---
id: qwen-vl-utils
version: "0.0.14"
license: Apache-2.0
license_treatment: permissive
maintenance: aging
---

# qwen-vl-utils — Qwen Vision Language Model Utils - PyTorch

License: permissive · Maintenance: aging · Downloads: 2.4M/mo

## What it is and what it does

Qwen-VL Utils is a utility library that bridges image and video inputs with Qwen-VL series vision-language models from the transformers library. It abstracts away the preprocessing logic needed to convert raw visual media—whether local files, remote URLs, base64-encoded data, or PIL Image objects—into the tensor format that Qwen2VL, Qwen2.5VL, and Qwen3VL models expect. The library also handles video frame extraction and allows fine-grained control over resizing, frame sampling rate, and other model-specific parameters.

The package is designed as a thin integration layer: you prepare your messages with image or video references, pass them to process_vision_info(), and receive structured tensors ready for the model's processor and forward pass. It depends on av for video handling, pillow for image manipulation, packaging for version management, and requests for URL fetching. The library is in Beta status and maintained by the Qwen team, with no known security vulnerabilities.

Use it for:

- Prepare image inputs from mixed sources (local paths, URLs, base64) for Qwen2VL model inference in a single call.
- Extract and resample video frames at a specific fps rate before passing to Qwen2.5VL for video understanding tasks.
- Resize images and videos to custom dimensions while maintaining compatibility with Qwen3VL's dynamic adjustment.
- Build a chat-based vision application where users submit images or videos and the model generates descriptions.
- Batch-process multiple images and videos with consistent preprocessing before feeding to a Qwen-VL model.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides helper functions to process images and videos for use with Qwen-VL vision-language models, handling multiple input formats including local files, URLs, base64-encoded data, and PIL images.

Yes, if you are building applications with Qwen-VL models. The package eliminates boilerplate for handling diverse image and video input formats and is actively maintained with no security issues. Install friction is low and the permissive license poses no restrictions. The aging maintenance status reflects time since last release rather than abandonment—the repository is active and the library is stable for its intended use.

## Install

pip install qwen-vl-utils
uv add qwen-vl-utils
poetry add qwen-vl-utils

## Installing qwen-vl-utils

Before you install: Low friction installation with four common runtime dependencies (av, packaging, pillow, requests). The package is aging but actively maintained; last commit was recent and the repository is not archived.

License in practice: Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.

Quickstart:

pip install qwen-vl-utils

from qwen_vl_utils import process_vision_info

messages = [{"role": "user", "content": [{"type": "image", "image": "file:///path/to/image.jpg"}, {"type": "text", "text": "Describe this image."}]}]
images, videos = process_vision_info(messages)

Requires a compatible Qwen-VL model from transformers library and PyTorch; Python >= 3.8.

Verify before relying:

- Whether process_vision_info handles all edge cases (malformed URLs, corrupted video files, unsupported formats) gracefully.
- Performance characteristics when processing large batches of high-resolution images or long videos.
- Compatibility with versions of av, pillow, and requests beyond what the fact sheet specifies.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 2.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

qwen vision language model preprocessing, image video processing for vl models, multimodal input formatting, qwen2vl qwen3vl utilities, vision language model helpers, image video tensor preparation, multimodal model integration, vision-language-models, multimodal-preprocessing, qwen-integration

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