mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-24 14:42:11 -03:00
Enhance Lora and recipe integration with improved filtering and UI updates
- Added support for filtering LoRAs by hash in both API and UI components. - Implemented session storage management for custom filter states when navigating between recipes and LoRAs. - Introduced a new button in the recipe modal to view associated LoRAs, enhancing user navigation. - Updated CSS styles for new UI elements, including a custom filter indicator and LoRA view button. - Refactored existing JavaScript components to streamline the handling of filter parameters and improve maintainability.
This commit is contained in:
Will Miao
@@ -132,13 +132,9 @@ class ApiRoutes:
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page = int(request.query.get('page', '1'))
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page_size = int(request.query.get('page_size', '20'))
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sort_by = request.query.get('sort_by', 'name')
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folder = request.query.get('folder')
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search = request.query.get('search', '').lower()
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fuzzy = request.query.get('fuzzy', 'false').lower() == 'true'
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# Parse base models filter parameter
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base_models = request.query.get('base_models', '').split(',')
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base_models = [model.strip() for model in base_models if model.strip()]
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folder = request.query.get('folder', None)
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search = request.query.get('search', None)
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fuzzy_search = request.query.get('fuzzy', 'false').lower() == 'true'
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# Parse search options
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search_filename = request.query.get('search_filename', 'true').lower() == 'true'
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@@ -146,62 +142,68 @@ class ApiRoutes:
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search_tags = request.query.get('search_tags', 'false').lower() == 'true'
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recursive = request.query.get('recursive', 'false').lower() == 'true'
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# Validate parameters
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if page < 1 or page_size < 1 or page_size > 100:
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return web.json_response({
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'error': 'Invalid pagination parameters'
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}, status=400)
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# Get filter parameters
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base_models = request.query.get('base_models', None)
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tags = request.query.get('tags', None)
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if sort_by not in ['date', 'name']:
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return web.json_response({
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'error': 'Invalid sort parameter'
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}, status=400)
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# New parameters for recipe filtering
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lora_hash = request.query.get('lora_hash', None)
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lora_hashes = request.query.get('lora_hashes', None)
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# Parse tags filter parameter
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tags = request.query.get('tags', '').split(',')
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tags = [tag.strip() for tag in tags if tag.strip()]
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# Parse filter parameters
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filters = {}
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if base_models:
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filters['base_model'] = base_models.split(',')
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if tags:
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filters['tags'] = tags.split(',')
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# Get paginated data with search and filters
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result = await self.scanner.get_paginated_data(
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page=page,
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page_size=page_size,
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sort_by=sort_by,
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# Add search options to filters
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search_options = {
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'filename': search_filename,
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'modelname': search_modelname,
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'tags': search_tags,
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'recursive': recursive
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}
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# Add lora hash filtering options
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hash_filters = {}
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if lora_hash:
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hash_filters['single_hash'] = lora_hash.lower()
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elif lora_hashes:
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hash_filters['multiple_hashes'] = [h.lower() for h in lora_hashes.split(',')]
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# Get file data
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data = await self.scanner.get_paginated_data(
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page,
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page_size,
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sort_by=sort_by,
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folder=folder,
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search=search,
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fuzzy=fuzzy,
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base_models=base_models, # Pass base models filter
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tags=tags, # Add tags parameter
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search_options={
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'filename': search_filename,
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'modelname': search_modelname,
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'tags': search_tags,
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'recursive': recursive
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}
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fuzzy_search=fuzzy_search,
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base_models=filters.get('base_model', None),
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tags=filters.get('tags', None),
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search_options=search_options,
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hash_filters=hash_filters
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)
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# Format the response data
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formatted_items = [
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self._format_lora_response(item)
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for item in result['items']
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]
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# Get all available folders from cache
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cache = await self.scanner.get_cached_data()
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return web.json_response({
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'items': formatted_items,
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'total': result['total'],
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'page': result['page'],
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'page_size': result['page_size'],
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'total_pages': result['total_pages'],
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'folders': cache.folders
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})
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# Convert output to match expected format
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result = {
