diff --git a/cli/test-tagger.py b/cli/test-tagger.py
index 2694a0e1f..9c5f858bf 100644
--- a/cli/test-tagger.py
+++ b/cli/test-tagger.py
@@ -2,7 +2,7 @@
 """
 Tagger Settings Test Suite
 
-Tests all WD14 and DeepBooru tagger settings to verify they're properly
+Tests all WaifuDiffusion and DeepBooru tagger settings to verify they're properly
 mapped and affect output correctly.
 
 Usage:
@@ -71,7 +71,7 @@ class TaggerTest:
     def __init__(self):
         self.results = {'passed': [], 'failed': [], 'skipped': []}
         self.test_image = None
-        self.wd14_loaded = False
+        self.waifudiffusion_loaded = False
         self.deepbooru_loaded = False
 
     def log_pass(self, msg):
@@ -116,11 +116,11 @@ class TaggerTest:
 
         # Load models
         print("\nLoading models...")
-        from modules.interrogate import wd14, deepbooru
+        from modules.interrogate import waifudiffusion, deepbooru
 
         t0 = time.time()
-        self.wd14_loaded = wd14.load_model()
-        print(f"  WD14: {'loaded' if self.wd14_loaded else 'FAILED'} ({time.time()-t0:.1f}s)")
+        self.waifudiffusion_loaded = waifudiffusion.load_model()
+        print(f"  WaifuDiffusion: {'loaded' if self.waifudiffusion_loaded else 'FAILED'} ({time.time()-t0:.1f}s)")
 
         t0 = time.time()
         self.deepbooru_loaded = deepbooru.load_model()
@@ -132,10 +132,10 @@ class TaggerTest:
         print("CLEANUP")
         print("=" * 70)
 
-        from modules.interrogate import wd14, deepbooru
+        from modules.interrogate import waifudiffusion, deepbooru
         from modules import devices
 
-        wd14.unload_model()
+        waifudiffusion.unload_model()
         deepbooru.unload_model()
         devices.torch_gc(force=True)
         print("  Models unloaded")
@@ -205,14 +205,14 @@ class TaggerTest:
             else:
                 self.log_fail(f"Provider '{provider}' configured but not available")
 
-        # Test 5: If WD14 loaded, check session providers
-        if self.wd14_loaded:
-            from modules.interrogate import wd14
-            if wd14.tagger.session is not None:
-                session_providers = wd14.tagger.session.get_providers()
-                self.log_pass(f"WD14 session providers: {session_providers}")
+        # Test 5: If WaifuDiffusion loaded, check session providers
+        if self.waifudiffusion_loaded:
+            from modules.interrogate import waifudiffusion
+            if waifudiffusion.tagger.session is not None:
+                session_providers = waifudiffusion.tagger.session.get_providers()
+                self.log_pass(f"WaifuDiffusion session providers: {session_providers}")
             else:
-                self.log_skip("WD14 session not initialized")
+                self.log_skip("WaifuDiffusion session not initialized")
 
     # =========================================================================
     # TEST: Memory Management (Offload/Reload/Unload)
@@ -251,7 +251,7 @@ class TaggerTest:
         import torch
         import gc
         from modules import devices
-        from modules.interrogate import wd14, deepbooru
+        from modules.interrogate import waifudiffusion, deepbooru
 
         # Memory leak tolerance (MB) - some variance is expected
         GPU_LEAK_TOLERANCE_MB = 50
@@ -362,10 +362,10 @@ class TaggerTest:
             deepbooru.load_model()
 
         # =====================================================================
-        # WD14: Test session lifecycle with memory monitoring
+        # WaifuDiffusion: Test session lifecycle with memory monitoring
         # =====================================================================
-        if self.wd14_loaded:
-            print("\n  WD14 Memory Management:")
+        if self.waifudiffusion_loaded:
+            print("\n  WaifuDiffusion Memory Management:")
 
             # Baseline memory
             gc.collect()
@@ -375,74 +375,74 @@ class TaggerTest:
             print(f"    Baseline: GPU={baseline['gpu_allocated']:.1f}MB, RAM={baseline['ram_used']:.1f}MB")
 
             # Test 1: Session exists
-            if wd14.tagger.session is not None:
-                self.log_pass("WD14: session loaded")
+            if waifudiffusion.tagger.session is not None:
+                self.log_pass("WaifuDiffusion: session loaded")
             else:
-                self.log_fail("WD14: session not loaded")
+                self.log_fail("WaifuDiffusion: session not loaded")
                 return
 
             # Test 2: Get current providers
-            providers = wd14.tagger.session.get_providers()
+            providers = waifudiffusion.tagger.session.get_providers()
             print(f"    Active providers: {providers}")
-            self.log_pass(f"WD14: using providers {providers}")
+            self.log_pass(f"WaifuDiffusion: using providers {providers}")
 
             # Test 3: Run inference
             try:
-                tags = wd14.tagger.predict(self.test_image, max_tags=3)
+                tags = waifudiffusion.tagger.predict(self.test_image, max_tags=3)
                 after_infer = self.get_memory_stats()
                 print(f"    After inference: GPU={after_infer['gpu_allocated']:.1f}MB, RAM={after_infer['ram_used']:.1f}MB")
                 if tags:
-                    self.log_pass(f"WD14: inference works ({tags[:30]}...)")
+                    self.log_pass(f"WaifuDiffusion: inference works ({tags[:30]}...)")
                 else:
-                    self.log_fail("WD14: inference returned empty")
+                    self.log_fail("WaifuDiffusion: inference returned empty")
             except Exception as e:
-                self.log_fail(f"WD14: inference failed: {e}")
+                self.log_fail(f"WaifuDiffusion: inference failed: {e}")
 
             # Test 4: Unload session with memory check
-            model_name = wd14.tagger.model_name
-            wd14.unload_model()
+            model_name = waifudiffusion.tagger.model_name
+            waifudiffusion.unload_model()
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
             after_unload = self.get_memory_stats()
             print(f"    After unload: GPU={after_unload['gpu_allocated']:.1f}MB, RAM={after_unload['ram_used']:.1f}MB")
 
-            if wd14.tagger.session is None:
-                self.log_pass("WD14: unload successful")
+            if waifudiffusion.tagger.session is None:
+                self.log_pass("WaifuDiffusion: unload successful")
             else:
-                self.log_fail("WD14: unload failed, session still exists")
+                self.log_fail("WaifuDiffusion: unload failed, session still exists")
 
