README.md

---
license: apache-2.0
dataset_info:
- config_name: class2image
  features:
  - name: pair_id
    dtype: string
  - name: subset
    dtype: string
  - name: category
    dtype: string
  - name: image_name
    dtype: string
  - name: input_relpath
    dtype: string
  - name: output_relpath
    dtype: string
  - name: recognized_text
    dtype: string
  - name: input_image
    dtype: image
  - name: output_image
    dtype: image
  splits:
  - name: train
    num_bytes: 40402985
    num_examples: 100
  download_size: 40414187
  dataset_size: 40402985
- config_name: doodles
  features:
  - name: pair_id
    dtype: string
  - name: subset
    dtype: string
  - name: category
    dtype: string
  - name: image_name
    dtype: string
  - name: input_relpath
    dtype: string
  - name: output_relpath
    dtype: string
  - name: recognized_text
    dtype: string
  - name: input_image
    dtype: image
  - name: output_image
    dtype: image
  splits:
  - name: train
    num_bytes: 40127584
    num_examples: 50
  download_size: 40130903
  dataset_size: 40127584
- config_name: force
  features:
  - name: pair_id
    dtype: string
  - name: subset
    dtype: string
  - name: category
    dtype: string
  - name: image_name
    dtype: string
  - name: input_relpath
    dtype: string
  - name: output_relpath
    dtype: string
  - name: recognized_text
    dtype: string
  - name: input_image
    dtype: image
  - name: output_image
    dtype: image
  splits:
  - name: train
    num_bytes: 82976644
    num_examples: 150
  download_size: 82966446
  dataset_size: 82976644
- config_name: text2image
  features:
  - name: pair_id
    dtype: string
  - name: subset
    dtype: string
  - name: category
    dtype: string
  - name: image_name
    dtype: string
  - name: input_relpath
    dtype: string
  - name: output_relpath
    dtype: string
  - name: recognized_text
    dtype: string
  - name: input_image
    dtype: image
  - name: output_image
    dtype: image
  splits:
  - name: train
    num_bytes: 22163018
    num_examples: 50
  download_size: 22166087
  dataset_size: 22163018
- config_name: text_box_control
  features:
  - name: pair_id
    dtype: string
  - name: subset
    dtype: string
  - name: category
    dtype: string
  - name: image_name
    dtype: string
  - name: input_relpath
    dtype: string
  - name: output_relpath
    dtype: string
  - name: recognized_text
    dtype: string
  - name: input_image
    dtype: image
  - name: output_image
    dtype: image
  splits:
  - name: train
    num_bytes: 37039690
    num_examples: 50
  download_size: 37045318
  dataset_size: 37039690
- config_name: text_in_image
  features:
  - name: pair_id
    dtype: string
  - name: subset
    dtype: string
  - name: category
    dtype: string
  - name: image_name
    dtype: string
  - name: input_relpath
    dtype: string
  - name: output_relpath
    dtype: string
  - name: recognized_text
    dtype: string
  - name: input_image
    dtype: image
  - name: output_image
    dtype: image
  splits:
  - name: train
    num_bytes: 214739988
    num_examples: 290
  download_size: 214733929
  dataset_size: 214739988
- config_name: trajectory
  features:
  - name: pair_id
    dtype: string
  - name: subset
    dtype: string
  - name: category
    dtype: string
  - name: image_name
    dtype: string
  - name: input_relpath
    dtype: string
  - name: output_relpath
    dtype: string
  - name: recognized_text
    dtype: string
  - name: input_image
    dtype: image
  - name: output_image
    dtype: image
  splits:
  - name: train
    num_bytes: 8089366
    num_examples: 50
  download_size: 8090278
  dataset_size: 8089366
- config_name: vismarker
  features:
  - name: pair_id
    dtype: string
  - name: subset
    dtype: string
  - name: category
    dtype: string
  - name: image_name
    dtype: string
  - name: input_relpath
    dtype: string
  - name: output_relpath
    dtype: string
  - name: recognized_text
    dtype: string
  - name: input_image
    dtype: image
  - name: output_image
    dtype: image
  splits:
  - name: train
    num_bytes: 241608849
    num_examples: 320
  download_size: 241592510
  dataset_size: 241608849
configs:
- config_name: class2image
  data_files:
  - split: train
    path: class2image/train-*
- config_name: doodles
  data_files:
  - split: train
    path: doodles/train-*
- config_name: force
  data_files:
  - split: train
    path: force/train-*
- config_name: text2image
  data_files:
  - split: train
    path: text2image/train-*
- config_name: text_box_control
  data_files:
  - split: train
    path: text_box_control/train-*
- config_name: text_in_image
  data_files:
  - split: train
    path: text_in_image/train-*
- config_name: trajectory
  data_files:
  - split: train
    path: trajectory/train-*
- config_name: vismarker
  data_files:
  - split: train
    path: vismarker/train-*
task_categories:
- image-to-image
- text-to-image
language:
- en
size_categories:
- 1K<n<10K
---
<div align="center">
  <h2 align="center" style="margin-top: 0; margin-bottom: 15px;">
    <span style="color:#0052CC">F</span><span style="color:#135FD0">l</span><span style="color:#266CD4">o</span><span style="color:#3979D7">w</span><span style="color:#4C86DB">I</span><span style="color:#6093DF">n</span><span style="color:#73A0E3">O</span><span style="color:#86ADE7">n</span><span style="color:#99BAEB">e</span>: Unifying Multimodal Generation as 
    <span style="color:#0052CC">I</span><span style="color:#0958CE">m</span><span style="color:#125ED0">a</span><span style="color:#1B64D2">g</span><span style="color:#246AD4">e</span><span style="color:#2D70D6">-</span><span style="color:#3676D8">i</span><span style="color:#3F7CDA">n</span><span style="color:#4882DC">,</span>&nbsp;<span style="color:#5188DE">I</span><span style="color:#5A8EE0">m</span><span style="color:#6394E2">a</span><span style="color:#6C9AE4">g</span><span style="color:#75A0E6">e</span><span style="color:#7EA6E8">-</span><span style="color:#87ACEA">o</span><span style="color:#90B2EC">u</span><span style="color:#99B8EE">t</span> Flow Matching
  </h2>
  <p align="center" style="font-size: 15px;">
    <span style="color:#E74C3C; font-weight: bold;">TL;DR:</span> <strong>The first vision-centric image-in, image-out image generation model.</strong>
  </p>
  <p align="center" style="font-size: 16px;">
    <a href="https://csu-jpg.github.io/FlowInOne.github.io/" style="text-decoration: none;">🌐 Homepage</a> | 
    <a href="https://github.com/CSU-JPG/FlowInOne" style="text-decoration: none;">💻 Code</a> | 
    <a href="https://arxiv.org/pdf/2604.06757" style="text-decoration: none;">📄 Paper</a> | 
    <a href="https://huggingface.co/datasets/CSU-JPG/VisPrompt5M" style="text-decoration: none;">📁 Dataset</a> | 
    <a href="https://huggingface.co/datasets/CSU-JPG/VPBench" style="text-decoration: none;">🌏 Benchmark</a> | 
    <a href="https://huggingface.co/CSU-JPG/FlowInOne" style="text-decoration: none;">🤗 Model</a>
</p>
</div>

