| --- |
| 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> <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} |
| } |
| ``` |
