refactor(ui): group example image download actions into a submenu

Move the 'Download Missing' / 'Re-process All' example image actions
under a single 'Download Example Images' submenu item in the single-model
and bulk context menus, matching the existing send-to-workflow submenu
pattern. Shorten the submenu labels and update all locale translations.

locales/en.json

@@ -773,6 +773,8 @@
       "deleteAll": "Delete Selected",
       "downloadMissingLoras": "Download Missing LoRAs",
       "downloadExamples": "Download Example Images",
+      "downloadMissingExamples": "Download Missing",
+      "reprocessExamples": "Re-process All",
       "clear": "Clear Selection",
       "skipMetadataRefreshCount": "Skip ({count} models)",
       "resumeMetadataRefreshCount": "Resume ({count} models)",
@@ -808,6 +810,8 @@
       "sendToWorkflowReplace": "Send to Workflow (Replace)",
       "openExamples": "Open Examples Folder",
       "downloadExamples": "Download Example Images",
+      "downloadMissingExamples": "Download Missing",
+      "reprocessExamples": "Re-process All",
       "replacePreview": "Replace Preview",
       "setContentRating": "Set Content Rating",
       "moveToFolder": "Move to Folder",

py/metadata_collector/node_extractors.py

@@ -2,7 +2,8 @@ import json
 import os
 import re
 
-from .constants import CLIP_SKIP_SENTINEL, MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER, OVERWRITE, METADATA_OVERWRITE_FIELDS
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER, OVERWRITE
+from .overwrite_utils import collect_overwrite_params
 
 
 def _store_checkpoint_metadata(metadata, node_id, model_name):
@@ -1233,14 +1234,7 @@ class MetadataOverwriteExtractor(NodeMetadataExtractor):
         if not inputs:
             return
 
-        overwrite_params = {}
-        for key in METADATA_OVERWRITE_FIELDS:
-            value = inputs.get(key)
-            if key == "clip_skip":
-                if value != CLIP_SKIP_SENTINEL:
-                    overwrite_params[key] = value
-            elif value:  # truthy — only overwrite when user provided a real value
-                overwrite_params[key] = value
+        overwrite_params = collect_overwrite_params(inputs)
 
         if overwrite_params:
             metadata.setdefault(OVERWRITE, {})

py/metadata_collector/overwrite_utils.py

@@ -0,0 +1,42 @@
+"""Shared helpers for Metadata Overwrite node metadata collection.
+
+Used by both the MetadataOverwriteLM node (execution time) and the
+MetadataOverwriteExtractor (hook time) so the conversion/filtering logic
+cannot drift between the two paths.
+"""
+
+import logging
+from typing import Any, Dict
+
+from ..utils.utils import model_patcher_to_name
+from .constants import CLIP_SKIP_SENTINEL, METADATA_OVERWRITE_FIELDS
+
+logger = logging.getLogger(__name__)
+
+
+def collect_overwrite_params(values: Dict[str, Any]) -> Dict[str, Any]:
+    """Convert node input values into non-default overwrite parameters.
+
+    For most fields, a falsy value (empty string, 0) means "not set" and is
+    skipped.  clip_skip uses a dedicated sentinel (-25) so that a wired value
+    of 0 is preserved.  The ``model`` field accepts either a manual string or
+    a wired MODEL (ModelPatcher) connection; in the latter case the source
+    model name is extracted from the patcher's ``cached_patcher_init`` and
+    stored as a ComfyUI-style relative path.
+    """
+    result: Dict[str, Any] = {}
+    for key in METADATA_OVERWRITE_FIELDS:
+        value = values.get(key)
+        if key == "model" and not isinstance(value, str):
+            value = model_patcher_to_name(value)
+            if value is None:
+                logger.warning(
+                    "Could not extract model name from wired MODEL input "
+                    "(no cached_patcher_init); model metadata overwrite skipped"
+                )
+        if key == "clip_skip":
+            if value != CLIP_SKIP_SENTINEL:
+                result[key] = value
+        elif value:
+            result[key] = value
+    return result

py/nodes/metadata_overwrite.py

@@ -9,10 +9,8 @@ but users may wire 0 to express "no clip skip / default".
 
 from typing import Any
 
-from ..metadata_collector.constants import (
-    CLIP_SKIP_SENTINEL as _CLIP_SKIP_SENTINEL,
-    METADATA_OVERWRITE_FIELDS,
-)
+from ..metadata_collector.constants import CLIP_SKIP_SENTINEL as _CLIP_SKIP_SENTINEL
+from ..metadata_collector.overwrite_utils import collect_overwrite_params
 
 
 class MetadataOverwriteLM:
@@ -87,12 +85,16 @@ class MetadataOverwriteLM:
                     },
                 ),
                 "model": (
-                    "STRING",
+                    "STRING,MODEL",
                     {
                         "default": "",
+                        "widgetType": "STRING",
                         "tooltip": (
                             "The checkpoint or diffusion model (UNet) used "
-                            "for generation. Only overwrites when non-empty."
+                            "for generation. Fill in the name manually or "
+                            "connect a MODEL output — the model name is then "
+                            "extracted automatically. Only overwrites when "
+                            "non-empty."
                         ),
                     },
                 ),
@@ -158,13 +160,10 @@ class MetadataOverwriteLM:
         For most fields, a falsy value (empty string, 0) means "not set"
         and is skipped.  clip_skip uses a dedicated sentinel (-25) so that
         a wired value of 0 is preserved and reaches the metadata pipeline.
+
+        The ``model`` field accepts either a manual string or a wired MODEL
+        (ModelPatcher) connection; in the latter case the underlying model
+        name is extracted from the patcher's ``cached_patcher_init`` and
+        stored as a ComfyUI-style relative path.
         """
-        result: dict[str, Any] = {}
-        for key in METADATA_OVERWRITE_FIELDS:
-            value = kwargs.get(key)
-            if key == "clip_skip":
-                if value != _CLIP_SKIP_SENTINEL:
-                    result[key] = value
-            elif value:
-                result[key] = value
-        return (result,)
+        return (collect_overwrite_params(kwargs),)

py/nodes/unet_loader.py

@@ -7,6 +7,21 @@ from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_c
 logger = logging.getLogger(__name__)
 