             # Check for memory leaks after unload
             gpu_leak = after_unload['gpu_allocated'] - baseline['gpu_allocated']
             ram_leak = after_unload['ram_used'] - baseline['ram_used']
             if gpu_leak <= GPU_LEAK_TOLERANCE_MB:
-                self.log_pass(f"WD14: no GPU memory leak after unload (diff={gpu_leak:.1f}MB)")
+                self.log_pass(f"WaifuDiffusion: no GPU memory leak after unload (diff={gpu_leak:.1f}MB)")
             else:
-                self.log_fail(f"WD14: GPU memory leak detected (diff={gpu_leak:.1f}MB > {GPU_LEAK_TOLERANCE_MB}MB)")
+                self.log_fail(f"WaifuDiffusion: GPU memory leak detected (diff={gpu_leak:.1f}MB > {GPU_LEAK_TOLERANCE_MB}MB)")
 
             if ram_leak <= RAM_LEAK_TOLERANCE_MB:
-                self.log_pass(f"WD14: no RAM leak after unload (diff={ram_leak:.1f}MB)")
+                self.log_pass(f"WaifuDiffusion: no RAM leak after unload (diff={ram_leak:.1f}MB)")
             else:
-                self.log_warn(f"WD14: RAM increased after unload (diff={ram_leak:.1f}MB) - may be caching")
+                self.log_warn(f"WaifuDiffusion: RAM increased after unload (diff={ram_leak:.1f}MB) - may be caching")
 
             # Test 5: Reload session
-            wd14.load_model(model_name)
+            waifudiffusion.load_model(model_name)
             after_reload = self.get_memory_stats()
             print(f"    After reload: GPU={after_reload['gpu_allocated']:.1f}MB, RAM={after_reload['ram_used']:.1f}MB")
-            if wd14.tagger.session is not None:
-                self.log_pass("WD14: reload successful")
+            if waifudiffusion.tagger.session is not None:
+                self.log_pass("WaifuDiffusion: reload successful")
             else:
-                self.log_fail("WD14: reload failed")
+                self.log_fail("WaifuDiffusion: reload failed")
 
             # Test 6: Inference after reload
             try:
-                tags = wd14.tagger.predict(self.test_image, max_tags=3)
+                tags = waifudiffusion.tagger.predict(self.test_image, max_tags=3)
                 if tags:
-                    self.log_pass("WD14: inference after reload works")
+                    self.log_pass("WaifuDiffusion: inference after reload works")
                 else:
-                    self.log_fail("WD14: inference after reload returned empty")
+                    self.log_fail("WaifuDiffusion: inference after reload returned empty")
             except Exception as e:
-                self.log_fail(f"WD14: inference after reload failed: {e}")
+                self.log_fail(f"WaifuDiffusion: inference after reload failed: {e}")
 
             # Final memory check after full cycle
             gc.collect()
@@ -471,8 +471,8 @@ class TaggerTest:
             ('tagger_escape_brackets', bool),
             ('tagger_exclude_tags', str),
             ('tagger_show_scores', bool),
-            ('wd14_model', str),
-            ('wd14_character_threshold', float),
+            ('waifudiffusion_model', str),
+            ('waifudiffusion_character_threshold', float),
             ('interrogate_offload', bool),
         ]
 
@@ -486,23 +486,23 @@ class TaggerTest:
     # =========================================================================
     # TEST: Parameter Effect - Tests a single parameter on both taggers
     # =========================================================================
-    def test_parameter(self, param_name, test_func, wd14_supported=True, deepbooru_supported=True):
-        """Test a parameter on both WD14 and DeepBooru."""
+    def test_parameter(self, param_name, test_func, waifudiffusion_supported=True, deepbooru_supported=True):
+        """Test a parameter on both WaifuDiffusion and DeepBooru."""
         print(f"\n  Testing: {param_name}")
 
-        if wd14_supported and self.wd14_loaded:
+        if waifudiffusion_supported and self.waifudiffusion_loaded:
             try:
-                result = test_func('wd14')
+                result = test_func('waifudiffusion')
                 if result is True:
-                    self.log_pass(f"WD14: {param_name}")
+                    self.log_pass(f"WaifuDiffusion: {param_name}")
                 elif result is False:
-                    self.log_fail(f"WD14: {param_name}")
+                    self.log_fail(f"WaifuDiffusion: {param_name}")
                 else:
-                    self.log_skip(f"WD14: {param_name} - {result}")
+                    self.log_skip(f"WaifuDiffusion: {param_name} - {result}")
             except Exception as e:
-                self.log_fail(f"WD14: {param_name} - {e}")
-        elif wd14_supported:
-            self.log_skip(f"WD14: {param_name} - model not loaded")
+                self.log_fail(f"WaifuDiffusion: {param_name} - {e}")
+        elif waifudiffusion_supported:
+            self.log_skip(f"WaifuDiffusion: {param_name} - model not loaded")
 
         if deepbooru_supported and self.deepbooru_loaded:
             try:
@@ -520,9 +520,9 @@ class TaggerTest:
 
     def tag(self, tagger, **kwargs):
         """Helper to call the appropriate tagger."""
-        if tagger == 'wd14':
-            from modules.interrogate import wd14
-            return wd14.tagger.predict(self.test_image, **kwargs)
+        if tagger == 'waifudiffusion':
+            from modules.interrogate import waifudiffusion
+            return waifudiffusion.tagger.predict(self.test_image, **kwargs)
         else:
             from modules.interrogate import deepbooru
             return deepbooru.model.tag(self.test_image, **kwargs)
@@ -759,16 +759,16 @@ class TaggerTest:
         self.test_parameter('include_rating', check_include_rating)
 
     # =========================================================================
-    # TEST: character_threshold (WD14 only)
+    # TEST: character_threshold (WaifuDiffusion only)
     # =========================================================================
     def test_character_threshold(self):
-        """Test that character_threshold affects character tag count (WD14 only)."""
+        """Test that character_threshold affects character tag count (WaifuDiffusion only)."""
         print("\n" + "=" * 70)
-        print("TEST: character_threshold effect (WD14 only)")
+        print("TEST: character_threshold effect (WaifuDiffusion only)")
         print("=" * 70)
 
         def check_character_threshold(tagger):
-            if tagger != 'wd14':
+            if tagger != 'waifudiffusion':
                 return "not supported"
 
