent(event);
+
+    expect(importManager.recipeTags).toEqual([]);
+    expect(tagInput.value).toBe('候補');
+    expect(event.defaultPrevented).toBe(false);
+  });
+});

tests/frontend/managers/settingsManager.downloadSkipBaseModels.test.js

@@ -0,0 +1,153 @@
+import { describe, it, expect, beforeEach, vi } from 'vitest';
+
+vi.mock('../../../static/js/managers/ModalManager.js', () => ({
+    modalManager: {
+        closeModal: vi.fn(),
+    },
+}));
+
+vi.mock('../../../static/js/utils/uiHelpers.js', () => ({
+    showToast: vi.fn(),
+}));
+
+vi.mock('../../../static/js/state/index.js', () => {
+    const settings = {};
+    return {
+        state: {
+            global: {
+                settings,
+            },
+        },
+        createDefaultSettings: () => ({
+            language: 'en',
+            download_skip_base_models: [],
+        }),
+    };
+});
+
+vi.mock('../../../static/js/api/modelApiFactory.js', () => ({
+    resetAndReload: vi.fn(),
+}));
+
+vi.mock('../../../static/js/utils/constants.js', () => ({
+    DOWNLOAD_PATH_TEMPLATES: {},
+    DEFAULT_PATH_TEMPLATES: {},
+    MAPPABLE_BASE_MODELS: ['Flux.1 D', 'Pony', 'SDXL 1.0', 'Other'],
+    PATH_TEMPLATE_PLACEHOLDERS: {},
+    DEFAULT_PRIORITY_TAG_CONFIG: {
+        lora: 'character, style',
+        checkpoint: 'base, guide',
+        embedding: 'hint',
+    },
+    getMappableBaseModelsDynamic: () => ['Flux.1 D', 'Pony', 'SDXL 1.0', 'Other'],
+}));
+
+vi.mock('../../../static/js/utils/i18nHelpers.js', () => ({
+    translate: (key, params, fallback) => {
+        if (key === 'settings.downloadSkipBaseModels.summary.none') {
+            return 'None selected';
+        }
+        if (key === 'settings.downloadSkipBaseModels.summary.count') {
+            return `${params?.count ?? 0} selected`;
+        }
+        return fallback ?? '';
+    },
+}));
+
+vi.mock('../../../static/js/i18n/index.js', () => ({
+    i18n: {
+        getCurrentLocale: () => 'en',
+        setLanguage: vi.fn().mockResolvedValue(),
+    },
+}));
+
+vi.mock('../../../static/js/components/shared/ModelCard.js', () => ({
+    configureModelCardVideo: vi.fn(),
+}));
+
+vi.mock('../../../static/js/managers/BannerService.js', () => ({
+    bannerService: {
+        registerBanner: vi.fn(),
+    },
+}));
+
+vi.mock('../../../static/js/components/SidebarManager.js', () => ({
+    sidebarManager: {
+        setSidebarEnabled: vi.fn().mockResolvedValue(),
+    },
+}));
+
+import { SettingsManager } from '../../../static/js/managers/SettingsManager.js';
+import { state } from '../../../static/js/state/index.js';
+
+const createManager = () => {
+    const initSettingsSpy = vi
+        .spyOn(SettingsManager.prototype, 'initializeSettings')
+        .mockResolvedValue();
+    const initializeSpy = vi
+        .spyOn(SettingsManager.prototype, 'initialize')
+        .mockImplementation(() => {});
+
+    const manager = new SettingsManager();
+
+    initSettingsSpy.mockRestore();
+    initializeSpy.mockRestore();
+
+    return manager;
+};
+
+const appendDownloadSkipUi = () => {
+    document.body.innerHTML = `
+        <button id="downloadSkipBaseModelsToggle" aria-expanded="false">
+            <span id="downloadSkipBaseModelsSummary"></span>
+            <span class="base-model-skip-toggle-label"></span>
+        </button>
+        <div id="downloadSkipBaseModelsPanel" hidden>
+            <input id="downloadSkipBaseModelsSearch" />
+            <button id="downloadSkipBaseModelsClear" type="button">Clear</button>
+            <div id="downloadSkipBaseModelsContainer"></div>
+            <div id="downloadSkipBaseModelsEmpty" hidden></div>
+        </div>
+        <div id="downloadSkipBaseModelsError"></div>
+    `;
+};
+
+describe('SettingsManager download skip base models UI', () => {
+    beforeEach(() => {
+        document.body.innerHTML = '';
+        vi.clearAllMocks();
+        state.global.settings = {
+            download_skip_base_models: [],
+        };
+    });
+
+    it('renders a compact summary for selected base models', () => {
+        appendDownloadSkipUi();
+        state.global.settings.download_skip_base_models = ['Flux.1 D', 'Pony'];
+        const manager = createManager();
+
+        manager.renderDownloadSkipBaseModels();
+
+        expect(document.getElementById('downloadSkipBaseModelsSummary').textContent).toBe('Flux.1 D, Pony');
+        expect(document.querySelectorAll('#downloadSkipBaseModelsContainer input')).toHaveLength(3);
+    });
+
+    it('filters the list using the search input and shows an empty state', () => {
+        appendDownloadSkipUi();
+        state.global.settings.download_skip_base_models = ['Flux.1 D'];
+        const manager = createManager();
+        const searchInput = document.getElementById('downloadSkipBaseModelsSearch');
+
+        searchInput.value = 'pony';
+        manager.renderDownloadSkipBaseModels();
+
+        expect(document.querySelectorAll('#downloadSkipBaseModelsContainer input')).toHaveLength(1);
+        expect(document.querySelector('#downloadSkipBaseModelsContainer input').value).toBe('Pony');
+
+        searchInput.value = 'zzz';
+        manager.renderDownloadSkipBaseModels();
+
+        expect(document.querySelectorAll('#downloadSkipBaseModelsContainer input')).toHaveLength(0);
+        expect(document.getElementById('downloadSkipBaseModelsEmpty').hidden).toBe(false);
+    });
+});

tests/frontend/managers/settingsManager.library.test.js

@@ -42,6 +42,7 @@ vi.mock('../../../static/js/utils/constants.js', () => ({
         checkpoint: 'base, guide',
         embedding: 'hint',
     },
+    getMappableBaseModelsDynamic: () => [],
 }));
 
 vi.mock('../../../static/js/utils/i18nHelpers.js', () => ({

tests/frontend/pages/recipesPage.test.js

@@ -6,6 +6,7 @@ const initializePageFeaturesMock = vi.fn();
 const getCurrentPageStateMock = vi.fn();
 const getSessionItemMock = vi.fn();
 const removeSessionItemMock = vi.fn();
+const getStorageItemMock = vi.fn();
 const RecipeContextMenuMock = vi.fn();
 const refreshVirtualScrollMock = vi.fn();
 const refreshRecipesMock = vi.fn();
@@ -51,6 +52,7 @@ vi.mock('../../../static/js/state/index.js', () => ({
 vi.mock('../../../static/js/utils/storageHelpers.js', () => ({
   getSessionItem: getSessionItemMock,
   removeSessionItem: removeSessionItemMock,
+  getStorageItem: getStorageItemMock,
 }));
 
 vi.mock('../../../static/js/components/ContextMenu/index.js', () => ({
@@ -117,11 +119,14 @@ describe('RecipeManager', () => {
       const map = {
         lora_to_recipe_filterLoraName: 'Flux Dream',
         lora_to_recipe_filterLoraHash: 'abc123',
+        checkpoint_to_recipe_filterCheckpointName: null,
+        checkpoint_to_recipe_filterCheckpointHash: null,
         viewRecipeId: '42',
       };
       return map[key] ?? null;
     });
     removeSessionItemMock.mockImplementation(() => { });
+    getStorageItemMock.mockImplementation((_, defaultValue = null) => defaultValue);
 
     renderRecipesPage();
 
@@ -166,6 +171,8 @@ describe('RecipeManager', () => {
       active: true,
       loraName: 'Flux Dream',
       loraHash: 'abc123',
+      checkpointName: null,
+      checkpointHash: null,
       recipeId: '42',
     });
 
