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17 changed files with 796 additions and 50 deletions

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@@ -34,6 +34,14 @@ Enhance your Civitai browsing experience with our companion browser extension! S
## Release Notes
### v0.8.22
* **Embeddings Management** - Added Embeddings page for comprehensive embedding model management.
* **Advanced Sorting Options** - Introduced flexible sorting controls, allowing sorting by name, added date, or file size in both ascending and descending order.
* **Custom Download Path Templates & Base Model Mapping** - Implemented UI settings for configuring download path templates and base model path mappings, allowing customized model organization and storage location when downloading models via LM Civitai Extension.
* **LM Civitai Extension Enhancements** - Improved concurrent download performance and stability, with new support for canceling active downloads directly from the extension interface.
* **Update Feature** - Added update functionality, allowing users to update LoRA Manager to the latest release version directly from the LoRA Manager UI.
* **Bulk Operations: Refresh All** - Added bulk refresh functionality, allowing users to update Civitai metadata across multiple LoRAs.
### v0.8.20
* **LM Civitai Extension** - Released [browser extension through Chrome Web Store](https://chromewebstore.google.com/detail/lm-civitai-extension/capigligggeijgmocnaflanlbghnamgm?utm_source=item-share-cb) that works seamlessly with LoRA Manager to enhance Civitai browsing experience, showing which models are already in your local library, enabling one-click downloads, and providing queue and parallel download support
* **Enhanced Lora Loader** - Added support for nunchaku, improving convenience when working with ComfyUI-nunchaku workflows, plus new template workflows for quick onboarding