 
+def _reload_gguf_unet(
+    unet_path: str, weight_dtype: str, disable_dynamic: bool = False
+) -> object:
+    """Reload a GGUF diffusion model from disk (cached_patcher_init factory).
+
+    Mirrors the GGUF branch of UNETLoaderLM.load_unet so ModelPatcher
+    deepclone/dynamic machinery can rebuild GGUF models with the correct
+    GGMLOps. ``disable_dynamic`` is accepted for signature compatibility
+    with core ComfyUI loaders.
+    """
+    loader = UNETLoaderLM()
+    model, = loader._load_gguf_unet(unet_path, unet_path, weight_dtype)
+    return model
+
+
 class UNETLoaderLM:
     """UNET Loader with support for extra folder paths
 
@@ -196,6 +211,12 @@ class UNETLoaderLM:
             # Wrap with GGUFModelPatcher
             model = GGUFModelPatcher.clone(model)
 
+            # Register a reload factory so the MODEL carries its source path
+            # (cached_patcher_init) like core ComfyUI loaders do — required
+            # for model-name extraction downstream and for ModelPatcher
+            # deepclone/dynamic machinery.
+            model.cached_patcher_init = (_reload_gguf_unet, (unet_path, weight_dtype))
+
             return (model,)
 
         except Exception as e:

py/utils/example_images_download_manager.py

@@ -172,6 +172,7 @@ class DownloadManager:
         model_types = data.get("model_types", ["lora", "checkpoint"])
         delay = float(data.get("delay", 0.2))
         force = data.get("force", False)
+        model_hashes = data.get("model_hashes", [])
 
         # Step 2: Validate configuration (fast lookup)
         settings_manager = get_settings_manager()
@@ -241,6 +242,7 @@ class DownloadManager:
                         delay,
                         active_library,
                         force,
+                        model_hashes,
                     )
                 )
 
@@ -577,8 +579,9 @@ class DownloadManager:
         delay,
         library_name,
         force: bool = False,
+        model_hashes: list[str] | None = None,
     ):
-        """Download example images for all models."""
+        """Download example images for all models (or only the given hashes)."""
 
         downloader = await get_downloader()
 
@@ -606,6 +609,18 @@ class DownloadManager:
                         if model.get("sha256"):
                             all_models.append((scanner_type, model, scanner))
 
+            # Restrict to the requested hashes when provided (empty = all models).
+            # Explicit targets are a directed user request, so previously failed
+            # models are retried instead of skipped.
+            explicit_targets = bool(model_hashes)
+            if model_hashes:
+                hash_set = {h.lower() for h in model_hashes}
+                all_models = [
+                    (scanner_type, model, scanner)
+                    for scanner_type, model, scanner in all_models
+                    if model.get("sha256", "").lower() in hash_set
+                ]
+
             # Update total count
             self._progress["total"] = len(all_models)
             logger.debug(f"Found {self._progress['total']} models to process")
@@ -629,6 +644,7 @@ class DownloadManager:
                     downloader,
                     library_name,
                     force,
+                    explicit_targets,
                 )
 
                 # Update progress
@@ -725,6 +741,7 @@ class DownloadManager:
         downloader,
         library_name,
         force: bool = False,
+        explicit_targets: bool = False,
     ):
         """Process a single model download."""
 
@@ -747,8 +764,9 @@ class DownloadManager:
             self._progress["current_model"] = f"{model_name} ({model_hash[:8]})"
             await self._broadcast_progress(status="running")
 
-            # Skip if already in failed models (unless force mode is enabled)
-            if not force and model_hash in self._progress["failed_models"]:
+            # Skip if already in failed models (unless force mode is enabled or
+            # the model was explicitly targeted by hash)
+            if not force and not explicit_targets and model_hash in self._progress["failed_models"]:
                 logger.debug(f"Skipping known failed model: {model_name}")
                 return False
 
@@ -757,30 +775,34 @@ class DownloadManager:
             )
             existing_files = _model_directory_has_files(model_dir)
 
-            # Skip if already processed AND directory exists with files
-            if model_hash in self._progress["processed_models"]:
-                if existing_files:
-                    logger.debug(f"Skipping already processed model: {model_name}")
+            # Model-level guard: a populated folder counts as done. Explicitly
+            # targeted models bypass it so the per-image existence pre-check can
+            # fill individual gaps without re-fetching existing files.
+            if not explicit_targets:
+                # Skip if already processed AND directory exists with files
+                if model_hash in self._progress["processed_models"]:
+                    if existing_files:
+                        logger.debug(f"Skipping already processed model: {model_name}")
+                        return False
+
+                    logger.debug(
+                        "Model %s (%s) marked as processed but folder empty or missing, reprocessing triggered",
+                        model_name,
+                        model_hash,
+                    )
+                    # Track that we are reprocessing this model for summary logging
+                    self._progress["reprocessed_models"].add(model_hash)
+                    # Remove from processed models since we need to reprocess
+                    self._progress["processed_models"].discard(model_hash)
+
+                if existing_files and model_hash not in self._progress["processed_models"]:
+                    logger.debug(
+                        "Model folder already populated for %s, marking as processed without download",
+                        model_name,
+                    )
+                    self._progress["processed_models"].add(model_hash)
                     return False
 
-                logger.debug(
-                    "Model %s (%s) marked as processed but folder empty or missing, reprocessing triggered",
-                    model_name,
-                    model_hash,
-                )
-                # Track that we are reprocessing this model for summary logging
-                self._progress["reprocessed_models"].add(model_hash)
-                # Remove from processed models since we need to reprocess
-                self._progress["processed_models"].discard(model_hash)
-
-            if existing_files and model_hash not in self._progress["processed_models"]:
-                logger.debug(
-                    "Model folder already populated for %s, marking as processed without download",
-                    model_name,
-                )
-                self._progress["processed_models"].add(model_hash)
-                return False
-
             if not model_dir:
                 logger.warning(
                     "Unable to resolve example images folder for model %s (%s)",
@@ -884,7 +906,7 @@ class DownloadManager:
                         model_name,
                     )
                     # Clear failed_models so non-force runs can retry
-                    if force and model_hash in self._progress["failed_models"]:
+                    if (force or explicit_targets) and model_hash in self._progress["failed_models"]:
                         self._progress["failed_models"].discard(model_hash)
                         logger.info(
                             f"Removed {model_name} from failed_models after force retry with rate-limited images"
@@ -904,7 +926,7 @@ class DownloadManager:
                     )
                 elif success:
                     self._progress["processed_models"].add(model_hash)
-                    if force and model_hash in self._progress["failed_models"]:
+                    if (force or explicit_targets) and model_hash in self._progress["failed_models"]:
                         self._progress["failed_models"].discard(model_hash)
                         logger.info(
                             f"Removed {model_name} from failed_models after successful force retry"

py/utils/example_images_processor.py

@@ -113,6 +113,26 @@ class ExampleImagesProcessor:
         message = str(error).lower()
         return '404' in message or 'file not found' in message
 