@@ -177,6 +184,8 @@ describe('RecipeManager', () => {
 
     expect(removeSessionItemMock).toHaveBeenCalledWith('lora_to_recipe_filterLoraName');
     expect(removeSessionItemMock).toHaveBeenCalledWith('lora_to_recipe_filterLoraHash');
+    expect(removeSessionItemMock).toHaveBeenCalledWith('checkpoint_to_recipe_filterCheckpointName');
+    expect(removeSessionItemMock).toHaveBeenCalledWith('checkpoint_to_recipe_filterCheckpointHash');
     expect(removeSessionItemMock).toHaveBeenCalledWith('viewRecipeId');
     expect(pageState.customFilter.active).toBe(false);
     expect(indicator.classList.contains('hidden')).toBe(true);
@@ -227,4 +236,36 @@ describe('RecipeManager', () => {
     await manager.refreshRecipes();
     expect(refreshRecipesMock).toHaveBeenCalledTimes(1);
   });
+
+  it('restores checkpoint recipe filter state and indicator text', async () => {
+    getSessionItemMock.mockImplementation((key) => {
+      const map = {
+        lora_to_recipe_filterLoraName: null,
+        lora_to_recipe_filterLoraHash: null,
+        checkpoint_to_recipe_filterCheckpointName: 'Flux Base',
+        checkpoint_to_recipe_filterCheckpointHash: 'ckpt123',
+        viewRecipeId: null,
+      };
+      return map[key] ?? null;
+    });
+
+    const manager = new RecipeManager();
+    await manager.initialize();
+
+    expect(pageState.customFilter).toEqual({
+      active: true,
+      loraName: null,
+      loraHash: null,
+      checkpointName: 'Flux Base',
+      checkpointHash: 'ckpt123',
+      recipeId: null,
+    });
+
+    const indicator = document.getElementById('customFilterIndicator');
+    const filterText = indicator.querySelector('#customFilterText');
+
+    expect(filterText.innerHTML).toContain('Recipes using checkpoint:');
+    expect(filterText.innerHTML).toContain('Flux Base');
+    expect(filterText.getAttribute('title')).toBe('Flux Base');
+  });
 });

tests/frontend/state/index.test.js

@@ -15,7 +15,8 @@ describe('state module', () => {
     expect(defaultSettings).toMatchObject({
       civitai_api_key: '',
       language: 'en',
-      blur_mature_content: true
+      blur_mature_content: true,
+      mature_blur_level: 'R'
     });
 
     expect(defaultSettings.download_path_templates).toEqual(DEFAULT_PATH_TEMPLATES);

tests/frontend/utils/constants.matureBlurThreshold.test.js

@@ -0,0 +1,18 @@
+import { describe, expect, it } from 'vitest';
+
+import { NSFW_LEVELS, getMatureBlurThreshold } from '../../../static/js/utils/constants.js';
+
+describe('getMatureBlurThreshold', () => {
+  it('returns configured PG13 threshold', () => {
+    expect(getMatureBlurThreshold({ mature_blur_level: 'PG13' })).toBe(NSFW_LEVELS.PG13);
+  });
+
+  it('normalizes lowercase values', () => {
+    expect(getMatureBlurThreshold({ mature_blur_level: 'x' })).toBe(NSFW_LEVELS.X);
+  });
+
+  it('falls back to R when value is invalid or missing', () => {
+    expect(getMatureBlurThreshold({ mature_blur_level: 'invalid' })).toBe(NSFW_LEVELS.R);
+    expect(getMatureBlurThreshold({})).toBe(NSFW_LEVELS.R);
+  });
+});

tests/nodes/test_lora_cycler.py

@@ -0,0 +1,109 @@
+"""Tests for preset strength behavior in LoraCyclerLM."""
+
+from unittest.mock import AsyncMock
+
+import pytest
+
+from py.nodes.lora_cycler import LoraCyclerLM
+from py.services import service_registry
+
+
+@pytest.fixture
+def cycler_node():
+    return LoraCyclerLM()
+
+
+@pytest.fixture
+def cycler_config():
+    return {
+        "current_index": 1,
+        "model_strength": 0.8,
+        "clip_strength": 0.6,
+        "use_same_clip_strength": False,
+        "use_preset_strength": True,
+        "preset_strength_scale": 1.5,
+        "include_no_lora": False,
+    }
+
+
+@pytest.mark.asyncio
+async def test_cycler_uses_scaled_preset_strength_when_available(
+    cycler_node, cycler_config, mock_scanner, monkeypatch
+):
+    monkeypatch.setattr(
+        service_registry.ServiceRegistry,
+        "get_lora_scanner",
+        AsyncMock(return_value=mock_scanner),
+    )
+
+    mock_scanner._cache.raw_data = [
+        {
+            "file_name": "preset_lora.safetensors",
+            "file_path": "/models/loras/preset_lora.safetensors",
+            "folder": "",
+            "usage_tips": '{"strength": 0.7, "clipStrength": 0.5}',
+        }
+    ]
+
+    result = await cycler_node.cycle(cycler_config)
+
+    assert result["result"][0] == [
+        ("/models/loras/preset_lora.safetensors", 1.05, 0.75)
+    ]
+
+
+@pytest.mark.asyncio
+async def test_cycler_falls_back_to_manual_strength_when_preset_missing(
+    cycler_node, cycler_config, mock_scanner, monkeypatch
+):
+    monkeypatch.setattr(
+        service_registry.ServiceRegistry,
+        "get_lora_scanner",
+        AsyncMock(return_value=mock_scanner),
+    )
+
+    mock_scanner._cache.raw_data = [
+        {
+            "file_name": "manual_lora.safetensors",
+            "file_path": "/models/loras/manual_lora.safetensors",
+            "folder": "",
+            "usage_tips": "",
+        }
+    ]
+
+    result = await cycler_node.cycle(cycler_config)
+
+    assert result["result"][0] == [
+        ("/models/loras/manual_lora.safetensors", 0.8, 0.6)
+    ]
+
+
+@pytest.mark.asyncio
+async def test_cycler_syncs_clip_to_model_when_same_clip_strength_enabled(
+    cycler_node, cycler_config, mock_scanner, monkeypatch
+):
+    monkeypatch.setattr(
+        service_registry.ServiceRegistry,
+        "get_lora_scanner",
+        AsyncMock(return_value=mock_scanner),
+    )
+
+    mock_scanner._cache.raw_data = [
+        {
+            "file_name": "preset_lora.safetensors",
+            "file_path": "/models/loras/preset_lora.safetensors",
+            "folder": "",
+            "usage_tips": '{"strength": 0.7, "clipStrength": 0.3}',
+        }
+    ]
+
+    result = await cycler_node.cycle(
+        {
+            **cycler_config,
+            "use_same_clip_strength": True,
+        }
+    )
+
+    assert result["result"][0] == [
+        ("/models/loras/preset_lora.safetensors", 1.05, 1.05)
+    ]