             # Character threshold only affects character tags
@@ -796,17 +796,17 @@ class TaggerTest:
 
         from modules.interrogate import tagger
 
-        # Test WD14 through unified interface
-        if self.wd14_loaded:
+        # Test WaifuDiffusion through unified interface
+        if self.waifudiffusion_loaded:
             try:
                 models = tagger.get_models()
-                wd14_model = next((m for m in models if m != 'DeepBooru'), None)
-                if wd14_model:
-                    tags = tagger.tag(self.test_image, model_name=wd14_model, max_tags=5)
-                    print(f"    WD14 ({wd14_model}): {tags[:50]}...")
-                    self.log_pass("Unified interface: WD14")
+                waifudiffusion_model = next((m for m in models if m != 'DeepBooru'), None)
+                if waifudiffusion_model:
+                    tags = tagger.tag(self.test_image, model_name=waifudiffusion_model, max_tags=5)
+                    print(f"    WaifuDiffusion ({waifudiffusion_model}): {tags[:50]}...")
+                    self.log_pass("Unified interface: WaifuDiffusion")
             except Exception as e:
-                self.log_fail(f"Unified interface: WD14 - {e}")
+                self.log_fail(f"Unified interface: WaifuDiffusion - {e}")
 
         # Test DeepBooru through unified interface
         if self.deepbooru_loaded:
diff --git a/modules/interrogate/deepbooru.py b/modules/interrogate/deepbooru.py
index 5ee2df751..88f0ab44f 100644
--- a/modules/interrogate/deepbooru.py
+++ b/modules/interrogate/deepbooru.py
@@ -80,20 +80,13 @@ class DeepDanbooru:
             Formatted tag string
         """
         # Use settings defaults if not specified
-        if general_threshold is None:
-            general_threshold = shared.opts.tagger_threshold
-        if include_rating is None:
-            include_rating = shared.opts.tagger_include_rating
-        if exclude_tags is None:
-            exclude_tags = shared.opts.tagger_exclude_tags
-        if max_tags is None:
-            max_tags = shared.opts.tagger_max_tags
-        if sort_alpha is None:
-            sort_alpha = shared.opts.tagger_sort_alpha
-        if use_spaces is None:
-            use_spaces = shared.opts.tagger_use_spaces
-        if escape_brackets is None:
-            escape_brackets = shared.opts.tagger_escape_brackets
+        general_threshold = general_threshold or shared.opts.tagger_threshold
+        include_rating = include_rating if include_rating is not None else shared.opts.tagger_include_rating
+        exclude_tags = exclude_tags or shared.opts.tagger_exclude_tags
+        max_tags = max_tags or shared.opts.tagger_max_tags
+        sort_alpha = sort_alpha if sort_alpha is not None else shared.opts.tagger_sort_alpha
+        use_spaces = use_spaces if use_spaces is not None else shared.opts.tagger_use_spaces
+        escape_brackets = escape_brackets if escape_brackets is not None else shared.opts.tagger_escape_brackets
 
         if isinstance(pil_image, list):
             pil_image = pil_image[0] if len(pil_image) > 0 else None
@@ -137,6 +130,31 @@ class DeepDanbooru:
 model = DeepDanbooru()
 
 
+def _save_tags_to_file(img_path, tags_str: str, save_append: bool) -> bool:
+    """Save tags to a text file with error handling.
+
+    Args:
+        img_path: Path to the image file
+        tags_str: Tags string to save
+        save_append: If True, append to existing file; otherwise overwrite
+
+    Returns:
+        True if save succeeded, False otherwise
+    """
+    try:
+        txt_path = img_path.with_suffix('.txt')
+        if save_append and txt_path.exists():
+            with open(txt_path, 'a', encoding='utf-8') as f:
+                f.write(f', {tags_str}')
+        else:
+            with open(txt_path, 'w', encoding='utf-8') as f:
+                f.write(tags_str)
+        return True
+    except Exception as e:
+        shared.log.error(f'DeepBooru batch: failed to save file="{img_path}" error={e}')
+        return False
+
+
 def get_models() -> list:
     """Return list of available DeepBooru models (just one)."""
     return ["DeepBooru"]
@@ -179,7 +197,7 @@ def tag(image, **kwargs) -> str:
 
     try:
         result = model.tag(image, **kwargs)
-        shared.log.debug(f'DeepBooru: complete time={time.time()-t0:.2f}s tags={len(result.split(", ")) if result else 0}')
+        shared.log.debug(f'DeepBooru: complete time={time.time()-t0:.2f} tags={len(result.split(", ")) if result else 0}')
     except Exception as e:
         result = f"Exception {type(e)}"
         shared.log.error(f'DeepBooru: {e}')
@@ -299,13 +317,7 @@ def batch(
                 tags_str = model.tag_multi(image, **kwargs)
 
                 if save_output:
-                    txt_path = img_path.with_suffix('.txt')
-                    if save_append and txt_path.exists():
-                        with open(txt_path, 'a', encoding='utf-8') as f:
-                            f.write(f', {tags_str}')
-                    else:
-                        with open(txt_path, 'w', encoding='utf-8') as f:
-                            f.write(tags_str)
+                    _save_tags_to_file(img_path, tags_str, save_append)
 
                 results.append(f'{img_path.name}: {tags_str[:100]}...' if len(tags_str) > 100 else f'{img_path.name}: {tags_str}')
 
