fix(recipes): enable bulk content rating for selected recipes

The model field now accepts either a manual string or a MODEL connection.
When wired, the model name is extracted from the patcher's
cached_patcher_init (registered by core loaders load_checkpoint_guess_config
and load_diffusion_model, preserved through LoRA clones) and converted to a
ComfyUI-style relative name via config model roots.

- model input declared as "STRING,MODEL" with widgetType STRING, so the
  text widget and the dual-type connection slot coexist; non-STRING/MODEL
  links are rejected by frontend and backend type validation
- UNETLoaderLM GGUF branch now registers a custom cached_patcher_init reload
  factory so GGUF models participate in name extraction and ModelPatcher
  deepclone/dynamic machinery
- shared collect_overwrite_params() helper keeps the node and the metadata
  extractor conversion logic in sync; extraction failures are logged instead
  of silently dropping the overwrite

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 = {}
+        overwrite_params = collect_overwrite_params(inputs)
-        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
 
         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 (
+from ..metadata_collector.constants import CLIP_SKIP_SENTINEL as _CLIP_SKIP_SENTINEL
-    CLIP_SKIP_SENTINEL as _CLIP_SKIP_SENTINEL,
+from ..metadata_collector.overwrite_utils import collect_overwrite_params
-    METADATA_OVERWRITE_FIELDS,
-)
 
 
 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] = {}
+        return (collect_overwrite_params(kwargs),)
-        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,)

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/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/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++;

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();
+  });
+});