# VP-Bench
**VP-Bench** is the official evaluation benchmark for [**FlowInOne**](https://csu-jpg.github.io/FlowInOne.github.io/).
It is a rigorously curated benchmark assessing **instruction faithfulness**, **spatial precision**, **visual realism**, and **content consistency** across eight distinct visual prompting tasks.

## Evaluation
Our evaluation scripts are now available on [GitHub](https://github.com/CSU-JPG/FlowInOne)!
## Dataset Subsets
The dataset contains **8 subsets**, each corresponding to a distinct visual instruction task:
| Subset | Abbrev. | Description |
|--------|---------|-------------|
| `class2image` | C2I | Class label rendered in input image → generate corresponding image |
| `text2image` | T2I | Text instruction rendered in input image → generate image |
| `text_in_image` | TIE | Edit text content within an image |
| `force` | FU | Physics-aware force understanding (3 categories) |
| `text_box_control` | TBE | Text and bounding box editing |
| `trajectory` | TU | Trajectory understanding and prediction |
| `vismarker` | VME | Visual marker guided editing (8 categories) |
| `doodles` | DE | Doodle-based editing |
## Dataset Features
- **input_image** (`image`): The input visual prompt image (with rendered instruction).
- **output_image** (`image`): The ground-truth output image.
- **recognized_text** (`string`): The text instruction rendered in the input image (extracted via OCR annotation).
- **subset** (`string`): The subset name.
- **category** (`string`): Sub-category within a subset (empty string if not applicable).
- **image_name** (`string`): The image filename.
- **input_relpath** (`string`): Relative path of the input image within the subset.
- **output_relpath** (`string`): Relative path of the output image within the subset.
- **pair_id** (`string`): Stable SHA1 identifier for each input-output pair.
## Loading the Dataset
```python
# class2image
from datasets import load_dataset
ds = load_dataset("CSU-JPG/VPBench", "class2image", split="train")
# text2image
from datasets import load_dataset
ds = load_dataset("CSU-JPG/VPBench", "text2image", split="train")
# text_in_image
from datasets import load_dataset
ds = load_dataset("CSU-JPG/VPBench", "text_in_image", split="train")
# force
from datasets import load_dataset
ds = load_dataset("CSU-JPG/VPBench", "force", split="train")
# text_box_control
from datasets import load_dataset
ds = load_dataset("CSU-JPG/VPBench", "text_box_control", split="train")
# trajectory
from datasets import load_dataset
ds = load_dataset("CSU-JPG/VPBench", "trajectory", split="train")
# vismarker
from datasets import load_dataset
ds = load_dataset("CSU-JPG/VPBench", "vismarker", split="train")
# doodles
from datasets import load_dataset
ds = load_dataset("CSU-JPG/VPBench", "doodles", split="train")
# Load All Subsets
from datasets import load_dataset, concatenate_datasets
subsets = ["class2image", "text2image", "text_in_image", "force",
           "text_box_control", "trajectory", "vismarker", "doodles"]
ds_all = concatenate_datasets([
    load_dataset("CSU-JPG/VPBench", name=s, split="train") for s in subsets
])
```
## Evaluation Results
We evaluate multiple methods on VP-Bench using three state-of-the-art VLM evaluators (Gemini3, GPT-5.2, Qwen3.5) and human judges. The metric is success ratio (higher is better). Total denotes the average success rate across all eight task categories.

