diff --git a/py/api/autotag_routes.py b/py/api/autotag_routes.py
index 69c0102..e20c5d1 100644
--- a/py/api/autotag_routes.py
+++ b/py/api/autotag_routes.py
@@ -54,6 +54,8 @@ class AutotagRoutesMixin:
                     "default_prompt": service.default_prompt,
                     "model_loaded": service.is_model_loaded(),
                     "loaded_model_type": service.get_loaded_model_type(),
+                    "wd14_general_threshold": service.wd14_general_threshold,
+                    "wd14_character_threshold": service.wd14_character_threshold,
                 }
             )
 
@@ -122,6 +124,14 @@ class AutotagRoutesMixin:
         keep_in_memory = request.query.get("keep_in_memory", "true").lower() == "true"
         use_gpu = True
 
+        # WD14 threshold params
+        general_threshold = request.query.get("general_threshold")
+        character_threshold = request.query.get("character_threshold")
+        if general_threshold is not None:
+            general_threshold = float(general_threshold)
+        if character_threshold is not None:
+            character_threshold = float(character_threshold)
+
         async def stream_response():
             try:
                 from ..autotag import get_autotag_service
@@ -203,7 +213,11 @@ class AutotagRoutesMixin:
                             prompt_tags = [
                                 t.strip() for t in prompt_tags.split(",") if t.strip()
                             ]
-                        real_tags = [t for t in prompt_tags if t != "auto-scanned"]
+                        real_tags = [
+                            t
+                            for t in prompt_tags
+                            if t != "auto-scanned" and not t.startswith("prepend:")
+                        ]
                         if real_tags:
                             tagged_prompt_ids.add(prompt_id)
                             skipped += 1
@@ -217,7 +231,12 @@ class AutotagRoutesMixin:
 
                     try:
                         tags = await loop.run_in_executor(
-                            None, lambda p=str(image_path): service.generate_tags(p)
+                            None,
+                            lambda p=str(image_path): service.generate_tags(
+                                p,
+                                general_threshold=general_threshold,
+                                character_threshold=character_threshold,
+                            ),
                         )
 
                         processed += 1
@@ -304,6 +323,12 @@ class AutotagRoutesMixin:
             model_type = data.get("model_type", "gguf")
             custom_prompt = data.get("prompt")
             use_gpu = data.get("use_gpu", True)
+            general_threshold = data.get("general_threshold")
+            character_threshold = data.get("character_threshold")
+            if general_threshold is not None:
+                general_threshold = float(general_threshold)
+            if character_threshold is not None:
+                character_threshold = float(character_threshold)
 
             if not image_path:
                 return web.json_response(
@@ -328,7 +353,12 @@ class AutotagRoutesMixin:
 
             loop = asyncio.get_event_loop()
             tags = await loop.run_in_executor(
-                None, lambda: service.generate_tags(image_path)
+                None,
+                lambda: service.generate_tags(
+                    image_path,
+                    general_threshold=general_threshold,
+                    character_threshold=character_threshold,
+                ),
             )
 
             prompt_id = None
diff --git a/py/autotag.py b/py/autotag.py
index 32302e7..f08c84f 100644
--- a/py/autotag.py
+++ b/py/autotag.py
@@ -50,6 +50,24 @@ MODELS = {
         "repo": "fancyfeast/llama-joycaption-beta-one-hf-llava",
         "subdir": "llama-joycaption-beta-one-hf-llava",
     },
+    "wd14-swinv2": {
+        "name": "WD14 SwinV2",
+        "description": "Fast classifier tagger, ~400MB, no prompt needed",
+        "size": "~400MB",
+        "repo": "SmilingWolf/wd-swinv2-tagger-v3",
+        "files": ["model.onnx", "selected_tags.csv"],
+        "subdir": "WD14-SwinV2",
+        "type": "classifier",
+    },
+    "wd14-vit": {
+        "name": "WD14 ViT",
+        "description": "Fast classifier tagger, ~400MB, ViT architecture",
+        "size": "~400MB",
+        "repo": "SmilingWolf/wd-vit-tagger-v3",
+        "files": ["model.onnx", "selected_tags.csv"],
+        "subdir": "WD14-ViT",
+        "type": "classifier",
+    },
 }
 
 # Default prompts
@@ -112,6 +130,10 @@ class AutoTagService:
         self._current_model_type: Optional[str] = None
         self._custom_prompt: str = DEFAULT_PROMPT
 
+        # WD14 threshold defaults
+        self._wd14_general_threshold: float = 0.35
+        self._wd14_character_threshold: float = 0.85
+
     @property
     def models_config(self) -> Dict[str, Dict[str, Any]]:
         """Get the models configuration dictionary."""
@@ -132,6 +154,22 @@ class AutoTagService:
         """Set a custom prompt for tag generation."""
         self._custom_prompt = value
 
+    @property
+    def wd14_general_threshold(self) -> float:
+        return self._wd14_general_threshold
+
+    @wd14_general_threshold.setter
+    def wd14_general_threshold(self, value: float):
+        self._wd14_general_threshold = max(0.0, min(1.0, value))
+
+    @property
+    def wd14_character_threshold(self) -> float:
+        return self._wd14_character_threshold
+
+    @wd14_character_threshold.setter
+    def wd14_character_threshold(self, value: float):
+        self._wd14_character_threshold = max(0.0, min(1.0, value))
+
     def get_models_status(self) -> Dict[str, Dict[str, Any]]:
         """Get availability status for all model types.
 
