A model file could only ever be linked to huggingface.co: `set_hf_url`
validated the URL with a huggingface-only regex, the agent fetched the card
from a hardcoded HF URL, and the readme processor built every relative image
path off `https://huggingface.co/{repo}/resolve/main`. ModelScope publishes the
same model-card convention (README.md + YAML frontmatter, often carrying
`base_model:` and `trigger_words:`) behind a public, key-less API, so the
enrichment pipeline could already serve it - it was the plumbing that was
HF-shaped, not the idea.
Make the external source a first-class, provider-driven concept:
- New `py/services/model_sources/` registry. A `ModelSource` owns URL
recognition (lenient for stored values, strict for user input), the
canonical page URL, model-card fetching, the asset base URL and the
capability flags. `HuggingFaceSource` is the previous logic relocated;
`ModelScopeSource` reads `/models/{o}/{n}/resolve/{master|main}/README.md`
and falls back to `/api/v1/models/{o}/{n}/repo`. `TensorArtSource` is
link-only on purpose: tensor.art answers plain HTTP clients with a
Cloudflare challenge and its internal API (ap-east-1.tensorart.cloud /
cn.tensorart.net) rejects every /v1/model/* route with "invalid
authorization header", so it declares supports_enrichment=False rather than
failing silently later.
- Metadata gains `source_platform` + `source_url`; `hf_url` stays as a
read/write alias, written only for Hugging Face, so existing sidecars,
cached rows and third-party consumers keep working. Normalisation runs at
the scanner, the persistent cache (both directions, plus two new columns
behind an ALTER migration) and the linking handler - which is what stops a
user who switches sources from leaving a stale `hf_url` on a ModelScope
model.
- The agent pipeline keys off the provider instead of `hf_url`: the fast-fail
gate now explains *why* a model is skipped (no source / unknown source /
source without a reachable card), the prompt context exposes
source_url/source_id/source_label/asset_base_url while still filling the
legacy hf_url/repo aliases, and the four README image extractors take a
base_url (defaulting to HF) so relative paths resolve against the right
site. Version grouping generalises to hf: / ms: / ta: keys.
- `POST /api/lm/set-hf-url` keeps its path and its legacy payload keys but
accepts `source_url`, validates against every provider and returns the
platform. `GET /api/lm/model-sources` lets the UI render the supported-site
list from the server.
- Frontend: a `modelSourceHelpers` mirror of the registry drives the link
dialog, the card/modal globe (branded "View on ModelScope/TensorArt"), the
version-group key and the enrichment gate; the versions tab no longer sends
ms:/ta: keys to the CivitAI API.
TensorArt stays in the list because provenance is worth keeping even when the
card is unreadable - the dialog says so plainly ("Sites that don't expose one
(currently TensorArt) can only be linked") and the context menu disables
enrichment with a matching tooltip, instead of the user getting
"Unsupported URL".
Verified against the real ModelScope API: jj3550945163/Krea-2-LORA returns a
1882-byte card whose frontmatter carries base_model/tags/trigger_words, and
relative images resolve to .../resolve/master/....
Tests: backend 2815 passed; frontend 1130 JS + 91 Vue passed; pytest
tests/i18n and a Jinja compile pass over templates/. The nine locales carry
[TODO: Translate] for the new strings, completed in the next commit.
The download modal's location step decided between checkpoint and unet
roots using only the CivitAI file-type signal, while the backend also
falls back to DIFFUSION_MODEL_BASE_MODELS. Models like Anima (file type
"Model") were offered checkpoint roots in the UI even though
use_default_paths would route them to the unet root.
- Extract the two-tier decision into py/services/download_routing.py and
reuse it in DownloadManager._execute_download
- Add POST /api/lm/download/routing so the UI asks the backend for the
routing decision; fall back to the local file-type check on failure
- ModelVersionsTab: search both checkpoint and unet roots when resolving
an existing version's download path
initialization.js falls back to polling /api/lm/init-status when the
/ws/init-progress WebSocket cannot be established, but no route ever
registered that path — each poll 404'd and the page never reloaded after
the scan completed. Report the aggregate status of all four scanners and
omit pageType so every initialization page accepts the update.
