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TextEdit

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 ---
pretty_name: TextEdit-Bench
license: mit
task_categories:
- image-to-image
tags:
- computer-vision
- image-editing
- benchmark
configs:
- config_name: default
data_files:
- split: train
path: metadata.jsonl
dataset_info:
features:
- name: original_image
dtype: image
- name: gt_image
dtype: image
- name: id
dtype: int64
- name: category
dtype: string
- name: source_text
dtype: string
- name: target_text
dtype: string
- name: prompt
dtype: string
- name: gt_caption
dtype: string
---
<div align="center">
# TextEdit: A High-Quality, Multi-Scenario Text Editing Benchmark for Generation Models
<p align="center">
<a href="https://arxiv.org/abs/2603.09877">
<img src="https://img.shields.io/badge/ArXiv-2603.09877-b31b1b?logo=arxiv" alt="arXiv">
</a>
<a href="https://github.com/open-compass/TextEdit">
<img src="https://img.shields.io/badge/GitHub-TextEdit-black?style=flat&logo=github" alt="GitHub Repo">
</a>
[Danni Yang](https://scholar.google.com/citations?user=qDsgBJAAAAAJ&hl=zh-CN&oi=sra),
[Sitao Chen](https://github.com/fudan-chen),
[Changyao Tian](https://scholar.google.com/citations?user=kQ3AisQAAAAJ&hl=zh-CN&oi=ao)
If you find our work helpful, please give us a ⭐ or cite our paper. See the InternVL-U technical report appendix for more details.
</div>
## 🎉 News
- **[2026/03/06]** TextEdit benchmark released.
- **[2026/03/06]** Evaluation code and initial baselines released.
- **[2026/03/06]** Leaderboard updated with latest models.
## 📖 Introduction
<img src="assets/intro.png" width="100%">
Text editing is a fundamental yet challenging capability for modern image generation and editing models. An increasing number of powerful multimodal generation models, such as Qwen-Image and Nano-Banana-Pro, are emerging with strong text rendering and editing capabilities.
For text editing task, unlike general image editing, text manipulation requires:
- Precise spatial alignment
- Font and style consistency
- Background preservation
- Layout-constrained reasoning
We introduce **TextEdit**, a **high-quality**, **multi-scenario benchmark** designed to evaluate **fine-grained text editing capabilities** in image generation models.
TextEdit covers a diverse set of real-world and virtual scenarios, spanning **18 subcategories** with a total of **2,148 high-quality source images** and **manually annotated edited ground-truth images**.
To comprehensively assess model performance, we combine **classic OCR, image-fidelity metrics and modern multimodal LLM-based evaluation** across _target accuracy_, _text preservation_, _scene integrity_, _local realism_ and _visual coherence_. This dual-track protocol enables comprehensive assessment.
Our goal is to provide a **standardized, realistic, and scalable** benchmark for text editing research.
---
## 🏆 LeadBoard
<details>
<summary><strong>📊 Full Benchmark Results</strong></summary>
<div style="max-width:1050px; margin:auto;">
<table>
<thead>
<tr>
<th rowspan="2" align="left">Models</th>
<th rowspan="2" align="center"># Params</th>
<th colspan="7" align="center">Real</th>
<th colspan="7" align="center">Virtual</th>