diff --git a/modules/interrogate/interrogate.py b/modules/interrogate/interrogate.py
index 4efc32732..4e06fb36f 100644
--- a/modules/interrogate/interrogate.py
+++ b/modules/interrogate/interrogate.py
@@ -21,13 +21,13 @@ def interrogate(image):
         shared.log.debug(f'Interrogate: time={time.time()-t0:.2f} answer="{prompt}"')
         return prompt
     elif shared.opts.interrogate_default_type == 'Tagger':
-        shared.log.info(f'Interrogate: type={shared.opts.interrogate_default_type} model="{shared.opts.wd14_model}"')
+        shared.log.info(f'Interrogate: type={shared.opts.interrogate_default_type} model="{shared.opts.waifudiffusion_model}"')
         from modules.interrogate import tagger
         prompt = tagger.tag(
             image=image,
-            model_name=shared.opts.wd14_model,
+            model_name=shared.opts.waifudiffusion_model,
             general_threshold=shared.opts.tagger_threshold,
-            character_threshold=shared.opts.wd14_character_threshold,
+            character_threshold=shared.opts.waifudiffusion_character_threshold,
             include_rating=shared.opts.tagger_include_rating,
             exclude_tags=shared.opts.tagger_exclude_tags,
             max_tags=shared.opts.tagger_max_tags,
diff --git a/modules/interrogate/openclip.py b/modules/interrogate/openclip.py
index 68de085b0..ca69ad8dd 100644
--- a/modules/interrogate/openclip.py
+++ b/modules/interrogate/openclip.py
@@ -117,7 +117,7 @@ def load_interrogator(clip_model, blip_model):
         ci = clip_interrogator.Interrogator(interrogator_config)
         if blip_model.startswith('blip2-'):
             _apply_blip2_fix(ci.caption_model, ci.caption_processor)
-        shared.log.debug(f'CLIP load: time={time.time()-t0:.2f}s')
+        shared.log.debug(f'CLIP load: time={time.time()-t0:.2f}')
     elif clip_model != ci.config.clip_model_name or blip_model != ci.config.caption_model_name:
         t0 = time.time()
         if clip_model != ci.config.clip_model_name:
@@ -134,7 +134,7 @@ def load_interrogator(clip_model, blip_model):
             ci.load_caption_model()
             if blip_model.startswith('blip2-'):
                 _apply_blip2_fix(ci.caption_model, ci.caption_processor)
-        shared.log.debug(f'CLIP load: time={time.time()-t0:.2f}s')
+        shared.log.debug(f'CLIP load: time={time.time()-t0:.2f}')
     else:
         debug_log(f'CLIP: models already loaded clip="{clip_model}" blip="{blip_model}"')
 
@@ -172,7 +172,7 @@ def interrogate(image, mode, caption=None):
         prompt = ci.interrogate_negative(image, max_flavors=shared.opts.interrogate_clip_max_flavors)
     else:
         raise RuntimeError(f"Unknown mode {mode}")
-    debug_log(f'CLIP: mode="{mode}" time={time.time()-t0:.2f}s result="{prompt[:100]}..."' if len(prompt) > 100 else f'CLIP: mode="{mode}" time={time.time()-t0:.2f}s result="{prompt}"')
+    debug_log(f'CLIP: mode="{mode}" time={time.time()-t0:.2f} result="{prompt[:100]}..."' if len(prompt) > 100 else f'CLIP: mode="{mode}" time={time.time()-t0:.2f} result="{prompt}"')
     return prompt
 
 
@@ -189,7 +189,7 @@ def interrogate_image(image, clip_model, blip_model, mode):
         image = image.convert('RGB')
         prompt = interrogate(image, mode)
         devices.torch_gc()
-        shared.log.debug(f'CLIP: complete time={time.time()-t0:.2f}s')
+        shared.log.debug(f'CLIP: complete time={time.time()-t0:.2f}')
     except Exception as e:
         prompt = f"Exception {type(e)}"
         shared.log.error(f'CLIP: {e}')
@@ -243,7 +243,7 @@ def interrogate_batch(batch_files, batch_folder, batch_str, clip_model, blip_mod
     ci.config.quiet = False
     unload_clip_model()
     shared.state.end(jobid)
-    shared.log.info(f'CLIP batch: complete images={len(prompts)} time={time.time()-t0:.2f}s')
+    shared.log.info(f'CLIP batch: complete images={len(prompts)} time={time.time()-t0:.2f}')
     return '\n\n'.join(prompts)
 
 
@@ -264,7 +264,7 @@ def analyze_image(image, clip_model, blip_model):
     movement_ranks = dict(sorted(zip(top_movements, ci.similarities(image_features, top_movements)), key=lambda x: x[1], reverse=True))
     trending_ranks = dict(sorted(zip(top_trendings, ci.similarities(image_features, top_trendings)), key=lambda x: x[1], reverse=True))
     flavor_ranks = dict(sorted(zip(top_flavors, ci.similarities(image_features, top_flavors)), key=lambda x: x[1], reverse=True))
-    shared.log.debug(f'CLIP analyze: complete time={time.time()-t0:.2f}s')
+    shared.log.debug(f'CLIP analyze: complete time={time.time()-t0:.2f}')
 
     # Format labels as text
     def format_category(name, ranks):
diff --git a/modules/interrogate/tagger.py b/modules/interrogate/tagger.py
index cd2374d04..51516adaa 100644
--- a/modules/interrogate/tagger.py
+++ b/modules/interrogate/tagger.py
@@ -1,4 +1,4 @@
-# Unified Tagger Interface - Dispatches to WD14 or DeepBooru based on model selection
+# Unified Tagger Interface - Dispatches to WaifuDiffusion or DeepBooru based on model selection
 # Provides a common interface for the Booru Tags tab
 
 from modules import shared
@@ -7,9 +7,9 @@ DEEPBOORU_MODEL = "DeepBooru"
 
 
 def get_models() -> list:
-    """Return combined list: DeepBooru + WD14 models."""
-    from modules.interrogate import wd14
-    return [DEEPBOORU_MODEL] + wd14.get_models()
+    """Return combined list: DeepBooru + WaifuDiffusion models."""
+    from modules.interrogate import waifudiffusion
+    return [DEEPBOORU_MODEL] + waifudiffusion.get_models()
 
 
 def refresh_models() -> list:
@@ -28,15 +28,15 @@ def load_model(model_name: str) -> bool:
         from modules.interrogate import deepbooru
         return deepbooru.load_model()
     else:
-        from modules.interrogate import wd14
-        return wd14.load_model(model_name)
+        from modules.interrogate import waifudiffusion
+        return waifudiffusion.load_model(model_name)
 
 
 def unload_model():
     """Unload both backends to ensure memory is freed."""
-    from modules.interrogate import deepbooru, wd14
+    from modules.interrogate import deepbooru, waifudiffusion
     deepbooru.unload_model()
-    wd14.unload_model()
+    waifudiffusion.unload_model()
 
 
 def tag(image, model_name: str = None, **kwargs) -> str:
@@ -44,28 +44,28 @@ def tag(image, model_name: str = None, **kwargs) -> str:
 
     Args:
         image: PIL Image to tag
-        model_name: Model to use (DeepBooru or WD14 model name)
+        model_name: Model to use (DeepBooru or WaifuDiffusion model name)
         **kwargs: Additional arguments passed to the backend
 
     Returns:
         Formatted tag string
     """
     if model_name is None:
-        model_name = shared.opts.wd14_model
+        model_name = shared.opts.waifudiffusion_model
 
     if is_deepbooru(model_name):
         from modules.interrogate import deepbooru
         return deepbooru.tag(image, **kwargs)
     else:
-        from modules.interrogate import wd14
-        return wd14.tag(image, model_name=model_name, **kwargs)
+        from modules.interrogate import waifudiffusion
+        return waifudiffusion.tag(image, model_name=model_name, **kwargs)
 
 
 def batch(model_name: str, **kwargs) -> str:
     """Unified batch processing.
 