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@@ -20,6 +20,7 @@ class StatsRoutes:
def __init__(self):
self.lora_scanner = None
self.checkpoint_scanner = None
self.embedding_scanner = None
self.usage_stats = None
self.template_env = jinja2.Environment(
loader=jinja2.FileSystemLoader(config.templates_path),
@@ -30,6 +31,7 @@ class StatsRoutes:
"""Initialize services from ServiceRegistry"""
self.lora_scanner = await ServiceRegistry.get_lora_scanner()
self.checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
self.embedding_scanner = await ServiceRegistry.get_embedding_scanner()
self.usage_stats = UsageStats()
async def handle_stats_page(self, request: web.Request) -> web.Response:
@@ -49,7 +51,12 @@ class StatsRoutes:
(hasattr(self.checkpoint_scanner, '_is_initializing') and self.checkpoint_scanner._is_initializing)
)
is_initializing = lora_initializing or checkpoint_initializing
embedding_initializing = (
self.embedding_scanner._cache is None or
(hasattr(self.embedding_scanner, 'is_initializing') and self.embedding_scanner.is_initializing())
)
is_initializing = lora_initializing or checkpoint_initializing or embedding_initializing
template = self.template_env.get_template('statistics.html')
rendered = template.render(
@@ -85,21 +92,29 @@ class StatsRoutes:
checkpoint_count = len(checkpoint_cache.raw_data)
checkpoint_size = sum(cp.get('size', 0) for cp in checkpoint_cache.raw_data)
# Get Embedding statistics
embedding_cache = await self.embedding_scanner.get_cached_data()
embedding_count = len(embedding_cache.raw_data)
embedding_size = sum(emb.get('size', 0) for emb in embedding_cache.raw_data)
# Get usage statistics
usage_data = await self.usage_stats.get_stats()
return web.json_response({
'success': True,
'data': {
'total_models': lora_count + checkpoint_count,
'total_models': lora_count + checkpoint_count + embedding_count,
'lora_count': lora_count,
'checkpoint_count': checkpoint_count,
'total_size': lora_size + checkpoint_size,
'embedding_count': embedding_count,
'total_size': lora_size + checkpoint_size + embedding_size,
'lora_size': lora_size,
'checkpoint_size': checkpoint_size,
'embedding_size': embedding_size,
'total_generations': usage_data.get('total_executions', 0),
'unused_loras': self._count_unused_models(lora_cache.raw_data, usage_data.get('loras', {})),
'unused_checkpoints': self._count_unused_models(checkpoint_cache.raw_data, usage_data.get('checkpoints', {}))
'unused_checkpoints': self._count_unused_models(checkpoint_cache.raw_data, usage_data.get('checkpoints', {})),
'unused_embeddings': self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {}))
}
})
@@ -121,14 +136,17 @@ class StatsRoutes:
# Get model data for enrichment
lora_cache = await self.lora_scanner.get_cached_data()
checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
embedding_cache = await self.embedding_scanner.get_cached_data()
# Create hash to model mapping
lora_map = {lora['sha256']: lora for lora in lora_cache.raw_data}
checkpoint_map = {cp['sha256']: cp for cp in checkpoint_cache.raw_data}
embedding_map = {emb['sha256']: emb for emb in embedding_cache.raw_data}
# Prepare top used models
top_loras = self._get_top_used_models(usage_data.get('loras', {}), lora_map, 10)
top_checkpoints = self._get_top_used_models(usage_data.get('checkpoints', {}), checkpoint_map, 10)
top_embeddings = self._get_top_used_models(usage_data.get('embeddings', {}), embedding_map, 10)
# Prepare usage timeline (last 30 days)
timeline = self._get_usage_timeline(usage_data, 30)
@@ -138,6 +156,7 @@ class StatsRoutes:
'data': {
'top_loras': top_loras,
'top_checkpoints': top_checkpoints,
'top_embeddings': top_embeddings,
'usage_timeline': timeline,
'total_executions': usage_data.get('total_executions', 0)
}
@@ -158,16 +177,19 @@ class StatsRoutes:
# Get model data
lora_cache = await self.lora_scanner.get_cached_data()
checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
embedding_cache = await self.embedding_scanner.get_cached_data()
# Count by base model
lora_base_models = Counter(lora.get('base_model', 'Unknown') for lora in lora_cache.raw_data)
checkpoint_base_models = Counter(cp.get('base_model', 'Unknown') for cp in checkpoint_cache.raw_data)
embedding_base_models = Counter(emb.get('base_model', 'Unknown') for emb in embedding_cache.raw_data)
return web.json_response({
'success': True,
'data': {
'loras': dict(lora_base_models),
'checkpoints': dict(checkpoint_base_models)
'checkpoints': dict(checkpoint_base_models),
'embeddings': dict(embedding_base_models)
}
})
@@ -186,6 +208,7 @@ class StatsRoutes:
# Get model data
lora_cache = await self.lora_scanner.get_cached_data()
checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
embedding_cache = await self.embedding_scanner.get_cached_data()
# Count tag frequencies
all_tags = []
@@ -193,6 +216,8 @@ class StatsRoutes:
all_tags.extend(lora.get('tags', []))
for cp in checkpoint_cache.raw_data:
all_tags.extend(cp.get('tags', []))
for emb in embedding_cache.raw_data:
all_tags.extend(emb.get('tags', []))
tag_counts = Counter(all_tags)
@@ -225,6 +250,7 @@ class StatsRoutes:
# Get model data
lora_cache = await self.lora_scanner.get_cached_data()
checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
embedding_cache = await self.embedding_scanner.get_cached_data()
# Create models with usage data
lora_storage = []
@@ -255,15 +281,31 @@ class StatsRoutes:
'base_model': cp.get('base_model', 'Unknown')
})
embedding_storage = []
for emb in embedding_cache.raw_data:
usage_count = 0
if emb['sha256'] in usage_data.get('embeddings', {}):
usage_count = usage_data['embeddings'][emb['sha256']].get('total', 0)
embedding_storage.append({
'name': emb['model_name'],
'size': emb.get('size', 0),
'usage_count': usage_count,
'folder': emb.get('folder', ''),
'base_model': emb.get('base_model', 'Unknown')
})
# Sort by size
lora_storage.sort(key=lambda x: x['size'], reverse=True)
checkpoint_storage.sort(key=lambda x: x['size'], reverse=True)
embedding_storage.sort(key=lambda x: x['size'], reverse=True)
return web.json_response({
'success': True,
'data': {
'loras': lora_storage[:20], # Top 20 by size
'checkpoints': checkpoint_storage[:20]
'checkpoints': checkpoint_storage[:20],
'embeddings': embedding_storage[:20]
}
})
@@ -285,15 +327,18 @@ class StatsRoutes:
# Get model data
lora_cache = await self.lora_scanner.get_cached_data()
checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
embedding_cache = await self.embedding_scanner.get_cached_data()
insights = []
# Calculate unused models
unused_loras = self._count_unused_models(lora_cache.raw_data, usage_data.get('loras', {}))
unused_checkpoints = self._count_unused_models(checkpoint_cache.raw_data, usage_data.get('checkpoints', {}))
unused_embeddings = self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {}))
total_loras = len(lora_cache.raw_data)
total_checkpoints = len(checkpoint_cache.raw_data)
total_embeddings = len(embedding_cache.raw_data)
if total_loras > 0:
unused_lora_percent = (unused_loras / total_loras) * 100
@@ -315,9 +360,20 @@ class StatsRoutes:
'suggestion': 'Review and consider removing checkpoints you no longer need.'
})
if total_embeddings > 0:
unused_embedding_percent = (unused_embeddings / total_embeddings) * 100
if unused_embedding_percent > 50:
insights.append({
'type': 'warning',
'title': 'High Number of Unused Embeddings',
'description': f'{unused_embedding_percent:.1f}% of your embeddings ({unused_embeddings}/{total_embeddings}) have never been used.',
'suggestion': 'Consider organizing or archiving unused embeddings to optimize your collection.'
})
# Storage insights
total_size = sum(lora.get('size', 0) for lora in lora_cache.raw_data) + \
sum(cp.get('size', 0) for cp in checkpoint_cache.raw_data)
sum(cp.get('size', 0) for cp in checkpoint_cache.raw_data) + \
sum(emb.get('size', 0) for emb in embedding_cache.raw_data)
if total_size > 100 * 1024 * 1024 * 1024: # 100GB
insights.append({
@@ -390,6 +446,7 @@ class StatsRoutes:
lora_usage = 0
checkpoint_usage = 0
embedding_usage = 0
# Count usage for this date
for model_usage in usage_data.get('loras', {}).values():
@@ -400,11 +457,16 @@ class StatsRoutes:
if isinstance(model_usage, dict) and 'history' in model_usage:
checkpoint_usage += model_usage['history'].get(date_str, 0)
for model_usage in usage_data.get('embeddings', {}).values():
if isinstance(model_usage, dict) and 'history' in model_usage:
embedding_usage += model_usage['history'].get(date_str, 0)
timeline.append({
'date': date_str,
'lora_usage': lora_usage,
'checkpoint_usage': checkpoint_usage,
'total_usage': lora_usage + checkpoint_usage
'embedding_usage': embedding_usage,
'total_usage': lora_usage + checkpoint_usage + embedding_usage
})
return list(reversed(timeline)) # Oldest to newest