+    @staticmethod
+    def _example_image_file_exists(model_dir: str, index: int, media_type_hint: str | None = None) -> bool:
+        """Return True when the file that would be written for a media index already exists.
+
+        The final filename (``image_{index}{extension}``) depends on the downloaded
+        content, so the extension cannot be known ahead of time. The post-download
+        check skips the write when the exact target file exists; this pre-check
+        approximates that with the candidate extensions for the media type (videos
+        only when the metadata hints at a video) so the network request is avoided
+        for files that already exist on disk.
+        """
+        if media_type_hint == "video":
+            extensions = SUPPORTED_MEDIA_EXTENSIONS['videos']
+        else:
+            extensions = SUPPORTED_MEDIA_EXTENSIONS['images']
+        return any(
+            os.path.exists(os.path.join(model_dir, f"image_{index}{ext}"))
+            for ext in extensions
+        )
+
     @staticmethod
     async def download_model_images(model_hash, model_name, model_images, model_dir, optimize, downloader):
         """Download images for a single model
@@ -139,7 +159,12 @@ class ExampleImagesProcessor:
             original_url = image_url
             if optimize and 'civitai.com' in image_url:
                 image_url = ExampleImagesProcessor.get_civitai_optimized_url(image_url)
-            
+
+            # Skip the download when the file already exists on disk
+            if ExampleImagesProcessor._example_image_file_exists(model_dir, i, image.get("type")):
+                logger.debug("File already exists, skipping download for %s", image_url)
+                continue
+
             # Download the file first to determine the actual file type
             try:
                 logger.debug(f"Downloading media file {i} for {model_name}")
@@ -229,6 +254,11 @@ class ExampleImagesProcessor:
             if optimize and 'civitai.com' in image_url:
                 image_url = ExampleImagesProcessor.get_civitai_optimized_url(image_url)
 
+            # Skip the download when the file already exists on disk
+            if ExampleImagesProcessor._example_image_file_exists(model_dir, i, image.get("type")):
+                logger.debug("File already exists, skipping download for %s", image_url)
+                continue
+
             async def _attempt_download() -> tuple:
                 logger.debug("Downloading media file %s for %s", i, model_name)
                 return await downloader.download_to_memory(

py/utils/utils.py

@@ -1,7 +1,7 @@
 from difflib import SequenceMatcher
 import os
 import re
-from typing import Dict
+from typing import Any, Dict, List, Optional
 from ..services.service_registry import ServiceRegistry
 from ..config import config
 from ..services.settings_manager import get_settings_manager
@@ -294,6 +294,53 @@ def _format_model_name_for_comfyui(file_path: str, model_roots: list) -> str:
     return os.path.basename(file_path)
 
 
+def model_patcher_to_name(model_patcher: Any) -> Optional[str]:
+    """Extract a ComfyUI-style model name from a MODEL (ModelPatcher) object.
+
+    Core ComfyUI loaders record the absolute weight file path on the patcher's
+    ``cached_patcher_init`` attribute:
+      - load_checkpoint_guess_config -> (fn, (ckpt_path, ...), index)
+      - load_diffusion_model -> (fn, (unet_path, model_options))
+    Patcher clones (LoRA loaders, model merges, ...) preserve the attribute,
+    so the name is recoverable anywhere downstream of a core loader — including
+    from LoRA Manager's own loaders (CheckpointLoaderLM / UNETLoaderLM), which
+    call the same core load functions.
+
+    The absolute path is converted to the ComfyUI-style relative name used by
+    the metadata pipeline (covering standard ComfyUI roots and LoRA Manager
+    extra folder paths).
+
+    Returns None when the path cannot be recovered (e.g. third-party loaders
+    that never set ``cached_patcher_init``).
+    """
+    init = getattr(model_patcher, "cached_patcher_init", None)
+    if not isinstance(init, (tuple, list)) or len(init) < 2:
+        return None
+    args = init[1]
+    abs_path = args[0] if args else None
+    if not isinstance(abs_path, str) or not abs_path:
+        return None
+    return _abs_model_path_to_name(abs_path)
+
+
+def _abs_model_path_to_name(abs_path: str) -> str:
+    """Convert an absolute model path to a ComfyUI-style relative name.
+
+    Tries standard ComfyUI model roots plus LoRA Manager extra folder paths;
+    falls back to the bare filename.
+    """
+    try:
+        roots: List[str] = list(config.base_models_roots or [])
+        roots.extend(config.extra_checkpoints_roots or [])
+        roots.extend(config.extra_unet_roots or [])
+        formatted = _format_model_name_for_comfyui(abs_path, roots)
+        if formatted:
+            return formatted
+    except Exception:
+        pass
+    return os.path.basename(abs_path)
+
+
 def fuzzy_match(text: str, pattern: str, threshold: float = 0.85) -> bool:
     """
     Check if text matches pattern using fuzzy matching.

static/js/api/apiConfig.js

@@ -184,7 +184,8 @@ export const DOWNLOAD_ENDPOINTS = {
     downloadGet: '/api/lm/download-model-get',
     cancelGet: '/api/lm/cancel-download-get',
     progress: '/api/lm/download-progress',
-    exampleImages: '/api/lm/force-download-example-images' // New endpoint for downloading example images
+    exampleImages: '/api/lm/force-download-example-images', // Re-process example images ignoring previous status
+    exampleImagesMissing: '/api/lm/download-example-images' // Download only missing example images
 };
 
