diff --git a/cli/test-caption-api.py b/cli/test-caption-api.py
index f5ce205ee..7c9e3726a 100755
--- a/cli/test-caption-api.py
+++ b/cli/test-caption-api.py
@@ -44,6 +44,9 @@ OCR_TEST_IMAGE = 'models/Reference/HiDream-ai--HiDream-I1-Fast.jpg'
 # Bracket test image (must produce tags with parentheses, e.g. pokemon_(creature))
 BRACKET_TEST_IMAGE = 'models/Reference/SDXL-Flash_Mini.jpg'
 
+# Custom prefill text used for dual-prefill verification across tests
+CUSTOM_PREFILL = "Vlado is the best, and I'm looking at his robot which"
+
 
 class CaptionAPITest:
     """Test harness for Caption API endpoints."""
@@ -257,6 +260,20 @@ class CaptionAPITest:
             return False
         return True
 
+    def _check_prefill(self, base_request: dict, test_label: str):
+        """Re-run a VQA request with custom prefill and verify it appears in output."""
+        req = {**base_request, 'prefill': CUSTOM_PREFILL, 'keep_prefill': True}
+        data = self.post('/sdapi/v1/vqa', req)
+        if 'error' in data:
+            self.log_skip(f"{test_label} prefill: API error")
+        elif data.get('answer') and not self.is_error_answer(data['answer']):
+            if data['answer'].startswith(CUSTOM_PREFILL):
+                self.log_pass(f"{test_label} prefill: output starts with custom prefill")
+            else:
+                self.log_fail(f"{test_label} prefill: expected '{CUSTOM_PREFILL[:30]}...' but got '{data['answer'][:30]}...'")
+        else:
+            self.log_fail(f"{test_label} prefill: empty/error")
+
     def get_model_family(self, model_name):
         """Determine model family from model name."""
         name_lower = model_name.lower()
@@ -1138,6 +1155,9 @@ class CaptionAPITest:
             if results['Long Caption'] < results['Normal Caption']:
                 self.log_info(f"NOTE: Long ({results['Long Caption']}) < Normal ({results['Normal Caption']}); LLM may interpret length prompts differently per run")
 
+        # Dual prefill: re-run 'Normal Caption' with custom prefill
+        self._check_prefill({'image': self.image_b64, 'question': 'Normal Caption'}, "different_prompts")
+
     # =========================================================================
     # TEST: POST /sdapi/v1/vqa - Annotated Image
     # =========================================================================
@@ -1256,6 +1276,9 @@ class CaptionAPITest:
         else:
             self.log_fail("Custom system prompt returned empty answer")
 
+        # Dual prefill: re-run with custom system prompt + prefill
+        self._check_prefill({'image': self.image_b64, 'question': 'describe the image', 'system': custom_system}, "system_prompt")
+
     # =========================================================================
     # TEST: POST /sdapi/v1/vqa - Invalid Inputs
     # =========================================================================
@@ -1328,6 +1351,9 @@ class CaptionAPITest:
             else:
                 self.log_skip("Detection prompt may require specific model")
 
+        # Dual prefill: re-run 'Use Prompt' with custom prefill
+        self._check_prefill({'image': self.image_b64, 'question': 'Use Prompt', 'prompt': custom_prompt}, "prompt_field")
+
     # =========================================================================
     # TEST: POST /sdapi/v1/vqa - Generation Parameters
     # =========================================================================
@@ -1434,6 +1460,9 @@ class CaptionAPITest:
         else:
             self.log_fail("top_k/top_p returned empty/error")
 
+        # Dual prefill: re-run temp=0 request with custom prefill
+        self._check_prefill({'image': self.image_b64, 'question': 'describe the image briefly', 'temperature': 0.0}, "generation_params")
+
     # =========================================================================
     # TEST: POST /sdapi/v1/vqa - Sampling Controls
     # =========================================================================
@@ -1500,6 +1529,9 @@ class CaptionAPITest:
         else:
             self.log_fail("num_beams=4 returned empty/error")
 
+        # Dual prefill: re-run greedy request with custom prefill
+        self._check_prefill({'image': self.image_b64, 'question': 'describe the image', 'do_sample': False}, "sampling")
+
     # =========================================================================
     # TEST: POST /sdapi/v1/vqa - Thinking Mode
     # =========================================================================
diff --git a/modules/caption/deepseek.py b/modules/caption/deepseek.py
index 680f2e497..e7d3eac0c 100644
--- a/modules/caption/deepseek.py
+++ b/modules/caption/deepseek.py
@@ -51,6 +51,7 @@ def load(repo: str):
         vl_gpt = AutoModelForCausalLM.from_pretrained(
             repo,
             trust_remote_code=True,
+            use_safetensors=True,
             cache_dir=shared.opts.hfcache_dir,
         )
         vl_gpt.to(dtype=devices.dtype)
diff --git a/modules/caption/joycaption.py b/modules/caption/joycaption.py
index 778d72cf2..3fc990ba8 100644
--- a/modules/caption/joycaption.py
+++ b/modules/caption/joycaption.py
@@ -69,6 +69,7 @@ def load(repo: str = None):
         llava_model = LlavaForConditionalGeneration.from_pretrained(
             repo,
             torch_dtype=devices.dtype,
+            use_safetensors=True,
             cache_dir=shared.opts.hfcache_dir,
             **quant_args,
         )
diff --git a/modules/caption/joytag.py b/modules/caption/joytag.py
index 960e79ec6..97a05b8b5 100644
--- a/modules/caption/joytag.py
+++ b/modules/caption/joytag.py
@@ -120,6 +120,10 @@ class VisionModel(nn.Module):
         self.image_size = image_size
         self.n_tags = n_tags
 