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@@ -4,6 +4,9 @@ import aiohttp
import logging
import toml
import git
import zipfile
import shutil
import tempfile
from datetime import datetime
from aiohttp import web
from typing import Dict, List
@@ -101,34 +104,36 @@ class UpdateRoutes:
@staticmethod
async def perform_update(request):
"""
Perform Git-based update to latest release tag or main branch
Perform Git-based update to latest release tag or main branch.
If .git is missing, fallback to ZIP download.
"""
try:
# Parse request body
body = await request.json() if request.has_body else {}
nightly = body.get('nightly', False)
# Get current plugin directory
current_dir = os.path.dirname(os.path.abspath(__file__))
plugin_root = os.path.dirname(os.path.dirname(current_dir))
# Backup settings.json if it exists
settings_path = os.path.join(plugin_root, 'settings.json')
settings_backup = None
if os.path.exists(settings_path):
with open(settings_path, 'r', encoding='utf-8') as f:
settings_backup = f.read()
logger.info("Backed up settings.json")
# Perform Git update
success, new_version = await UpdateRoutes._perform_git_update(plugin_root, nightly)
# Restore settings.json if we backed it up
git_folder = os.path.join(plugin_root, '.git')
if os.path.exists(git_folder):
# Git update
success, new_version = await UpdateRoutes._perform_git_update(plugin_root, nightly)
else:
# Fallback: Download ZIP and replace files
success, new_version = await UpdateRoutes._download_and_replace_zip(plugin_root)
if settings_backup and success:
with open(settings_path, 'w', encoding='utf-8') as f:
f.write(settings_backup)
logger.info("Restored settings.json")
if success:
return web.json_response({
'success': True,
@@ -138,15 +143,86 @@ class UpdateRoutes:
else:
return web.json_response({
'success': False,
'error': 'Failed to complete Git update'
'error': 'Failed to complete update'
})
except Exception as e:
logger.error(f"Failed to perform update: {e}", exc_info=True)
return web.json_response({
'success': False,
'error': str(e)
})
@staticmethod
async def _download_and_replace_zip(plugin_root: str) -> tuple[bool, str]:
"""
Download latest release ZIP from GitHub and replace plugin files.
Skips settings.json.
"""
repo_owner = "willmiao"
repo_name = "ComfyUI-Lora-Manager"
github_api = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
try:
async with aiohttp.ClientSession() as session:
async with session.get(github_api) as resp:
if resp.status != 200:
logger.error(f"Failed to fetch release info: {resp.status}")
return False, ""
data = await resp.json()
zip_url = data.get("zipball_url")
version = data.get("tag_name", "unknown")
# Download ZIP
async with session.get(zip_url) as zip_resp:
if zip_resp.status != 200:
logger.error(f"Failed to download ZIP: {zip_resp.status}")
return False, ""
with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as tmp_zip:
tmp_zip.write(await zip_resp.read())
zip_path = tmp_zip.name
UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=['settings.json'])
# Extract ZIP to temp dir
with tempfile.TemporaryDirectory() as tmp_dir:
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(tmp_dir)
# Find extracted folder (GitHub ZIP contains a root folder)
extracted_root = next(os.scandir(tmp_dir)).path
# Copy files, skipping settings.json
for item in os.listdir(extracted_root):
src = os.path.join(extracted_root, item)
dst = os.path.join(plugin_root, item)
if os.path.isdir(src):
# Remove old folder, then copy
if os.path.exists(dst):
shutil.rmtree(dst)
shutil.copytree(src, dst, ignore=shutil.ignore_patterns('settings.json'))
else:
if item == 'settings.json':
continue
shutil.copy2(src, dst)
os.remove(zip_path)
logger.info(f"Updated plugin via ZIP to {version}")
return True, version
except Exception as e:
logger.error(f"ZIP update failed: {e}", exc_info=True)
return False, ""
def _clean_plugin_folder(plugin_root, skip_files=None):
skip_files = skip_files or []
for item in os.listdir(plugin_root):
if item in skip_files:
continue
path = os.path.join(plugin_root, item)
if os.path.isdir(path):
shutil.rmtree(path)
else:
os.remove(path)
@staticmethod
async def _get_nightly_version() -> tuple[str, List[str]]:

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@@ -1,7 +1,7 @@
[project]
name = "comfyui-lora-manager"
description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
version = "0.8.21"
version = "0.8.22"
license = {file = "LICENSE"}
dependencies = [
"aiohttp",

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@@ -0,0 +1,245 @@
/* Banner Container */
.banner-container {
position: relative;
width: 100%;
z-index: calc(var(--z-header) - 1);
border-bottom: 1px solid var(--border-color);
background: var(--card-bg);
margin-bottom: var(--space-2);
}
/* Individual Banner */
.banner-item {
position: relative;
padding: var(--space-2) var(--space-3);
background: linear-gradient(135deg,
oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.05),
oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.02)
);
border-left: 4px solid var(--lora-accent);
animation: banner-slide-down 0.3s ease-in-out;
}
/* Banner Content Layout */
.banner-content {
display: flex;
align-items: center;
justify-content: space-between;
gap: var(--space-3);
max-width: 1400px;
margin: 0 auto;
}
/* Banner Text Section */
.banner-text {
flex: 1;
min-width: 0;
}
.banner-title {
margin: 0 0 4px 0;
font-size: 1.1em;
font-weight: 600;
color: var(--text-color);
line-height: 1.3;
}
.banner-description {
margin: 0;
font-size: 0.9em;
color: var(--text-muted);
line-height: 1.4;
}
/* Banner Actions */
.banner-actions {
display: flex;
align-items: center;
gap: var(--space-1);
flex-shrink: 0;
}
.banner-action {
display: inline-flex;
align-items: center;
gap: 6px;
padding: 6px 12px;
border-radius: var(--border-radius-xs);
text-decoration: none;
font-size: 0.85em;
font-weight: 500;
transition: all 0.2s ease;
white-space: nowrap;
border: 1px solid transparent;
}
.banner-action i {
font-size: 0.9em;
}
/* Primary Action Button */
.banner-action-primary {
background: var(--lora-accent);
color: white;
border-color: var(--lora-accent);
}
.banner-action-primary:hover {
background: oklch(calc(var(--lora-accent-l) - 5%) var(--lora-accent-c) var(--lora-accent-h));
transform: translateY(-1px);
box-shadow: 0 3px 6px oklch(var(--lora-accent) / 0.3);
}
/* Secondary Action Button */
.banner-action-secondary {
background: var(--card-bg);
color: var(--text-color);
border-color: var(--border-color);
}
.banner-action-secondary:hover {
background: var(--lora-accent);
color: white;
border-color: var(--lora-accent);
transform: translateY(-1px);
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
/* Tertiary Action Button */
.banner-action-tertiary {
background: transparent;
color: var(--lora-accent);
border-color: var(--lora-accent);
}
.banner-action-tertiary:hover {
background: var(--lora-accent);
color: white;
transform: translateY(-1px);
}
/* Dismiss Button */
.banner-dismiss {
position: absolute;
top: 8px;
right: 8px;
width: 24px;
height: 24px;
border: none;
background: transparent;
color: var(--text-muted);
cursor: pointer;
border-radius: 50%;
display: flex;
align-items: center;
justify-content: center;
transition: all 0.2s ease;
font-size: 0.8em;
}
.banner-dismiss:hover {
background: oklch(var(--lora-accent) / 0.1);
color: var(--lora-accent);
transform: scale(1.1);
}
/* Animations */
@keyframes banner-slide-down {
from {
opacity: 0;
transform: translateY(-100%);
}
to {
opacity: 1;
transform: translateY(0);
}
}
@keyframes banner-slide-up {
from {
opacity: 1;
transform: translateY(0);
max-height: 200px;
}
to {
opacity: 0;
transform: translateY(-20px);
max-height: 0;
padding-top: 0;
padding-bottom: 0;
}
}
/* Responsive Design */
@media (max-width: 768px) {
.banner-content {
flex-direction: column;
align-items: flex-start;
gap: var(--space-2);
}
.banner-actions {
width: 100%;
flex-wrap: wrap;
justify-content: flex-start;
}
.banner-action {
flex: 1;
min-width: 0;
justify-content: center;
}
.banner-dismiss {
top: 6px;
right: 6px;
}
.banner-item {
padding: var(--space-2);
}
.banner-title {
font-size: 1em;
}
.banner-description {
font-size: 0.85em;
}
}
@media (max-width: 480px) {
.banner-actions {
flex-direction: column;
width: 100%;
}
.banner-action {
width: 100%;
justify-content: center;
}
.banner-content {
gap: var(--space-1);
}
}
/* Dark theme adjustments */
[data-theme="dark"] .banner-item {
background: linear-gradient(135deg,
oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.08),
oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.03)
);
}
/* Prevent text selection */
.banner-item,
.banner-title,
.banner-description,
.banner-action,
.banner-dismiss {
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}

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@@ -56,6 +56,24 @@
color: var(--lora-error);
}
/* Update color scheme to include embeddings */
:root {
--embedding-color: oklch(68% 0.28 120); /* Green for embeddings */
}
/* Update metric cards and chart colors to support embeddings */
.metric-card.embedding .metric-icon {
color: var(--embedding-color);
}
.model-item.embedding {
border-left: 3px solid var(--embedding-color);
}
.model-item.embedding:hover {
border-color: var(--embedding-color);
}
/* Dashboard Content */
.dashboard-content {
background: var(--card-bg);

