feat: add model metadata refresh functionality and enhance download progress tracking. https://github.com/willmiao/ComfyUI-Lora-Manager/issues/151

py/routes/misc_routes.py

@@ -10,6 +10,8 @@ from ..utils.usage_stats import UsageStats
 from ..services.service_registry import ServiceRegistry
 from ..utils.exif_utils import ExifUtils
 from ..utils.constants import EXAMPLE_IMAGE_WIDTH, SUPPORTED_MEDIA_EXTENSIONS
+from ..services.civitai_client import CivitaiClient
+from ..utils.routes_common import ModelRouteUtils
 
 logger = logging.getLogger(__name__)
 
@@ -25,7 +27,8 @@ download_progress = {
     'last_error': None,
     'start_time': None,
     'end_time': None,
-    'processed_models': set()  # Track models that have been processed
+    'processed_models': set(),  # Track models that have been processed
+    'refreshed_models': set()  # Track models that had metadata refreshed
 }
 
 class MiscRoutes:
@@ -149,6 +152,7 @@ class MiscRoutes:
             # Create a copy for JSON serialization
             response_progress = download_progress.copy()
             response_progress['processed_models'] = list(download_progress['processed_models'])
+            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
             
             return web.json_response({
                 'success': False,
@@ -211,6 +215,7 @@ class MiscRoutes:
             # Create a copy for JSON serialization
             response_progress = download_progress.copy()
             response_progress['processed_models'] = list(download_progress['processed_models'])
+            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
             
             return web.json_response({
                 'success': True,
@@ -233,6 +238,7 @@ class MiscRoutes:
         # Create a copy of the progress dict with the set converted to a list for JSON serialization
         response_progress = download_progress.copy()
         response_progress['processed_models'] = list(download_progress['processed_models'])
+        response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
         
         return web.json_response({
             'success': True,
@@ -283,102 +289,186 @@ class MiscRoutes:
             }, status=400)
 
     @staticmethod
-    async def _download_all_example_images(output_dir, optimize, model_types, delay):
+    async def _refresh_model_metadata(model_hash, model_name, scanner_type, scanner):
-        """Download example images for all models
+        """Refresh model metadata from CivitAI
         
         Args:
-            output_dir: Base directory to save example images
+            model_hash: SHA256 hash of the model
-            optimize: Whether to optimize images
+            model_name: Name of the model (for logging)
-            model_types: List of model types to process
+            scanner_type: Type of scanner ('lora' or 'checkpoint')
-            delay: Delay between downloads to avoid rate limiting
+            scanner: Scanner instance for this model type
+            
+        Returns:
+            bool: True if metadata was successfully refreshed, False otherwise
         """
-        global is_downloading, download_progress
+        global download_progress
-        
-        # Create an independent session for downloading example images
-        # This avoids interference with the CivitAI client's session
-        connector = aiohttp.TCPConnector(
-            ssl=True,
-            limit=3,
-            force_close=False,
-            enable_cleanup_closed=True
-        )
-        timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=60)
-        
-        # Create a dedicated session just for this download task
-        independent_session = aiohttp.ClientSession(
-            connector=connector,
-            trust_env=True,
-            timeout=timeout
-        )
         
         try:
-            # Get the scanners
+            # Find the model in the scanner cache
-            scanners = []
-            if 'lora' in model_types:
-                lora_scanner = await ServiceRegistry.get_lora_scanner()
-                scanners.append(('lora', lora_scanner))
-            
-            if 'checkpoint' in model_types:
-                checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
-                scanners.append(('checkpoint', checkpoint_scanner))
-            
-            # Get all models from all scanners
-            all_models = []
-            for scanner_type, scanner in scanners:
             cache = await scanner.get_cached_data()
-                if cache and cache.raw_data:
+            model_data = None
-                    for model in cache.raw_data:
-                        # Only process models with images and a valid sha256
-                        if model.get('civitai') and model.get('civitai', {}).get('images') and model.get('sha256'):
-                            all_models.append((scanner_type, model))
             