</tr>
<tr>
<th>OA</th>
<th>OP</th>
<th>OR</th>
<th>F1</th>
<th>NED</th>
<th>CLIP</th>
<th>AES</th>
<th>OA</th>
<th>OP</th>
<th>OR</th>
<th>F1</th>
<th>NED</th>
<th>CLIP</th>
<th>AES</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="16"><strong><em>Generation Models</em></strong></td>
</tr>
<tr>
<td>Qwen-Image-Edit</td>
<td align="center">20B</td>
<td>0.75</td><td>0.68</td><td>0.66</td><td>0.67</td><td>0.71</td><td>0.75</td><td>5.72</td>
<td>0.78</td><td>0.75</td><td>0.73</td><td>0.74</td><td>0.75</td><td>0.81</td><td>5.21</td>
</tr>
<tr>
<td>GPT-Image-1.5</td>
<td align="center">-</td>
<td>0.74</td><td>0.69</td><td>0.67</td><td>0.68</td><td>0.68</td><td>0.75</td><td>5.78</td>
<td>0.73</td><td>0.72</td><td>0.71</td><td>0.71</td><td>0.70</td><td>0.80</td><td>5.28</td>
</tr>
<tr>
<td>Nano Banana Pro</td>
<td align="center">-</td>
<td>0.77</td><td>0.72</td><td>0.70</td><td>0.71</td><td>0.72</td><td>0.75</td><td>5.79</td>
<td>0.80</td><td>0.78</td><td>0.77</td><td>0.78</td><td>0.78</td><td>0.81</td><td>5.28</td>
</tr>
<tr>
<td colspan="16"><strong><em>Unified Models</em></strong></td>
</tr>
<tr>
<td>Lumina-DiMOO</td>
<td align="center">8B</td>
<td>0.22</td><td>0.23</td><td>0.19</td><td>0.20</td><td>0.19</td><td>0.69</td><td>5.53</td>
<td>0.22</td><td>0.25</td><td>0.21</td><td>0.22</td><td>0.20</td><td>0.72</td><td>4.76</td>
</tr>
<tr>
<td>Ovis-U1</td>
<td align="center">2.4B+1.2B</td>
<td>0.40</td><td>0.37</td><td>0.34</td><td>0.35</td><td>0.35</td><td>0.72</td><td>5.32</td>
<td>0.37</td><td>0.40</td><td>0.38</td><td>0.39</td><td>0.33</td><td>0.75</td><td>4.66</td>
</tr>
<tr>
<td>BAGEL</td>
<td align="center">7B+7B</td>
<td>0.60</td><td>0.59</td><td>0.53</td><td>0.55</td><td>0.55</td><td>0.74</td><td>5.71</td>
<td>0.57</td><td>0.60</td><td>0.56</td><td>0.57</td><td>0.54</td><td>0.78</td><td>5.19</td>
</tr>
<tr>
<td>InternVL-U</td>
<td align="center">2B+1.7B</td>
<td>0.77</td><td>0.73</td><td>0.70</td><td>0.71</td><td>0.72</td><td>0.75</td><td>5.70</td>
<td>0.79</td><td>0.77</td><td>0.75</td><td>0.75</td><td>0.77</td><td>0.80</td><td>5.12</td>
</tr>
</tbody>
</table>
</div>
<div style="max-width:1050px; margin:auto;">
<table>
<thead>
<tr>
<th rowspan="2" align="left">Models</th>
<th rowspan="2" align="center"># Params</th>
<th colspan="6" align="center">Real</th>
<th colspan="6" align="center">Virtual</th>
</tr>
<tr>
<th>TA</th>
<th>TP</th>
<th>SI</th>
<th>LR</th>
<th>VC</th>
<th>Avg</th>
<th>TA</th>
<th>TP</th>
<th>SI</th>
<th>LR</th>
<th>VC</th>
<th>Avg</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="14"><strong><em>Generation Models</em></strong></td>
</tr>
<tr>
<td>Qwen-Image-Edit</td>
<td align="center">20B</td>
<td>0.92</td><td>0.82</td><td>0.75</td><td>0.57</td><td>0.80</td><td>0.77</td>
<td>0.57</td><td>0.79</td><td>0.92</td><td>0.80</td><td>0.77</td><td>0.77</td>
</tr>
<tr>
<td>GPT-Image-1.5</td>
<td align="center">-</td>
<td>0.96</td><td>0.94</td><td>0.86</td><td>0.80</td><td>0.93</td><td>0.90</td>
<td>0.82</td><td>0.93</td><td>0.96</td><td>0.91</td><td>0.87</td><td>0.90</td>
</tr>
<tr>
<td>Nano Banana Pro</td>
<td align="center">-</td>
<td>0.96</td><td>0.95</td><td>0.85</td><td>0.88</td><td>0.93</td><td>0.91</td>