Add GET variants of the two POST endpoints used by the send-to-workflow
feature. Parameters are read from query string instead of JSON body,
supporting both simple repeated node_id params and JSON-encoded node_ids
for complex graph references.
- Merge Relink to Civitai and new Link to HuggingFace into a single
'Link Model' submenu with sub-options for each source
- Add POST /api/lm/set-hf-url endpoint to associate a model with a
HuggingFace repo URL, saving hf_url to .metadata.json
- Add link_hf_modal.html for URL input, following relink-civitai pattern
- Use update_single_model_cache instead of add_model_to_cache to
prevent duplicate cache entries after linking
- Remove os.path.realpath usage for consistency with relink-civitai
- Raise errors instead of silently falling back to LoRA scanner when
model root cannot be determined
- Scope .input-group CSS rules to modal IDs to fix style conflicts
with download-modal.css
- Add i18n keys across all 10 locales with translations for
zh-CN, zh-TW, ja, ko, de, es, fr, he, ru
- Replace hardcoded provider list with PROVIDER_PRESETS (OpenAI, Ollama,
DeepSeek, Groq, OpenRouter, OpenCode Go, Custom)
- Load model lists from models.dev/api.json catalog at startup
- Add Combobox vanilla JS component for model/base-URL selection
- Fetch local Ollama models via live API instead of catalog
- Hide API key values from frontend (boolean-only llm_api_key_set)
- Add i18n translations for all 9+ locales
- Update snapshot tests for new response fields
Introduce an agent skill framework for LLM-driven metadata enrichment:
- AgentCLI (py/agent_cli/): in-process wrappers around internal services
using standard relative imports, eliminating the need for sys.path hacks
- LLMService: centralized BYOK (bring-your-own-key) LLM client supporting
OpenAI, Ollama, and custom OpenAI-compatible endpoints
- PostProcessor: deterministic engine that applies LLM output via AgentCLI
(replaces old handler.py + _BASE_MODEL_ALIASES approach)
- SkillRegistry: filesystem-based skill discovery (skill.yaml + prompt.md)
- AgentService: orchestrates skill execution with WebSocket progress
- Frontend AgentManager: WebSocket listeners, skill execution, config UI
- Context menu entries (single + bulk) for "Enrich Metadata (Agent)"
- Settings UI for AI Provider configuration (BYOK)
- Full i18n support across 9 locales
Bug fixes found during review:
- aiohttp.web.json_response: status_code= -> status=
- settings_modal cancelEditApiKey: wrong argument position
- AgentManager.isLlmConfigured: allow Ollama without API key
- PostProcessor._merge_tags: lowercase all tags to match TagUpdateService
New endpoint: GET /api/lm/check-models-exist?modelIds=1,2,3,...
Accepts comma-separated modelIds, returns a results array with one
entry per modelId. Uses a single scanner lookup batch - three
service-registry calls total, regardless of model count. Skips
history checks entirely (same rationale as the singleton endpoint:
when models exist locally, history is redundant).
Expected: reduces 231 HTTP round-trips to 1 for the browser
extension's model-card indicator flow. Combined with the prior
SQLite-connection and history-skip fixes, total wall-clock time
for a 175K-lora user's page load drops from ~9.4s to <10ms.
Detects when multiple model files share the same basename (causing
ambiguity in LoRA resolution), logs warnings during scanning, and
provides a "Resolve Conflicts" button in the Doctor panel. Resolution
renames duplicates with hash-prefixed unique filenames, migrates all
sidecar and preview files, and updates the cache and frontend scroller
in-place so the model modal immediately reflects the new filename.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Implement automatic fetching of base models from Civitai API to keep
data up-to-date without manual updates.
