diff --git a/modules/interrogate/vqa.py b/modules/interrogate/vqa.py
index cb1780677..fcb7464bc 100644
--- a/modules/interrogate/vqa.py
+++ b/modules/interrogate/vqa.py
@@ -22,6 +22,12 @@ vlm_models = {
     "Alibaba Qwen 2.0 VL 2B": "Qwen/Qwen2-VL-2B-Instruct",
     "Alibaba Qwen 2.5 Omni 3B": "Qwen/Qwen2.5-Omni-3B",
     "Alibaba Qwen 2.5 VL 3B": "Qwen/Qwen2.5-VL-3B-Instruct",
+    "Alibaba Qwen 3 VL 2B": "Qwen/Qwen3-VL-2B-Instruct",
+    "Alibaba Qwen 3 VL 2B Thinking": "Qwen/Qwen3-VL-2B-Thinking",
+    "Alibaba Qwen 3 VL 4B": "Qwen/Qwen3-VL-4B-Instruct",
+    "Alibaba Qwen 3 VL 4B Thinking": "Qwen/Qwen3-VL-4B-Thinking",
+    "Alibaba Qwen 3 VL 8B": "Qwen/Qwen3-VL-8B-Instruct",
+    "Alibaba Qwen 3 VL 8B Thinking": "Qwen/Qwen3-VL-8B-Thinking",
     "Huggingface Smol VL2 0.5B": "HuggingFaceTB/SmolVLM-500M-Instruct",
     "Huggingface Smol VL2 2B": "HuggingFaceTB/SmolVLM-Instruct",
     "Apple FastVLM 0.5B": "apple/FastVLM-0.5B",
@@ -181,10 +187,14 @@ def qwen(question: str, image: Image.Image, repo: str = None, system_prompt: str
     if model is None or loaded != repo:
         shared.log.debug(f'Interrogate load: vlm="{repo}"')
         model = None
-        if '2.5' in repo:
+        if 'Qwen3-VL' in repo or 'Qwen3VL' in repo:
+            cls_name = transformers.Qwen3VLForConditionalGeneration
+        elif 'Qwen2.5-VL' in repo or 'Qwen2_5_VL' in repo:
             cls_name = transformers.Qwen2_5_VLForConditionalGeneration
-        else:
+        elif 'Qwen2-VL' in repo or 'Qwen2VL' in repo:
             cls_name = transformers.Qwen2VLForConditionalGeneration
+        else:
+            cls_name = transformers.AutoModelForCausalLM
         model = cls_name.from_pretrained(
             repo,
             torch_dtype=devices.dtype,
diff --git a/scripts/prompt_enhance.py b/scripts/prompt_enhance.py
index 221d815dc..1925a05d8 100644
--- a/scripts/prompt_enhance.py
+++ b/scripts/prompt_enhance.py
@@ -32,6 +32,14 @@ def b64(image):
 class Options:
     img2img = [
         'google/gemma-3-4b-it',
+        'allura-org/Gemma-3-Glitter-4B',
+        'Qwen/Qwen2.5-VL-3B-Instruct',
+        'Qwen/Qwen3-VL-2B-Instruct',
+        'Qwen/Qwen3-VL-2B-Thinking',
+        'Qwen/Qwen3-VL-4B-Instruct',
+        'Qwen/Qwen3-VL-4B-Thinking',
+        'Qwen/Qwen3-VL-8B-Instruct',
+        'Qwen/Qwen3-VL-8B-Thinking',
     ]
     models = {
         'google/gemma-3-1b-it': {},
@@ -49,6 +57,12 @@ class Options:
         'Qwen/Qwen2.5-1.5B-Instruct': {},
         'Qwen/Qwen2.5-3B-Instruct': {},
         'Qwen/Qwen2.5-VL-3B-Instruct': {},
+        'Qwen/Qwen3-VL-2B-Instruct': {},
+        'Qwen/Qwen3-VL-2B-Thinking': {},
+        'Qwen/Qwen3-VL-4B-Instruct': {},
+        'Qwen/Qwen3-VL-4B-Thinking': {},
+        'Qwen/Qwen3-VL-8B-Instruct': {},
+        'Qwen/Qwen3-VL-8B-Thinking': {},
         'microsoft/Phi-4-mini-instruct': {},
         'HuggingFaceTB/SmolLM2-135M-Instruct': {},
         'HuggingFaceTB/SmolLM2-360M-Instruct': {},
@@ -154,7 +168,17 @@ class Script(scripts_manager.Script):
             load_args = { 'pretrained_model_name_or_path': model_repo if not gguf_args else model_gguf }
             if model_subfolder:
                 load_args['subfolder'] = model_subfolder # Comma was incorrect here
-            self.llm = transformers.AutoModelForCausalLM.from_pretrained(
+
+            if 'Qwen3-VL' in model_repo or 'Qwen3VL' in model_repo:
+                cls_name = transformers.Qwen3VLForConditionalGeneration
+            elif 'Qwen2.5-VL' in model_repo or 'Qwen2_5_VL' in model_repo:
+                cls_name = transformers.Qwen2_5_VLForConditionalGeneration
+            elif 'Qwen2-VL' in model_repo or 'Qwen2VL' in model_repo:
+                cls_name = transformers.Qwen2VLForConditionalGeneration
+            else:
+                cls_name = transformers.AutoModelForCausalLM
+
+            self.llm = cls_name.from_pretrained(
                 **load_args,
                 trust_remote_code=True,
                 torch_dtype=devices.dtype,
@@ -288,6 +312,25 @@ class Script(scripts_manager.Script):
         except Exception:
             current_image = None
 
+        # Resize large images to match VQA performance (Qwen3-VL performance is sensitive to resolution)
+        # Create a copy to avoid modifying the original image used by img2img
+        if current_image is not None and isinstance(current_image, Image.Image):
+            original_size = (current_image.width, current_image.height)
+            needs_resize = current_image.width > 768 or current_image.height > 768
+            needs_rgb = current_image.mode != 'RGB'
+
+            if needs_resize or needs_rgb:
+                # Copy the image before any modifications to preserve the original
+                current_image = current_image.copy()
+
+                if needs_resize:
+                    current_image.thumbnail((768, 768), Image.Resampling.LANCZOS)
+                    debug_log(f'Prompt enhance: Resized image from {original_size} to {(current_image.width, current_image.height)}')
+
+                if needs_rgb:
+                    current_image = current_image.convert('RGB')
+                    debug_log('Prompt enhance: Converted image to RGB mode')
+
         has_system = system is not None and len(system) > 4
         mode = 'custom' if has_system else ''