tests/nodes/test_lora_loader.py

@@ -0,0 +1,201 @@
+import types
+
+import pytest
+
+from py.nodes.lora_loader import LoraLoaderLM, LoraTextLoaderLM
+
+
+class _ModelContainer:
+    def __init__(self, diffusion_model):
+        self.diffusion_model = diffusion_model
+
+
+class _Model:
+    def __init__(self, diffusion_model):
+        self.model = _ModelContainer(diffusion_model)
+
+
+def test_lora_loader_standard_model_uses_comfy_loader(monkeypatch):
+    loader = LoraLoaderLM()
+    model = _Model(object())
+    clip = object()
+
+    monkeypatch.setattr(
+        "py.nodes.lora_loader.get_lora_info_absolute",
+        lambda name: (f"/abs/{name}.safetensors", [f"{name}_trigger"]),
+    )
+
+    load_calls = []
+
+    def mock_load_torch_file(path, safe_load=True):
+        load_calls.append((path, safe_load))
+        return {"path": path}
+
+    def mock_load_lora_for_models(model_arg, clip_arg, lora_arg, model_strength, clip_strength):
+        return model_arg, clip_arg
+
+    monkeypatch.setattr("comfy.utils.load_torch_file", mock_load_torch_file)
+    monkeypatch.setattr("comfy.sd.load_lora_for_models", mock_load_lora_for_models)
+
+    result_model, result_clip, trigger_words, loaded_loras = loader.load_loras(
+        model,
+        "",
+        clip=clip,
+        loras={
+            "__value__": [
+                {"active": True, "name": "demo", "strength": 0.75, "clipStrength": 0.5},
+            ]
+        },
+    )
+
+    assert result_model is model
+    assert result_clip is clip
+    assert load_calls == [("/abs/demo.safetensors", True)]
+    assert trigger_words == "demo_trigger"
+    assert loaded_loras == "<lora:demo:0.75:0.5>"
+
+
+def test_lora_loader_formats_widget_lora_names_with_colons(monkeypatch):
+    loader = LoraLoaderLM()
+    model = _Model(object())
+    clip = object()
+
+    monkeypatch.setattr(
+        "py.nodes.lora_loader.get_lora_info_absolute",
+        lambda name: (f"/abs/{name}.safetensors", [f"{name}_trigger"]),
+    )
+    monkeypatch.setattr("comfy.utils.load_torch_file", lambda path, safe_load=True: {"path": path})
+    monkeypatch.setattr(
+        "comfy.sd.load_lora_for_models",
+        lambda model_arg, clip_arg, lora_arg, model_strength, clip_strength: (model_arg, clip_arg),
+    )
+
+    _, _, trigger_words, loaded_loras = loader.load_loras(
+        model,
+        "",
+        clip=clip,
+        loras={
+            "__value__": [
+                {"active": True, "name": "demo:variant", "strength": 0.75, "clipStrength": 0.5},
+                {"active": True, "name": "demo:single", "strength": 0.3},
+            ]
+        },
+    )
+
+    assert trigger_words == "demo:variant_trigger,, demo:single_trigger"
+    assert loaded_loras == "<lora:demo:variant:0.75:0.5> <lora:demo:single:0.3>"
+
+
+def test_lora_loader_flux_model_uses_flux_helper(monkeypatch):
+    flux_model = _Model(type("ComfyFluxWrapper", (), {})())
+    loader = LoraLoaderLM()
+
+    monkeypatch.setattr(
+        "py.nodes.lora_loader.get_lora_info_absolute",
+        lambda name: (f"/abs/{name}.safetensors", [f"{name}_trigger"]),
+    )
+
+    calls = []
+
+    def mock_nunchaku_load_lora(model_arg, lora_name, strength):
+        calls.append((lora_name, strength))
+        return model_arg
+
+    monkeypatch.setattr("py.nodes.lora_loader.nunchaku_load_lora", mock_nunchaku_load_lora)
+
+    _, _, trigger_words, loaded_loras = loader.load_loras(
+        flux_model,
+        "",
+        lora_stack=[("stack_lora.safetensors", 0.4, 0.2)],
+        loras={"__value__": [{"active": True, "name": "widget_lora", "strength": 0.8}]},
+    )
+
+    assert calls == [("stack_lora.safetensors", 0.4), ("/abs/widget_lora.safetensors", 0.8)]
+    assert trigger_words == "stack_lora_trigger,, widget_lora_trigger"
+    assert loaded_loras == "<lora:stack_lora:0.4> <lora:widget_lora:0.8>"
+
+
+def test_lora_loader_qwen_model_batches_loras(monkeypatch):
+    qwen_model = _Model(type("NunchakuQwenImageTransformer2DModel", (), {})())
+    loader = LoraLoaderLM()
+
+    monkeypatch.setattr(
+        "py.nodes.lora_loader.get_lora_info_absolute",
+        lambda name: (f"/abs/{name}.safetensors", [f"{name}_trigger"]),
+    )
+
+    batched_calls = []
+
+    def mock_nunchaku_load_qwen_loras(model_arg, lora_configs):
+        batched_calls.append((model_arg, lora_configs))
+        return model_arg
+
+    monkeypatch.setattr("py.nodes.lora_loader._get_nunchaku_load_qwen_loras", lambda: mock_nunchaku_load_qwen_loras)
+
+    _, result_clip, trigger_words, loaded_loras = loader.load_loras(
+        qwen_model,
+        "",
+        clip="clip",
+        lora_stack=[("stack_qwen.safetensors", 0.6, 0.1)],
+        loras={"__value__": [{"active": True, "name": "widget_qwen", "strength": 0.9, "clipStrength": 0.3}]},
+    )
+
+    assert result_clip == "clip"
+    assert len(batched_calls) == 1
+    assert batched_calls[0][0] is qwen_model
+    assert batched_calls[0][1] == [
+        ("/abs/stack_qwen.safetensors", 0.6),
+        ("/abs/widget_qwen.safetensors", 0.9),
+    ]
+    assert trigger_words == "stack_qwen_trigger,, widget_qwen_trigger"
+    assert loaded_loras == "<lora:stack_qwen:0.6> <lora:widget_qwen:0.9>"
+
+
+def test_lora_text_loader_qwen_batches_text_and_stack(monkeypatch):
+    qwen_model = _Model(type("NunchakuQwenImageTransformer2DModel", (), {})())
+    loader = LoraTextLoaderLM()
+
+    monkeypatch.setattr(
+        "py.nodes.lora_loader.get_lora_info_absolute",
+        lambda name: (f"/abs/{name}.safetensors", [f"{name}_trigger"]),
+    )
+
+    batched_calls = []
+    monkeypatch.setattr(
+        "py.nodes.lora_loader._get_nunchaku_load_qwen_loras",
+        lambda: (lambda model_arg, lora_configs: batched_calls.append(lora_configs) or model_arg),
+    )
+
+    _, _, trigger_words, loaded_loras = loader.load_loras_from_text(
+        qwen_model,
+        "<lora:text_qwen:1.2:0.4>",
+        clip="clip",
+        lora_stack=[("stack_qwen.safetensors", 0.6, 0.1)],
+    )
+
+    assert batched_calls == [[("/abs/stack_qwen.safetensors", 0.6), ("/abs/text_qwen.safetensors", 1.2)]]
+    assert trigger_words == "stack_qwen_trigger,, text_qwen_trigger"
+    assert loaded_loras == "<lora:stack_qwen:0.6> <lora:text_qwen:1.2>"
+
+
+def test_lora_loader_qwen_model_raises_clear_error_when_helper_import_fails(monkeypatch):
+    qwen_model = _Model(type("NunchakuQwenImageTransformer2DModel", (), {})())
+    loader = LoraLoaderLM()
+
+    monkeypatch.setattr(
+        "py.nodes.lora_loader.get_lora_info_absolute",
+        lambda name: (f"/abs/{name}.safetensors", [f"{name}_trigger"]),
+    )
+    monkeypatch.setattr(
+        "py.nodes.lora_loader._get_nunchaku_load_qwen_loras",
+        lambda: (_ for _ in ()).throw(  # pragma: no branch
+            RuntimeError("Qwen-Image LoRA loading requires the ComfyUI runtime with its torch dependency available.")
+        ),
+    )
+
+    with pytest.raises(RuntimeError, match="Qwen-Image LoRA loading requires the ComfyUI runtime"):
+        loader.load_loras(
+            qwen_model,
+            "",
+            lora_stack=[("stack_qwen.safetensors", 0.6, 0.1)],
+        )

tests/nodes/test_lora_stack_combiner.py

@@ -0,0 +1,51 @@
+from py.nodes.lora_stack_combiner import LoraStackCombinerLM
+
+
+def test_combine_stacks_preserves_order():
+    node = LoraStackCombinerLM()
+    stack_a = [
+        ("folder/a.safetensors", 0.7, 0.6),
+        ("folder/b.safetensors", 0.8, 0.8),
+    ]
+    stack_b = [
+        ("folder/c.safetensors", 1.0, 0.9),
+    ]
+
+    (combined_stack,) = node.combine_stacks(stack_a, stack_b)
+
+    assert combined_stack == stack_a + stack_b
+
+
+def test_combine_stacks_returns_second_when_first_empty():
+    node = LoraStackCombinerLM()
+    stack_b = [("folder/c.safetensors", 1.0, 0.9)]
+
+    (combined_stack,) = node.combine_stacks([], stack_b)
+
+    assert combined_stack == stack_b
+
+
+def test_combine_stacks_returns_first_when_second_empty():
+    node = LoraStackCombinerLM()
+    stack_a = [("folder/a.safetensors", 0.7, 0.6)]
+
+    (combined_stack,) = node.combine_stacks(stack_a, [])
+
+    assert combined_stack == stack_a
+
+
+def test_combine_stacks_returns_empty_when_both_empty():
+    node = LoraStackCombinerLM()
+
+    (combined_stack,) = node.combine_stacks([], [])
+
+    assert combined_stack == []
+
+
+def test_combine_stacks_allows_duplicate_entries():
+    node = LoraStackCombinerLM()
+    duplicate_entry = ("folder/shared.safetensors", 0.9, 0.5)
+
+    (combined_stack,) = node.combine_stacks([duplicate_entry], [duplicate_entry])
+
+    assert combined_stack == [duplicate_entry, duplicate_entry]