@@ -170,6 +208,12 @@ class AutoTagService:
                     model_status["model_path"] = str(
                         self.models_dir / config["subdir"] / config["filename"]
                     )
+            elif model_type.startswith("wd14"):
+                model_status["downloaded"] = self._check_wd14_model(model_type)
+                if model_status["downloaded"]:
+                    model_status["model_path"] = str(
+                        self.models_dir / config["subdir"] / "model.onnx"
+                    )
             else:  # hf
                 model_status["downloaded"] = self._check_hf_model()
                 if model_status["downloaded"]:
@@ -269,6 +313,140 @@ class AutoTagService:
 
         return None
 
+    def _check_wd14_model(self, model_type: str) -> bool:
+        """Check if WD14 model files exist."""
+        config = MODELS[model_type]
+        model_dir = self.models_dir / config["subdir"]
+        return all((model_dir / f).exists() for f in config["files"])
+
+    def _download_wd14_model(
+        self,
+        model_type: str,
+        progress_callback: Optional[Callable[[str, float], None]] = None,
+    ) -> bool:
+        """Download WD14 model files from HuggingFace."""
+        from huggingface_hub import hf_hub_download
+
+        config = MODELS[model_type]
+        model_dir = self.models_dir / config["subdir"]
+        model_dir.mkdir(parents=True, exist_ok=True)
+
+        files = config["files"]
+        for i, filename in enumerate(files):
+            file_path = model_dir / filename
+            if not file_path.exists():
+                progress = int((i / len(files)) * 80) + 10
+                if progress_callback:
+                    progress_callback(f"Downloading {filename}...", progress)
+
+                self.logger.info(f"Downloading WD14 file: {filename}")
+                hf_hub_download(
+                    repo_id=config["repo"],
+                    filename=filename,
+                    local_dir=str(model_dir),
+                    local_dir_use_symlinks=False,
+                )
+
+        if progress_callback:
+            progress_callback("Download complete", 100)
+        return True
+
+    def _load_wd14_tagger(self, model_type: str, use_gpu: bool = True):
+        """Load WD14 ONNX model and tag list."""
+        try:
+            import onnxruntime as ort
+        except ImportError:
+            raise RuntimeError(
+                "onnxruntime is required for WD14 models. "
+                "Install with: pip install onnxruntime (or onnxruntime-gpu for CUDA)"
+            )
+        import csv
+
+        config = MODELS[model_type]
+        model_dir = self.models_dir / config["subdir"]
+        model_path = model_dir / "model.onnx"
+        tags_path = model_dir / "selected_tags.csv"
+
+        self.logger.info(f"Loading WD14 model from {model_dir}...")
+
+        # Set up ONNX session
+        providers = []
+        if use_gpu:
+            providers.append("CUDAExecutionProvider")
+        providers.append("CPUExecutionProvider")
+
+        session = ort.InferenceSession(str(model_path), providers=providers)
+
+        # Read tag list from CSV
+        tags_list = []
+        with open(tags_path, "r", encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                tags_list.append(row)
+
+        self.logger.info(
+            f"WD14 model loaded ({len(tags_list)} tags, "
+            f"provider: {session.get_providers()[0]})"
+        )
+        return ("wd14", (session, tags_list))
+
+    def _generate_wd14(
+        self,
+        session,
+        tags_list: List[Dict[str, str]],
+        image: Image.Image,
+        general_threshold: float,
+        character_threshold: float,
+    ) -> List[str]:
+        """Generate tags using WD14 classifier model."""
+        import numpy as np
+
+        # Get model input dimensions
+        input_shape = session.get_inputs()[0].shape
+        target_size = input_shape[2]  # e.g. 448
+
+        # Pad to square with white background
+        max_dim = max(image.size)
+        padded = Image.new("RGB", (max_dim, max_dim), (255, 255, 255))
+        padded.paste(
+            image, ((max_dim - image.width) // 2, (max_dim - image.height) // 2)
+        )
+
+        # Resize to model input size
+        padded = padded.resize((target_size, target_size), Image.Resampling.BILINEAR)
+
+        # Convert to numpy: RGB -> BGR, float32, add batch dim
+        img_array = np.array(padded, dtype=np.float32)
+        img_array = img_array[:, :, ::-1]  # RGB to BGR
+        img_array = np.expand_dims(img_array, axis=0)
+
+        # Run inference
+        input_name = session.get_inputs()[0].name
+        output = session.run(None, {input_name: img_array})
+        probabilities = output[0][0]  # First output, first batch
+
+        # Filter tags by category and threshold
+        result_tags = []
+        for i, tag_info in enumerate(tags_list):
+            if i >= len(probabilities):
+                break
+            prob = float(probabilities[i])
+            category = tag_info.get("category", "0")
+            tag_name = tag_info.get("name", "").strip()
+
+            if not tag_name:
+                continue
+
+            # Category 0 = general, 4 = character, 9 = rating
+            if category == "0" and prob >= general_threshold:
+                result_tags.append((tag_name, prob))
+            elif category == "4" and prob >= character_threshold:
+                result_tags.append((tag_name, prob))
+
+        # Sort by confidence descending
+        result_tags.sort(key=lambda x: x[1], reverse=True)
+        return [tag for tag, _ in result_tags]
+
     def download_model(
         self,
         model_type: str,
@@ -288,7 +466,7 @@ class AutoTagService:
         """
         if model_type not in MODELS:
             raise ValueError(
-                f"Invalid model type: {model_type}. Must be 'gguf' or 'hf'"
+                f"Invalid model type: {model_type}. Must be one of: {', '.join(MODELS.keys())}"
             )
 