<td>0.87</td><td>0.92</td><td>0.96</td><td>0.94</td><td>0.89</td><td>0.92</td>
</tr>
<tr>
<td colspan="14"><strong><em>Unified Models</em></strong></td>
</tr>
<tr>
<td>Lumina-DiMOO</td>
<td align="center">8B</td>
<td>0.17</td><td>0.06</td><td>0.04</td><td>0.02</td><td>0.05</td><td>0.09</td>
<td>0.02</td><td>0.06</td><td>0.16</td><td>0.05</td><td>0.03</td><td>0.08</td>
</tr>
<tr>
<td>Ovis-U1</td>
<td align="center">2.4B+1.2B</td>
<td>0.31</td><td>0.12</td><td>0.12</td><td>0.07</td><td>0.18</td><td>0.18</td>
<td>0.06</td><td>0.16</td><td>0.31</td><td>0.14</td><td>0.13</td><td>0.19</td>
</tr>
<tr>
<td>BAGEL</td>
<td align="center">7B+7B</td>
<td>0.68</td><td>0.60</td><td>0.38</td><td>0.35</td><td>0.56</td><td>0.53</td>
<td>0.38</td><td>0.51</td><td>0.68</td><td>0.62</td><td>0.42</td><td>0.54</td>
</tr>
<tr>
<td>InternVL-U</td>
<td align="center">2B+1.7B</td>
<td>0.94</td><td>0.90</td><td>0.71</td><td>0.80</td><td>0.80</td><td>0.88</td>
<td>0.87</td><td>0.86</td><td>0.91</td><td>0.82</td><td>0.62</td><td>0.83</td>
</tr>
</tbody>
</table>
</div>
</details>
<details>
<summary><strong>📊 Mini-set Benchmark Results(500 samples)</strong></summary>
<div style="max-width:1050px; margin:auto;">
<table>
<thead>
<tr>
<th rowspan="2" align="left">Models</th>
<th rowspan="2" align="center"># Params</th>
<th colspan="7" align="center">Real</th>
<th colspan="7" align="center">Virtual</th>
</tr>
<tr>
<th>OA</th>
<th>OP</th>
<th>OR</th>
<th>F1</th>
<th>NED</th>
<th>CLIP</th>
<th>AES</th>
<th>OA</th>
<th>OP</th>
<th>OR</th>
<th>F1</th>
<th>NED</th>
<th>CLIP</th>
<th>AES</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="16"><strong><em>Generation Models</em></strong></td>
</tr>
<tr>
<td>Qwen-Image-Edit</td>
<td align="center">20B</td>
<td>0.76</td><td>0.69</td><td>0.67</td><td>0.67</td><td>0.70</td><td>0.75</td><td>5.81</td>
<td>0.74</td><td>0.71</td><td>0.70</td><td>0.70</td><td>0.70</td><td>0.80</td><td>5.27</td>
</tr>
<tr>
<td>GPT-Image-1.5</td>
<td align="center">-</td>
<td>0.72</td><td>0.68</td><td>0.66</td><td>0.67</td><td>0.67</td><td>0.75</td><td>5.85</td>
<td>0.68</td><td>0.69</td><td>0.68</td><td>0.68</td><td>0.65</td><td>0.80</td><td>5.32</td>
</tr>
<tr>
<td>Nano Banana Pro</td>
<td align="center">-</td>
<td>0.76</td><td>0.71</td><td>0.69</td><td>0.70</td><td>0.70</td><td>0.75</td><td>5.86</td>
<td>0.77</td><td>0.76</td><td>0.75</td><td>0.75</td><td>0.76</td><td>0.81</td><td>5.32</td>
</tr>
<tr>
<td colspan="16"><strong><em>Unified Models</em></strong></td>
</tr>
<tr>
<td>Lumina-DiMOO</td>
<td align="center">8B</td>
<td>0.20</td><td>0.22</td><td>0.18</td><td>0.19</td><td>0.19</td><td>0.70</td><td>5.58</td>
<td>0.22</td><td>0.25</td><td>0.21</td><td>0.22</td><td>0.19</td><td>0.73</td><td>4.87</td>
</tr>
<tr>
<td>Ovis-U1</td>
<td align="center">2.4B+1.2B</td>
<td>0.37</td><td>0.34</td><td>0.32</td><td>0.32</td><td>0.33</td><td>0.72</td><td>5.39</td>
<td>0.39</td><td>0.41</td><td>0.38</td><td>0.39</td><td>0.33</td><td>0.74</td><td>4.75</td>
</tr>
<tr>
<td>BAGEL</td>
<td align="center">7B+7B</td>
<td>0.61</td><td>0.59</td><td>0.52</td><td>0.54</td><td>0.54</td><td>0.74</td><td>5.79</td>
<td>0.53</td><td>0.58</td><td>0.53</td><td>0.55</td><td>0.51</td><td>0.78</td><td>5.25</td>
</tr>
<tr>