tests/nodes/test_save_image.py

@@ -0,0 +1,153 @@
+import json
+
+import numpy as np
+import piexif
+from PIL import Image
+
+from py.nodes.save_image import SaveImageLM
+
+
+class _DummyTensor:
+    def __init__(self, array):
+        self._array = array
+        self.shape = array.shape
+
+    def cpu(self):
+        return self
+
+    def numpy(self):
+        return self._array
+
+
+def _make_image():
+    return _DummyTensor(
+        np.array(
+            [
+                [[0.0, 0.1, 0.2], [0.3, 0.4, 0.5]],
+                [[0.6, 0.7, 0.8], [0.9, 1.0, 0.0]],
+            ],
+            dtype="float32",
+        )
+    )
+
+
+def _configure_save_paths(monkeypatch, tmp_path):
+    monkeypatch.setattr("folder_paths.get_output_directory", lambda: str(tmp_path), raising=False)
+    monkeypatch.setattr(
+        "folder_paths.get_save_image_path",
+        lambda *_args, **_kwargs: (str(tmp_path), "sample", 1, "", "sample"),
+        raising=False,
+    )
+
+
+def _configure_metadata(monkeypatch, metadata_dict):
+    monkeypatch.setattr("py.nodes.save_image.get_metadata", lambda: {"raw": "metadata"})
+    monkeypatch.setattr(
+        "py.nodes.save_image.MetadataProcessor.to_dict",
+        lambda raw_metadata, node_id: metadata_dict,
+    )
+
+
+def test_save_image_defaults_to_writing_png_metadata(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
+
+    node = SaveImageLM()
+    node.save_images([_make_image()], "ComfyUI", "png", id="node-1")
+
+    image_path = tmp_path / "sample_00001_.png"
+    with Image.open(image_path) as img:
+        assert img.info["parameters"] == "prompt text\nSeed: 123"
+
+
+def test_save_image_skips_png_parameters_when_metadata_disabled_and_keeps_workflow(
+    monkeypatch, tmp_path
+):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
+
+    node = SaveImageLM()
+    workflow = {"nodes": [{"id": 1}]}
+    node.save_images(
+        [_make_image()],
+        "ComfyUI",
+        "png",
+        id="node-1",
+        embed_workflow=True,
+        extra_pnginfo={"workflow": workflow},
+        save_with_metadata=False,
+    )
+
+    image_path = tmp_path / "sample_00001_.png"
+    with Image.open(image_path) as img:
+        assert "parameters" not in img.info
+        assert img.info["workflow"] == json.dumps(workflow)
+
+
+def test_save_image_skips_jpeg_metadata_when_disabled(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
+
+    node = SaveImageLM()
+    node.save_images(
+        [_make_image()],
+        "ComfyUI",
+        "jpeg",
+        id="node-1",
+        save_with_metadata=False,
+    )
+
+    image_path = tmp_path / "sample_00001_.jpg"
+    exif_dict = piexif.load(str(image_path))
+    assert piexif.ExifIFD.UserComment not in exif_dict.get("Exif", {})
+
+
+def test_save_image_skips_webp_metadata_when_disabled(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
+
+    node = SaveImageLM()
+    node.save_images(
+        [_make_image()],
+        "ComfyUI",
+        "webp",
+        id="node-1",
+        save_with_metadata=False,
+    )
+
+    image_path = tmp_path / "sample_00001_.webp"
+    exif_dict = piexif.load(str(image_path))
+    assert piexif.ExifIFD.UserComment not in exif_dict.get("Exif", {})
+
+
+def test_process_image_returns_passthrough_result_and_ui_images(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
+
+    images = [_make_image()]
+    node = SaveImageLM()
+
+    result = node.process_image(images, id="node-1")
+
+    assert result["result"] == (images,)
+    assert result["ui"] == {
+        "images": [{"filename": "sample_00001_.png", "subfolder": "", "type": "output"}]
+    }
+
+
+def test_process_image_returns_empty_ui_images_when_save_fails(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
+
+    def _raise_save_error(*args, **kwargs):
+        raise OSError("disk full")
+
+    monkeypatch.setattr(Image.Image, "save", _raise_save_error)
+
+    images = [_make_image()]
+    node = SaveImageLM()
+
+    result = node.process_image(images, id="node-1")
+
+    assert result["result"] == (images,)
+    assert result["ui"] == {"images": []}

tests/routes/test_base_model_routes_smoke.py

@@ -719,3 +719,42 @@ def test_auto_organize_conflict_when_running(mock_service):
             await client.close()
 
     asyncio.run(scenario())
+
+
+
+def test_download_model_returns_skipped_success(mock_service, download_manager_stub):
+    async def scenario():
+        download_manager_stub.last_progress_snapshot = None
+
+        async def fake_download(**kwargs):
+            download_manager_stub.calls.append(kwargs)
+            return {
+                "success": True,
+                "skipped": True,
+                "status": "skipped",
+                "reason": "base_model_excluded",
+                "message": "Skipped by settings",
+                "base_model": "SDXL 1.0",
+                "file_name": "demo.safetensors",
+            }
+
+        download_manager_stub.download_from_civitai = fake_download
+
+        client = await create_test_client(mock_service)
+        try:
+            response = await client.post(
+                "/api/lm/download-model",
+                json={"model_version_id": 123},
+            )
+            payload = await response.json()
+
+            assert response.status == 200
+            assert payload["success"] is True
+            assert payload["skipped"] is True
+            assert payload["reason"] == "base_model_excluded"
+            assert payload["base_model"] == "SDXL 1.0"
+            assert payload["file_name"] == "demo.safetensors"
+        finally:
+            await client.close()
+
+    asyncio.run(scenario())

tests/routes/test_recipe_routes.py

@@ -43,6 +43,9 @@ class StubRecipeScanner:
         self.cached_raw: List[Dict[str, Any]] = []
         self.recipes: Dict[str, Dict[str, Any]] = {}
         self.removed: List[str] = []
+        self.last_paginated_params: Dict[str, Any] | None = None
+        self.lora_lookup: Dict[str, List[Dict[str, Any]]] = {}
+        self.checkpoint_lookup: Dict[str, List[Dict[str, Any]]] = {}
 
         async def _noop_get_cached_data(force_refresh: bool = False) -> None:  # noqa: ARG001 - signature mirrors real scanner
             return None
@@ -56,6 +59,7 @@ class StubRecipeScanner:
         return SimpleNamespace(raw_data=list(self.cached_raw))
 
     async def get_paginated_data(self, **params: Any) -> Dict[str, Any]:
+        self.last_paginated_params = params
         items = [dict(item) for item in self.listing_items]
         page = int(params.get("page", 1))
         page_size = int(params.get("page_size", 20))
@@ -70,6 +74,14 @@ class StubRecipeScanner:
     async def get_recipe_by_id(self, recipe_id: str) -> Optional[Dict[str, Any]]:
         return self.recipes.get(recipe_id)
 