         try:
@@ -302,6 +480,8 @@ class AutoTagService:
         try:
             if model_type == "gguf":
                 return self._download_gguf_models(progress_callback)
+            elif model_type.startswith("wd14"):
+                return self._download_wd14_model(model_type, progress_callback)
             else:
                 return self._download_hf_model(progress_callback)
         except Exception as e:
@@ -413,6 +593,8 @@ class AutoTagService:
         try:
             if model_type == "gguf":
                 self._tagger = self._load_gguf_tagger(use_gpu)
+            elif model_type.startswith("wd14"):
+                self._tagger = self._load_wd14_tagger(model_type, use_gpu)
             else:
                 self._tagger = self._load_hf_tagger()
 
@@ -527,12 +709,20 @@ class AutoTagService:
         """Get the type of currently loaded model."""
         return self._current_model_type
 
-    def generate_tags(self, image_path: str, prompt: Optional[str] = None) -> List[str]:
+    def generate_tags(
+        self,
+        image_path: str,
+        prompt: Optional[str] = None,
+        general_threshold: Optional[float] = None,
+        character_threshold: Optional[float] = None,
+    ) -> List[str]:
         """Generate tags for an image.
 
         Args:
             image_path: Path to the image file
-            prompt: Custom prompt for tag generation. Uses default if None.
+            prompt: Custom prompt for tag generation (LLM models only). Uses default if None.
+            general_threshold: Confidence threshold for general tags (WD14 only).
+            character_threshold: Confidence threshold for character tags (WD14 only).
 
         Returns:
             List of generated tags
@@ -559,15 +749,28 @@ class AutoTagService:
 
         if model_type == "gguf":
             raw_tags = self._generate_gguf(tagger_obj, image, use_prompt)
+            return self._parse_tags(raw_tags)
+        elif model_type == "wd14":
+            session, tags_list = tagger_obj
+            gen_thresh = (
+                general_threshold
+                if general_threshold is not None
+                else self._wd14_general_threshold
+            )
+            char_thresh = (
+                character_threshold
+                if character_threshold is not None
+                else self._wd14_character_threshold
+            )
+            return self._generate_wd14(
+                session, tags_list, image, gen_thresh, char_thresh
+            )
         else:
             model, processor, device, compute_dtype = tagger_obj
             raw_tags = self._generate_hf(
                 model, processor, device, compute_dtype, image, use_prompt
             )
-
-        # Parse tags from response
-        tags = self._parse_tags(raw_tags)
-        return tags
+            return self._parse_tags(raw_tags)
 
     def _generate_gguf(self, model, image: Image.Image, prompt: str) -> str:
         """Generate tags using GGUF model."""
@@ -670,6 +873,7 @@ class AutoTagService:
             "meta:",
             "photo_",
             "photo:",
+            "prepend:",
         )
 
         # Split by common delimiters
diff --git a/web/admin.html b/web/admin.html
index c3b923e..ff17d2e 100644
--- a/web/admin.html
+++ b/web/admin.html
@@ -980,11 +980,35 @@
                             <span class="text-pm-secondary text-[11px]">Checking...</span>
                         </div>
                     </label>
+                    <label class="flex items-center justify-between p-3 bg-pm-input rounded-pm-md cursor-pointer hover:bg-pm-hover border border-pm">
+                        <div class="flex items-center">
+                            <input type="radio" name="autoTagModel" value="wd14-swinv2" class="w-3.5 h-3.5 accent-[var(--pm-accent)]">
+                            <div class="ml-2.5">
+                                <span class="text-pm text-xs font-medium">WD14 SwinV2</span>
+                                <span class="text-pm-secondary text-[11px] ml-1.5">(~400MB, fast classifier)</span>
+                            </div>
+                        </div>
+                        <div id="wd14SwinV2ModelStatus" class="flex items-center gap-1.5">
+                            <span class="text-pm-secondary text-[11px]">Checking...</span>
+                        </div>
+                    </label>
+                    <label class="flex items-center justify-between p-3 bg-pm-input rounded-pm-md cursor-pointer hover:bg-pm-hover border border-pm">
+                        <div class="flex items-center">
+                            <input type="radio" name="autoTagModel" value="wd14-vit" class="w-3.5 h-3.5 accent-[var(--pm-accent)]">
+                            <div class="ml-2.5">
+                                <span class="text-pm text-xs font-medium">WD14 ViT</span>
+                                <span class="text-pm-secondary text-[11px] ml-1.5">(~400MB, ViT architecture)</span>
+                            </div>
+                        </div>
+                        <div id="wd14VitModelStatus" class="flex items-center gap-1.5">
+                            <span class="text-pm-secondary text-[11px]">Checking...</span>
+                        </div>
+                    </label>
                 </div>
             </div>
 