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
     @staticmethod
     def load_model(path: str) -> 'VisionModel':
         with open(Path(path) / 'config.json', 'r', encoding='utf8') as f:
diff --git a/modules/caption/moondream3.py b/modules/caption/moondream3.py
index e6e6b9a9b..12c28b1b6 100644
--- a/modules/caption/moondream3.py
+++ b/modules/caption/moondream3.py
@@ -54,6 +54,7 @@ def load_model(repo: str):
             repo,
             trust_remote_code=True,
             torch_dtype=devices.dtype,
+            use_safetensors=True,
             cache_dir=shared.opts.hfcache_dir,
         )
 
diff --git a/modules/caption/vqa.py b/modules/caption/vqa.py
index 5d6ef6c14..b0905ef82 100644
--- a/modules/caption/vqa.py
+++ b/modules/caption/vqa.py
@@ -366,7 +366,7 @@ def get_keep_thinking():
 def strip_think_xml_tags(text: str, keep: bool = False) -> str:
     """Strip or reformat XML-style <think>...</think> blocks from model output.
 
-    Applies to models that use HuggingFace chat templates with <think>/<\/think>
+    Applies to models that use HuggingFace chat templates with <think>/</think>
     tokens (Qwen, Gemma, SmolVLM). Models with structured reasoning APIs
     (e.g. Moondream) handle their reasoning output separately.
 
@@ -374,7 +374,7 @@ def strip_think_xml_tags(text: str, keep: bool = False) -> str:
     response may only contain </think> without a matching <think>.
 
     Args:
-        text: Model output text potentially containing <think>/<\/think> tags.
+        text: Model output text potentially containing <think>/</think> tags.
         keep: If True, reformat tags as human-readable Reasoning/Answer sections.
               If False, strip thinking blocks entirely.
     """
@@ -550,6 +550,7 @@ class VQA:
                 repo,
                 torch_dtype=devices.dtype,
                 trust_remote_code=True,
+                use_safetensors=True,
                 cache_dir=shared.opts.hfcache_dir,
                 **quant_args,
             )
@@ -585,6 +586,13 @@ class VQA:
         answer = self.processor.decode(outputs[0], skip_special_tokens=True)
         return answer
 
+    # Map Qwen VL config model_type strings to their model classes.
+    _QWEN_VL_MODEL_TYPE_MAP = {
+        'qwen3_vl': 'Qwen3VLForConditionalGeneration',
+        'qwen2_5_vl': 'Qwen2_5_VLForConditionalGeneration',
+        'qwen2_vl': 'Qwen2VLForConditionalGeneration',
+    }
+
     def _load_qwen(self, repo: str):
         """Load Qwen VL model and processor."""
         if self.model is None or self.loaded != repo:
@@ -597,11 +605,17 @@ class VQA:
             elif 'Qwen2-VL' in repo or 'Qwen2VL' in repo:
                 cls_name = transformers.Qwen2VLForConditionalGeneration
             else:
-                cls_name = transformers.AutoModelForCausalLM
+                # Fine-tunes (e.g. ToriiGate) may not have "Qwen" in the repo name.
+                # Detect the correct class from the config's model_type.
+                config = transformers.AutoConfig.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
+                model_type = getattr(config, 'model_type', '')
+                cls_attr = self._QWEN_VL_MODEL_TYPE_MAP.get(model_type)
+                cls_name = getattr(transformers, cls_attr) if cls_attr else transformers.AutoModelForCausalLM
             quant_args = model_quant.create_config(module='LLM')
             self.model = cls_name.from_pretrained(
                 repo,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
                 cache_dir=shared.opts.hfcache_dir,
                 **quant_args,
             )
@@ -716,6 +730,7 @@ class VQA:
             self.model = cls.from_pretrained(
                 repo,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
                 cache_dir=shared.opts.hfcache_dir,
                 **quant_args,
             )
@@ -826,6 +841,7 @@ class VQA:
                 repo,
                 cache_dir=shared.opts.hfcache_dir,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
             )
             self.loaded = repo
             devices.torch_gc()
@@ -850,13 +866,22 @@ class VQA:
         if self.model is None or self.loaded != repo:
             shared.log.debug(f'Caption load: vlm="{repo}"')
             self.model = None
-            self.model = transformers.AutoModelForCausalLM.from_pretrained(
-                repo,
-                torch_dtype=devices.dtype,
-                multimodal_max_length=32768,
-                trust_remote_code=True,
-                cache_dir=shared.opts.hfcache_dir,
-            )
+            # Ovis remote code calls AutoConfig.register("aimv2", ...) at module scope
+            # without exist_ok=True, which fails on reload or when the type is already
+            # registered by a newer transformers version.
+            _orig = transformers.AutoConfig.register.__func__ if hasattr(transformers.AutoConfig.register, '__func__') else transformers.AutoConfig.register
+            transformers.AutoConfig.register = staticmethod(lambda model_type, config, exist_ok=False: _orig(model_type, config, exist_ok=True))
+            try:
+                self.model = transformers.AutoModelForCausalLM.from_pretrained(
+                    repo,
+                    torch_dtype=devices.dtype,
+                    multimodal_max_length=32768,
+                    trust_remote_code=True,
+                    use_safetensors=True,
+                    cache_dir=shared.opts.hfcache_dir,
+                )
+            finally:
+                transformers.AutoConfig.register = _orig
             self.loaded = repo
             devices.torch_gc()
 