+    async def get_recipes_for_lora(self, lora_hash: str) -> List[Dict[str, Any]]:
+        return list(self.lora_lookup.get(lora_hash.lower(), []))
+
+    async def get_recipes_for_checkpoint(
+        self, checkpoint_hash: str
+    ) -> List[Dict[str, Any]]:
+        return list(self.checkpoint_lookup.get(checkpoint_hash.lower(), []))
+
     async def get_recipe_json_path(self, recipe_id: str) -> Optional[str]:
         candidate = Path(self.recipes_dir) / f"{recipe_id}.recipe.json"
         return str(candidate) if candidate.exists() else None
@@ -132,6 +144,7 @@ class StubPersistenceService:
         self.save_calls: List[Dict[str, Any]] = []
         self.delete_calls: List[str] = []
         self.move_calls: List[Dict[str, str]] = []
+        self.update_calls: List[Dict[str, Any]] = []
         self.save_result = SimpleNamespace(
             payload={"success": True, "recipe_id": "stub-id"}, status=200
         )
@@ -182,7 +195,14 @@ class StubPersistenceService:
 
     async def update_recipe(
         self, *, recipe_scanner, recipe_id: str, updates: Dict[str, Any]
-    ) -> SimpleNamespace:  # pragma: no cover - unused by smoke tests
+    ) -> SimpleNamespace:
+        self.update_calls.append(
+            {
+                "recipe_scanner": recipe_scanner,
+                "recipe_id": recipe_id,
+                "updates": updates,
+            }
+        )
         return SimpleNamespace(
             payload={"success": True, "recipe_id": recipe_id, "updates": updates},
             status=200,
@@ -342,6 +362,47 @@ async def test_list_recipes_provides_file_urls(monkeypatch, tmp_path: Path) -> N
         assert payload["items"][0]["loras"] == []
 
 
+async def test_list_recipes_passes_checkpoint_hash_filter(
+    monkeypatch, tmp_path: Path
+) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.get("/api/lm/recipes?checkpoint_hash=ckpt123")
+        payload = await response.json()
+
+        assert response.status == 200
+        assert payload["items"] == []
+        assert harness.scanner.last_paginated_params["checkpoint_hash"] == "ckpt123"
+
+
+async def test_get_recipes_for_checkpoint(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        harness.scanner.checkpoint_lookup["abc123"] = [
+            {"id": "recipe-1", "title": "Linked recipe"}
+        ]
+
+        response = await harness.client.get(
+            "/api/lm/recipes/for-checkpoint?hash=ABC123"
+        )
+        payload = await response.json()
+
+        assert response.status == 200
+        assert payload == {
+            "success": True,
+            "recipes": [{"id": "recipe-1", "title": "Linked recipe"}],
+        }
+
+
+async def test_get_recipes_for_checkpoint_requires_hash(
+    monkeypatch, tmp_path: Path
+) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.get("/api/lm/recipes/for-checkpoint")
+        payload = await response.json()
+
+        assert response.status == 400
+        assert payload["success"] is False
+
+
 async def test_save_and_delete_recipe_round_trip(monkeypatch, tmp_path: Path) -> None:
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         form = FormData()
@@ -509,6 +570,33 @@ async def test_import_remote_recipe_falls_back_to_request_base_model(
         assert provider_calls == ["77"]
 
 
+async def test_update_recipe_accepts_gen_params(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        payload = {
+            "gen_params": {
+                "prompt": "updated prompt",
+                "negative_prompt": "updated negative",
+                "steps": 30,
+            }
+        }
+
+        response = await harness.client.put(
+            "/api/lm/recipe/recipe-42/update",
+            json=payload,
+        )
+        data = await response.json()
+
+        assert response.status == 200
+        assert data["success"] is True
+        assert harness.persistence.update_calls == [
+            {
+                "recipe_scanner": harness.scanner,
+                "recipe_id": "recipe-42",
+                "updates": payload,
+            }
+        ]
+
+
 async def test_import_remote_video_recipe(monkeypatch, tmp_path: Path) -> None:
     async def fake_get_default_metadata_provider():
         return SimpleNamespace(get_model_version_info=lambda id: ({}, None))

tests/services/test_civitai_base_model_service.py

@@ -0,0 +1,133 @@
+"""Tests for CivitaiBaseModelService."""
+
+import pytest
+from unittest.mock import patch
+
+from py.services.civitai_base_model_service import CivitaiBaseModelService
+
+
+class TestCivitaiBaseModelService:
+    """Test suite for CivitaiBaseModelService."""
+
+    @pytest.fixture(autouse=True)
+    def setup_service(self):
+        """Create a fresh service instance for each test."""
+        self.service = CivitaiBaseModelService()
+        # Reset cache
+        self.service._cache = None
+        self.service._cache_timestamp = None
+        yield
+
+    def test_generate_abbreviation_known_models(self):
+        """Test abbreviation generation for known models."""
+        test_cases = [
+            ("SD 1.5", "SD1"),
+            ("SDXL 1.0", "XL"),
+            ("Flux.1 D", "F1D"),
+            ("Wan Video 2.5 T2V", "WAN"),
+            ("Pony V7", "PNY7"),
+            ("CogVideoX", "CVX"),
+            ("Mochi", "MCHI"),
+            ("Anima", "ANI"),
+        ]
+
+        for model_name, expected in test_cases:
+            result = self.service.generate_abbreviation(model_name)
+            assert result == expected, (
+                f"Failed for {model_name}: got {result}, expected {expected}"
+            )
+
+    def test_generate_abbreviation_unknown_models(self):
+        """Test abbreviation generation for unknown models."""
+        result = self.service.generate_abbreviation("New Model 2.0")
+        assert len(result) <= 4
+        assert result.isupper()
+
+    def test_generate_abbreviation_edge_cases(self):
+        """Test abbreviation generation edge cases."""
+        assert self.service.generate_abbreviation("") == "OTH"
+        assert self.service.generate_abbreviation(None) == "OTH"
+
+    def test_cache_status_no_cache(self):
+        """Test cache status when no cache exists."""
+        status = self.service.get_cache_status()
+
+        assert status["has_cache"] is False
+        assert status["last_updated"] is None
+        assert status["is_expired"] is True
+        assert status["age_seconds"] is None
+
+    @pytest.mark.asyncio
+    async def test_get_base_models_fallback(self):
+        """Test that fallback to hardcoded models works."""
+        with patch.object(self.service, "_fetch_from_civitai", return_value=None):
+            result = await self.service.get_base_models()
+
+        assert result["source"] == "fallback"
+        assert len(result["models"]) > 0
+        assert result["hardcoded_count"] > 0
+        assert result["remote_count"] == 0
+
+    @pytest.mark.asyncio
+    async def test_get_base_models_from_api(self):
+        """Test fetching models from API."""
+        mock_models = {"SD 1.5", "SDXL 1.0", "New Model"}
+
+        with patch.object(
+            self.service, "_fetch_from_civitai", return_value=mock_models
+        ):
+            result = await self.service.get_base_models()
+
+        assert result["source"] == "api"
+        assert result["remote_count"] == 3
+        assert "New Model" in result["models"]
+
+    @pytest.mark.asyncio
+    async def test_get_base_models_uses_cache(self):
+        """Test that cached data is used when available and not expired."""
+        # First call - populate cache
+        mock_models = {"SD 1.5", "SDXL 1.0"}
+        with patch.object(
+            self.service, "_fetch_from_civitai", return_value=mock_models
+        ):
+            await self.service.get_base_models()
+
+        # Second call - should use cache
+        with patch.object(self.service, "_fetch_from_civitai") as mock_fetch:
+            result = await self.service.get_base_models()
+            mock_fetch.assert_not_called()
+
+        assert result["source"] == "cache"
+
+    @pytest.mark.asyncio
+    async def test_refresh_cache(self):
+        """Test force refresh clears cache and fetches fresh data."""
+        # Populate cache
+        mock_models = {"SD 1.5"}
+        with patch.object(
+            self.service, "_fetch_from_civitai", return_value=mock_models
+        ):
+            await self.service.get_base_models()
+
+        # Force refresh with different data
+        new_models = {"SD 1.5", "SDXL 1.0", "New Model"}
+        with patch.object(self.service, "_fetch_from_civitai", return_value=new_models):
+            result = await self.service.refresh_cache()
+
+        assert result["source"] == "api"
+        assert result["remote_count"] == 3
+
+    def test_get_model_categories(self):
+        """Test model categories are returned."""
+        categories = self.service.get_model_categories()
+
+        assert "Stable Diffusion 1.x" in categories
+        assert "Video Models" in categories
+        assert "Flux Models" in categories
+        assert "Other Models" in categories
+
+        # Check that video models include new additions
+        video_models = categories["Video Models"]
+        assert "CogVideoX" in video_models
+        assert "Mochi" in video_models
+        assert "Wan Video 2.5 T2V" in video_models