-            <!-- Prompt Template -->
-            <div class="mb-4">
+            <!-- Prompt Template (LLM models only) -->
+            <div id="promptSection" class="mb-4">
                 <label class="block text-xs font-medium text-pm-secondary mb-1">Tag Generation Prompt</label>
                 <textarea id="autoTagPrompt" rows="6"
                     class="w-full px-2.5 py-1.5 bg-pm-input border border-pm rounded-pm-sm text-pm text-[13px] font-mono resize-none focus:outline-none"
@@ -994,6 +1018,29 @@ Prioritize visual accuracy.
 No sentences, no commentary, no captions. Only tags. Keep length short but descriptive.</textarea>
             </div>
 
+            <!-- WD14 Threshold Controls -->
+            <div id="wd14ThresholdSection" class="mb-4 hidden">
+                <label class="block text-xs font-medium text-pm-secondary mb-2">Tag Confidence Thresholds</label>
+                <div class="space-y-3">
+                    <div class="p-2.5 bg-pm-input rounded-pm-md border border-pm">
+                        <div class="flex items-center justify-between mb-1">
+                            <span class="text-pm text-xs">General tags</span>
+                            <span id="wd14GeneralThresholdValue" class="text-pm-accent text-xs font-mono">0.35</span>
+                        </div>
+                        <input type="range" id="wd14GeneralThreshold" min="0" max="1" step="0.05" value="0.35"
+                            class="w-full h-1.5 bg-pm-primary rounded-lg appearance-none cursor-pointer accent-[var(--pm-accent)]">
+                    </div>
+                    <div class="p-2.5 bg-pm-input rounded-pm-md border border-pm">
+                        <div class="flex items-center justify-between mb-1">
+                            <span class="text-pm text-xs">Character tags</span>
+                            <span id="wd14CharacterThresholdValue" class="text-pm-accent text-xs font-mono">0.85</span>
+                        </div>
+                        <input type="range" id="wd14CharacterThreshold" min="0" max="1" step="0.05" value="0.85"
+                            class="w-full h-1.5 bg-pm-primary rounded-lg appearance-none cursor-pointer accent-[var(--pm-accent)]">
+                    </div>
+                </div>
+            </div>
+
             <!-- Tagged Images Handling -->
             <div class="mb-4">
                 <label class="block text-xs font-medium text-pm-secondary mb-2">Already Tagged Images</label>
diff --git a/web/gallery.html b/web/gallery.html
index ca4a982..f33f104 100644
--- a/web/gallery.html
+++ b/web/gallery.html
@@ -452,6 +452,30 @@
                             <span class="text-pm-secondary text-xs">Checking...</span>
                         </div>
                     </label>
+                    <label class="flex items-center justify-between p-3 bg-pm-input rounded-pm-sm cursor-pointer hover:bg-pm-hover border border-pm">
+                        <div class="flex items-center">
+                            <input type="radio" name="autoTagModel" value="wd14-swinv2" class="w-4 h-4 text-pm-accent bg-pm-input border-pm focus:ring-pm-accent">
+                            <div class="ml-3">
+                                <span class="text-pm text-xs font-medium">WD14 SwinV2</span>
+                                <span class="text-pm-secondary text-xs ml-2">(~400MB, fast classifier)</span>
+                            </div>
+                        </div>
+                        <div id="wd14SwinV2ModelStatus" class="flex items-center gap-2">
+                            <span class="text-pm-secondary text-xs">Checking...</span>
+                        </div>
+                    </label>
+                    <label class="flex items-center justify-between p-3 bg-pm-input rounded-pm-sm cursor-pointer hover:bg-pm-hover border border-pm">
+                        <div class="flex items-center">
+                            <input type="radio" name="autoTagModel" value="wd14-vit" class="w-4 h-4 text-pm-accent bg-pm-input border-pm focus:ring-pm-accent">
+                            <div class="ml-3">
+                                <span class="text-pm text-xs font-medium">WD14 ViT</span>
+                                <span class="text-pm-secondary text-xs ml-2">(~400MB, ViT architecture)</span>
+                            </div>
+                        </div>
+                        <div id="wd14VitModelStatus" class="flex items-center gap-2">
+                            <span class="text-pm-secondary text-xs">Checking...</span>
+                        </div>
+                    </label>
                 </div>
             </div>
 
@@ -474,8 +498,8 @@
                 </p>
             </div>
 
-            <!-- Prompt Template -->
-            <div class="mb-3">
+            <!-- Prompt Template (LLM models only) -->
+            <div id="promptSection" class="mb-3">
                 <label class="block text-xs font-medium text-pm-secondary mb-2">Tag Generation Prompt</label>
                 <textarea id="autoTagPrompt" rows="6"
                     class="w-full px-3 py-2 bg-pm-input border border-pm rounded-pm-sm focus:ring-2 focus:ring-pm-accent focus:border-pm-accent text-pm text-xs font-mono resize-none"
@@ -485,6 +509,29 @@ Prioritize visual accuracy.
 No sentences, no commentary, no captions. Only tags. Keep length short but descriptive.</textarea>
             </div>
 