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@@ -6,6 +6,7 @@
/* Import Components */
@import 'components/header.css';
@import 'components/banner.css';
@import 'components/card.css';
@import 'components/modal/_base.css';
@import 'components/modal/delete-modal.css';

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@@ -454,7 +454,7 @@ export function createModelCard(model, modelType) {
card.innerHTML = `
<div class="card-preview ${shouldBlur ? 'blurred' : ''}">
${isVideo ?
`<video ${videoAttrs}>
`<video ${videoAttrs} style="pointer-events: none;">
<source src="${versionedPreviewUrl}" type="video/mp4">
</video>` :
`<img src="${versionedPreviewUrl}" alt="${model.model_name}">`

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@@ -7,6 +7,7 @@ import { HeaderManager } from './components/Header.js';
import { settingsManager } from './managers/SettingsManager.js';
import { exampleImagesManager } from './managers/ExampleImagesManager.js';
import { helpManager } from './managers/HelpManager.js';
import { bannerService } from './managers/BannerService.js';
import { showToast, initTheme, initBackToTop } from './utils/uiHelpers.js';
import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
import { migrateStorageItems } from './utils/storageHelpers.js';
@@ -27,6 +28,7 @@ export class AppCore {
state.loadingManager = new LoadingManager();
modalManager.initialize();
updateService.initialize();
bannerService.initialize();
window.modalManager = modalManager;
window.settingsManager = settingsManager;
window.exampleImagesManager = exampleImagesManager;

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@@ -0,0 +1,176 @@
import { getStorageItem, setStorageItem } from '../utils/storageHelpers.js';
/**
* Banner Service for managing notification banners
*/
class BannerService {
constructor() {
this.banners = new Map();
this.container = null;
this.initialized = false;
}
/**
* Initialize the banner service
*/
initialize() {
if (this.initialized) return;
this.container = document.getElementById('banner-container');
if (!this.container) {
console.warn('Banner container not found');
return;
}
// Register default banners
this.registerBanner('civitai-extension', {
id: 'civitai-extension',
title: 'New Tool Available: LM Civitai Extension!',
content: 'LM Civitai Extension is a browser extension designed to work seamlessly with LoRA Manager to significantly enhance your Civitai browsing experience! See which models you already have, download new ones with a single click, and manage your downloads efficiently.',
actions: [
{
text: 'Chrome Web Store',
icon: 'fab fa-chrome',
url: 'https://chromewebstore.google.com/detail/capigligggeijgmocnaflanlbghnamgm?utm_source=item-share-cb',
type: 'secondary'
},
{
text: 'Firefox Extension',
icon: 'fab fa-firefox-browser',
url: 'https://github.com/willmiao/lm-civitai-extension-firefox/releases/latest/download/extension.xpi',
type: 'secondary'
},
{
text: 'Read more...',
icon: 'fas fa-book',
url: 'https://github.com/willmiao/ComfyUI-Lora-Manager/wiki/LoRA-Manager-Civitai-Extension-(Chrome-Extension)',
type: 'tertiary'
}
],
dismissible: true,
priority: 1
});
this.showActiveBanners();
this.initialized = true;
}
/**
* Register a new banner
* @param {string} id - Unique banner ID
* @param {Object} bannerConfig - Banner configuration
*/
registerBanner(id, bannerConfig) {
this.banners.set(id, bannerConfig);
}
/**
* Check if a banner has been dismissed
* @param {string} bannerId - Banner ID
* @returns {boolean}
*/
isBannerDismissed(bannerId) {
const dismissedBanners = getStorageItem('dismissed_banners', []);
return dismissedBanners.includes(bannerId);
}
/**
* Dismiss a banner
* @param {string} bannerId - Banner ID
*/
dismissBanner(bannerId) {
const dismissedBanners = getStorageItem('dismissed_banners', []);
if (!dismissedBanners.includes(bannerId)) {
dismissedBanners.push(bannerId);
setStorageItem('dismissed_banners', dismissedBanners);
}
// Remove banner from DOM
const bannerElement = document.querySelector(`[data-banner-id="${bannerId}"]`);
if (bannerElement) {
bannerElement.style.animation = 'banner-slide-up 0.3s ease-in-out forwards';
setTimeout(() => {
bannerElement.remove();
this.updateContainerVisibility();
}, 300);
}
}
/**
* Show all active (non-dismissed) banners
*/
showActiveBanners() {
if (!this.container) return;
const activeBanners = Array.from(this.banners.values())
.filter(banner => !this.isBannerDismissed(banner.id))
.sort((a, b) => (b.priority || 0) - (a.priority || 0));
activeBanners.forEach(banner => {
this.renderBanner(banner);
});
this.updateContainerVisibility();
}
/**
* Render a banner to the DOM
* @param {Object} banner - Banner configuration
*/
renderBanner(banner) {
const bannerElement = document.createElement('div');
bannerElement.className = 'banner-item';
bannerElement.setAttribute('data-banner-id', banner.id);
const actionsHtml = banner.actions ? banner.actions.map(action =>
`<a href="${action.url}" target="_blank" class="banner-action banner-action-${action.type}" rel="noopener noreferrer">
<i class="${action.icon}"></i>
<span>${action.text}</span>
</a>`
).join('') : '';
const dismissButtonHtml = banner.dismissible ?
`<button class="banner-dismiss" onclick="bannerService.dismissBanner('${banner.id}')" title="Dismiss">
<i class="fas fa-times"></i>
</button>` : '';
bannerElement.innerHTML = `
<div class="banner-content">
<div class="banner-text">
<h4 class="banner-title">${banner.title}</h4>
<p class="banner-description">${banner.content}</p>
</div>
<div class="banner-actions">
${actionsHtml}
</div>
</div>
${dismissButtonHtml}
`;
this.container.appendChild(bannerElement);
}
/**
* Update container visibility based on active banners
*/
updateContainerVisibility() {
if (!this.container) return;
const hasActiveBanners = this.container.children.length > 0;
this.container.style.display = hasActiveBanners ? 'block' : 'none';
}
/**
* Clear all dismissed banners (for testing/admin purposes)
*/
clearDismissedBanners() {
setStorageItem('dismissed_banners', []);
location.reload();
}
}
// Create and export singleton instance
export const bannerService = new BannerService();
// Make it globally available
window.bannerService = bannerService;