tests/services/test_civitai_image_parser.py

@@ -184,7 +184,10 @@ async def test_parse_metadata_populates_checkpoint_and_rewrites_thumbnails(monke
     assert result["model"] is not None
     assert result["model"]["name"] == "Checkpoint Example"
     assert result["model"]["type"] == "checkpoint"
-    assert result["model"]["thumbnailUrl"] == "https://image.civitai.com/checkpoints/width=450,optimized=true"
+    assert (
+        result["model"]["thumbnailUrl"]
+        == "https://image.civitai.com/checkpoints/width=450,optimized=true"
+    )
     assert result["model"]["modelId"] == 111
     assert result["model"]["size"] == 1024 * 1024
     assert result["model"]["hash"] == "ffaa0011"
@@ -192,5 +195,106 @@ async def test_parse_metadata_populates_checkpoint_and_rewrites_thumbnails(monke
 
     assert result["loras"]
     assert result["loras"][0]["name"] == "Example Lora Model"
-    assert result["loras"][0]["thumbnailUrl"] == "https://image.civitai.com/loras/width=450,optimized=true"
+    assert (
+        result["loras"][0]["thumbnailUrl"]
+        == "https://image.civitai.com/loras/width=450,optimized=true"
+    )
     assert result["loras"][0]["hash"] == "abc123"
+
+
+@pytest.mark.asyncio
+async def test_parse_metadata_handles_modelVersionIds(monkeypatch):
+    """Test that modelVersionIds from Civitai image API are properly processed."""
+    lora_info_1 = {
+        "id": 2398829,
+        "modelId": 123456,
+        "model": {"name": "Dance LoRA 1", "type": "lora"},
+        "name": "Version 1.0",
+        "images": [{"url": "https://image.civitai.com/lora1/original=true"}],
+        "baseModel": "SDXL",
+        "downloadUrl": "https://civitai.com/lora1/download",
+        "files": [
+            {
+                "type": "Model",
+                "primary": True,
+                "sizeKB": 10240,
+                "name": "dance_lora_1.safetensors",
+                "hashes": {"SHA256": "aabbccdd0011"},
+            }
+        ],
+    }
+
+    lora_info_2 = {
+        "id": 2398838,
+        "modelId": 123457,
+        "model": {"name": "Style LoRA 2", "type": "lora"},
+        "name": "Version 2.0",
+        "images": [{"url": "https://image.civitai.com/lora2/original=true"}],
+        "baseModel": "SDXL",
+        "downloadUrl": "https://civitai.com/lora2/download",
+        "files": [
+            {
+                "type": "Model",
+                "primary": True,
+                "sizeKB": 20480,
+                "name": "style_lora_2.safetensors",
+                "hashes": {"SHA256": "aabbccdd0022"},
+            }
+        ],
+    }
+
+    async def fake_metadata_provider():
+        class Provider:
+            async def get_model_version_info(self, version_id):
+                if version_id == "2398829":
+                    return lora_info_1, None
+                if version_id == "2398838":
+                    return lora_info_2, None
+                return None, "Model not found"
+
+        return Provider()
+
+    monkeypatch.setattr(
+        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
+        fake_metadata_provider,
+    )
+
+    parser = CivitaiApiMetadataParser()
+
+    # This simulates the metadata from Civitai image API where modelVersionIds
+    # is at the root level and meta only contains basic prompt info
+    metadata = {
+        "id": 109882763,
+        "meta": {
+            "id": 109882763,
+            "meta": {"prompt": "A woman does the hip bump dance."},
+        },
+        "modelVersionIds": [2398829, 2398838],
+    }
+
+    assert parser.is_metadata_matching(metadata)
+
+    result = await parser.parse_metadata(metadata)
+
+    # Verify both LoRAs were created from modelVersionIds
+    assert len(result["loras"]) == 2
+
+    # Check first LoRA
+    lora1 = result["loras"][0]
+    assert lora1["id"] == 2398829
+    assert lora1["name"] == "Dance LoRA 1"
+    assert lora1["type"] == "lora"
+    assert lora1["hash"] == "aabbccdd0011"
+    assert lora1["baseModel"] == "SDXL"
+    assert (
+        lora1["thumbnailUrl"]
+        == "https://image.civitai.com/lora1/width=450,optimized=true"
+    )
+
+    # Check second LoRA
+    lora2 = result["loras"][1]
+    assert lora2["id"] == 2398838
+    assert lora2["name"] == "Style LoRA 2"
+    assert lora2["type"] == "lora"
+    assert lora2["hash"] == "aabbccdd0022"
+    assert lora2["baseModel"] == "SDXL"

tests/services/test_download_manager_basic.py

@@ -38,6 +38,7 @@ def isolate_settings(monkeypatch, tmp_path):
                 "embedding": "{base_model}/{first_tag}",
             },
             "base_model_path_mappings": {"BaseModel": "MappedModel"},
+            "download_skip_base_models": [],
         }
     )
     monkeypatch.setattr(manager, "settings", default_settings)
@@ -443,3 +444,49 @@ def test_distribute_preview_to_entries_keeps_existing_file(tmp_path):
 
     assert targets[0] == str(existing_preview)
     assert Path(targets[1]).read_bytes() == b"preview"
+
+
+
+@pytest.mark.asyncio
+async def test_download_skips_excluded_base_model(monkeypatch, scanners, metadata_provider):
+    manager = DownloadManager()
+    get_settings_manager().settings["download_skip_base_models"] = ["SDXL 1.0"]
+
+    metadata_provider.get_model_version = AsyncMock(
+        return_value={
+            "id": 42,
+            "model": {"type": "LoRA", "tags": ["fantasy"]},
+            "baseModel": "SDXL 1.0",
+            "creator": {"username": "Author"},
+            "files": [
+                {
+                    "type": "Model",
+                    "primary": True,
+                    "downloadUrl": "https://example.invalid/file.safetensors",
+                    "name": "file.safetensors",
+                }
+            ],
+        }
+    )
+
+    execute_download = AsyncMock()
+    monkeypatch.setattr(
+        DownloadManager, "_execute_download", execute_download, raising=False
+    )
+
+    result = await manager.download_from_civitai(
+        model_version_id=99,
+        use_default_paths=True,
+        progress_callback=None,
+        source=None,
+    )
+
+    assert result["success"] is True
+    assert result["skipped"] is True
+    assert result["status"] == "skipped"
+    assert result["reason"] == "base_model_excluded"
+    assert result["base_model"] == "SDXL 1.0"
+    assert result["file_name"] == "file.safetensors"
+    assert "file.safetensors" in result["message"]
+    execute_download.assert_not_called()
+    assert manager._active_downloads[result["download_id"]]["status"] == "skipped"

tests/services/test_download_manager_error.py

@@ -281,8 +281,6 @@ async def test_execute_download_extracts_zip_single_model(monkeypatch, tmp_path)
         DownloadManager, "_get_lora_scanner", AsyncMock(return_value=dummy_scanner)
     )
     monkeypatch.setattr(MetadataManager, "save_metadata", AsyncMock(return_value=True))
-    hash_calculator = AsyncMock(return_value="hash-single")
-    monkeypatch.setattr(download_manager, "calculate_sha256", hash_calculator)
 
     result = await manager._execute_download(
         download_urls=download_urls,
@@ -299,10 +297,10 @@ async def test_execute_download_extracts_zip_single_model(monkeypatch, tmp_path)
     assert not zip_path.exists()
     extracted = save_dir / "model.safetensors"
     assert extracted.exists()
-    assert hash_calculator.await_args.args[0] == str(extracted)
     saved_call = MetadataManager.save_metadata.await_args
     assert saved_call.args[0] == str(extracted)
-    assert saved_call.args[1].sha256 == "hash-single"
+    # SHA256 comes from metadata (API value), not recalculated
+    assert saved_call.args[1].sha256 == "sha256"
     assert dummy_scanner.add_model_to_cache.await_count == 1
 
