metadata

license: mit
language:
  - en
pretty_name: KontextBench
size_categories:
  - 1K<n<10K

Kontext Bench

Kontext Bench is a benchmark for image editing models consisting of source images paired with image editing instructions and category tags.

The benchmark comprises 1026 unique image-prompt pairs derived from 108 base images from diverse sources. It spans five core tasks: local instruction editing, global instruction editing, text editing, style reference, and character reference. We found that the scale of the benchmark provides a good balance between reliable human evaluation and comprehensive coverage of real-world applications.

Benchmark Structure

kontext-bench/
└── test/
    ├── images/
    │   ├── 0000.jpg
    │   ├── 0001.jpg
    │   └── ...
    └── metadata.jsonl

Fields

Each line in metadata.jsonl consists of

file_name: Path to the image file relative to the split directory
instruction: The editing instruction to apply to the image
category: Category of the editing instruction
key: Unique identifier for this image-instruction pair
image_idx: Index of the source image
prompt_idx: Index of the prompt for this image

An example entry is shown below:

{
    "file_name": "images/0000.jpg"
    "instruction": "give the cat a tophat",
    "category": "Instruction Editing - Local",
    "key": "0000_01",
    "img_idx": "0000",
    "prompt_idx": "01"
}

Benchmark Statistics

Total entries: 1026
Unique images: 108

Category Statistics

Character Reference: 193 entries
Instruction Editing - Global: 262 entries
Instruction Editing - Local: 416 entries
Style Reference: 63 entries
Text Editing: 92 entries

License

The benchmark is released under the MIT License. This benchmark and the included Images are made available for scientific and research purposes only. We gratefully acknowledge all contributing photographers, Unsplash, Pexels for making their visuals available to the research community.

Citation

@misc{labs2025flux1kontextflowmatching,
      title={FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space}, Add commentMore actions
      author={Black Forest Labs and Stephen Batifol and Andreas Blattmann and Frederic Boesel and Saksham Consul and Cyril Diagne and Tim Dockhorn and Jack English and Zion English and Patrick Esser and Sumith Kulal and Kyle Lacey and Yam Levi and Cheng Li and Dominik Lorenz and Jonas Müller and Dustin Podell and Robin Rombach and Harry Saini and Axel Sauer and Luke Smith},
      year={2025},
      eprint={2506.15742},
      archivePrefix={arXiv},
      primaryClass={cs.GR},
      url={https://arxiv.org/abs/2506.15742},
}