[model-gateway] add qwen3_vl model image processor (#14377)

sgl-router/scripts/generate_vision_golden.py

@@ -46,6 +46,11 @@ MODELS = {
         "processor_class": "Qwen2VLImageProcessor",
         "description": "Dynamic resolution with smart resize",
     },
+    "qwen3_vl": {
+        "model_id": "Qwen/Qwen3-VL-8B-Instruct",
+        "processor_class": "Qwen2VLImageProcessorFast",
+        "description": "Dynamic resolution with patch_size=16 and [0.5,0.5,0.5] normalization",
+    },
 }
 
 # Default test images
@@ -283,6 +288,72 @@ def save_golden(model_key: str, image_name: str, data: dict, output_dir: str):
         print(f"  Saved: {config_path}")
 
 
+def generate_golden_qwen3_vl(image_path: str, output_dir: str) -> dict:
+    """Generate golden output for Qwen3-VL.
+
+    Qwen3-VL uses dynamic resolution with smart resize similar to Qwen2-VL
+    but with different parameters:
+    - patch_size: 16 (vs 14 in Qwen2-VL)
+    - factor: 32 (vs 28 in Qwen2-VL)
+    - normalization: [0.5, 0.5, 0.5] mean/std (vs CLIP values in Qwen2-VL)
+
+    Default parameters:
+    - patch_size: 16
+    - merge_size: 2
+    - temporal_patch_size: 2
+    """
+    from transformers import AutoProcessor
+
+    processor = AutoProcessor.from_pretrained(
+        "Qwen/Qwen3-VL-8B-Instruct", trust_remote_code=True
+    )
+    image = Image.open(image_path).convert("RGB")
+    original_size = image.size
+
+    # Process image using the image processor directly
+    outputs = processor.image_processor(images=image, return_tensors="pt")
+
+    # Convert to numpy for saving
+    pixel_values = outputs["pixel_values"].numpy()
+    image_grid_thw = outputs.get("image_grid_thw")
+    if image_grid_thw is not None:
+        image_grid_thw = image_grid_thw.numpy()
+
+    # Get config values
+    img_processor = processor.image_processor
+    patch_size = getattr(img_processor, "patch_size", 16)
+    merge_size = getattr(img_processor, "merge_size", 2)
+    temporal_patch_size = getattr(img_processor, "temporal_patch_size", 2)
+
+    # Calculate number of tokens
+    if image_grid_thw is not None:
+        grid_thw = image_grid_thw[0]
+        num_tokens = int(np.prod(grid_thw) / (merge_size**2))
+    else:
+        num_tokens = None
+
+    result = {
+        "pixel_values": pixel_values,
+        "original_size": original_size,
+        "processor_config": img_processor.to_dict(),
+    }
+
+    if image_grid_thw is not None:
+        result["image_grid_thw"] = image_grid_thw
+
+    if num_tokens is not None:
+        result["num_tokens"] = num_tokens
+
+    # Add debug info
+    result["config_info"] = {
+        "patch_size": patch_size,
+        "merge_size": merge_size,
+        "temporal_patch_size": temporal_patch_size,
+    }
+
+    return result
+
+
 def generate_for_model(model_key: str, image_paths: list, output_dir: str):
     """Generate golden outputs for a specific model."""
     print(f"\nGenerating golden outputs for {model_key}...")
@@ -292,6 +363,7 @@ def generate_for_model(model_key: str, image_paths: list, output_dir: str):
         "llava_pad": generate_golden_llava_pad,
         "llava_next": generate_golden_llava_next,
         "qwen2_vl": generate_golden_qwen2_vl,
+        "qwen3_vl": generate_golden_qwen3_vl,
     }.get(model_key)
 
     if generator_fn is None: