Qwen-Image-Bench - A benchmark for evaluating text-based image models launched by Tongyi Qianwen.

What is Qwen-Image-Bench?

Qwen-Image-Bench is a dataset for evaluating text-based image models, developed by the Tongyi Qianwen team. It is [size missing]. 1k test samplesIt provides bilingual (Chinese and English) prompts, supports multi-dimensional evaluation of the generation effects of various text-to-image models, and adopts... Apache 2.0 is an open-source license.It provides a standardized and reproducible evaluation framework for image generation models, covering core capabilities such as text rendering, image editing, and general generation, helping developers and researchers objectively compare the actual performance of different models.

Main functions of Qwen-Image-Bench

• Bilingual evaluation (Chinese and English)The test samples cover both Chinese and English prompts, which can comprehensively evaluate the model's multilingual image generation capabilities.
• Multidimensional ability assessmentIt supports comprehensive evaluation of text-to-image models in dimensions such as text rendering, image editing, general generation, and semantic consistency.
• Standardized testing processIt provides a unified evaluation script and data format to ensure fair comparison between different models.
• Open source datasets1,000 meticulously designed test cases covering a variety of complex scenarios and fine-grained tasks.
• Automated scoring supportIt supports automated evaluation by combining multiple benchmark metrics (such as GenEval, DPG, GEdit, etc.).

How to use Qwen-Image-Bench

• Clone the repository to your local machineAccess the GitHub repository QwenLM/Qwen-Image-Bench,use git clone Download the project code to your local environment.
• Install dependenciesAccording to the warehouse requirements.txt Alternatively, refer to the documentation to install Python dependencies (such as PyTorch, Diffusers, Transformers, and other tools required for image generation and evaluation).
• Prepare the model to be evaluatedConfigure the text graph model to be tested, supporting local loading of model weights (such as Qwen-Image, FLUX, Stable Diffusion, etc.), or accessing remote model services via API.
• Load the evaluation datasetThe 1,000 bilingual (Chinese and English) test samples provided by Qwen-Image-Bench were loaded into the evaluation process. The dataset contains prompts for various dimensions, including general generation, text rendering, and image editing.
• Batch image generationRun the inference script. The model generates corresponding images one by one based on the text prompts in the dataset. It is recommended to unify the output resolution (e.g., 1024×1024) and inference parameters to ensure consistent evaluation.
• Run automated evaluation scriptsThe built-in evaluation tools in the repository are used to automatically score the generated results from dimensions such as text rendering accuracy, semantic consistency, image quality, and editing fidelity.
• Output and compare the evaluation resultsGenerate structured evaluation reports, view the model's scores across various dimensions, and support horizontal comparative analysis with other models.
• Custom extended evaluation (optional)Custom test cases can be added or evaluation metric weights can be adjusted according to actual needs to adapt to the evaluation requirements of specific business scenarios.

Qwen-Image-Bench's core advantages

• Highly targeted to Chinese scenarios: Special emphasis is placed on enhancing the assessment of Chinese text rendering and cultural element comprehension, addressing the shortcomings of insufficient Chinese coverage in existing benchmarks.
• Comprehensive evaluation dimensionsIt covers a wide range of tasks, including general image generation, precise image editing, and complex text rendering, and is not limited to a single capability.
• Moderate in size and easy to reproduceUsing 1,000 samples reduces the reproduction threshold and computational cost while ensuring the representativeness of the evaluation.
• Good ecological compatibilityIt is natively compatible with models such as Qwen-Image and Qwen-Image-Edit, and can also be used to evaluate third-party text-based image models.
• Open source license friendlyThe Apache 2.0 license allows for commercial use and free secondary development.

Qwen-Image-Bench project address

• Github repositoryhttps://github.com/QwenLM/Qwen-Image-Bench
• HuggingFace model libraryhttps://huggingface.co/datasets/Qwen/Qwen-Image-Bench
• arXiv technical paper: https://arxiv.org/pdf/2605.28091

Comparison with similar products from Qwen-Image-Bench

Application scenarios of Qwen-Image-Bench

• Standardized evaluation before model releaseBefore the text-based image model is officially launched or open-sourced, 1,000 bilingual (Chinese and English) test cases on Qwen-Image-Bench are used to systematically verify the model's generation quality and stability in dimensions such as general generation, text rendering, and image editing, ensuring that the model meets the release standards.
• Multi-model horizontal capability comparisonThis study provides a horizontal comparison of the overall performance of different text-to-image models, including Qwen-Image, FLUX, GPT Image 1, SeedDream, and Stable Diffusion. The scores from various dimensions clearly demonstrate the advantages and disadvantages of each model, assisting in technology selection.
• Chinese generation ability testThe main focus is to verify the text rendering effect of the model in applications such as Chinese posters, PPTs, e-commerce images, and cultural scenarios, and to evaluate the model's understanding and visual expression capabilities of Chinese semantics, layout, and cultural elements.
• Image editing ability assessmentThe test model performs well in image editing tasks such as style transfer, local modification, text replacement, and object addition/deletion, measuring semantic consistency and visual fidelity before and after editing.
• Academic research benchmark citationAs an authoritative benchmark dataset for model evaluation in papers, it enhances the credibility and reproducibility of research results and supports the publication of basic research in the field of image generation.