@@ -903,6 +928,7 @@ class VQA:
                 repo,
                 cache_dir=shared.opts.hfcache_dir,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
                 **quant_args,
             )
             self.processor = transformers.AutoProcessor.from_pretrained(repo, max_pixels=1024*1024, cache_dir=shared.opts.hfcache_dir)
@@ -995,6 +1021,7 @@ class VQA:
             self.model = transformers.GitForCausalLM.from_pretrained(
                 repo,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
                 cache_dir=shared.opts.hfcache_dir,
             )
             self.processor = transformers.GitProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
@@ -1025,6 +1052,7 @@ class VQA:
             self.model = transformers.BlipForQuestionAnswering.from_pretrained(
                 repo,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
                 cache_dir=shared.opts.hfcache_dir,
             )
             self.processor = transformers.BlipProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
@@ -1049,6 +1077,7 @@ class VQA:
             self.model = transformers.ViltForQuestionAnswering.from_pretrained(
                 repo,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
                 cache_dir=shared.opts.hfcache_dir,
             )
             self.processor = transformers.ViltProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
@@ -1075,6 +1104,7 @@ class VQA:
             self.model = transformers.Pix2StructForConditionalGeneration.from_pretrained(
                 repo,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
                 cache_dir=shared.opts.hfcache_dir,
             )
             self.processor = transformers.Pix2StructProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
@@ -1085,9 +1115,10 @@ class VQA:
         self._load_pix(repo)
         sd_models.move_model(self.model, devices.device)
         if len(question) > 0:
-            inputs = self.processor(images=image, text=question, return_tensors="pt").to(devices.device)
+            inputs = self.processor(images=image, text=question, return_tensors="pt")
         else:
-            inputs = self.processor(images=image, return_tensors="pt").to(devices.device)
+            inputs = self.processor(images=image, return_tensors="pt")
+        inputs = {k: v.to(devices.device, devices.dtype) if v.is_floating_point() else v.to(devices.device) for k, v in inputs.items()}
         with devices.inference_context():
             outputs = self.model.generate(**inputs)
         response = self.processor.decode(outputs[0], skip_special_tokens=True)
@@ -1103,6 +1134,7 @@ class VQA:
                 revision="2025-06-21",
                 trust_remote_code=True,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
                 cache_dir=shared.opts.hfcache_dir,
             )
             self.processor = transformers.AutoTokenizer.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
@@ -1201,6 +1233,7 @@ class VQA:
                 repo_name,
                 revision=effective_revision,
                 torch_dtype=devices.dtype,
+                use_safetensors=True,
                 cache_dir=shared.opts.hfcache_dir,
                 **quant_args,
             )
@@ -1254,6 +1287,7 @@ class VQA:
                 torch_dtype=devices.dtype,
                 low_cpu_mem_usage=True,
                 use_flash_attn=False,
+                use_safetensors=True,
                 trust_remote_code=True)
             self.model = self.model.eval()  # required: trust_remote_code model
             self.processor = transformers.AutoTokenizer.from_pretrained(
diff --git a/modules/ui_caption.py b/modules/ui_caption.py
index 90e94387f..6498d463b 100644
--- a/modules/ui_caption.py
+++ b/modules/ui_caption.py
@@ -3,7 +3,7 @@ from modules import shared, ui_common, generation_parameters_copypaste
 from modules.caption import openclip
 
 
-default_task = "Short Caption"
+default_task = "Normal Caption"
 
 def vlm_caption_wrapper(question, system_prompt, prompt, image, model_name, prefill, thinking_mode):
     """Wrapper for vqa.caption that handles annotated image display."""
@@ -19,7 +19,8 @@ def update_vlm_prompts_for_model(model_name):
     """Update the task dropdown choices based on selected model."""
     from modules.caption import vqa
     prompts = vqa.get_prompts_for_model(model_name)
-    return gr.update(choices=prompts, value=prompts[0] if prompts else default_task)
+    value = default_task if default_task in prompts else (prompts[0] if prompts else default_task)
+    return gr.update(choices=prompts, value=value)
 
 
 def update_vlm_prompt_placeholder(question):