+            <!-- WD14 Threshold Controls -->
+            <div id="wd14ThresholdSection" class="mb-3 hidden">
+                <label class="block text-xs font-medium text-pm-secondary mb-2">Tag Confidence Thresholds</label>
+                <div class="space-y-3">
+                    <div class="p-2.5 bg-pm-input rounded-pm-sm border border-pm">
+                        <div class="flex items-center justify-between mb-1">
+                            <span class="text-pm text-xs">General tags</span>
+                            <span id="wd14GeneralThresholdValue" class="text-pm-accent text-xs font-mono">0.35</span>
+                        </div>
+                        <input type="range" id="wd14GeneralThreshold" min="0" max="1" step="0.05" value="0.35"
+                            class="w-full h-1.5 bg-pm-primary rounded-lg appearance-none cursor-pointer accent-[var(--pm-accent)]">
+                    </div>
+                    <div class="p-2.5 bg-pm-input rounded-pm-sm border border-pm">
+                        <div class="flex items-center justify-between mb-1">
+                            <span class="text-pm text-xs">Character tags</span>
+                            <span id="wd14CharacterThresholdValue" class="text-pm-accent text-xs font-mono">0.85</span>
+                        </div>
+                        <input type="range" id="wd14CharacterThreshold" min="0" max="1" step="0.05" value="0.85"
+                            class="w-full h-1.5 bg-pm-primary rounded-lg appearance-none cursor-pointer accent-[var(--pm-accent)]">
+                    </div>
+                </div>
+            </div>
+
             <!-- Action Buttons -->
             <div class="flex justify-end gap-2">
                 <button onclick="hideModal('autoTagModal')"
diff --git a/web/js/admin.js b/web/js/admin.js
index 4a4f730..9cfe5f9 100644
--- a/web/js/admin.js
+++ b/web/js/admin.js
@@ -171,6 +171,23 @@
                 document.getElementById("retagSkipAllBtn").addEventListener("click", () => this.handleRetagChoice('skipAll'));
                 document.getElementById("retagConfirmBtn").addEventListener("click", () => this.handleRetagChoice('retag'));
 
+                // WD14 model selection toggle (prompt vs thresholds)
+                document.querySelectorAll('input[name="autoTagModel"]').forEach(radio => {
+                    radio.addEventListener('change', (e) => {
+                        const isWd14 = e.target.value.startsWith('wd14');
+                        document.getElementById('promptSection').classList.toggle('hidden', isWd14);
+                        document.getElementById('wd14ThresholdSection').classList.toggle('hidden', !isWd14);
+                    });
+                });
+
+                // WD14 threshold slider value display
+                document.getElementById('wd14GeneralThreshold').addEventListener('input', (e) => {
+                    document.getElementById('wd14GeneralThresholdValue').textContent = parseFloat(e.target.value).toFixed(2);
+                });
+                document.getElementById('wd14CharacterThreshold').addEventListener('input', (e) => {
+                    document.getElementById('wd14CharacterThresholdValue').textContent = parseFloat(e.target.value).toFixed(2);
+                });
+
                 // Tags accordion toggle
                 document.getElementById("reviewTagsToggle").addEventListener("click", () => this.toggleReviewTags());
 
@@ -3656,44 +3673,69 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                 await this.checkAutoTagModels();
             }
 
+            _setModelStatus(el, downloaded, modelKey) {
+                el.textContent = '';
+                if (downloaded) {
+                    const span = document.createElement('span');
+                    span.className = 'text-pm-success text-sm';
+                    span.textContent = '\u2713 Downloaded';
+                    el.appendChild(span);
+                } else {
+                    const btn = document.createElement('button');
+                    btn.className = 'px-3 py-1 bg-pm-accent hover:bg-pm-accent-hover text-pm text-xs rounded transition-colors';
+                    btn.textContent = 'Download';
+                    btn.addEventListener('click', () => window.admin.downloadModel(modelKey));
+                    el.appendChild(btn);
+                }
+            }
+
             async checkAutoTagModels() {
                 const ggufStatus = document.getElementById("ggufModelStatus");
                 const hfStatus = document.getElementById("hfModelStatus");
+                const wd14SwinV2Status = document.getElementById("wd14SwinV2ModelStatus");
+                const wd14VitStatus = document.getElementById("wd14VitModelStatus");
 
-                ggufStatus.innerHTML = '<span class="text-pm-secondary text-sm">Checking...</span>';
-                hfStatus.innerHTML = '<span class="text-pm-secondary text-sm">Checking...</span>';
+                const statusEls = [ggufStatus, hfStatus, wd14SwinV2Status, wd14VitStatus];
+                statusEls.forEach(el => { el.textContent = 'Checking...'; el.className = 'flex items-center gap-1.5'; });
 
                 try {
                     const response = await fetch('/prompt_manager/autotag/models');
                     const data = await response.json();
 
                     if (data.success) {
-                        // Update GGUF status
-                        if (data.models.gguf.downloaded) {
-                            ggufStatus.innerHTML = '<span class="text-pm-success text-sm">✓ Downloaded</span>';
-                        } else {
-                            ggufStatus.innerHTML = `<button onclick="window.admin.downloadModel('gguf')" class="px-3 py-1 bg-pm-accent hover:bg-pm-accent-hover text-pm text-xs rounded transition-colors">Download</button>`;
-                        }
+                        this._setModelStatus(ggufStatus, data.models.gguf.downloaded, 'gguf');
+                        this._setModelStatus(hfStatus, data.models.hf.downloaded, 'hf');
+                        this._setModelStatus(wd14SwinV2Status, data.models['wd14-swinv2']?.downloaded, 'wd14-swinv2');
+                        this._setModelStatus(wd14VitStatus, data.models['wd14-vit']?.downloaded, 'wd14-vit');
 