 // Hugging Face API endpoints

static/js/api/baseModelApi.js

@@ -1641,7 +1641,7 @@ export class BaseModelApiClient {
         }
     }
 
-    async downloadExampleImages(modelHashes, modelTypes = null) {
+    async downloadExampleImages(modelHashes, modelTypes = null, { force = true } = {}) {
         let ws = null;
 
         await state.loadingManager.showWithProgress(async (loading) => {
@@ -1700,8 +1700,13 @@ export class BaseModelApiClient {
                 // Determine optimize setting
                 const optimize = state.global?.settings?.optimize_example_images ?? true;
 
+                // force=false routes to the regular endpoint, which skips already-processed models
+                const endpoint = force
+                    ? DOWNLOAD_ENDPOINTS.exampleImages
+                    : DOWNLOAD_ENDPOINTS.exampleImagesMissing;
+
                 // Make the API request to start the download process
-                const response = await fetch(DOWNLOAD_ENDPOINTS.exampleImages, {
+                const response = await fetch(endpoint, {
                     method: 'POST',
                     headers: {
                         'Content-Type': 'application/json'
@@ -1710,6 +1715,7 @@ export class BaseModelApiClient {
                         model_hashes: modelHashes,
                         output_dir: outputDir,
                         optimize: optimize,
+                        force: force,
                         model_types: modelTypes || [this.apiConfig.config.singularName]
                     })
                 });

static/js/components/ContextMenu/BulkContextMenu.js

@@ -137,11 +137,10 @@ export class BulkContextMenu extends BaseContextMenu {
             downloadMissingLorasItem.style.display = currentModelType === 'recipes' ? 'flex' : 'none';
         }
 
-        const downloadExampleImagesItem = this.menu.querySelector('[data-action="download-example-images"]');
-        if (downloadExampleImagesItem) {
+        const downloadExampleImagesSubmenu = this.menu.querySelector('[data-has-submenu="download-example-images"]');
+        if (downloadExampleImagesSubmenu) {
             // Show on model pages (loras, checkpoints, embeddings), hide on recipes
-            const modelPages = ['loras', 'checkpoints', 'embeddings'];
-            downloadExampleImagesItem.style.display = modelPages.includes(currentModelType) ? 'flex' : 'none';
+            downloadExampleImagesSubmenu.style.display = ['loras', 'checkpoints', 'embeddings'].includes(currentModelType) ? 'flex' : 'none';
         }
 
         const skipMetadataRefreshItem = this.menu.querySelector('[data-action="skip-metadata-refresh"]');
@@ -294,8 +293,11 @@ export class BulkContextMenu extends BaseContextMenu {
             case 'download-missing-loras':
                 this.handleDownloadMissingLoras();
                 break;
+            case 'download-missing-example-images':
+                this.handleDownloadExampleImages({ force: false });
+                break;
             case 'download-example-images':
-                this.handleDownloadExampleImages();
+                this.handleDownloadExampleImages({ force: true });
                 break;
             case 'clear':
                 bulkManager.clearSelection();
@@ -340,7 +342,7 @@ export class BulkContextMenu extends BaseContextMenu {
         await bulkMissingLoraDownloadManager.downloadMissingLoras(selectedRecipes);
     }
 
-    async handleDownloadExampleImages() {
+    async handleDownloadExampleImages({ force = true } = {}) {
         if (state.selectedModels.size === 0) {
             return;
         }
@@ -361,7 +363,7 @@ export class BulkContextMenu extends BaseContextMenu {
 
         try {
             const apiClient = getModelApiClient();
-            await apiClient.downloadExampleImages([...hashes]);
+            await apiClient.downloadExampleImages([...hashes], null, { force });
         } catch (error) {
             console.error('Bulk download example images failed:', error);
         }

static/js/components/ContextMenu/ModelContextMenuMixin.js

@@ -347,7 +347,10 @@ export const ModelContextMenuMixin = {
                 openExampleImagesFolder(this.currentCard.dataset.sha256);
                 return true;
             case 'download-examples':
-                this.downloadExampleImages();
+                this.downloadExampleImages(false);
+                return true;
+            case 'download-examples-force':
+                this.downloadExampleImages(true);
                 return true;
             case 'civitai':
                 if (this.currentCard.dataset.from_civitai === 'true') {
@@ -378,7 +381,7 @@ export const ModelContextMenuMixin = {
     },
 
     // Download example images method
-    async downloadExampleImages() {
+    async downloadExampleImages(force = false) {
         const modelHash = this.currentCard.dataset.sha256;
         if (!modelHash) {
             showToast('toast.contextMenu.missingHash', {}, 'error');
@@ -387,7 +390,7 @@ export const ModelContextMenuMixin = {
 
         try { 
             const apiClient = getModelApiClient();
-            await apiClient.downloadExampleImages([modelHash]);
+            await apiClient.downloadExampleImages([modelHash], null, { force });
         } catch (error) {
             console.error('Error downloading example images:', error);
         }

static/js/managers/BulkManager.js

@@ -90,7 +90,7 @@ export class BulkManager {
                 moveAll: true,
                 autoOrganize: false,
                 deleteAll: true,
-                setContentRating: false,
+                setContentRating: true,
                 skipMetadataRefresh: false,
                 setFavorite: true,
                 unfavorite: true,
@@ -1528,14 +1528,18 @@ export class BulkManager {
         let failureCount = 0;
 
         try {
-            const apiClient = getModelApiClient();
+            const isRecipesPage = state.currentPageType === 'recipes';
             for (const filePath of targets) {
                 if (cancelled) {
                     showToast('toast.api.operationCancelled', {}, 'info');
                     break;
                 }
                 try {
-                    await apiClient.saveModelMetadata(filePath, { preview_nsfw_level: level });
+                    if (isRecipesPage) {
+                        await updateRecipeMetadata(filePath, { preview_nsfw_level: level });
+                    } else {
+                        await getModelApiClient().saveModelMetadata(filePath, { preview_nsfw_level: level });
+                    }
                     successCount++;
                 } catch (error) {
                     failureCount++;