     Args:
-        model_name: Model to use (DeepBooru or WD14 model name)
+        model_name: Model to use (DeepBooru or WaifuDiffusion model name)
         **kwargs: Additional arguments passed to the backend
 
     Returns:
@@ -75,5 +75,5 @@ def batch(model_name: str, **kwargs) -> str:
         from modules.interrogate import deepbooru
         return deepbooru.batch(model_name=model_name, **kwargs)
     else:
-        from modules.interrogate import wd14
-        return wd14.batch(model_name=model_name, **kwargs)
+        from modules.interrogate import waifudiffusion
+        return waifudiffusion.batch(model_name=model_name, **kwargs)
diff --git a/modules/interrogate/wd14.py b/modules/interrogate/waifudiffusion.py
similarity index 69%
rename from modules/interrogate/wd14.py
rename to modules/interrogate/waifudiffusion.py
index fcdb360b0..71951a47f 100644
--- a/modules/interrogate/wd14.py
+++ b/modules/interrogate/waifudiffusion.py
@@ -1,4 +1,4 @@
-# WD14/WaifuDiffusion Tagger - ONNX-based anime/illustration tagging
+# WaifuDiffusion Tagger - ONNX-based anime/illustration tagging
 # Based on SmilingWolf's tagger models: https://huggingface.co/SmilingWolf
 
 import os
@@ -17,8 +17,8 @@ debug_log = shared.log.trace if debug_enabled else lambda *args, **kwargs: None
 re_special = re.compile(r'([\\()])')
 load_lock = threading.Lock()
 
-# WD14 model repository mappings
-WD14_MODELS = {
+# WaifuDiffusion model repository mappings
+WAIFUDIFFUSION_MODELS = {
     # v3 models (latest, recommended)
     "wd-eva02-large-tagger-v3": "SmilingWolf/wd-eva02-large-tagger-v3",
     "wd-vit-tagger-v3": "SmilingWolf/wd-vit-tagger-v3",
@@ -38,8 +38,8 @@ CATEGORY_CHARACTER = 4
 CATEGORY_RATING = 9
 
 
-class WD14Tagger:
-    """WD14/WaifuDiffusion Tagger using ONNX inference."""
+class WaifuDiffusionTagger:
+    """WaifuDiffusion Tagger using ONNX inference."""
 
     def __init__(self):
         self.session = None
@@ -54,63 +54,63 @@ class WD14Tagger:
         import huggingface_hub
 
         if model_name is None:
-            model_name = shared.opts.wd14_model
-        if model_name not in WD14_MODELS:
-            shared.log.error(f'WD14: unknown model "{model_name}"')
+            model_name = shared.opts.waifudiffusion_model
+        if model_name not in WAIFUDIFFUSION_MODELS:
+            shared.log.error(f'WaifuDiffusion: unknown model "{model_name}"')
             return False
 
         with load_lock:
             if self.session is not None and self.model_name == model_name:
-                debug_log(f'WD14: model already loaded model="{model_name}"')
+                debug_log(f'WaifuDiffusion: model already loaded model="{model_name}"')
                 return True  # Already loaded
 
             # Unload previous model if different
             if self.model_name != model_name and self.session is not None:
-                debug_log(f'WD14: switching model from "{self.model_name}" to "{model_name}"')
+                debug_log(f'WaifuDiffusion: switching model from "{self.model_name}" to "{model_name}"')
                 self.unload()
 
-            repo_id = WD14_MODELS[model_name]
+            repo_id = WAIFUDIFFUSION_MODELS[model_name]
             t0 = time.time()
-            shared.log.info(f'WD14 load: model="{model_name}" repo="{repo_id}"')
+            shared.log.info(f'WaifuDiffusion load: model="{model_name}" repo="{repo_id}"')
 
             try:
                 # Download only ONNX model and tags CSV (skip safetensors/msgpack variants)
-                debug_log(f'WD14 load: downloading from HuggingFace cache_dir="{shared.opts.hfcache_dir}"')
+                debug_log(f'WaifuDiffusion load: downloading from HuggingFace cache_dir="{shared.opts.hfcache_dir}"')
                 self.model_path = huggingface_hub.snapshot_download(
                     repo_id,
                     cache_dir=shared.opts.hfcache_dir,
                     allow_patterns=["model.onnx", "selected_tags.csv"],
                 )
-                debug_log(f'WD14 load: model_path="{self.model_path}"')
+                debug_log(f'WaifuDiffusion load: model_path="{self.model_path}"')
 
                 # Load ONNX model
                 model_file = os.path.join(self.model_path, "model.onnx")
                 if not os.path.exists(model_file):
-                    shared.log.error(f'WD14 load: model file not found: {model_file}')
+                    shared.log.error(f'WaifuDiffusion load: model file not found: {model_file}')
                     return False
 
                 import onnxruntime as ort
 
-                debug_log(f'WD14 load: onnxruntime version={ort.__version__}')
+                debug_log(f'WaifuDiffusion load: onnxruntime version={ort.__version__}')
 
                 self.session = ort.InferenceSession(model_file, providers=devices.onnx)
                 self.model_name = model_name
 
                 # Get actual providers used
                 actual_providers = self.session.get_providers()
-                debug_log(f'WD14 load: active providers={actual_providers}')
+                debug_log(f'WaifuDiffusion load: active providers={actual_providers}')
 