-                        // Update HF status
-                        if (data.models.hf.downloaded) {
-                            hfStatus.innerHTML = '<span class="text-pm-success text-sm">✓ Downloaded</span>';
-                        } else {
-                            hfStatus.innerHTML = `<button onclick="window.admin.downloadModel('hf')" class="px-3 py-1 bg-pm-accent hover:bg-pm-accent-hover text-pm text-xs rounded transition-colors">Download</button>`;
+                        // Update WD14 threshold defaults from server
+                        if (data.wd14_general_threshold !== undefined) {
+                            const genSlider = document.getElementById('wd14GeneralThreshold');
+                            genSlider.value = data.wd14_general_threshold;
+                            document.getElementById('wd14GeneralThresholdValue').textContent = parseFloat(data.wd14_general_threshold).toFixed(2);
+                        }
+                        if (data.wd14_character_threshold !== undefined) {
+                            const charSlider = document.getElementById('wd14CharacterThreshold');
+                            charSlider.value = data.wd14_character_threshold;
+                            document.getElementById('wd14CharacterThresholdValue').textContent = parseFloat(data.wd14_character_threshold).toFixed(2);
                         }
                     } else {
-                        ggufStatus.innerHTML = '<span class="text-pm-error text-sm">Error</span>';
-                        hfStatus.innerHTML = '<span class="text-pm-error text-sm">Error</span>';
+                        statusEls.forEach(el => { el.textContent = 'Error'; });
                     }
                 } catch (error) {
                     console.error('Error checking models:', error);
-                    ggufStatus.innerHTML = '<span class="text-pm-error text-sm">Error</span>';
-                    hfStatus.innerHTML = '<span class="text-pm-error text-sm">Error</span>';
+                    statusEls.forEach(el => { el.textContent = 'Error'; });
                 }
             }
 
             async downloadModel(modelType) {
-                const modelName = modelType === 'gguf' ? 'GGUF Model' : 'HuggingFace Model';
+                const modelNames = {
+                    'gguf': 'GGUF Model',
+                    'hf': 'HuggingFace Model',
+                    'wd14-swinv2': 'WD14 SwinV2',
+                    'wd14-vit': 'WD14 ViT'
+                };
+                const modelName = modelNames[modelType] || modelType;
                 document.getElementById('downloadModelName').textContent = modelName;
                 document.getElementById('downloadStatus').textContent = 'Preparing...';
                 document.getElementById('downloadProgressPercent').textContent = '0%';
@@ -3780,9 +3822,15 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                 try {
                     const formData = new URLSearchParams();
                     formData.append('model_type', modelType);
-                    formData.append('prompt', prompt);
                     formData.append('skip_tagged', skipTagged ? 'true' : 'false');
 
+                    if (modelType.startsWith('wd14')) {
+                        formData.append('general_threshold', document.getElementById('wd14GeneralThreshold').value);
+                        formData.append('character_threshold', document.getElementById('wd14CharacterThreshold').value);
+                    } else {
+                        formData.append('prompt', prompt);
+                    }
+
                     this.autoTagState.eventSource = new EventSource(`/prompt_manager/autotag/start?${formData.toString()}`);
 
                     this.autoTagState.eventSource.onmessage = (event) => {
@@ -3870,6 +3918,8 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                     this.autoTagState.reviewIndex = 0;
                     this.autoTagState.modelType = modelType;
                     this.autoTagState.prompt = document.getElementById('autoTagPrompt').value;
+                    this.autoTagState.generalThreshold = parseFloat(document.getElementById('wd14GeneralThreshold').value);
+                    this.autoTagState.characterThreshold = parseFloat(document.getElementById('wd14CharacterThreshold').value);
                     this.autoTagState.modelLoaded = false;
                     this.autoTagState.skipAllTagged = false;  // Reset skip flag for new session
 
@@ -3892,10 +3942,10 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                 }
             }
 
-            // Check if tags array has "real" tags (excluding auto-scanned)
+            // Check if tags array has "real" tags (excluding auto-scanned and prepend:* metadata)
             getRealTags(tags) {
                 if (!tags || !Array.isArray(tags)) return [];
-                return tags.filter(tag => tag !== 'auto-scanned');
+                return tags.filter(tag => tag !== 'auto-scanned' && !tag.startsWith('prepend:'));
             }
 