Backend:
- Add CivitaiBaseModelService with 7-day TTL caching
- Add /api/lm/base-models endpoints for fetching and refreshing
- Merge hardcoded and remote models for backward compatibility
- Smart abbreviation generation for unknown models
Frontend:
- Add civitaiBaseModelApi client for API communication
- Dynamic base model loading on app initialization
- Update SettingsManager to use merged model lists
- Add support for 8 new models: Anima, CogVideoX, LTXV 2.3, Mochi,
Pony V7, Wan Video 2.5 T2V/I2V
API Endpoints:
- GET /api/lm/base-models - Get merged models
- POST /api/lm/base-models/refresh - Force refresh
- GET /api/lm/base-models/categories - Get categories
- GET /api/lm/base-models/cache-status - Check cache status
Closes#854
- Add GET /api/lm/example-workflows endpoint to list available templates
- Add GET /api/lm/example-workflows/{filename} to retrieve specific workflow
- Add 'New Tab Template Workflow' setting in LoRA Manager settings
- Automatically apply 80% zoom level when loading template workflows
- Override workflow's saved view settings to prevent visual zoom flicker
The feature allows users to select a template workflow from example_workflows/
directory to load when creating new workflow tabs, with a hardcoded 0.8 zoom
level for better initial view experience.
- Add SupportersHandler in misc_handlers.py to serve /api/lm/supporters
- Register new endpoint in misc_route_registrar.py
- Remove supporters from page load template context in model_handlers.py
- Create supportersService.js for frontend data fetching
- Update Header.js to fetch supporters when support modal opens
- Modify support_modal.html to use client-side rendering
This change improves page load performance by loading supporters data
on-demand instead of during initial page render.
Remove all autocomplete.txt parsing logic and fallback code, simplifying
the service to use only TagFTSIndex for Danbooru/e621 tag search
with category filtering.
- Remove WordEntry dataclass and _words_cache, _file_path attributes
- Remove _determine_file_path(), get_file_path(), load_words(), save_words(),
get_content(), _parse_csv_content() methods
- Simplify search_words() to only use TagFTSIndex, always returning
enriched results with {tag_name, category, post_count}
- Remove GET/POST /api/lm/custom-words endpoints (unused)
- Keep GET /api/lm/custom-words/search for frontend autocomplete
- Rewrite tests to focus on TagFTSIndex integration
This reduces code by 446 lines and removes untested pysssss plugin
integration. Feature is unreleased so no backward compatibility needed.
Adds custom words autocomplete functionality similar to comfyui-custom-scripts,
with the following features:
Backend (Python):
- Create CustomWordsService for CSV parsing and priority-based search
- Add API endpoints: GET/POST /api/lm/custom-words and
GET /api/lm/custom-words/search
- Share storage with pysssss plugin (checks for their user/autocomplete.txt first)
- Fallback to Lora Manager's user directory for storage
Frontend (JavaScript/Vue):
- Add 'custom_words' and 'prompt' model types to autocomplete system
- Prompt node now supports dual-mode autocomplete:
* Type 'emb:' prefix → search embeddings
* Type normally → search custom words (no prefix required)
- Add AUTOCOMPLETE_TEXT_PROMPT widget type
- Update Vue component and composable types
Key Features:
- CSV format: word[,priority] compatible with danbooru-tags.txt
- Priority-based sorting: 20% top priority + prefix + include matches
- Preview tooltip for embeddings (not for custom words)
- Dynamic endpoint switching based on prefix detection
Breaking Changes:
- Prompt (LoraManager) node widget type changed from
AUTOCOMPLETE_TEXT_EMBEDDINGS to AUTOCOMPLETE_TEXT_PROMPT
- Removed standalone web/comfyui/prompt.js (integrated into main widgets)
Fixes comfy_dir path calculation by prioritizing folder_paths.base_path
from ComfyUI when available, with fallback to computed path.
- Add `open_settings_location` method to `FileSystemHandler` to open OS file explorer at settings file location
- Register new POST route `/api/lm/settings/open-location` for settings file access
- Inject `SettingsManager` dependency into `FileSystemHandler` constructor
- Add cross-platform support for Windows, macOS, and Linux file explorers
- Include error handling for missing settings files and system exceptions
- Add capabilities parsing and validation for node registration
- Implement widget_names extraction from capabilities with type safety
- Add supports_lora boolean conversion in capabilities
- Include comfy_class fallback to node_type when missing
- Add new update_node_widget API endpoint for bulk widget updates
- Improve error handling and input validation for widget updates
- Remove unused parameters from node selector event setup function
These changes improve node metadata handling and enable dynamic widget management capabilities.
Will Miao