templates/checkpoints.html

@@ -32,7 +32,12 @@
     <div class="context-menu-separator menu-section-break"></div>
     <!-- Media / Preview -->
     <div class="context-menu-item" data-action="preview"><i class="fas fa-folder-open"></i> {{ t('loras.contextMenu.openExamples') }}</div>
-    <div class="context-menu-item" data-action="download-examples"><i class="fas fa-download"></i> {{ t('loras.contextMenu.downloadExamples') }}</div>
+    <div class="context-menu-item has-submenu" data-has-submenu="download-examples"><i class="fas fa-download"></i> {{ t('loras.contextMenu.downloadExamples') }} <i class="fas fa-chevron-right submenu-arrow"></i>
+        <div class="context-submenu">
+            <div class="context-menu-item" data-action="download-examples"><i class="fas fa-download"></i> {{ t('loras.contextMenu.downloadMissingExamples') }}</div>
+            <div class="context-menu-item" data-action="download-examples-force"><i class="fas fa-redo-alt"></i> {{ t('loras.contextMenu.reprocessExamples') }}</div>
+        </div>
+    </div>
     <div class="context-menu-item" data-action="replace-preview"><i class="fas fa-image"></i> {{ t('loras.contextMenu.replacePreview') }}</div>
     <div class="context-menu-separator menu-section-break"></div>
     <!-- Attributes -->

templates/components/context_menu.html

@@ -44,8 +44,18 @@
     <div class="context-menu-item" data-action="preview">
         <i class="fas fa-folder-open"></i> <span>{{ t('loras.contextMenu.openExamples') }}</span>
     </div>
-    <div class="context-menu-item" data-action="download-examples">
-        <i class="fas fa-download"></i> <span>{{ t('loras.contextMenu.downloadExamples') }}</span>
+    <div class="context-menu-item has-submenu" data-has-submenu="download-examples">
+        <i class="fas fa-download"></i>
+        <span>{{ t('loras.contextMenu.downloadExamples') }}</span>
+        <i class="fas fa-chevron-right submenu-arrow"></i>
+        <div class="context-submenu">
+            <div class="context-menu-item" data-action="download-examples">
+                <i class="fas fa-download"></i> <span>{{ t('loras.contextMenu.downloadMissingExamples') }}</span>
+            </div>
+            <div class="context-menu-item" data-action="download-examples-force">
+                <i class="fas fa-redo-alt"></i> <span>{{ t('loras.contextMenu.reprocessExamples') }}</span>
+            </div>
+        </div>
     </div>
     <div class="context-menu-item" data-action="replace-preview">
         <i class="fas fa-image"></i> <span>{{ t('loras.contextMenu.replacePreview') }}</span>
@@ -136,8 +146,18 @@
     </div>
     <div class="context-menu-section" data-section="download">
         <div class="context-menu-section-header">{{ t('loras.bulkOperations.sections.download') }}</div>
-        <div class="context-menu-item" data-action="download-example-images">
-            <i class="fas fa-download"></i> <span>{{ t('loras.bulkOperations.downloadExamples') }}</span>
+        <div class="context-menu-item has-submenu" data-has-submenu="download-example-images">
+            <i class="fas fa-download"></i>
+            <span>{{ t('loras.bulkOperations.downloadExamples') }}</span>
+            <i class="fas fa-chevron-right submenu-arrow"></i>
+            <div class="context-submenu">
+                <div class="context-menu-item" data-action="download-missing-example-images">
+                    <i class="fas fa-download"></i> <span>{{ t('loras.bulkOperations.downloadMissingExamples') }}</span>
+                </div>
+                <div class="context-menu-item" data-action="download-example-images">
+                    <i class="fas fa-redo-alt"></i> <span>{{ t('loras.bulkOperations.reprocessExamples') }}</span>
+                </div>
+            </div>
         </div>
         <div class="context-menu-item" data-action="download-missing-loras">
             <i class="fas fa-download"></i> <span>{{ t('loras.bulkOperations.downloadMissingLoras') }}</span>

templates/embeddings.html

@@ -32,7 +32,12 @@
     <div class="context-menu-separator menu-section-break"></div>
     <!-- Media / Preview -->
     <div class="context-menu-item" data-action="preview"><i class="fas fa-folder-open"></i> {{ t('loras.contextMenu.openExamples') }}</div>
-    <div class="context-menu-item" data-action="download-examples"><i class="fas fa-download"></i> {{ t('loras.contextMenu.downloadExamples') }}</div>
+    <div class="context-menu-item has-submenu" data-has-submenu="download-examples"><i class="fas fa-download"></i> {{ t('loras.contextMenu.downloadExamples') }} <i class="fas fa-chevron-right submenu-arrow"></i>
+        <div class="context-submenu">
+            <div class="context-menu-item" data-action="download-examples"><i class="fas fa-download"></i> {{ t('loras.contextMenu.downloadMissingExamples') }}</div>
+            <div class="context-menu-item" data-action="download-examples-force"><i class="fas fa-redo-alt"></i> {{ t('loras.contextMenu.reprocessExamples') }}</div>
+        </div>
+    </div>
     <div class="context-menu-item" data-action="replace-preview"><i class="fas fa-image"></i> {{ t('loras.contextMenu.replacePreview') }}</div>
     <div class="context-menu-separator menu-section-break"></div>
     <!-- Attributes -->