-            # Update total count
+            for item in cache.raw_data:
-            download_progress['total'] = len(all_models)
+                if item.get('sha256') == model_hash:
-            logger.info(f"Found {download_progress['total']} models with example images")
+                    model_data = item
-            
-            # Process each model
-            for scanner_type, model in all_models:
-                # Check if download is paused
-                while download_progress['status'] == 'paused':
-                    await asyncio.sleep(1)
-                
-                # Check if download should continue
-                if download_progress['status'] != 'running':
-                    logger.info(f"Download stopped: {download_progress['status']}")
                     break
             
-                model_success = True  # Track if all images for this model download successfully
+            if not model_data:
+                logger.warning(f"Model {model_name} with hash {model_hash} not found in cache")
+                return False
             
+            file_path = model_data.get('file_path')
+            if not file_path:
+                logger.warning(f"Model {model_name} has no file path")
+                return False
+            
+            # Track that we're refreshing this model
+            download_progress['refreshed_models'].add(model_hash)
+            
+            # Use ModelRouteUtils to refresh the metadata
+            async def update_cache_func(old_path, new_path, metadata):
+                return await scanner.update_single_model_cache(old_path, new_path, metadata)
+            
+            success = await ModelRouteUtils.fetch_and_update_model(
+                model_hash, 
+                file_path, 
+                model_data,
+                update_cache_func
+            )
+            
+            if success:
+                logger.info(f"Successfully refreshed metadata for {model_name}")
+                return True
+            else:
+                logger.warning(f"Failed to refresh metadata for {model_name}")
+                return False
+                
+        except Exception as e:
+            error_msg = f"Error refreshing metadata for {model_name}: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            download_progress['errors'].append(error_msg)
+            download_progress['last_error'] = error_msg
+            return False
+    
+    @staticmethod
+    async def _process_model_images(model_hash, model_name, model_images, model_dir, optimize, independent_session, delay):
+        """Process and download images for a single model
+        
+        Args:
+            model_hash: SHA256 hash of the model
+            model_name: Name of the model
+            model_images: List of image objects from CivitAI
+            model_dir: Directory to save images to
+            optimize: Whether to optimize images
+            independent_session: aiohttp session for downloads
+            delay: Delay between downloads
+            
+        Returns:
+            bool: True if all images were processed successfully, False otherwise
+        """
+        global download_progress
+        
+        model_success = True
+        
+        for i, image in enumerate(model_images, 1):
+            image_url = image.get('url')
+            if not image_url:
+                continue
+            
+            # Get image filename from URL
+            image_filename = os.path.basename(image_url.split('?')[0])
+            image_ext = os.path.splitext(image_filename)[1].lower()
+            
+            # Handle both images and videos
+            is_image = image_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
+            is_video = image_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
+            
+            if not (is_image or is_video):
+                logger.debug(f"Skipping unsupported file type: {image_filename}")
+                continue
+            
+            save_filename = f"image_{i}{image_ext}"
+            
+            # Check if already downloaded
+            save_path = os.path.join(model_dir, save_filename)
+            if os.path.exists(save_path):
+                logger.debug(f"File already exists: {save_path}")
+                continue
+            
+            # Download the file
             try:
-                    # Update current model info
+                logger.debug(f"Downloading {save_filename} for {model_name}")
-                    model_hash = model.get('sha256', '').lower()
-                    model_name = model.get('model_name', 'Unknown')
-                    model_file_path = model.get('file_path', '')
-                    model_file_name = model.get('file_name', '')
-                    download_progress['current_model'] = f"{model_name} ({model_hash[:8]})"
                 
-                    # Skip if already processed
+                # Direct download using the independent session
-                    if model_hash in download_progress['processed_models']:
+                async with independent_session.get(image_url, timeout=60) as response:
-                        logger.debug(f"Skipping already processed model: {model_name}")
+                    if response.status == 200:
-                        download_progress['completed'] += 1
+                        if is_image and optimize:
-                        continue
+                            # For images, optimize if requested
+                            image_data = await response.read()
+                            optimized_data, ext = ExifUtils.optimize_image(
+                                image_data, 
+                                target_width=EXAMPLE_IMAGE_WIDTH, 
+                                format='webp', 
+                                quality=85, 
+                                preserve_metadata=False
+                            )
                             
