mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-23 14:12:11 -03:00
feat(example-images): add use case orchestration
This commit is contained in:
pixelpaws
@@ -1,7 +1,6 @@
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import logging
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import os
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import re
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import tempfile
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import random
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import string
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from aiohttp import web
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@@ -13,6 +12,14 @@ from ..utils.metadata_manager import MetadataManager
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logger = logging.getLogger(__name__)
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class ExampleImagesImportError(RuntimeError):
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"""Base error for example image import operations."""
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class ExampleImagesValidationError(ExampleImagesImportError):
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"""Raised when input validation fails."""
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class ExampleImagesProcessor:
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"""Processes and manipulates example images"""
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@@ -299,90 +306,29 @@ class ExampleImagesProcessor:
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return False
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@staticmethod
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async def import_images(request):
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"""
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Import local example images
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Accepts:
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- multipart/form-data form with model_hash and files fields
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or
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- JSON request with model_hash and file_paths
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Returns:
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- Success status and list of imported files
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"""
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async def import_images(model_hash: str, files_to_import: list[str]):
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"""Import local example images for a model."""
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if not model_hash:
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raise ExampleImagesValidationError('Missing model_hash parameter')
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if not files_to_import:
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raise ExampleImagesValidationError('No files provided to import')
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try:
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model_hash = None
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files_to_import = []
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temp_files_to_cleanup = []
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# Check if it's a multipart form-data request (direct file upload)
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if request.content_type and 'multipart/form-data' in request.content_type:
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reader = await request.multipart()
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# First get model_hash
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field = await reader.next()
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if field.name == 'model_hash':
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model_hash = await field.text()
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# Then process all files
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while True:
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field = await reader.next()
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if field is None:
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break
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if field.name == 'files':
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# Create a temporary file with appropriate suffix for type detection
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file_name = field.filename
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file_ext = os.path.splitext(file_name)[1].lower()
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with tempfile.NamedTemporaryFile(suffix=file_ext, delete=False) as tmp_file:
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temp_path = tmp_file.name
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temp_files_to_cleanup.append(temp_path) # Track for cleanup
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# Write chunks to the temporary file
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while True:
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chunk = await field.read_chunk()
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if not chunk:
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break
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tmp_file.write(chunk)
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# Add to the list of files to process
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files_to_import.append(temp_path)
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else:
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# Parse JSON request (legacy method using file paths)
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data = await request.json()
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model_hash = data.get('model_hash')
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files_to_import = data.get('file_paths', [])
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if not model_hash:
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return web.json_response({
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'success': False,
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'error': 'Missing model_hash parameter'
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}, status=400)
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if not files_to_import:
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return web.json_response({
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'success': False,
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'error': 'No files provided to import'
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}, status=400)
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# Get example images path
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example_images_path = settings.get('example_images_path')
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if not example_images_path:
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return web.json_response({
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'success': False,
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'error': 'No example images path configured'
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}, status=400)
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raise ExampleImagesValidationError('No example images path configured')
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# Find the model and get current metadata
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lora_scanner = await ServiceRegistry.get_lora_scanner()
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checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
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embedding_scanner = await ServiceRegistry.get_embedding_scanner()
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model_data = None
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scanner = None
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# Check both scanners to find the model
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for scan_obj in [lora_scanner, checkpoint_scanner, embedding_scanner]:
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cache = await scan_obj.get_cached_data()
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@@ -393,21 +339,20 @@ class ExampleImagesProcessor:
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break
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if model_data:
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break
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if not model_data:
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return web.json_response({
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'success': False,
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'error': f"Model with hash {model_hash} not found in cache"
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}, status=404)
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raise ExampleImagesImportError(
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f"Model with hash {model_hash} not found in cache"
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)
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# Create model folder
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model_folder = os.path.join(example_images_path, model_hash)
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os.makedirs(model_folder, exist_ok=True)
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imported_files = []
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errors = []
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newly_imported_paths = []
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# Process each file path
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for file_path in files_to_import:
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try:
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@@ -415,26 +360,26 @@ class ExampleImagesProcessor:
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if not os.path.isfile(file_path):
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errors.append(f"File not found: {file_path}")
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continue
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# Check if file type is supported
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file_ext = os.path.splitext(file_path)[1].lower()
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if not (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or
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if not (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or
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file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']):
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errors.append(f"Unsupported file type: {file_path}")
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continue
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# Generate new filename using short ID instead of UUID
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short_id = ExampleImagesProcessor.generate_short_id()
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new_filename = f"custom_{short_id}{file_ext}"
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dest_path = os.path.join(model_folder, new_filename)
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# Copy the file
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import shutil
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shutil.copy2(file_path, dest_path)
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# Store both the dest_path and the short_id
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newly_imported_paths.append((dest_path, short_id))
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# Add to imported files list
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imported_files.append({
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'name': new_filename,
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@@ -444,39 +389,31 @@ class ExampleImagesProcessor:
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})
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except Exception as e:
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errors.append(f"Error importing {file_path}: {str(e)}")
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# Update metadata with new example images
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regular_images, custom_images = await MetadataUpdater.update_metadata_after_import(
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model_hash,
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model_hash,
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model_data,
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scanner,
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newly_imported_paths
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)
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return web.json_response({
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return {
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'success': len(imported_files) > 0,
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'message': f'Successfully imported {len(imported_files)} files' +
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'message': f'Successfully imported {len(imported_files)} files' +
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(f' with {len(errors)} errors' if errors else ''),
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'files': imported_files,
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'errors': errors,
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'regular_images': regular_images,
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'custom_images': custom_images,
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"model_file_path": model_data.get('file_path', ''),
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})
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}
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except ExampleImagesImportError:
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raise
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except Exception as e:
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logger.error(f"Failed to import example images: {e}", exc_info=True)
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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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finally:
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# Clean up temporary files
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for temp_file in temp_files_to_cleanup:
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try:
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os.remove(temp_file)
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except Exception as e:
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logger.error(f"Failed to remove temporary file {temp_file}: {e}")
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raise ExampleImagesImportError(str(e)) from e
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@staticmethod
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async def delete_custom_image(request):
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