Abbreviations: C2I: class-to-image · T2I: text-to-image · TIE: text-in-image edit · FU: force understanding · TBE: text & bbox edit · TU: trajectory understanding · VME: visual marker edit · DE: doodles edit

**Evaluator: Gemini3**
| Method | C2I | T2I | TIE | FU | TBE | TU | VME | DE | **Total** |
|--------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---------:|
| Nano Banana (Google, 2025) | .650 | .980 | .423 | .520 | .614 | .020 | .548 | .721 | .560 |
| Omnigen2 (Wu et al., 2025) | .020 | .020 | .017 | .020 | .000 | .000 | .000 | .000 | .007 |
| Kontext (Labs et al., 2025) | .050 | .020 | .048 | .007 | .000 | .020 | .010 | .000 | .019 |
| Qwen-IE-2509 (Wu et al., 2025) | .230 | .040 | .069 | .000 | .000 | .020 | .023 | .000 | .048 |
| **FlowInOne (Ours)** | **.890** | **.700** | **.355** | **.727** | **.302** | **.520** | **.292** | **.535** | **.540** |

**Evaluator: GPT-5.2**
| Method | C2I | T2I | TIE | FU | TBE | TU | VME | DE | **Total** |
|--------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---------:|
| Nano Banana (Google, 2025) | .680 | .959 | .152 | .127 | .023 | .040 | .136 | .302 | .302 |
| Omnigen2 (Wu et al., 2025) | .110 | .020 | .000 | .000 | .000 | .000 | .000 | .023 | .019 |
| Kontext (Labs et al., 2025) | .090 | .020 | .028 | .020 | .000 | .080 | .003 | .093 | .042 |
| Qwen-IE-2509 (Wu et al., 2025) | .240 | .120 | .080 | .020 | .022 | .060 | .020 | .047 | .076 |
| **FlowInOne (Ours)** | **.850** | **.800** | .079 | **.500** | **.116** | **.240** | .083 | **.465** | **.392** |

**Evaluator: Qwen3.5**
| Method | C2I | T2I | TIE | FU | TBE | TU | VME | DE | **Total** |
|--------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---------:|
| Nano Banana (Google, 2025) | .600 | .959 | .386 | .367 | .257 | .040 | .321 | .744 | .469 |
| Omnigen2 (Wu et al., 2025) | .030 | .020 | .017 | .034 | .000 | .000 | .003 | .047 | .019 |
| Kontext (Labs et al., 2025) | .050 | .020 | .042 | .133 | .000 | .060 | .047 | .093 | .056 |
| Qwen-IE-2509 (Wu et al., 2025) | .270 | .060 | .080 | .087 | .047 | .040 | .033 | .047 | .083 |
| **FlowInOne (Ours)** | **.859** | **.720** | **.354** | **.713** | **.272** | **.320** | **.306** | **.481** | **.503** |

**Evaluator: Human**
| Method | C2I | T2I | TIE | FU | TBE | TU | VME | DE | **Total** |
|--------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---------:|
| Nano Banana (Google, 2025) | .602 | .904 | .271 | .250 | .200 | .050 | .229 | .742 | .406 |
| Omnigen2 (Wu et al., 2025) | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 | .000 |
| Kontext (Labs et al., 2025) | .000 | .000 | .043 | .000 | .000 | .000 | .000 | .100 | .018 |
| Qwen-IE-2509 (Wu et al., 2025) | .067 | .000 | .029 | .000 | .000 | .000 | .000 | .000 | .012 |
| **FlowInOne (Ours)** | **.800** | **.645** | **.242** | **.705** | **.255** | **.280** | **.255** | **.400** | **.449** |

## Citation

If you found our work useful, please consider citing:
```
@article{yi2026flowinoneunifyingmultimodalgenerationimagein,
      title={FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching}, 
      author={Junchao Yi and Rui Zhao and Jiahao Tang and Weixian Lei and Linjie Li and Qisheng Su and Zhengyuan Yang and Lijuan Wang and Xiaofeng Zhu and Alex Jinpeng Wang},    
      journal={arXiv preprint arXiv:2604.06757},
      year={2026}
}
```