View File

@@ -67,6 +67,11 @@ export class SettingsManager {
if (state.global.settings.base_model_path_mappings === undefined) {
state.global.settings.base_model_path_mappings = {};
}
// Set default for defaultEmbeddingRoot if undefined
if (state.global.settings.default_embedding_root === undefined) {
state.global.settings.default_embedding_root = '';
}
}
initialize() {
@@ -151,6 +156,9 @@ export class SettingsManager {
// Load default checkpoint root
await this.loadCheckpointRoots();
// Load default embedding root
await this.loadEmbeddingRoots();
// Backend settings are loaded from the template directly
}
@@ -233,6 +241,45 @@ export class SettingsManager {
}
}
async loadEmbeddingRoots() {
try {
const defaultEmbeddingRootSelect = document.getElementById('defaultEmbeddingRoot');
if (!defaultEmbeddingRootSelect) return;
// Fetch embedding roots
const response = await fetch('/api/embeddings/roots');
if (!response.ok) {
throw new Error('Failed to fetch embedding roots');
}
const data = await response.json();
if (!data.roots || data.roots.length === 0) {
throw new Error('No embedding roots found');
}
// Clear existing options except the first one (No Default)
const noDefaultOption = defaultEmbeddingRootSelect.querySelector('option[value=""]');
defaultEmbeddingRootSelect.innerHTML = '';
defaultEmbeddingRootSelect.appendChild(noDefaultOption);
// Add options for each root
data.roots.forEach(root => {
const option = document.createElement('option');
option.value = root;
option.textContent = root;
defaultEmbeddingRootSelect.appendChild(option);
});
// Set selected value from settings
const defaultRoot = state.global.settings.default_embedding_root || '';
defaultEmbeddingRootSelect.value = defaultRoot;
} catch (error) {
console.error('Error loading embedding roots:', error);
showToast('Failed to load embedding roots: ' + error.message, 'error');
}
}
loadBaseModelMappings() {
const mappingsContainer = document.getElementById('baseModelMappingsContainer');
if (!mappingsContainer) return;
@@ -508,6 +555,8 @@ export class SettingsManager {
state.global.settings.default_loras_root = value;
} else if (settingKey === 'default_checkpoint_root') {
state.global.settings.default_checkpoint_root = value;
} else if (settingKey === 'default_embedding_root') {
state.global.settings.default_embedding_root = value;
} else if (settingKey === 'display_density') {
state.global.settings.displayDensity = value;
@@ -528,7 +577,7 @@ export class SettingsManager {
try {
// For backend settings, make API call
if (settingKey === 'default_lora_root' || settingKey === 'default_checkpoint_root' || settingKey === 'download_path_template') {
if (settingKey === 'default_lora_root' || settingKey === 'default_checkpoint_root' || settingKey === 'default_embedding_root' || settingKey === 'download_path_template') {
const payload = {};
payload[settingKey] = value;

View File

@@ -358,9 +358,10 @@ export class UpdateService {
<i class="fas fa-check-circle" style="margin-right: 8px;"></i>
Successfully updated to ${newVersion}!
<br><br>
<small style="opacity: 0.8;">
Please restart ComfyUI to complete the update process.
</small>
<div style="opacity: 0.95; color: var(--lora-error); font-size: 1em;">
Please restart ComfyUI or LoRA Manager to apply update.<br>
Make sure to reload your browser for both LoRA Manager and ComfyUI.
</div>
</div>
`;
}
@@ -370,10 +371,10 @@ export class UpdateService {
this.updateAvailable = false;
// Refresh the modal content
setTimeout(() => {
this.updateModalContent();
this.showUpdateProgress(false);
}, 2000);
// setTimeout(() => {
// this.updateModalContent();
// this.showUpdateProgress(false);
// }, 2000);
}
// Simple markdown parser for changelog items

