> ## Documentation Index
> Fetch the complete documentation index at: https://fireworks.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# How many tokens does a Qwen2.5 VL image use?

> Count tokens for one Qwen2.5 VL image at common resolutions, or measure your own image with the Qwen2.5 VL processor.

Image token counts depend on the model and its image processor. The table and code below are for Qwen2.5 VL.

## Common resolution token counts

The following table shows the token counts for a single image for Qwen2.5 VL at different image resolutions:

| Resolution | Token Count |
| - | - |
| 336×336 | 144 |
| 672×672 | 576 |
| 1024×1024 | 1,369 |
| 1280×720 | 1,196 |
| 1920×1080 | 2,769 |
| 2560×1440 | 4,641 |
| 3840×2160 | 10,549 |

## Calculating exact token count for your images

You can determine exact token usage by processing your images through the model's tokenizer.
For instance, for Qwen2.5 VL, you can use the following code:

**1. Install dependencies**

```bash theme={null}
pip install torch torchvision transformers pillow
```

**2. Tokenize your image**

```python Tokenizing your image theme={null}
import requests
from PIL import Image
from transformers import AutoProcessor
import os

# Your image source - can be URL or local path
IMAGE_URL_OR_PATH = "https://images.unsplash.com/photo-1519125323398-675f0ddb6308"

def load_image(source):
    """Load image from URL or local file path"""
    if source.startswith(('http://', 'https://')):
        print(f"Downloading image from URL: {source}")
        response = requests.get(source)
        response.raise_for_status()
        return Image.open(requests.get(source, stream=True).raw)
    else:
        print(f"Loading image from path: {source}")
        if not os.path.exists(source):
            raise FileNotFoundError(f"Image file not found: {source}")
        return Image.open(source)

def count_image_tokens(image):
    """Count how many tokens an image takes using Qwen 2.5 VL processor"""
    processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
    
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": "What's in this image?"},
            ],
        }
    ]
    
    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor(text=text, images=[image], return_tensors="pt")
    input_ids = inputs["input_ids"][0]
    
    # Count the image pad tokens (151655 is Qwen2.5 VL's image token ID)
    image_tokens = (input_ids == 151655).sum().item()
    
    return image_tokens, input_ids

def main():
    import sys
    
    image_source = sys.argv[1] if len(sys.argv) > 1 else IMAGE_URL_OR_PATH
    
    print(f"Processing image: {image_source}")
    image = load_image(image_source)
    print(f"Image size: {image.size}")
    print(f"Image mode: {image.mode}")
    
    print("\nCalculating tokens...")
    image_tokens, input_ids = count_image_tokens(image)
    
    print(f"Total tokens: {len(input_ids)}")
    print(f"Image tokens: {image_tokens}")
    print(f"Text tokens: {len(input_ids) - image_tokens}")
    
if __name__ == "__main__":
    main()
```

```bash Usage theme={null}
# Calculate tokens for an image URL
python token_calculator.py "https://example.com/image.jpg"

# Calculate tokens for a local image
python token_calculator.py "path/to/your/image.png"
```


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