-                    # Create model directory
+                            # Update save filename if format changed
-                    model_dir = os.path.join(output_dir, model_hash)
+                            if ext == '.webp':
-                    os.makedirs(model_dir, exist_ok=True)
+                                save_filename = os.path.splitext(save_filename)[0] + '.webp'
+                                save_path = os.path.join(model_dir, save_filename)
                             
-                    # Process images for this model
+                            # Save the optimized image
-                    images = model.get('civitai', {}).get('images', [])
+                            with open(save_path, 'wb') as f:
+                                f.write(optimized_data)
+                        else:
+                            # For videos or unoptimized images, save directly
+                            with open(save_path, 'wb') as f:
+                                async for chunk in response.content.iter_chunked(8192):
+                                    if chunk:
+                                        f.write(chunk)
+                    elif response.status == 404:
+                        error_msg = f"Failed to download file: {image_url}, status code: 404 - Model metadata might be stale"
+                        logger.warning(error_msg)
+                        download_progress['errors'].append(error_msg)
+                        download_progress['last_error'] = error_msg
+                        model_success = False  # Mark model as failed due to 404
+                        # Return early to trigger metadata refresh attempt
+                        return False, True  # (success, is_stale_metadata)
+                    else:
+                        error_msg = f"Failed to download file: {image_url}, status code: {response.status}"
+                        logger.warning(error_msg)
+                        download_progress['errors'].append(error_msg)
+                        download_progress['last_error'] = error_msg
+                        model_success = False  # Mark model as failed
                 
-                    if not images:
+                # Add a delay between downloads for remote files only
-                        logger.debug(f"No images found for model: {model_name}")
+                await asyncio.sleep(delay)
-                        download_progress['processed_models'].add(model_hash)
+            except Exception as e:
-                        download_progress['completed'] += 1
+                error_msg = f"Error downloading file {image_url}: {str(e)}"
-                        continue
+                logger.error(error_msg)
+                download_progress['errors'].append(error_msg)
+                download_progress['last_error'] = error_msg
+                model_success = False  # Mark model as failed
+        
+        return model_success, False  # (success, is_stale_metadata)
+    
+    @staticmethod
+    async def _process_local_example_images(model_file_path, model_file_name, model_name, model_dir, optimize):
+        """Process local example images for a model
+        
+        Args:
+            model_file_path: Path to the model file
+            model_file_name: Filename of the model
+            model_name: Name of the model
+            model_dir: Directory to save processed images to
+            optimize: Whether to optimize images
+            
+        Returns:
+            bool: True if local images were processed successfully, False otherwise
+        """
+        global download_progress
         
-                    # First check if we have local example images for this model
-                    local_images_processed = False
-                    if model_file_path:
         try:
             model_dir_path = os.path.dirname(model_file_path)
             local_images = []
@@ -442,93 +532,174 @@ class MiscRoutes:
                             with open(save_path, 'wb') as dst_file:
                                 dst_file.write(src_file.read())
                 