View File

@@ -150,6 +150,12 @@ class StatisticsManager {
value: this.data.collection.checkpoint_count,
label: 'Checkpoints',
format: 'number'
},
{
icon: 'fas fa-code',
value: this.data.collection.embedding_count,
label: 'Embeddings',
format: 'number'
}
];
@@ -195,7 +201,9 @@ class StatisticsManager {
if (!this.data.collection) return 0;
const totalModels = this.data.collection.total_models;
const unusedModels = this.data.collection.unused_loras + this.data.collection.unused_checkpoints;
const unusedModels = this.data.collection.unused_loras +
this.data.collection.unused_checkpoints +
this.data.collection.unused_embeddings;
const usedModels = totalModels - unusedModels;
return totalModels > 0 ? (usedModels / totalModels) * 100 : 0;
@@ -233,12 +241,17 @@ class StatisticsManager {
if (!ctx || !this.data.collection) return;
const data = {
labels: ['LoRAs', 'Checkpoints'],
labels: ['LoRAs', 'Checkpoints', 'Embeddings'],
datasets: [{
data: [this.data.collection.lora_count, this.data.collection.checkpoint_count],
data: [
this.data.collection.lora_count,
this.data.collection.checkpoint_count,
this.data.collection.embedding_count
],
backgroundColor: [
'oklch(68% 0.28 256)',
'oklch(68% 0.28 200)'
'oklch(68% 0.28 200)',
'oklch(68% 0.28 120)'
],
borderWidth: 2,
borderColor: getComputedStyle(document.documentElement).getPropertyValue('--border-color')
@@ -266,8 +279,13 @@ class StatisticsManager {
const loraData = this.data.baseModels.loras;
const checkpointData = this.data.baseModels.checkpoints;
const embeddingData = this.data.baseModels.embeddings;
const allModels = new Set([...Object.keys(loraData), ...Object.keys(checkpointData)]);
const allModels = new Set([
...Object.keys(loraData),
...Object.keys(checkpointData),
...Object.keys(embeddingData)
]);
const data = {
labels: Array.from(allModels),
@@ -281,6 +299,11 @@ class StatisticsManager {
label: 'Checkpoints',
data: Array.from(allModels).map(model => checkpointData[model] || 0),
backgroundColor: 'oklch(68% 0.28 200 / 0.7)'
},
{
label: 'Embeddings',
data: Array.from(allModels).map(model => embeddingData[model] || 0),
backgroundColor: 'oklch(68% 0.28 120 / 0.7)'
}
]
};
@@ -325,6 +348,13 @@ class StatisticsManager {
borderColor: 'oklch(68% 0.28 200)',
backgroundColor: 'oklch(68% 0.28 200 / 0.1)',
fill: true
},
{
label: 'Embedding Usage',
data: timeline.map(item => item.embedding_usage),
borderColor: 'oklch(68% 0.28 120)',
backgroundColor: 'oklch(68% 0.28 120 / 0.1)',
fill: true
}
]
};
@@ -365,11 +395,13 @@ class StatisticsManager {
const topLoras = this.data.usage.top_loras || [];
const topCheckpoints = this.data.usage.top_checkpoints || [];
const topEmbeddings = this.data.usage.top_embeddings || [];
// Combine and sort all models by usage
const allModels = [
...topLoras.map(m => ({ ...m, type: 'LoRA' })),
...topCheckpoints.map(m => ({ ...m, type: 'Checkpoint' }))
...topCheckpoints.map(m => ({ ...m, type: 'Checkpoint' })),
...topEmbeddings.map(m => ({ ...m, type: 'Embedding' }))
].sort((a, b) => b.usage_count - a.usage_count).slice(0, 10);
const data = {
@@ -377,9 +409,14 @@ class StatisticsManager {
datasets: [{
label: 'Usage Count',
data: allModels.map(model => model.usage_count),
backgroundColor: allModels.map(model =>
model.type === 'LoRA' ? 'oklch(68% 0.28 256)' : 'oklch(68% 0.28 200)'
)
backgroundColor: allModels.map(model => {
switch(model.type) {
case 'LoRA': return 'oklch(68% 0.28 256)';
case 'Checkpoint': return 'oklch(68% 0.28 200)';
case 'Embedding': return 'oklch(68% 0.28 120)';
default: return 'oklch(68% 0.28 256)';
}
})
}]
};
@@ -404,12 +441,17 @@ class StatisticsManager {
if (!ctx || !this.data.collection) return;
const data = {
labels: ['LoRAs', 'Checkpoints'],
labels: ['LoRAs', 'Checkpoints', 'Embeddings'],
datasets: [{
data: [this.data.collection.lora_size, this.data.collection.checkpoint_size],
data: [
this.data.collection.lora_size,
this.data.collection.checkpoint_size,
this.data.collection.embedding_size
],
backgroundColor: [
'oklch(68% 0.28 256)',
'oklch(68% 0.28 200)'
'oklch(68% 0.28 200)',
'oklch(68% 0.28 120)'
]
}]
};
@@ -443,10 +485,12 @@ class StatisticsManager {
const loraData = this.data.storage.loras || [];
const checkpointData = this.data.storage.checkpoints || [];
const embeddingData = this.data.storage.embeddings || [];
const allData = [
...loraData.map(item => ({ ...item, type: 'LoRA' })),
...checkpointData.map(item => ({ ...item, type: 'Checkpoint' }))
...checkpointData.map(item => ({ ...item, type: 'Checkpoint' })),
...embeddingData.map(item => ({ ...item, type: 'Embedding' }))
];
const data = {
@@ -458,9 +502,14 @@ class StatisticsManager {
name: item.name,
type: item.type
})),
backgroundColor: allData.map(item =>
item.type === 'LoRA' ? 'oklch(68% 0.28 256 / 0.6)' : 'oklch(68% 0.28 200 / 0.6)'
)
backgroundColor: allData.map(item => {
switch(item.type) {
case 'LoRA': return 'oklch(68% 0.28 256 / 0.6)';
case 'Checkpoint': return 'oklch(68% 0.28 200 / 0.6)';
case 'Embedding': return 'oklch(68% 0.28 120 / 0.6)';
default: return 'oklch(68% 0.28 256 / 0.6)';
}
})
}]
};
@@ -502,6 +551,7 @@ class StatisticsManager {
renderTopModelsLists() {
this.renderTopLorasList();
this.renderTopCheckpointsList();
this.renderTopEmbeddingsList();
this.renderLargestModelsList();
}
@@ -555,17 +605,44 @@ class StatisticsManager {
`).join('');
}
renderTopEmbeddingsList() {
const container = document.getElementById('topEmbeddingsList');
if (!container || !this.data.usage?.top_embeddings) return;
const topEmbeddings = this.data.usage.top_embeddings;
if (topEmbeddings.length === 0) {
container.innerHTML = '<div class="loading-placeholder">No usage data available</div>';
return;
}
container.innerHTML = topEmbeddings.map(embedding => `
<div class="model-item">
<img src="${embedding.preview_url || '/loras_static/images/no-preview.png'}"
alt="${embedding.name}" class="model-preview"
onerror="this.src='/loras_static/images/no-preview.png'">
<div class="model-info">
<div class="model-name" title="${embedding.name}">${embedding.name}</div>
<div class="model-meta">${embedding.base_model}${embedding.folder}</div>
</div>
<div class="model-usage">${embedding.usage_count}</div>
</div>
`).join('');
}
renderLargestModelsList() {
const container = document.getElementById('largestModelsList');
if (!container || !this.data.storage) return;
const loraModels = this.data.storage.loras || [];
const checkpointModels = this.data.storage.checkpoints || [];
const embeddingModels = this.data.storage.embeddings || [];
// Combine and sort by size
const allModels = [
...loraModels.map(m => ({ ...m, type: 'LoRA' })),
...checkpointModels.map(m => ({ ...m, type: 'Checkpoint' }))
...checkpointModels.map(m => ({ ...m, type: 'Checkpoint' })),
...embeddingModels.map(m => ({ ...m, type: 'Embedding' }))
].sort((a, b) => b.size - a.size).slice(0, 10);
if (allModels.length === 0) {

