mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-25 07:05:43 -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
@@ -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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