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'items': [self._format_lora_response(lora) for lora in data['items']],
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'folders': cache.folders,
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'total': data['total'],
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'page': data['page'],
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'page_size': data['page_size'],
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'total_pages': data['total_pages']
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}
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return web.json_response(result)
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except Exception as e:
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logger.error(f"Error in get_loras: {str(e)}", exc_info=True)
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return web.json_response({
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'error': 'Internal server error'
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}, status=500)
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logger.error(f"Error retrieving loras: {e}", exc_info=True)
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return web.json_response({"error": str(e)}, status=500)
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def _format_lora_response(self, lora: Dict) -> Dict:
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"""Format LoRA data for API response"""
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@@ -831,7 +833,10 @@ class ApiRoutes:
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except Exception as e:
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logger.error(f"Error getting lora Civitai URL: {e}", exc_info=True)
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return web.Response(text=str(e), status=500)
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return web.json_response({
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'success': False,
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'error': str(e)
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}, status=500)
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async def move_models_bulk(self, request: web.Request) -> web.Response:
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"""Handle bulk model move request"""
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@@ -103,7 +103,6 @@ class RecipeRoutes:
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# New parameter: get LoRA hash filter
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lora_hash = request.query.get('lora_hash', None)
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bypass_filters = request.query.get('bypass_filters', 'false').lower() == 'true'
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# Parse filter parameters
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filters = {}
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@@ -128,8 +127,7 @@ class RecipeRoutes:
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search=search,
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filters=filters,
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search_options=search_options,
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lora_hash=lora_hash,
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bypass_filters=bypass_filters
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lora_hash=lora_hash
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)
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# Format the response data with static URLs for file paths
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@@ -136,9 +136,9 @@ class LoraScanner:
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)
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async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'name',
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folder: str = None, search: str = None, fuzzy: bool = False,
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folder: str = None, search: str = None, fuzzy_search: bool = False,
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base_models: list = None, tags: list = None,
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search_options: dict = None) -> Dict:
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search_options: dict = None, hash_filters: dict = None) -> Dict:
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"""Get paginated and filtered lora data
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Args:
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@@ -147,10 +147,11 @@ class LoraScanner:
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sort_by: Sort method ('name' or 'date')
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folder: Filter by folder path
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search: Search term
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fuzzy: Use fuzzy matching for search
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fuzzy_search: Use fuzzy matching for search
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base_models: List of base models to filter by
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tags: List of tags to filter by
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search_options: Dictionary with search options (filename, modelname, tags, recursive)
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hash_filters: Dictionary with hash filtering options (single_hash or multiple_hashes)
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"""
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cache = await self.get_cached_data()
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@@ -160,90 +161,108 @@ class LoraScanner:
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'filename': True,
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'modelname': True,
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'tags': False,
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'recursive': False
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'recursive': False,
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}
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# Get the base data set
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filtered_data = cache.sorted_by_date if sort_by == 'date' else cache.sorted_by_name
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# Apply hash filtering if provided (highest priority)
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if hash_filters:
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single_hash = hash_filters.get('single_hash')
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multiple_hashes = hash_filters.get('multiple_hashes')
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if single_hash:
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# Filter by single hash
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single_hash = single_hash.lower() # Ensure lowercase for matching
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filtered_data = [
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lora for lora in filtered_data
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if lora.get('sha256', '').lower() == single_hash
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]
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elif multiple_hashes:
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# Filter by multiple hashes
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hash_set = set(hash.lower() for hash in multiple_hashes) # Convert to set for faster lookup
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filtered_data = [
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lora for lora in filtered_data
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if lora.get('sha256', '').lower() in hash_set
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]
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# Jump to pagination
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total_items = len(filtered_data)
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start_idx = (page - 1) * page_size
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end_idx = min(start_idx + page_size, total_items)
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result = {
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'items': filtered_data[start_idx:end_idx],
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'total': total_items,
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'page': page,
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'page_size': page_size,
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'total_pages': (total_items + page_size - 1) // page_size
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}
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return result
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# Apply SFW filtering if enabled