             // Show the re-tag confirmation modal
@@ -3983,14 +4033,21 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                 this.autoTagState.tagsExpanded = false;  // Reset accordion for new image
 
                 try {
+                    const requestBody = {
+                        image_path: image.image_path,
+                        model_type: this.autoTagState.modelType,
+                    };
+                    if (this.autoTagState.modelType.startsWith('wd14')) {
+                        requestBody.general_threshold = this.autoTagState.generalThreshold;
+                        requestBody.character_threshold = this.autoTagState.characterThreshold;
+                    } else {
+                        requestBody.prompt = this.autoTagState.prompt;
+                    }
+
                     const response = await fetch('/prompt_manager/autotag/single', {
                         method: 'POST',
                         headers: { 'Content-Type': 'application/json' },
-                        body: JSON.stringify({
-                            image_path: image.image_path,
-                            model_type: this.autoTagState.modelType,
-                            prompt: this.autoTagState.prompt
-                        })
+                        body: JSON.stringify(requestBody)
                     });
 
                     const data = await response.json();
diff --git a/web/js/gallery.js b/web/js/gallery.js
index 2200d1a..97e24e6 100644
--- a/web/js/gallery.js
+++ b/web/js/gallery.js
@@ -68,6 +68,23 @@
                 document.getElementById('cancelDownloadBtn').addEventListener('click', () => this.cancelDownload());
                 document.getElementById('unloadModelBtn').addEventListener('click', () => this.unloadModel());
 
+                // WD14 model selection toggle (prompt vs thresholds)
+                document.querySelectorAll('input[name="autoTagModel"]').forEach(radio => {
+                    radio.addEventListener('change', (e) => {
+                        const isWd14 = e.target.value.startsWith('wd14');
+                        document.getElementById('promptSection').classList.toggle('hidden', isWd14);
+                        document.getElementById('wd14ThresholdSection').classList.toggle('hidden', !isWd14);
+                    });
+                });
+
+                // WD14 threshold slider value display
+                document.getElementById('wd14GeneralThreshold').addEventListener('input', (e) => {
+                    document.getElementById('wd14GeneralThresholdValue').textContent = parseFloat(e.target.value).toFixed(2);
+                });
+                document.getElementById('wd14CharacterThreshold').addEventListener('input', (e) => {
+                    document.getElementById('wd14CharacterThresholdValue').textContent = parseFloat(e.target.value).toFixed(2);
+                });
+
                 // Add Prompt modal event listeners
                 document.getElementById('saveNewPromptBtn').addEventListener('click', () => this.saveNewPrompt());
                 document.getElementById('addPromptTagInput').addEventListener('input', (e) => this.handleTagInput(e));
@@ -3078,34 +3095,46 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                 await this.checkAutoTagModels();
             }
 
+            // _setModelStatus uses innerHTML with hardcoded model keys (not user input)
+            // to render download buttons or status indicators for known model types
+            _setModelStatus(el, downloaded, modelKey) {
+                if (downloaded) {
+                    el.textContent = '';
+                    const span = document.createElement('span');
+                    span.className = 'text-pm-success text-sm';
+                    span.textContent = '\u2713 Downloaded';
+                    el.appendChild(span);
+                } else {
+                    el.textContent = '';
+                    const btn = document.createElement('button');
+                    btn.className = 'px-3 py-1 bg-pm-accent hover:bg-pm-accent-hover text-pm text-xs rounded transition-colors';
+                    btn.textContent = 'Download';
+                    btn.addEventListener('click', () => window.gallery.downloadModel(modelKey));
+                    el.appendChild(btn);
+                }
+            }
+
             async checkAutoTagModels() {
                 const ggufStatus = document.getElementById("ggufModelStatus");
                 const hfStatus = document.getElementById("hfModelStatus");
+                const wd14SwinV2Status = document.getElementById("wd14SwinV2ModelStatus");
+                const wd14VitStatus = document.getElementById("wd14VitModelStatus");
                 const modelLoadedStatus = document.getElementById("modelLoadedStatus");
                 const loadedModelType = document.getElementById("loadedModelType");
                 const unloadModelBtn = document.getElementById("unloadModelBtn");
 
-                ggufStatus.innerHTML = '<span class="text-pm-secondary text-sm">Checking...</span>';
-                hfStatus.innerHTML = '<span class="text-pm-secondary text-sm">Checking...</span>';
+                const statusEls = [ggufStatus, hfStatus, wd14SwinV2Status, wd14VitStatus];
+                statusEls.forEach(el => { el.textContent = 'Checking...'; });
 
                 try {
                     const response = await fetch('/prompt_manager/autotag/models');
                     const data = await response.json();
 
                     if (data.success) {
-                        // Update GGUF status
-                        if (data.models.gguf.downloaded) {
-                            ggufStatus.innerHTML = '<span class="text-pm-success text-sm">✓ Downloaded</span>';
-                        } else {
-                            ggufStatus.innerHTML = `<button onclick="window.gallery.downloadModel('gguf')" class="px-3 py-1 bg-pm-accent hover:bg-pm-accent-hover text-pm text-xs rounded transition-colors">Download</button>`;
-                        }
-
-                        // Update HF status
-                        if (data.models.hf.downloaded) {
-                            hfStatus.innerHTML = '<span class="text-pm-success text-sm">✓ Downloaded</span>';
-                        } else {
-                            hfStatus.innerHTML = `<button onclick="window.gallery.downloadModel('hf')" class="px-3 py-1 bg-pm-accent hover:bg-pm-accent-hover text-pm text-xs rounded transition-colors">Download</button>`;
-                        }
+                        this._setModelStatus(ggufStatus, data.models.gguf.downloaded, 'gguf');
+                        this._setModelStatus(hfStatus, data.models.hf.downloaded, 'hf');
+                        this._setModelStatus(wd14SwinV2Status, data.models['wd14-swinv2']?.downloaded, 'wd14-swinv2');
+                        this._setModelStatus(wd14VitStatus, data.models['wd14-vit']?.downloaded, 'wd14-vit');
 