                 # Load tags from CSV
                 self._load_tags()
 
                 load_time = time.time() - t0
-                shared.log.debug(f'WD14 load: time={load_time:.2f}s tags={len(self.tags)}')
-                debug_log(f'WD14 load: input_name={self.session.get_inputs()[0].name} output_name={self.session.get_outputs()[0].name}')
+                shared.log.debug(f'WaifuDiffusion load: time={load_time:.2f} tags={len(self.tags)}')
+                debug_log(f'WaifuDiffusion load: input_name={self.session.get_inputs()[0].name} output_name={self.session.get_outputs()[0].name}')
                 return True
 
             except Exception as e:
-                shared.log.error(f'WD14 load: failed error={e}')
-                errors.display(e, 'WD14 load')
+                shared.log.error(f'WaifuDiffusion load: failed error={e}')
+                errors.display(e, 'WaifuDiffusion load')
                 self.unload()
                 return False
 
@@ -120,7 +120,7 @@ class WD14Tagger:
 
         csv_path = os.path.join(self.model_path, "selected_tags.csv")
         if not os.path.exists(csv_path):
-            shared.log.error(f'WD14 load: tags file not found: {csv_path}')
+            shared.log.error(f'WaifuDiffusion load: tags file not found: {csv_path}')
             return
 
         self.tags = []
@@ -136,24 +136,24 @@ class WD14Tagger:
         category_counts = {}
         for cat in self.tag_categories:
             category_counts[cat] = category_counts.get(cat, 0) + 1
-        debug_log(f'WD14 load: tag categories={category_counts}')
+        debug_log(f'WaifuDiffusion load: tag categories={category_counts}')
 
     def unload(self):
         """Unload the model and free resources."""
         if self.session is not None:
-            shared.log.debug(f'WD14 unload: model="{self.model_name}"')
+            shared.log.debug(f'WaifuDiffusion unload: model="{self.model_name}"')
             self.session = None
             self.tags = None
             self.tag_categories = None
             self.model_name = None
             self.model_path = None
             devices.torch_gc(force=True)
-            debug_log('WD14 unload: complete')
+            debug_log('WaifuDiffusion unload: complete')
         else:
-            debug_log('WD14 unload: no model loaded')
+            debug_log('WaifuDiffusion unload: no model loaded')
 
     def preprocess_image(self, image: Image.Image) -> np.ndarray:
-        """Preprocess image for WD14 model input.
+        """Preprocess image for WaifuDiffusion model input.
 
         - Resize to 448x448 (standard for WD models)
         - Pad to square with white background
@@ -189,7 +189,7 @@ class WD14Tagger:
         # Add batch dimension
         img_array = np.expand_dims(img_array, axis=0)
 
-        debug_log(f'WD14 preprocess: original_size={original_size} mode={original_mode} padded_size={max_dim} output_shape={img_array.shape}')
+        debug_log(f'WaifuDiffusion preprocess: original_size={original_size} mode={original_mode} padded_size={max_dim} output_shape={img_array.shape}')
         return img_array
 
     def predict(
@@ -223,24 +223,16 @@ class WD14Tagger:
         t0 = time.time()
 
         # Use settings defaults if not specified
-        if general_threshold is None:
-            general_threshold = shared.opts.tagger_threshold
-        if character_threshold is None:
-            character_threshold = shared.opts.wd14_character_threshold
-        if include_rating is None:
-            include_rating = shared.opts.tagger_include_rating
-        if exclude_tags is None:
-            exclude_tags = shared.opts.tagger_exclude_tags
-        if max_tags is None:
-            max_tags = shared.opts.tagger_max_tags
-        if sort_alpha is None:
-            sort_alpha = shared.opts.tagger_sort_alpha
-        if use_spaces is None:
-            use_spaces = shared.opts.tagger_use_spaces
-        if escape_brackets is None:
-            escape_brackets = shared.opts.tagger_escape_brackets
+        general_threshold = general_threshold or shared.opts.tagger_threshold
+        character_threshold = character_threshold or shared.opts.waifudiffusion_character_threshold
+        include_rating = include_rating if include_rating is not None else shared.opts.tagger_include_rating
+        exclude_tags = exclude_tags or shared.opts.tagger_exclude_tags
+        max_tags = max_tags or shared.opts.tagger_max_tags
+        sort_alpha = sort_alpha if sort_alpha is not None else shared.opts.tagger_sort_alpha
+        use_spaces = use_spaces if use_spaces is not None else shared.opts.tagger_use_spaces
+        escape_brackets = escape_brackets if escape_brackets is not None else shared.opts.tagger_escape_brackets
 
-        debug_log(f'WD14 predict: general_threshold={general_threshold} character_threshold={character_threshold} max_tags={max_tags} include_rating={include_rating} sort_alpha={sort_alpha}')
+        debug_log(f'WaifuDiffusion predict: general_threshold={general_threshold} character_threshold={character_threshold} max_tags={max_tags} include_rating={include_rating} sort_alpha={sort_alpha}')
 
         # Handle input variations
         if isinstance(image, list):
@@ -248,7 +240,7 @@ class WD14Tagger:
         if isinstance(image, dict) and 'name' in image:
             image = Image.open(image['name'])
         if image is None:
-            shared.log.error('WD14 predict: no image provided')
+            shared.log.error('WaifuDiffusion predict: no image provided')
             return ''
 
         # Load model if needed
@@ -265,13 +257,13 @@ class WD14Tagger:
         output_name = self.session.get_outputs()[0].name
         probs = self.session.run([output_name], {input_name: img_input})[0][0]
         infer_time = time.time() - t_infer
-        debug_log(f'WD14 predict: inference time={infer_time:.3f}s output_shape={probs.shape}')
+        debug_log(f'WaifuDiffusion predict: inference time={infer_time:.3f}s output_shape={probs.shape}')
 
         # Build tag list with probabilities
         tag_probs = {}
         exclude_set = {x.strip().replace(' ', '_').lower() for x in exclude_tags.split(',') if x.strip()}
         if exclude_set:
-            debug_log(f'WD14 predict: exclude_tags={exclude_set}')
+            debug_log(f'WaifuDiffusion predict: exclude_tags={exclude_set}')
 
         general_count = 0
         character_count = 0
@@ -305,7 +297,7 @@ class WD14Tagger:
                 if prob >= general_threshold:
                     tag_probs[tag_name] = float(prob)
 