-                                # Mark as successfully processed if all local images were processed
+                return True
-                                download_progress['processed_models'].add(model_hash)
+            return False
-                                local_images_processed = True
-                                logger.info(f"Successfully processed local examples for {model_name}")
-                        
         except Exception as e:
             error_msg = f"Error processing local examples for {model_name}: {str(e)}"
             logger.error(error_msg)
             download_progress['errors'].append(error_msg)
             download_progress['last_error'] = error_msg
-                            # Continue to remote download if local processing fails
+            return False
+
+    @staticmethod
+    async def _download_all_example_images(output_dir, optimize, model_types, delay):
+        """Download example images for all models
+        
+        Args:
+            output_dir: Base directory to save example images
+            optimize: Whether to optimize images
+            model_types: List of model types to process
+            delay: Delay between downloads to avoid rate limiting
+        """
+        global is_downloading, download_progress
+        
+        # Create an independent session for downloading example images
+        # This avoids interference with the CivitAI client's session
+        connector = aiohttp.TCPConnector(
+            ssl=True,
+            limit=3,
+            force_close=False,
+            enable_cleanup_closed=True
+        )
+        timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=60)
+        
+        # Create a dedicated session just for this download task
+        independent_session = aiohttp.ClientSession(
+            connector=connector,
+            trust_env=True,
+            timeout=timeout
+        )
+        
+        try:
+            # Get the scanners
+            scanners = []
+            if 'lora' in model_types:
+                lora_scanner = await ServiceRegistry.get_lora_scanner()
+                scanners.append(('lora', lora_scanner))
+            
+            if 'checkpoint' in model_types:
+                checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+                scanners.append(('checkpoint', checkpoint_scanner))
+            
+            # Get all models from all scanners
+            all_models = []
+            for scanner_type, scanner in scanners:
+                cache = await scanner.get_cached_data()
+                if cache and cache.raw_data:
+                    for model in cache.raw_data:
+                        # Only process models with images and a valid sha256
+                        if model.get('civitai') and model.get('civitai', {}).get('images') and model.get('sha256'):
+                            all_models.append((scanner_type, model, scanner))
+            
+            # Update total count
+            download_progress['total'] = len(all_models)
+            logger.info(f"Found {download_progress['total']} models with example images")
+            
+            # Process each model
+            for scanner_type, model, scanner in all_models:
+                # Check if download is paused
+                while download_progress['status'] == 'paused':
+                    await asyncio.sleep(1)
+                
+                # Check if download should continue
+                if download_progress['status'] != 'running':
+                    logger.info(f"Download stopped: {download_progress['status']}")
+                    break
+                
+                model_hash = model.get('sha256', '').lower()
+                model_name = model.get('model_name', 'Unknown')
+                model_file_path = model.get('file_path', '')
+                model_file_name = model.get('file_name', '')
+                
+                try:
+                    # Update current model info
+                    download_progress['current_model'] = f"{model_name} ({model_hash[:8]})"
+                    
+                    # Skip if already processed
+                    if model_hash in download_progress['processed_models']:
+                        logger.debug(f"Skipping already processed model: {model_name}")
+                        download_progress['completed'] += 1
+                        continue
+                    
+                    # Create model directory
+                    model_dir = os.path.join(output_dir, model_hash)
+                    os.makedirs(model_dir, exist_ok=True)
+                    
+                    # Process images for this model
+                    images = model.get('civitai', {}).get('images', [])
+                    
+                    if not images:
+                        logger.debug(f"No images found for model: {model_name}")
+                        download_progress['processed_models'].add(model_hash)
+                        download_progress['completed'] += 1
+                        continue
+                    
+                    # First check if we have local example images for this model
+                    local_images_processed = False
+                    if model_file_path:
+                        local_images_processed = await MiscRoutes._process_local_example_images(
+                            model_file_path, 
+                            model_file_name, 
+                            model_name, 
+                            model_dir, 
+                            optimize
+                        )
+                        
+                        if local_images_processed:
+                            # Mark as successfully processed if all local images were processed
+                            download_progress['processed_models'].add(model_hash)
+                            logger.info(f"Successfully processed local examples for {model_name}")
                     
                     # If we didn't process local images, download from remote
                     if not local_images_processed:
-                        # Download example images
+                        # Try to download images
-                        for i, image in enumerate(images, 1):
+                        model_success, is_stale_metadata = await MiscRoutes._process_model_images(
-                            image_url = image.get('url')
+                            model_hash, 
-                            if not image_url:
+                            model_name, 
-                                continue
+                            images, 
-                            
+                            model_dir, 
-                            # Get image filename from URL
+                            optimize, 
-                            image_filename = os.path.basename(image_url.split('?')[0])
+                            independent_session, 
-                            image_ext = os.path.splitext(image_filename)[1].lower()
+                            delay
-                            
-                            # Handle both images and videos
-                            is_image = image_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
-                            is_video = image_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
-                            
-                            if not (is_image or is_video):
-                                logger.debug(f"Skipping unsupported file type: {image_filename}")
-                                continue
-                            
-                            save_filename = f"image_{i}{image_ext}"
-                            
-                            # Check if already downloaded
-                            save_path = os.path.join(model_dir, save_filename)
-                            if os.path.exists(save_path):
-                                logger.debug(f"File already exists: {save_path}")
-                                continue
-                            
-                            # Download the file
-                            try:
-                                logger.debug(f"Downloading {save_filename} for {model_name}")
-                                
-                                # Direct download using the independent session
-                                async with independent_session.get(image_url, timeout=60) as response:
-                                    if response.status == 200:
-                                        if is_image and optimize:
-                                            # For images, optimize if requested
-                                            image_data = await response.read()
-                                            optimized_data, ext = ExifUtils.optimize_image(
-                                                image_data, 
-                                                target_width=EXAMPLE_IMAGE_WIDTH, 
-                                                format='webp', 
-                                                quality=85, 
-                                                preserve_metadata=False
                         )
                         