<td>InternVL-U</td>
<td align="center">2B+1.7B</td>
<td>0.77</td><td>0.74</td><td>0.70</td><td>0.71</td><td>0.71</td><td>0.76</td><td>5.79</td>
<td>0.74</td><td>0.72</td><td>0.69</td><td>0.70</td><td>0.72</td><td>0.79</td><td>5.14</td>
</tr>
</tbody>
</table>
</div>
<div style="max-width:1050px; margin:auto;">
<table>
<thead>
<tr>
<th rowspan="2" align="left">Models</th>
<th rowspan="2" align="center"># Params</th>
<th colspan="6" align="center">Real</th>
<th colspan="6" align="center">Virtual</th>
</tr>
<tr>
<th>TA</th>
<th>TP</th>
<th>SI</th>
<th>LR</th>
<th>VC</th>
<th>Avg</th>
<th>TA</th>
<th>TP</th>
<th>SI</th>
<th>LR</th>
<th>VC</th>
<th>Avg</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="14"><strong><em>Generation Models</em></strong></td>
</tr>
<tr>
<td>Qwen-Image-Edit</td>
<td align="center">20B</td>
<td>0.93</td><td>0.85</td><td>0.77</td><td>0.55</td><td>0.78</td><td>0.80</td>
<td>0.60</td><td>0.82</td><td>0.91</td><td>0.81</td><td>0.74</td><td>0.76</td>
</tr>
<tr>
<td>GPT-Image-1.5</td>
<td align="center">-</td>
<td>0.97</td><td>0.94</td><td>0.86</td><td>0.79</td><td>0.92</td><td>0.91</td>
<td>0.85</td><td>0.93</td><td>0.95</td><td>0.92</td><td>0.83</td><td>0.88</td>
</tr>
<tr>
<td>Nano Banana Pro</td>
<td align="center">-</td>
<td>0.96</td><td>0.95</td><td>0.85</td><td>0.86</td><td>0.92</td><td>0.91</td>
<td>0.87</td><td>0.92</td><td>0.96</td><td>0.93</td><td>0.87</td><td>0.92</td>
</tr>
<tr>
<td colspan="14"><strong><em>Unified Models</em></strong></td>
</tr>
<tr>
<td>Lumina-DiMOO</td>
<td align="center">8B</td>
<td>0.16</td><td>0.04</td><td>0.04</td><td>0.02</td><td>0.06</td><td>0.08</td>
<td>0.02</td><td>0.05</td><td>0.19</td><td>0.07</td><td>0.03</td><td>0.10</td>
</tr>
<tr>
<td>Ovis-U1</td>
<td align="center">2.4B+1.2B</td>
<td>0.29</td><td>0.11</td><td>0.11</td><td>0.08</td><td>0.20</td><td>0.17</td>
<td>0.04</td><td>0.16</td><td>0.35</td><td>0.18</td><td>0.15</td><td>0.22</td>
</tr>
<tr>
<td>BAGEL</td>
<td align="center">7B+7B</td>
<td>0.68</td><td>0.61</td><td>0.38</td><td>0.34</td><td>0.59</td><td>0.53</td>
<td>0.36</td><td>0.52</td><td>0.69</td><td>0.64</td><td>0.40</td><td>0.54</td>
</tr>
<tr>
<td>InternVL-U</td>
<td align="center">2B+1.7B</td>
<td>0.94</td><td>0.91</td><td>0.72</td><td>0.73</td><td>0.75</td><td>0.89</td>
<td>0.88</td><td>0.87</td><td>0.90</td><td>0.78</td><td>0.57</td><td>0.79</td>
</tr>
</tbody>
</table>
</div>
</details>
## 🛠️ Quick Start
### 📂 1. Data Preparation
You can download images from [this page](https://huggingface.co/collections/OpenGVLab/TextEdit). The TextEdit benchmark data is organized under `data/` by and category:
- **Virtual** (categories `1.x.x`): Synthetic/virtual scene images
- **Real** (categories `2.x`): Real-world scene images
Evaluation prompts are provided under `eval_prompts/` in two subsets:
| Subset | Directory | Description |
|--------|-----------|-------------|
| **Fullset** | `eval_prompts/fullset/` | Complete benchmark with all samples |
| **Miniset (500)** | `eval_prompts/miniset/` | 500-sample subset uniformly sampled from the fullset |
Each `.jsonl` file contains per-sample fields: `id`, `prompt`, `original_image`, `gt_image`, `source_text`, `target_text`, `gt_caption`.