-        debug_log(f'WD14 predict: matched tags general={general_count} character={character_count} rating={rating_count} total={len(tag_probs)}')
+        debug_log(f'WaifuDiffusion predict: matched tags general={general_count} character={character_count} rating={rating_count} total={len(tag_probs)}')
 
         # Sort tags
         if sort_alpha:
@@ -316,7 +308,7 @@ class WD14Tagger:
         # Limit number of tags
         if max_tags > 0 and len(sorted_tags) > max_tags:
             sorted_tags = sorted_tags[:max_tags]
-            debug_log(f'WD14 predict: limited to max_tags={max_tags}')
+            debug_log(f'WaifuDiffusion predict: limited to max_tags={max_tags}')
 
         # Format output
         result = []
@@ -332,7 +324,7 @@ class WD14Tagger:
 
         output = ", ".join(result)
         total_time = time.time() - t0
-        debug_log(f'WD14 predict: complete tags={len(result)} time={total_time:.2f}s result="{output[:100]}..."' if len(output) > 100 else f'WD14 predict: complete tags={len(result)} time={total_time:.2f}s result="{output}"')
+        debug_log(f'WaifuDiffusion predict: complete tags={len(result)} time={total_time:.2f} result="{output[:100]}..."' if len(output) > 100 else f'WaifuDiffusion predict: complete tags={len(result)} time={total_time:.2f} result="{output}"')
 
         return output
 
@@ -342,12 +334,37 @@ class WD14Tagger:
 
 
 # Global tagger instance
-tagger = WD14Tagger()
+tagger = WaifuDiffusionTagger()
+
+
+def _save_tags_to_file(img_path, tags_str: str, save_append: bool) -> bool:
+    """Save tags to a text file with error handling.
+
+    Args:
+        img_path: Path to the image file
+        tags_str: Tags string to save
+        save_append: If True, append to existing file; otherwise overwrite
+
+    Returns:
+        True if save succeeded, False otherwise
+    """
+    try:
+        txt_path = img_path.with_suffix('.txt')
+        if save_append and txt_path.exists():
+            with open(txt_path, 'a', encoding='utf-8') as f:
+                f.write(f', {tags_str}')
+        else:
+            with open(txt_path, 'w', encoding='utf-8') as f:
+                f.write(tags_str)
+        return True
+    except Exception as e:
+        shared.log.error(f'WaifuDiffusion batch: failed to save file="{img_path}" error={e}')
+        return False
 
 
 def get_models() -> list:
-    """Return list of available WD14 model names."""
-    return list(WD14_MODELS.keys())
+    """Return list of available WaifuDiffusion model names."""
+    return list(WAIFUDIFFUSION_MODELS.keys())
 
 
 def refresh_models() -> list:
@@ -358,17 +375,17 @@ def refresh_models() -> list:
 
 
 def load_model(model_name: str = None) -> bool:
-    """Load the specified WD14 model."""
+    """Load the specified WaifuDiffusion model."""
     return tagger.load(model_name)
 
 
 def unload_model():
-    """Unload the current WD14 model."""
+    """Unload the current WaifuDiffusion model."""
     tagger.unload()
 
 
 def tag(image: Image.Image, model_name: str = None, **kwargs) -> str:
-    """Tag an image using WD14 tagger.
+    """Tag an image using WaifuDiffusion tagger.
 
     Args:
         image: PIL Image to tag
@@ -379,21 +396,21 @@ def tag(image: Image.Image, model_name: str = None, **kwargs) -> str:
         Formatted tag string
     """
     t0 = time.time()
-    jobid = shared.state.begin('WD14 Tag')
-    shared.log.info(f'WD14: model="{model_name or tagger.model_name or shared.opts.wd14_model}" image_size={image.size if image else None}')
+    jobid = shared.state.begin('WaifuDiffusion Tag')
+    shared.log.info(f'WaifuDiffusion: model="{model_name or tagger.model_name or shared.opts.waifudiffusion_model}" image_size={image.size if image else None}')
 
     try:
         if model_name and model_name != tagger.model_name:
             tagger.load(model_name)
         result = tagger.predict(image, **kwargs)
-        shared.log.debug(f'WD14: complete time={time.time()-t0:.2f}s tags={len(result.split(", ")) if result else 0}')
+        shared.log.debug(f'WaifuDiffusion: complete time={time.time()-t0:.2f} tags={len(result.split(", ")) if result else 0}')
         # Offload model if setting enabled
         if shared.opts.interrogate_offload:
             tagger.unload()
     except Exception as e:
         result = f"Exception {type(e)}"
-        shared.log.error(f'WD14: {e}')
-        errors.display(e, 'WD14 Tag')
+        shared.log.error(f'WaifuDiffusion: {e}')
+        errors.display(e, 'WaifuDiffusion Tag')
 
     shared.state.end(jobid)
     return result
@@ -485,19 +502,19 @@ def batch(
     image_files = unique_files
 
     if not image_files:
-        shared.log.warning('WD14 batch: no images found')
+        shared.log.warning('WaifuDiffusion batch: no images found')
         return ''
 
     t0 = time.time()
-    jobid = shared.state.begin('WD14 Batch')
-    shared.log.info(f'WD14 batch: model="{tagger.model_name}" images={len(image_files)} write={save_output} append={save_append} recursive={recursive}')
-    debug_log(f'WD14 batch: files={[str(f) for f in image_files[:5]]}{"..." if len(image_files) > 5 else ""}')
+    jobid = shared.state.begin('WaifuDiffusion Batch')
+    shared.log.info(f'WaifuDiffusion batch: model="{tagger.model_name}" images={len(image_files)} write={save_output} append={save_append} recursive={recursive}')
+    debug_log(f'WaifuDiffusion batch: files={[str(f) for f in image_files[:5]]}{"..." if len(image_files) > 5 else ""}')
 
     results = []
 