 
@@ -351,8 +349,6 @@ async def test_execute_download_extracts_zip_multiple_models(monkeypatch, tmp_pa
         DownloadManager, "_get_lora_scanner", AsyncMock(return_value=dummy_scanner)
     )
     monkeypatch.setattr(MetadataManager, "save_metadata", AsyncMock(return_value=True))
-    hash_calculator = AsyncMock(side_effect=["hash-one", "hash-two"])
-    monkeypatch.setattr(download_manager, "calculate_sha256", hash_calculator)
 
     result = await manager._execute_download(
         download_urls=download_urls,
@@ -372,15 +368,15 @@ async def test_execute_download_extracts_zip_multiple_models(monkeypatch, tmp_pa
     assert extracted_one.exists()
     assert extracted_two.exists()
 
-    assert hash_calculator.await_count == 2
     assert MetadataManager.save_metadata.await_count == 2
     assert dummy_scanner.add_model_to_cache.await_count == 2
 
     metadata_calls = MetadataManager.save_metadata.await_args_list
     assert metadata_calls[0].args[0] == str(extracted_one)
-    assert metadata_calls[0].args[1].sha256 == "hash-one"
+    # SHA256 comes from metadata (API value), not recalculated
+    assert metadata_calls[0].args[1].sha256 == "sha256"
     assert metadata_calls[1].args[0] == str(extracted_two)
-    assert metadata_calls[1].args[1].sha256 == "hash-two"
+    assert metadata_calls[1].args[1].sha256 == "sha256"
 
 
 @pytest.mark.asyncio
@@ -427,8 +423,6 @@ async def test_execute_download_extracts_zip_pt_embedding(monkeypatch, tmp_path)
         ServiceRegistry, "get_embedding_scanner", AsyncMock(return_value=dummy_scanner)
     )
     monkeypatch.setattr(MetadataManager, "save_metadata", AsyncMock(return_value=True))
-    hash_calculator = AsyncMock(return_value="hash-pt")
-    monkeypatch.setattr(download_manager, "calculate_sha256", hash_calculator)
 
     result = await manager._execute_download(
         download_urls=download_urls,
@@ -445,10 +439,10 @@ async def test_execute_download_extracts_zip_pt_embedding(monkeypatch, tmp_path)
     assert not zip_path.exists()
     extracted = save_dir / "embedding.pt"
     assert extracted.exists()
-    assert hash_calculator.await_args.args[0] == str(extracted)
     saved_call = MetadataManager.save_metadata.await_args
     assert saved_call.args[0] == str(extracted)
-    assert saved_call.args[1].sha256 == "hash-pt"
+    # SHA256 comes from metadata (API value), not recalculated
+    assert saved_call.args[1].sha256 == "sha256"
     assert dummy_scanner.add_model_to_cache.await_count == 1
 
 

tests/services/test_downloader_error_paths.py

@@ -9,95 +9,99 @@ from unittest.mock import AsyncMock, patch, MagicMock
 
 import aiohttp
 
-from py.services.downloader import Downloader, DownloadStalledError, DownloadRestartRequested
+from py.services.downloader import (
+    Downloader,
+    DownloadStalledError,
+    DownloadRestartRequested,
+)
 
 
 class TestDownloadStreamControl:
     """Test DownloadStreamControl functionality."""
-    
+
     def test_pause_clears_event(self):
         """Verify pause() clears the event."""
         from py.services.downloader import DownloadStreamControl
-        
+
         control = DownloadStreamControl()
         assert control.is_set() is True  # Initially set
-        
+
         control.pause()
         assert control.is_set() is False
         assert control.is_paused() is True
-    
+
     def test_resume_sets_event(self):
         """Verify resume() sets the event."""
         from py.services.downloader import DownloadStreamControl
-        
+
         control = DownloadStreamControl()
         control.pause()
         assert control.is_set() is False
-        
+
         control.resume()
         assert control.is_set() is True
         assert control.is_paused() is False
-    
+
     def test_reconnect_request_tracking(self):
         """Verify reconnect request tracking works correctly."""
         from py.services.downloader import DownloadStreamControl
-        
+
         control = DownloadStreamControl()
         assert control.has_reconnect_request() is False
-        
+
         control.request_reconnect()
         assert control.has_reconnect_request() is True
-        
+
         # Consume the request
         consumed = control.consume_reconnect_request()
         assert consumed is True
         assert control.has_reconnect_request() is False
-    
+
     def test_mark_progress_clears_reconnect(self):
         """Verify mark_progress clears reconnect requests."""
         from py.services.downloader import DownloadStreamControl
-        
+
         control = DownloadStreamControl()
         control.request_reconnect()
         assert control.has_reconnect_request() is True
-        
+
         control.mark_progress()
         assert control.has_reconnect_request() is False
         assert control.last_progress_timestamp is not None
-    
+
     def test_time_since_last_progress(self):
         """Verify time_since_last_progress calculation."""
         from py.services.downloader import DownloadStreamControl
         import time
-        
+
         control = DownloadStreamControl()
-        
+
         # Initially None
         assert control.time_since_last_progress() is None
-        
+
         # After marking progress
         now = time.time()
         control.mark_progress(timestamp=now)
-        
+
         elapsed = control.time_since_last_progress(now=now + 5)
         assert elapsed == 5.0
-    
+
     @pytest.mark.asyncio
     async def test_wait_for_resume(self):
         """Verify wait() blocks until resumed."""
         from py.services.downloader import DownloadStreamControl
         import asyncio
-        
+
         control = DownloadStreamControl()
         control.pause()
-        
+
         # Start a task that will wait
         wait_task = asyncio.create_task(control.wait())
-        
+
         # Give it a moment to start waiting
         await asyncio.sleep(0.01)
         assert not wait_task.done()
-        
+
         # Resume should unblock
         control.resume()
         await asyncio.wait_for(wait_task, timeout=0.1)
@@ -105,75 +109,76 @@ class TestDownloadStreamControl:
 
 class TestDownloaderConfiguration:
     """Test downloader configuration and initialization."""
-    
+
     def test_downloader_singleton_pattern(self):
         """Verify Downloader follows singleton pattern."""
         # Reset first
         Downloader._instance = None
-        
+
         # Both should return same instance
         async def get_instances():
             instance1 = await Downloader.get_instance()
             instance2 = await Downloader.get_instance()
             return instance1, instance2
-        
+
         import asyncio
+
         instance1, instance2 = asyncio.run(get_instances())
-        
+
         assert instance1 is instance2
-        
+
         # Cleanup
         Downloader._instance = None
-    
+
     def test_default_configuration_values(self):
         """Verify default configuration values are set correctly."""
         Downloader._instance = None
-        
+
         downloader = Downloader()
-        
-        assert downloader.chunk_size == 4 * 1024 * 1024  # 4MB
+
+        assert downloader.chunk_size == 16 * 1024 * 1024  # 16MB
         assert downloader.max_retries == 5
         assert downloader.base_delay == 2.0
         assert downloader.session_timeout == 300
-        
+
         # Cleanup
         Downloader._instance = None
-    
+
     def test_default_headers_include_user_agent(self):
         """Verify default headers include User-Agent."""
         Downloader._instance = None
-        
+
         downloader = Downloader()
-        
-        assert 'User-Agent' in downloader.default_headers
-        assert 'ComfyUI-LoRA-Manager' in downloader.default_headers['User-Agent']
-        assert downloader.default_headers['Accept-Encoding'] == 'identity'
-        
+
+        assert "User-Agent" in downloader.default_headers
+        assert "ComfyUI-LoRA-Manager" in downloader.default_headers["User-Agent"]
+        assert downloader.default_headers["Accept-Encoding"] == "identity"
+
         # Cleanup
         Downloader._instance = None
-    
+
     def test_stall_timeout_resolution(self):
         """Verify stall timeout is resolved correctly."""
         Downloader._instance = None
-        
+
         downloader = Downloader()
         timeout = downloader._resolve_stall_timeout()
-        
+
         # Should be at least 30 seconds
         assert timeout >= 30.0
-        
+
         # Cleanup
         Downloader._instance = None
 