### 🤖 2. Model Output Preparation
You need to use your model to perform image editing inference process. Please organize the outputs in the folder structure shown below to facilitate evaluation.
```
output/
├── internvl-u/ # Your Model Name
│ ├── 1.1.1 # Category Name
│ ├── 1007088003726.0.jpg # Model Output Images
│ ├── 1013932004096.0.jpg
│ ├── ...
│ ├── 1.1.2
│ ├── 1.1.3
│ ├── ...
│ └── 2.7
```
### 📏 3. Model Evaluation
#### 3.1 Classic Metrics Evaluation
Classic metrics evaluate text editing quality using **OCR-based text accuracy**, **image-text alignment**, and **aesthetic quality**. All metrics are reported separately for **Virtual** and **Real** splits.
#### Evaluated Metrics
| Abbreviation | Metric | Description |
|:---:|---|---|
| **OA** | OCR Accuracy | Whether the target text is correctly rendered in the editing region |
| **OP** | OCR Precision | Precision of text content (target + background) in the generated image |
| **OR** | OCR Recall | Recall of text content (target + background) in the generated image |
| **F1** | OCR F1 | Harmonic mean of OCR Precision and Recall |
| **NED** | Normalized Edit Distance | ROI-aware normalized edit distance between target and generated text |
| **CLIP** | CLIPScore | CLIP-based image-text alignment score |
| **AES** | Aesthetic Score | Predicted aesthetic quality score of the generated image |
#### Usage
Evaluation scripts are provided separately for **fullset** and **miniset**:
- `eval_scripts/classic_metrics_eval_full.sh` — evaluate on the full benchmark
- `eval_scripts/classic_metrics_eval_mini.sh` — evaluate on the 500-sample miniset
**Step 1. Modify the contents of the configure script according to your project directory.** (e.g., `eval_scripts/classic_metrics_eval_full.sh`):
```bash
MODELS="model-a,model-b,model-c" # Comma-separated list of model names to be evaluated
path="your_project_path_here"
CACHE_DIR="$path/TextEdit/checkpoint" # Directory for all model checkpoints (OCR, CLIP, etc.)
BENCHMARK_DIR="$path/TextEdit/eval_prompts/fullset"
GT_ROOT_DIR="$path/TextEdit/data" # Root path for original & GT images
MODEL_OUTPUT_ROOT="$path/TextEdit/output" # Root path for model infer outputs
OUTPUT_DIR="$path/TextEdit/result/classic_fullset" # Evaluation result root path for classic metric
```
> **Note:** All required model checkpoints (PaddleOCR, CLIP, aesthetic model, etc.) should be placed under the **`CACHE_DIR`** directory.
**Step 2.Run evaluation shell script to evaluate your model output.**
```bash
# Fullset evaluation
bash eval_scripts/classic_metrics_eval_full.sh
# Miniset evaluation
bash eval_scripts/classic_metrics_eval_mini.sh
```
Results are saved as `{model_name}.json` under the output directory, containing per-sample scores and aggregated metrics for both **Virtual** and **Real** splits.
---
#### 3.2 VLM-based Metrics Evaluation
Our VLM-based evaluation uses **Gemini-3-Pro-Preview** as an expert judge to score text editing quality across five fine-grained dimensions. The evaluation is a **two-step pipeline**.