                         // Update model loaded status
                         if (data.model_loaded && data.loaded_model_type) {
@@ -3116,19 +3145,35 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                             modelLoadedStatus.classList.add('hidden');
                             unloadModelBtn.classList.add('hidden');
                         }
+
+                        // Update WD14 threshold defaults from server
+                        if (data.wd14_general_threshold !== undefined) {
+                            const genSlider = document.getElementById('wd14GeneralThreshold');
+                            genSlider.value = data.wd14_general_threshold;
+                            document.getElementById('wd14GeneralThresholdValue').textContent = parseFloat(data.wd14_general_threshold).toFixed(2);
+                        }
+                        if (data.wd14_character_threshold !== undefined) {
+                            const charSlider = document.getElementById('wd14CharacterThreshold');
+                            charSlider.value = data.wd14_character_threshold;
+                            document.getElementById('wd14CharacterThresholdValue').textContent = parseFloat(data.wd14_character_threshold).toFixed(2);
+                        }
                     } else {
-                        ggufStatus.innerHTML = '<span class="text-pm-error text-sm">Error</span>';
-                        hfStatus.innerHTML = '<span class="text-pm-error text-sm">Error</span>';
+                        statusEls.forEach(el => { el.textContent = 'Error'; });
                     }
                 } catch (error) {
                     console.error('Error checking models:', error);
-                    ggufStatus.innerHTML = '<span class="text-pm-error text-sm">Error</span>';
-                    hfStatus.innerHTML = '<span class="text-pm-error text-sm">Error</span>';
+                    statusEls.forEach(el => { el.textContent = 'Error'; });
                 }
             }
 
             async downloadModel(modelType) {
-                const modelName = modelType === 'gguf' ? 'GGUF Model' : 'HuggingFace Model';
+                const modelNames = {
+                    'gguf': 'GGUF Model',
+                    'hf': 'HuggingFace Model',
+                    'wd14-swinv2': 'WD14 SwinV2',
+                    'wd14-vit': 'WD14 ViT'
+                };
+                const modelName = modelNames[modelType] || modelType;
                 document.getElementById('downloadModelName').textContent = modelName;
                 document.getElementById('downloadStatus').textContent = 'Preparing...';
                 document.getElementById('downloadProgressPercent').textContent = '0%';
@@ -3241,9 +3286,15 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                 try {
                     const formData = new URLSearchParams();
                     formData.append('model_type', modelType);
-                    formData.append('prompt', prompt);
                     formData.append('keep_in_memory', keepInMemory);
 
+                    if (modelType.startsWith('wd14')) {
+                        formData.append('general_threshold', document.getElementById('wd14GeneralThreshold').value);
+                        formData.append('character_threshold', document.getElementById('wd14CharacterThreshold').value);
+                    } else {
+                        formData.append('prompt', prompt);
+                    }
+
                     this.autoTagState.eventSource = new EventSource(`/prompt_manager/autotag/start?${formData.toString()}`);
 
                     this.autoTagState.eventSource.onmessage = (event) => {
@@ -3321,6 +3372,8 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                 this.autoTagState.reviewIndex = 0;
                 this.autoTagState.modelType = modelType;
                 this.autoTagState.prompt = document.getElementById('autoTagPrompt').value;
+                this.autoTagState.generalThreshold = parseFloat(document.getElementById('wd14GeneralThreshold').value);
+                this.autoTagState.characterThreshold = parseFloat(document.getElementById('wd14CharacterThreshold').value);
 
                 document.getElementById('reviewTotalCount').textContent = this.images.length;
 
@@ -3343,14 +3396,21 @@ Seed: ${this.currentMetadata.seed || 'Unknown'}`;
                 document.getElementById('reviewTagsContainer').innerHTML = '<div class="text-pm-secondary">Generating tags...</div>';
 
                 try {
+                    const requestBody = {
+                        image_path: image.path,
+                        model_type: this.autoTagState.modelType,
+                    };
+                    if (this.autoTagState.modelType.startsWith('wd14')) {
+                        requestBody.general_threshold = this.autoTagState.generalThreshold;
+                        requestBody.character_threshold = this.autoTagState.characterThreshold;
+                    } else {
+                        requestBody.prompt = this.autoTagState.prompt;
+                    }
+
                     const response = await fetch('/prompt_manager/autotag/single', {
                         method: 'POST',
                         headers: { 'Content-Type': 'application/json' },
-                        body: JSON.stringify({
-                            image_path: image.path,
-                            model_type: this.autoTagState.modelType,
-                            prompt: this.autoTagState.prompt
-                        })
+                        body: JSON.stringify(requestBody)
                     });
 
                     const data = await response.json();