-                                            # Update save filename if format changed
+                        # If metadata is stale (404 error), try to refresh it and download again
-                                            if ext == '.webp':
+                        if is_stale_metadata and model_hash not in download_progress['refreshed_models']:
-                                                save_filename = os.path.splitext(save_filename)[0] + '.webp'
+                            logger.info(f"Metadata seems stale for {model_name}, attempting to refresh...")
-                                                save_path = os.path.join(model_dir, save_filename)
                             
-                                            # Save the optimized image
+                            # Refresh metadata from CivitAI
-                                            with open(save_path, 'wb') as f:
+                            refresh_success = await MiscRoutes._refresh_model_metadata(
-                                                f.write(optimized_data)
+                                model_hash, 
-                                        else:
+                                model_name, 
-                                            # For videos or unoptimized images, save directly
+                                scanner_type, 
-                                            with open(save_path, 'wb') as f:
+                                scanner
-                                                async for chunk in response.content.iter_chunked(8192):
+                            )
-                                                    if chunk:
-                                                        f.write(chunk)
-                                    else:
-                                        error_msg = f"Failed to download file: {image_url}, status code: {response.status}"
-                                        logger.warning(error_msg)
-                                        download_progress['errors'].append(error_msg)
-                                        download_progress['last_error'] = error_msg
-                                        model_success = False  # Mark model as failed
                             
-                                # Add a delay between downloads for remote files only
+                            if refresh_success:
-                                await asyncio.sleep(delay)
+                                # Get updated model data
-                            except Exception as e:
+                                updated_cache = await scanner.get_cached_data()
-                                error_msg = f"Error downloading file {image_url}: {str(e)}"
+                                updated_model = None
-                                logger.error(error_msg)
+                                
-                                download_progress['errors'].append(error_msg)
+                                for item in updated_cache.raw_data:
-                                download_progress['last_error'] = error_msg
+                                    if item.get('sha256') == model_hash:
-                                model_success = False  # Mark model as failed
+                                        updated_model = item
+                                        break
+                                
+                                if updated_model and updated_model.get('civitai', {}).get('images'):
+                                    # Try downloading with updated metadata
+                                    logger.info(f"Retrying download with refreshed metadata for {model_name}")
+                                    updated_images = updated_model.get('civitai', {}).get('images', [])
+                                    
+                                    # Retry download with new images
+                                    model_success, _ = await MiscRoutes._process_model_images(
+                                        model_hash, 
+                                        model_name, 
+                                        updated_images, 
+                                        model_dir, 
+                                        optimize, 
+                                        independent_session, 
+                                        delay
+                                    )
                         
                         # Only mark model as processed if all images downloaded successfully
                         if model_success:
@@ -542,6 +713,7 @@ class MiscRoutes:
                         with open(progress_file, 'w', encoding='utf-8') as f:
                             json.dump({
                                 'processed_models': list(download_progress['processed_models']),
+                                'refreshed_models': list(download_progress['refreshed_models']),
                                 'completed': download_progress['completed'],
                                 'total': download_progress['total'],
                                 'last_update': time.time()
@@ -582,6 +754,7 @@ class MiscRoutes:
                 with open(progress_file, 'w', encoding='utf-8') as f:
                     json.dump({
                         'processed_models': list(download_progress['processed_models']),
+                        'refreshed_models': list(download_progress['refreshed_models']),
                         'completed': download_progress['completed'],
                         'total': download_progress['total'],
                         'last_update': time.time(),