 
 class TestDownloadProgress:
     """Test DownloadProgress dataclass."""
-    
+
     def test_download_progress_creation(self):
         """Verify DownloadProgress can be created with correct values."""
         from py.services.downloader import DownloadProgress
         from datetime import datetime
-        
+
         progress = DownloadProgress(
             percent_complete=50.0,
             bytes_downloaded=500,
@@ -181,7 +186,7 @@ class TestDownloadProgress:
             bytes_per_second=100.5,
             timestamp=datetime.now().timestamp(),
         )
-        
+
         assert progress.percent_complete == 50.0
         assert progress.bytes_downloaded == 500
         assert progress.total_bytes == 1000
@@ -191,121 +196,130 @@ class TestDownloadProgress:
 
 class TestDownloaderExceptions:
     """Test custom exception classes."""
-    
+
     def test_download_stalled_error(self):
         """Verify DownloadStalledError can be raised and caught."""
         with pytest.raises(DownloadStalledError) as exc_info:
             raise DownloadStalledError("Download stalled for 120 seconds")
-        
+
         assert "stalled" in str(exc_info.value).lower()
-    
+
     def test_download_restart_requested_error(self):
         """Verify DownloadRestartRequested can be raised and caught."""
         with pytest.raises(DownloadRestartRequested) as exc_info:
             raise DownloadRestartRequested("Reconnect requested after resume")
-        
-        assert "reconnect" in str(exc_info.value).lower() or "restart" in str(exc_info.value).lower()
+
+        assert (
+            "reconnect" in str(exc_info.value).lower()
+            or "restart" in str(exc_info.value).lower()
+        )
 
 
 class TestDownloaderAuthHeaders:
     """Test authentication header generation."""
-    
+
     def test_get_auth_headers_without_auth(self):
         """Verify auth headers without authentication."""
         Downloader._instance = None
         downloader = Downloader()
-        
+
         headers = downloader._get_auth_headers(use_auth=False)
-        
-        assert 'User-Agent' in headers
-        assert 'Authorization' not in headers
-        
+
+        assert "User-Agent" in headers
+        assert "Authorization" not in headers
+
         Downloader._instance = None
-    
+
     def test_get_auth_headers_with_auth_no_api_key(self, monkeypatch):
         """Verify auth headers with auth but no API key configured."""
         Downloader._instance = None
         downloader = Downloader()
-        
+
         # Mock settings manager to return no API key
         mock_settings = MagicMock()
         mock_settings.get.return_value = None
-        
-        with patch('py.services.downloader.get_settings_manager', return_value=mock_settings):
+
+        with patch(
+            "py.services.downloader.get_settings_manager", return_value=mock_settings
+        ):
             headers = downloader._get_auth_headers(use_auth=True)
-            
+
             # Should still have User-Agent but no Authorization
-            assert 'User-Agent' in headers
-            assert 'Authorization' not in headers
-        
+            assert "User-Agent" in headers
+            assert "Authorization" not in headers
+
         Downloader._instance = None
-    
+
     def test_get_auth_headers_with_auth_and_api_key(self, monkeypatch):
         """Verify auth headers with auth and API key configured."""
         Downloader._instance = None
         downloader = Downloader()
-        
+
         # Mock settings manager to return API key
         mock_settings = MagicMock()
         mock_settings.get.return_value = "test-api-key-12345"
-        
-        with patch('py.services.downloader.get_settings_manager', return_value=mock_settings):
+
+        with patch(
+            "py.services.downloader.get_settings_manager", return_value=mock_settings
+        ):
             headers = downloader._get_auth_headers(use_auth=True)
-            
+
             # Should have both User-Agent and Authorization
-            assert 'User-Agent' in headers
-            assert 'Authorization' in headers
-            assert 'test-api-key-12345' in headers['Authorization']
-            assert headers['Content-Type'] == 'application/json'
-        
+            assert "User-Agent" in headers
+            assert "Authorization" in headers
+            assert "test-api-key-12345" in headers["Authorization"]
+            assert headers["Content-Type"] == "application/json"
+
         Downloader._instance = None
 
 
 class TestDownloaderSessionManagement:
     """Test session management functionality."""
-    
+
     @pytest.mark.asyncio
     async def test_should_refresh_session_when_none(self):
         """Verify session refresh is needed when session is None."""
         Downloader._instance = None
         downloader = Downloader()
-        
+
         # Initially should need refresh
         assert downloader._should_refresh_session() is True
-        
+
         Downloader._instance = None
-    
+
     def test_should_not_refresh_new_session(self):
         """Verify new session doesn't need refresh."""
         Downloader._instance = None
         downloader = Downloader()
-        
+
         # Simulate a fresh session
         downloader._session_created_at = MagicMock()
         downloader._session = MagicMock()
-        
+
         # Mock datetime to return current time
         from datetime import datetime, timedelta
+
         current_time = datetime.now()
         downloader._session_created_at = current_time
-        
+
         # Should not need refresh for new session
         assert downloader._should_refresh_session() is False
-        
+
         Downloader._instance = None
-    
+
     def test_should_refresh_old_session(self):
         """Verify old session needs refresh."""
         Downloader._instance = None
         downloader = Downloader()
-        
+
         # Simulate an old session (older than timeout)
         from datetime import datetime, timedelta
+
         old_time = datetime.now() - timedelta(seconds=downloader.session_timeout + 1)
         downloader._session_created_at = old_time
         downloader._session = MagicMock()
-        
+
         # Should need refresh for old session
         assert downloader._should_refresh_session() is True
-        
+
         Downloader._instance = None

tests/services/test_model_scanner.py

@@ -73,6 +73,15 @@ class DummyScanner(ModelScanner):
         }
 
 
+class MultiRootDummyScanner(DummyScanner):
+    def __init__(self, roots: List[Path]):
+        self._roots = [str(root) for root in roots]
+        super().__init__(roots[0])
+
+    def get_model_roots(self) -> List[str]:
+        return list(self._roots)
+
+
 @pytest.fixture(autouse=True)
 def reset_model_scanner_singletons():
     ModelScanner._instances.clear()
@@ -559,3 +568,59 @@ async def test_count_model_files_handles_symlink_loops(tmp_path: Path):
     count = scanner._count_model_files()
 
     assert count == 2
+
+
+@pytest.mark.asyncio
+async def test_initialize_cache_dedupes_files_reachable_via_primary_symlink_and_extra_root(
+    tmp_path: Path,
+):
+    loras_root = tmp_path / "loras"
+    loras_root.mkdir()
+    extra_root = tmp_path / "extra"
+    extra_root.mkdir()
+    (extra_root / "one.txt").write_text("one", encoding="utf-8")
+    (loras_root / "link").symlink_to(extra_root, target_is_directory=True)
+
+    scanner = MultiRootDummyScanner([loras_root, extra_root])
+
+    await scanner._initialize_cache()
+    cache = await scanner.get_cached_data()
+
+    assert len(cache.raw_data) == 1
+    assert cache.raw_data[0]["file_path"] == _normalize_path(loras_root / "link" / "one.txt")
+
+
+@pytest.mark.asyncio
+async def test_reconcile_cache_removes_duplicate_alias_when_same_real_file_seen_once(
+    tmp_path: Path,
+):
+    loras_root = tmp_path / "loras"
+    loras_root.mkdir()
+    extra_root = tmp_path / "extra"
+    extra_root.mkdir()
+    real_file = extra_root / "one.txt"
+    real_file.write_text("one", encoding="utf-8")
+    (loras_root / "link").symlink_to(extra_root, target_is_directory=True)
+
+    scanner = MultiRootDummyScanner([loras_root, extra_root])
+    await scanner._initialize_cache()
+
+    duplicate_entry = {
+        "file_path": _normalize_path(extra_root / "one.txt"),
+        "folder": "",
+        "sha256": "hash-one",
+        "tags": ["alpha"],
+        "model_name": "one",
+        "size": 1,
+        "modified": 1.0,
+        "license_flags": DEFAULT_LICENSE_FLAGS,
+    }
+    scanner._cache.raw_data.append(duplicate_entry)
+    scanner._cache.add_to_version_index(duplicate_entry)
+    scanner._hash_index.add_entry("hash-one", duplicate_entry["file_path"])
+
+    await scanner._reconcile_cache()
+
+    cache = await scanner.get_cached_data()
+    cached_paths = {item["file_path"] for item in cache.raw_data}
+    assert cached_paths == {_normalize_path(loras_root / "link" / "one.txt")}