tests/frontend/components/contextMenu.interactions.test.js

@@ -2155,4 +2155,35 @@ describe('Interaction-level regression coverage', () => {
     excludedItem.dispatchEvent(new Event('click', { bubbles: true }));
     expect(window.pageControls.enterExcludedView).toHaveBeenCalledTimes(1);
   });
+
+  it('routes single-model example downloads to missing-only and force paths', async () => {
+    document.body.innerHTML = `
+      <div id="loraContextMenu" class="context-menu">
+        <div class="context-menu-item has-submenu" data-has-submenu="download-examples">
+          <div class="context-submenu">
+            <div class="context-menu-item" data-action="download-examples"></div>
+            <div class="context-menu-item" data-action="download-examples-force"></div>
+          </div>
+        </div>
+      </div>
+    `;
+
+    const { LoraContextMenu } = await import('../../../static/js/components/ContextMenu/LoraContextMenu.js');
+    const contextMenu = new LoraContextMenu();
+
+    const card = document.createElement('div');
+    card.className = 'model-card';
+    card.dataset.filepath = '/models/test.safetensors';
+    card.dataset.sha256 = 'abc123hash';
+    document.body.appendChild(card);
+
+    contextMenu.showMenu(100, 100, card);
+
+    document.querySelector('[data-action="download-examples"]').dispatchEvent(new Event('click', { bubbles: true }));
+    expect(downloadExampleImagesApiMock).toHaveBeenCalledWith(['abc123hash'], null, { force: false });
+
+    contextMenu.showMenu(100, 100, card);
+    document.querySelector('[data-action="download-examples-force"]').dispatchEvent(new Event('click', { bubbles: true }));
+    expect(downloadExampleImagesApiMock).toHaveBeenCalledWith(['abc123hash'], null, { force: true });
+  });
 });

tests/frontend/managers/BulkManager.contentRating.test.js

@@ -0,0 +1,133 @@
+import { describe, it, beforeEach, expect, vi } from 'vitest';
+
+const showToastMock = vi.fn();
+const translateMock = vi.fn((key, params, fallback) => (typeof fallback === 'string' ? fallback : key));
+const getNSFWLevelNameMock = vi.fn((level) => {
+  if (level >= 16) return 'XXX';
+  if (level >= 8) return 'X';
+  if (level >= 4) return 'R';
+  if (level >= 2) return 'PG13';
+  if (level >= 1) return 'PG';
+  return 'Unknown';
+});
+
+const loadingManagerStub = {
+  showSimpleLoading: vi.fn(),
+  showCancelButton: vi.fn(),
+  hide: vi.fn(),
+};
+
+const stateStub = {
+  currentPageType: 'recipes',
+  bulkMode: false,
+  selectedModels: new Set(),
+  loadingManager: loadingManagerStub,
+  virtualScroller: { updateSingleItem: vi.fn() },
+  global: { settings: {} },
+};
+
+const saveModelMetadataMock = vi.fn();
+const getModelApiClientMock = vi.fn(() => ({ saveModelMetadata: saveModelMetadataMock }));
+const updateRecipeMetadataMock = vi.fn(() => Promise.resolve({ success: true }));
+
+vi.mock('../../../static/js/state/index.js', () => ({
+  state: stateStub,
+  getCurrentPageState: vi.fn(),
+}));
+
+vi.mock('../../../static/js/utils/uiHelpers.js', () => ({
+  showToast: showToastMock,
+  copyToClipboard: vi.fn(),
+  sendLoraToWorkflow: vi.fn(),
+  sendEmbeddingToWorkflow: vi.fn(),
+  buildLoraSyntax: vi.fn(),
+  getNSFWLevelName: getNSFWLevelNameMock,
+}));
+
+vi.mock('../../../static/js/api/modelApiFactory.js', () => ({
+  getModelApiClient: getModelApiClientMock,
+  resetAndReload: vi.fn(),
+}));
+
+vi.mock('../../../static/js/api/recipeApi.js', () => ({
+  RecipeSidebarApiClient: class {},
+  updateRecipeMetadata: updateRecipeMetadataMock,
+  extractRecipeId: vi.fn(),
+}));
+
+vi.mock('../../../static/js/api/apiConfig.js', () => ({
+  MODEL_TYPES: { LORA: 'loras', CHECKPOINT: 'checkpoints', EMBEDDING: 'embeddings' },
+  MODEL_CONFIG: {},
+}));
+
+vi.mock('../../../static/js/managers/ModalManager.js', () => ({
+  modalManager: { showModal: vi.fn(), closeModal: vi.fn() },
+}));
+
+vi.mock('../../../static/js/components/shared/ModelCard.js', () => ({
+  updateCardsForBulkMode: vi.fn(),
+}));
+
+vi.mock('../../../static/js/utils/i18nHelpers.js', () => ({
+  translate: translateMock,
+}));
+
+vi.mock('../../../static/js/utils/priorityTagHelpers.js', () => ({
+  getPriorityTagSuggestions: vi.fn(),
+}));
+
+vi.mock('../../../static/js/components/shared/NsfwLevelSelector.js', () => ({
+  getNsfwLevelSelector: vi.fn(),
+}));
+
+describe('BulkManager bulk content rating', () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+    stateStub.currentPageType = 'recipes';
+    stateStub.bulkMode = false;
+    stateStub.selectedModels.clear();
+    saveModelMetadataMock.mockResolvedValue(undefined);
+    updateRecipeMetadataMock.mockResolvedValue({ success: true });
+  });
+
+  async function createBulkManager() {
+    const { BulkManager } = await import('../../../static/js/managers/BulkManager.js');
+    return new BulkManager();
+  }
+
+  it('exposes the content rating action on the recipes page action config', async () => {
+    const bulk = await createBulkManager();
+    expect(bulk.actionConfig.recipes.setContentRating).toBe(true);
+  });
+
+  it('persists the rating through the recipe API when on the recipes page', async () => {
+    const bulk = await createBulkManager();
+    stateStub.currentPageType = 'recipes';
+    stateStub.selectedModels.add('/recipes/test.webp');
+
+    const ok = await bulk.setBulkContentRating(4, ['/recipes/test.webp']);
+
+    expect(ok).toBe(true);
+    expect(updateRecipeMetadataMock).toHaveBeenCalledWith('/recipes/test.webp', { preview_nsfw_level: 4 });
+    expect(updateRecipeMetadataMock).toHaveBeenCalledTimes(1);
+    expect(saveModelMetadataMock).not.toHaveBeenCalled();
+    expect(showToastMock).toHaveBeenCalledWith(
+      'toast.models.bulkContentRatingSet',
+      { count: 1, level: 'R' },
+      'success'
+    );
+  });
+
+  it('persists the rating through the model API on model pages', async () => {
+    const bulk = await createBulkManager();
+    stateStub.currentPageType = 'loras';
+    stateStub.selectedModels.add('/models/test.safetensors');
+
+    const ok = await bulk.setBulkContentRating(8, ['/models/test.safetensors']);
+
+    expect(ok).toBe(true);
+    expect(saveModelMetadataMock).toHaveBeenCalledWith('/models/test.safetensors', { preview_nsfw_level: 8 });
+    expect(saveModelMetadataMock).toHaveBeenCalledTimes(1);
+    expect(updateRecipeMetadataMock).not.toHaveBeenCalled();
+  });
+});