#### Evaluated Metrics
| Abbreviation | Metric | Description |
|:---:|---|---|
| **TA** | Text Accuracy | Spelling correctness and completeness of the target text (1–5) |
| **TP** | Text Preservation | Preservation of non-target background text (1–5) |
| **SI** | Scene Integrity | Geometric stability of non-edited background areas (1–5) |
| **LR** | Local Realism | Inpainting quality, edge cleanness, and seamlessness (1–5) |
| **VC** | Visual Coherence | Style matching (font, lighting, shadow, texture harmony) (1–5) |
| **Avg** | Weighted Average | Weighted average of all five dimensions (default weights: 0.4 / 0.3 / 0.1 / 0.1 / 0.1) |
All raw scores (1–5) are normalized to 0–1 for reporting. A **cutoff mechanism** is available: if TA (Q1) < 4, the remaining dimensions are set to 0, reflecting that a failed text edit invalidates other quality dimensions.
#### Step 1: Gemini API Evaluation
Send (Original Image, GT Image, Edited Image) triplets to the Gemini API for scoring.
Configure and run `eval_scripts/vlm_metrics_eval_step1.sh`:
```bash
API_KEY="your_gemini_api_key_here"
BASE_URL="your_gemini_api_base_url_here"
python eval_pipeline/vlm_metrics_eval_step1.py \
--input_data_dir <your_path>/TextEdit/eval_prompts/fullset \
--model_output_root <your_path>/TextEdit/output \
--gt_data_root <your_path>/TextEdit/data \
--output_base_dir <your_path>/TextEdit/result/vlm_gemini_full_answers \
--model_name "gemini-3-pro-preview" \
--models "model-a,model-b,model-c" \
--api_key "$API_KEY" \
--base_url "$BASE_URL" \
--num_workers 64
```
Per-model `.jsonl` answer files are saved under the `output_base_dir`.
#### Step 2: Score Aggregation & Report
Aggregate the per-sample Gemini responses into a final report.
Configure and run `eval_scripts/vlm_metrics_eval_step2.sh`:
```bash
# Fullset report
python eval_pipeline/vlm_metrics_eval_step2.py \
--answer_dir <your_path>/TextEdit/result/vlm_gemini_full_answers \
--output_file <your_path>/TextEdit/result/gemini_report_fullset.json \
--weights 0.4 0.3 0.1 0.1 0.1 \
--enable_cutoff
# Miniset report
python eval_pipeline/vlm_metrics_eval_step2.py \
--answer_dir <your_path>/TextEdit/result/vlm_gemini_mini_answers \
--output_file <your_path>/TextEdit/result/gemini_report_miniset.json \
--weights 0.4 0.3 0.1 0.1 0.1 \
--enable_cutoff
```
**Key parameters:**
- `--weights`: Weights for Q1–Q5 (default: `0.4 0.3 0.1 0.1 0.1`).
- `--enable_cutoff`: Enable cutoff mechanism — if Q1 < 4, set Q2–Q5 to 0.
The output includes a JSON report, a CSV table, and a Markdown-formatted leaderboard printed to the console.
---
## 🎨 Visualization Ouput Example
<img src="assets/output.jpg" width="100%">
## Citation
If you find TextEdit Bench useful, please cite our technical report InternVL-U using this BibTeX.
```
@article{tian2026internvlu,
title={InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing},
author={Changyao Tian and Danni Yang and Guanzhou Chen and Erfei Cui and Zhaokai Wang and Yuchen Duan and Penghao Yin and Sitao Chen and Ganlin Yang and Mingxin Liu and Zirun Zhu and Ziqian Fan and Leyao Gu and Haomin Wang and Qi Wei and Jinhui Yin and Xue Yang and Zhihang Zhong and Qi Qin and Yi Xin and Bin Fu and Yihao Liu and Jiaye Ge and Qipeng Guo and Gen Luo and Hongsheng Li and Yu Qiao and Kai Chen and Hongjie Zhang},
year={2026},
eprint={2603.09877},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2603.09877}
}
```