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if settings.get('show_only_sfw', False):
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filtered_data = [
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item for item in filtered_data
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if not item.get('preview_nsfw_level') or item.get('preview_nsfw_level') < NSFW_LEVELS['R']
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lora for lora in filtered_data
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if not lora.get('preview_nsfw_level') or lora.get('preview_nsfw_level') < NSFW_LEVELS['NSFW']
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]
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# Apply folder filtering
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if folder is not None:
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if search_options.get('recursive', False):
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# Recursive mode: match all paths starting with this folder
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# Recursive folder filtering - include all subfolders
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filtered_data = [
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item for item in filtered_data
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if item['folder'].startswith(folder + '/') or item['folder'] == folder
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lora for lora in filtered_data
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if lora['folder'].startswith(folder)
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]
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else:
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# Non-recursive mode: match exact folder
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# Exact folder filtering
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filtered_data = [
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item for item in filtered_data
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if item['folder'] == folder
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lora for lora in filtered_data
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if lora['folder'] == folder
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]
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# Apply base model filtering
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if base_models and len(base_models) > 0:
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filtered_data = [
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item for item in filtered_data
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if item.get('base_model') in base_models
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lora for lora in filtered_data
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if lora.get('base_model') in base_models
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]
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# Apply tag filtering
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if tags and len(tags) > 0:
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filtered_data = [
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item for item in filtered_data
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if any(tag in item.get('tags', []) for tag in tags)
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lora for lora in filtered_data
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if any(tag in lora.get('tags', []) for tag in tags)
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]
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# Apply search filtering
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if search:
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search_results = []
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for item in filtered_data:
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# Check filename if enabled
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if search_options.get('filename', True):
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if fuzzy:
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if fuzzy_match(item.get('file_name', ''), search):
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search_results.append(item)
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continue
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else:
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if search.lower() in item.get('file_name', '').lower():
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search_results.append(item)
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continue
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# Check model name if enabled
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if search_options.get('modelname', True):
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if fuzzy:
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if fuzzy_match(item.get('model_name', ''), search):
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search_results.append(item)
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continue
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else:
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if search.lower() in item.get('model_name', '').lower():
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search_results.append(item)
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continue
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# Check tags if enabled
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if search_options.get('tags', False) and item.get('tags'):
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found_tag = False
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for tag in item['tags']:
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if fuzzy:
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if fuzzy_match(tag, search):
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found_tag = True
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break
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else:
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if search.lower() in tag.lower():
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found_tag = True
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break
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if found_tag:
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search_results.append(item)
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search_opts = search_options or {}
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for lora in filtered_data:
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# Search by file name
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if search_opts.get('filename', True):
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if fuzzy_match(lora.get('file_name', ''), search):
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search_results.append(lora)
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continue
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# Search by model name
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if search_opts.get('modelname', True):
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if fuzzy_match(lora.get('model_name', ''), search):
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search_results.append(lora)
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continue
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# Search by tags
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if search_opts.get('tags', False) and 'tags' in lora:
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if any(fuzzy_match(tag, search) for tag in lora['tags']):
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search_results.append(lora)
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continue
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filtered_data = search_results
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# Calculate pagination
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@@ -330,7 +330,7 @@ class RecipeScanner:
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logger.error(f"Error getting base model for lora: {e}")
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return None
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async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'date', search: str = None, filters: dict = None, search_options: dict = None, lora_hash: str = None, bypass_filters: bool = False):
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async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'date', search: str = None, filters: dict = None, search_options: dict = None, lora_hash: str = None, bypass_filters: bool = True):
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"""Get paginated and filtered recipe data
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Args:
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