tests/services/test_example_images_download_manager_async.py

@@ -529,7 +529,8 @@ async def test_not_found_example_images_are_cleaned(
 
     model_dir = images_root / model_hash
     model_dir.mkdir(parents=True, exist_ok=True)
-    (model_dir / "image_0.png").write_bytes(b"first")
+    # Pre-existing file collides with the valid image index (1) so the
+    # pre-download existence check must skip it without a network request
     (model_dir / "image_1.png").write_bytes(b"second")
 
     async def fake_process_local_examples(*_args, **_kwargs):
@@ -608,11 +609,188 @@ async def test_not_found_example_images_are_cleaned(
     ]
 
     files = sorted(p.name for p in model_dir.iterdir())
-    assert files == ["image_0.png", "image_1.png"]
-    assert (model_dir / "image_0.png").read_bytes() == b"first"
+    assert files == ["image_1.png"]
     assert (model_dir / "image_1.png").read_bytes() == b"second"
 
 
+async def test_failed_models_retried_when_explicitly_targeted(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path,
+    settings_manager,
+):
+    ws_manager = RecordingWebSocketManager()
+    manager = download_module.DownloadManager(ws_manager=ws_manager)
+
+    images_root = tmp_path / "examples"
+    monkeypatch.setitem(settings_manager.settings, "example_images_path", str(images_root))
+
+    model_hash = "a" * 64
+    model_path = tmp_path / "model.safetensors"
+    model_path.write_text("data", encoding="utf-8")
+
+    model_metadata = {
+        "sha256": model_hash,
+        "model_name": "Failed Example",
+        "file_path": str(model_path),
+        "file_name": "model.safetensors",
+        "civitai": {"images": [{"url": "https://example.com/valid.png"}]},
+    }
+
+    scanner = StubScanner([model_metadata.copy()])
+    _patch_scanner(monkeypatch, scanner)
+
+    # Persist a previous failure so the skip path is exercised
+    images_root.mkdir(parents=True, exist_ok=True)
+    (images_root / ".download_progress.json").write_text(
+        json.dumps(
+            {
+                "failed_models": [model_hash],
+                "processed_models": [],
+                "rate_limited_models": [],
+            }
+        ),
+        encoding="utf-8",
+    )
+
+    async def fake_process_local_examples(*_args, **_kwargs):
+        return False
+
+    async def fake_get_updated_model(model_hash_arg, _scanner):
+        return model_metadata
+
+    class DownloaderStub:
+        def __init__(self):
+            self.calls: list[str] = []
+
+        async def download_to_memory(self, url, *_args, **_kwargs):
+            self.calls.append(url)
+            return True, b"\x89PNG\r\n\x1a\n", {"content-type": "image/png"}
+
+    downloader = DownloaderStub()
+
+    async def fake_get_downloader():
+        return downloader
+
+    monkeypatch.setattr(
+        download_module.ExampleImagesProcessor,
+        "process_local_examples",
+        staticmethod(fake_process_local_examples),
+    )
+    monkeypatch.setattr(
+        download_module.MetadataUpdater,
+        "get_updated_model",
+        staticmethod(fake_get_updated_model),
+    )
+    monkeypatch.setattr(download_module, "get_downloader", fake_get_downloader)
+
+    # Without explicit hashes the previously failed model is skipped
+    skipped_manager = download_module.DownloadManager(ws_manager=RecordingWebSocketManager())
+    result = await skipped_manager.start_download({"model_types": ["lora"], "delay": 0})
+    assert result["success"] is True
+    if skipped_manager._download_task is not None:
+        await asyncio.wait_for(skipped_manager._download_task, timeout=1)
+    assert downloader.calls == []
+
+    # With explicit hashes the previously failed model is retried and cleared
+    result = await manager.start_download(
+        {"model_types": ["lora"], "delay": 0, "model_hashes": [model_hash]}
+    )
+    assert result["success"] is True
+    if manager._download_task is not None:
+        await asyncio.wait_for(manager._download_task, timeout=1)
+    assert downloader.calls == ["https://example.com/valid.png"]
+    assert manager._progress["failed_models"] == set()
+    assert model_hash in manager._progress["processed_models"]
+
+
+async def test_explicit_targets_fill_partial_example_gaps(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path,
+    settings_manager,
+):
+    ws_manager = RecordingWebSocketManager()
+
+    images_root = tmp_path / "examples"
+    monkeypatch.setitem(settings_manager.settings, "example_images_path", str(images_root))
+
+    model_hash = "b" * 64
+    model_path = tmp_path / "model.safetensors"
+    model_path.write_text("data", encoding="utf-8")
+
+    model_metadata = {
+        "sha256": model_hash,
+        "model_name": "Partial Example",
+        "file_path": str(model_path),
+        "file_name": "model.safetensors",
+        "civitai": {
+            "images": [
+                {"url": "https://example.com/first.png"},
+                {"url": "https://example.com/second.png"},
+            ]
+        },
+    }
+
+    scanner = StubScanner([model_metadata.copy()])
+    _patch_scanner(monkeypatch, scanner)
+
+    # Simulate a partially populated folder: index 0 already downloaded
+    model_dir = images_root / model_hash
+    model_dir.mkdir(parents=True, exist_ok=True)
+    (model_dir / "image_0.png").write_bytes(b"existing")
+
+    async def fake_process_local_examples(*_args, **_kwargs):
+        return False
+
+    async def fake_get_updated_model(model_hash_arg, _scanner):
+        return model_metadata
+
+    class DownloaderStub:
+        def __init__(self):
+            self.calls: list[str] = []
+
+        async def download_to_memory(self, url, *_args, **_kwargs):
+            self.calls.append(url)
+            return True, b"\x89PNG\r\n\x1a\n", {"content-type": "image/png"}
+
+    downloader = DownloaderStub()
+
+    async def fake_get_downloader():
+        return downloader
+
+    monkeypatch.setattr(
+        download_module.ExampleImagesProcessor,
+        "process_local_examples",
+        staticmethod(fake_process_local_examples),
+    )
+    monkeypatch.setattr(
+        download_module.MetadataUpdater,
+        "get_updated_model",
+        staticmethod(fake_get_updated_model),
+    )
+    monkeypatch.setattr(download_module, "get_downloader", fake_get_downloader)
+
+    # Untargeted run treats the populated folder as done
+    untargeted = download_module.DownloadManager(ws_manager=RecordingWebSocketManager())
+    result = await untargeted.start_download({"model_types": ["lora"], "delay": 0})
+    assert result["success"] is True
+    if untargeted._download_task is not None:
+        await asyncio.wait_for(untargeted._download_task, timeout=1)
+    assert downloader.calls == []
+
+    # Explicitly targeted run fills only the missing index, skipping the
+    # existing file without a network request
+    targeted = download_module.DownloadManager(ws_manager=ws_manager)
+    result = await targeted.start_download(
+        {"model_types": ["lora"], "delay": 0, "model_hashes": [model_hash]}
+    )
+    assert result["success"] is True
+    if targeted._download_task is not None:
+        await asyncio.wait_for(targeted._download_task, timeout=1)
+    assert downloader.calls == ["https://example.com/second.png"]
+    assert (model_dir / "image_1.png").exists()
+    assert (model_dir / "image_0.png").read_bytes() == b"existing"
+
+
 @pytest.fixture
 def settings_manager():
     return get_settings_manager()