     # Progress bar
     import rich.progress as rp
-    pbar = rp.Progress(rp.TextColumn('[cyan]WD14:'), rp.BarColumn(), rp.MofNCompleteColumn(), rp.TaskProgressColumn(), rp.TimeRemainingColumn(), rp.TimeElapsedColumn(), rp.TextColumn('[cyan]{task.description}'), console=shared.console)
+    pbar = rp.Progress(rp.TextColumn('[cyan]WaifuDiffusion:'), rp.BarColumn(), rp.MofNCompleteColumn(), rp.TaskProgressColumn(), rp.TimeRemainingColumn(), rp.TimeElapsedColumn(), rp.TextColumn('[cyan]{task.description}'), console=shared.console)
 
     with pbar:
         task = pbar.add_task(total=len(image_files), description='starting...')
@@ -505,31 +522,23 @@ def batch(
             pbar.update(task, advance=1, description=str(img_path.name))
             try:
                 if shared.state.interrupted:
-                    shared.log.info('WD14 batch: interrupted')
+                    shared.log.info('WaifuDiffusion batch: interrupted')
                     break
 
                 image = Image.open(img_path)
                 tags_str = tagger.predict(image, **kwargs)
 
                 if save_output:
-                    txt_path = img_path.with_suffix('.txt')
-                    if save_append and txt_path.exists():
-                        with open(txt_path, 'a', encoding='utf-8') as f:
-                            f.write(f', {tags_str}')
-                        debug_log(f'WD14 batch: appended to "{txt_path}"')
-                    else:
-                        with open(txt_path, 'w', encoding='utf-8') as f:
-                            f.write(tags_str)
-                        debug_log(f'WD14 batch: wrote to "{txt_path}"')
+                    _save_tags_to_file(img_path, tags_str, save_append)
 
                 results.append(f'{img_path.name}: {tags_str[:100]}...' if len(tags_str) > 100 else f'{img_path.name}: {tags_str}')
 
             except Exception as e:
-                shared.log.error(f'WD14 batch: file="{img_path}" error={e}')
+                shared.log.error(f'WaifuDiffusion batch: file="{img_path}" error={e}')
                 results.append(f'{img_path.name}: ERROR - {e}')
 
     elapsed = time.time() - t0
-    shared.log.info(f'WD14 batch: complete images={len(results)} time={elapsed:.1f}s')
+    shared.log.info(f'WaifuDiffusion batch: complete images={len(results)} time={elapsed:.1f}s')
     shared.state.end(jobid)
 
     return '\n'.join(results)
diff --git a/modules/shared.py b/modules/shared.py
index b143d65aa..2a6cd5ac0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -774,8 +774,8 @@ options_templates.update(options_section(('hidden_options', "Hidden options"), {
     "tagger_use_spaces": OptionInfo(False, "Tagger: use spaces for tags", gr.Checkbox, {"visible": False}),
     "tagger_escape_brackets": OptionInfo(True, "Tagger: escape brackets", gr.Checkbox, {"visible": False}),
     "tagger_exclude_tags": OptionInfo("", "Tagger: exclude tags", gr.Textbox, {"visible": False}),
-    "wd14_model": OptionInfo("wd-eva02-large-tagger-v3", "WD14: default model", gr.Dropdown, {"choices": [], "visible": False}),
-    "wd14_character_threshold": OptionInfo(0.85, "WD14: character tag threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": False}),
+    "waifudiffusion_model": OptionInfo("wd-eva02-large-tagger-v3", "WaifuDiffusion: default model", gr.Dropdown, {"choices": [], "visible": False}),
+    "waifudiffusion_character_threshold": OptionInfo(0.85, "WaifuDiffusion: character tag threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": False}),
 
     # control settings are handled separately
     "control_hires": OptionInfo(False, "Hires use Control", gr.Checkbox, {"visible": False}),
diff --git a/modules/ui_caption.py b/modules/ui_caption.py
index 4821be690..5ab4d74b7 100644
--- a/modules/ui_caption.py
+++ b/modules/ui_caption.py
@@ -98,9 +98,9 @@ def update_tagger_ui(model_name):
 
 def update_tagger_params(model_name, general_threshold, character_threshold, include_rating, max_tags, sort_alpha, use_spaces, escape_brackets, exclude_tags, show_scores):
     """Save all tagger parameters to shared.opts when UI controls change."""
-    shared.opts.wd14_model = model_name
+    shared.opts.waifudiffusion_model = model_name
     shared.opts.tagger_threshold = float(general_threshold)
-    shared.opts.wd14_character_threshold = float(character_threshold)
+    shared.opts.waifudiffusion_character_threshold = float(character_threshold)
     shared.opts.tagger_include_rating = bool(include_rating)
     shared.opts.tagger_max_tags = int(max_tags)
     shared.opts.tagger_sort_alpha = bool(sort_alpha)
@@ -250,7 +250,7 @@ def create_ui():
                 with gr.Tab("Tagger", elem_id='tab_tagger'):
                     from modules.interrogate import tagger
                     with gr.Row():
-                        wd_model = gr.Dropdown(tagger.get_models(), value=shared.opts.wd14_model, label='Tagger Model', elem_id='wd_model')
+                        wd_model = gr.Dropdown(tagger.get_models(), value=shared.opts.waifudiffusion_model, label='Tagger Model', elem_id='wd_model')
                         ui_common.create_refresh_button(wd_model, tagger.refresh_models, lambda: {"choices": tagger.get_models()}, 'wd_models_refresh')
                     with gr.Row():
                         wd_load_btn = gr.Button(value='Load', elem_id='wd_load', variant='secondary')
@@ -258,7 +258,7 @@ def create_ui():
                     with gr.Accordion(label='Tagger: Advanced Options', open=True, visible=True):
                         with gr.Row():
                             wd_general_threshold = gr.Slider(label='General threshold', value=shared.opts.tagger_threshold, minimum=0.0, maximum=1.0, step=0.01, elem_id='wd_general_threshold')
-                            wd_character_threshold = gr.Slider(label='Character threshold', value=shared.opts.wd14_character_threshold, minimum=0.0, maximum=1.0, step=0.01, elem_id='wd_character_threshold')
+                            wd_character_threshold = gr.Slider(label='Character threshold', value=shared.opts.waifudiffusion_character_threshold, minimum=0.0, maximum=1.0, step=0.01, elem_id='wd_character_threshold')
                         with gr.Row():
                             wd_max_tags = gr.Slider(label='Max tags', value=shared.opts.tagger_max_tags, minimum=1, maximum=512, step=1, elem_id='wd_max_tags')
                             wd_include_rating = gr.Checkbox(label='Include rating', value=shared.opts.tagger_include_rating, elem_id='wd_include_rating')