View File

@@ -141,7 +141,8 @@ export function migrateStorageItems() {
'recipes_search_prefs',
'checkpoints_search_prefs',
'show_update_notifications',
'last_update_check'
'last_update_check',
'dismissed_banners'
];
// Migrate each known key

View File

@@ -82,6 +82,11 @@
</button>
<div class="container">
<!-- Banner component -->
<div id="banner-container" class="banner-container" style="display: none;">
<!-- Banners will be dynamically inserted here -->
</div>
{% if is_initializing %}
<!-- Show initialization component when initializing -->
{% include 'components/initialization.html' %}

View File

@@ -128,6 +128,23 @@
Set the default checkpoint root directory for downloads, imports and moves
</div>
</div>
<div class="setting-item">
<div class="setting-row">
<div class="setting-info">
<label for="defaultEmbeddingRoot">Default Embedding Root</label>
</div>
<div class="setting-control select-control">
<select id="defaultEmbeddingRoot" onchange="settingsManager.saveSelectSetting('defaultEmbeddingRoot', 'default_embedding_root')">
<option value="">No Default</option>
<!-- Options will be loaded dynamically -->
</select>
</div>
</div>
<div class="input-help">
Set the default embedding root directory for downloads, imports and moves
</div>
</div>
</div>
<!-- Default Path Customization Section -->

View File

@@ -98,6 +98,14 @@
</div>
</div>
<!-- Top Used Embeddings -->
<div class="list-container">
<h3><i class="fas fa-code"></i> Most Used Embeddings</h3>
<div class="model-list" id="topEmbeddingsList">
<!-- List will be populated by JavaScript -->
</div>
</div>
<!-- Usage Distribution Chart -->
<div class="chart-container full-width">
<h3><i class="fas fa-chart-bar"></i> Usage Distribution</h3>