tests/utils/test_example_images_download_manager_unit.py

@@ -63,7 +63,7 @@ async def test_start_download_bootstraps_progress_and_task(
     release = asyncio.Event()
 
     async def fake_download(
-        self, output_dir, optimize, model_types, delay, library_name, force=False
+        self, output_dir, optimize, model_types, delay, library_name, force=False, model_hashes=None
     ):
         started.set()
         await release.wait()
@@ -93,6 +93,44 @@ async def test_start_download_bootstraps_progress_and_task(
     assert manager._progress["status"] == "completed"
 
 
+async def test_start_download_forwards_model_hashes(
+    monkeypatch: pytest.MonkeyPatch, tmp_path
+) -> None:
+    settings_manager = get_settings_manager()
+    settings_manager.settings["example_images_path"] = str(tmp_path)
+    settings_manager.settings["libraries"] = {"default": {}}
+    settings_manager.settings["active_library"] = "default"
+
+    manager = download_module.DownloadManager(ws_manager=RecordingWebSocketManager())
+
+    received: Dict[str, Any] = {}
+
+    async def fake_download(
+        self, output_dir, optimize, model_types, delay, library_name, force=False, model_hashes=None
+    ):
+        received["model_hashes"] = model_hashes
+        async with self._state_lock:
+            self._is_downloading = False
+            self._download_task = None
+        self._progress["status"] = "completed"
+
+    monkeypatch.setattr(
+        download_module.DownloadManager,
+        "_download_all_example_images",
+        fake_download,
+    )
+
+    result = await manager.start_download(
+        {"model_types": ["lora"], "delay": 0, "model_hashes": ["abc123", "def456"]}
+    )
+    assert result["success"] is True
+
+    task = manager._download_task
+    assert task is not None
+    await asyncio.wait_for(task, timeout=1)
+    assert received["model_hashes"] == ["abc123", "def456"]
+
+
 async def test_pause_and_resume_flow(monkeypatch: pytest.MonkeyPatch, tmp_path) -> None:
     settings_manager = get_settings_manager()
     settings_manager.settings["example_images_path"] = str(tmp_path)

tests/utils/test_example_images_processor_unit.py

@@ -100,6 +100,54 @@ def test_get_file_extension_media_type_hint_low_priority() -> None:
     assert ext == ".mp4"
 
 
+def test_example_image_file_exists_checks_plausible_extensions(tmp_path) -> None:
+    proc = processor_module.ExampleImagesProcessor
+    assert proc._example_image_file_exists(str(tmp_path), 0) is False
+    Path(tmp_path, "image_0.webp").write_bytes(b"x")
+    assert proc._example_image_file_exists(str(tmp_path), 0) is True
+    assert proc._example_image_file_exists(str(tmp_path), 1) is False
+
+
+def test_example_image_file_exists_video_hint_only_checks_video_extensions(tmp_path) -> None:
+    proc = processor_module.ExampleImagesProcessor
+    Path(tmp_path, "image_2.jpg").write_bytes(b"x")
+    # An existing image file must not satisfy a video-hinted lookup
+    assert proc._example_image_file_exists(str(tmp_path), 2, "video") is False
+    Path(tmp_path, "image_2.mp4").write_bytes(b"x")
+    assert proc._example_image_file_exists(str(tmp_path), 2, "video") is True
+
+
+async def test_download_model_images_with_tracking_skips_existing_files(tmp_path) -> None:
+    proc = processor_module.ExampleImagesProcessor
+    images = [
+        {"url": "https://image.civitai.com/a/b", "type": "image"},
+        {"url": "https://image.civitai.com/c/d", "type": "image"},
+    ]
+    Path(tmp_path, "image_0.jpg").write_bytes(b"existing")
+
+    class RecordingDownloader:
+        def __init__(self) -> None:
+            self.calls: list[str] = []
+
+        async def download_to_memory(self, url, use_auth=False, return_headers=False):
+            self.calls.append(url)
+            return True, b"\xff\xd8\xff" + b"data", {}
+
+    downloader = RecordingDownloader()
+    success, is_stale, failed, rate_limited = await proc.download_model_images_with_tracking(
+        "hash", "model", images, str(tmp_path), False, downloader
+    )
+
+    assert success is True
+    assert is_stale is False
+    assert failed == []
+    assert rate_limited == []
+    # Only the missing image is requested; the existing one is skipped without a network call
+    assert len(downloader.calls) == 1
+    assert "c/d" in downloader.calls[0]
+    assert Path(tmp_path, "image_1.jpg").exists()
+
+
 class StubScanner:
     def __init__(self, models: list[Dict[str, Any]]) -> None:
         self._cache = SimpleNamespace(raw_data=models)