Datasets:

FractalAIResearch
/

ProductConsistency-Benchmark

	---
	license: apache-2.0
	task_categories:
	- image-to-image
	language:
	- en
	tags:
	- image-editing
	size_categories:
	- n<1K
	---
	<div align="center">

	# ProductConsistency Benchmark

	</div>

	<div align="center">

	[![model](https://img.shields.io/badge/HFModel-Kalaido--qwenedit--lora-red?logo=huggingface)](https://huggingface.co/FractalAIResearch/Kalaido-qwenedit-lora)
	[![model](https://img.shields.io/badge/HFModel-Kalaido--qwen--2512--lora-red?logo=huggingface)](https://huggingface.co/FractalAIResearch/Kalaido-qwen-2512-lora)

	</div>

	<div align="center">

	[![report](https://img.shields.io/badge/📄_Technical_Report-ProductConsistency-blue?logo=arxiv)](https://arxiv.org/abs/2606.19103)
	[![Dataset License](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)

	</div>

	<div align="center">

	<img src="assets/distribution.png" alt="ProductConsistency Benchmark Distribution" width="70%">

	</div>

	---

	## Dataset

	The ProductConsistency Benchmark is a high-quality dataset for instruction-based, product-centric image editing in English. Every record is one evaluation tuple: an input product image plus a single edit instruction describing an advertisement-style scene change.

	- 870 samples formed from 174 synthetic product images (8 product categories) with 5 distinct edit instructions per image.
	- Images emphasize legible on-pack text and consistent branding; instructions are written to stress identity preservation under realistic marketing-style edits.



	Benchmark images are drawn with uniform coverage over categories and over the range of rendered text lengths, using the same synthetic product generation and OCR-based text verification pipeline as the broader ProductConsistency release. For each selected product image, a language model generates five edit instructions that are unique to that image. To ensure dataset quality, a final human verification step confirms that on-product text in each benchmark image matches the intended ground truth before instructions are paired and released.

	---

	## Evaluation on this benchmark

	\| Model \| CER ↓ \| Seg CLIP-I ↑ \| Seg DINO-I ↑ \| Product consistency ↑ \| Aesthetics ↑ \| Text fidelity ↑ \| Overall ↑ \|
	\|-------\|------:\|-------------:\|-------------:\|------------------------:\|-------------:\|-----------------:\|----------:\|
	\| HiDream-E1-1 \| 3.8774 \| 0.8390 \| 0.7240 \| 6.5828 \| 7.4134 \| 3.4477 \| 5.8146 \|
	\| OmniGen2 \| 1.7094 \| 0.8858 \| 0.7790 \| 7.6739 \| 7.8613 \| 4.9908 \| 6.8422 \|
	\| BAGEL \| 1.6810 \| 0.8767 \| 0.7515 \| 7.7260 \| 7.8088 \| 6.2203 \| 7.2520 \|
	\| Step1x-edit-v1p2 \| 1.1909 \| 0.8626 \| 0.7157 \| 7.5812 \| 7.4636 \| 7.0414 \| 7.3621 \|
	\| RePlan-Flux \| 0.2914 \| 0.9174 \| 0.8085 \| 8.4118 \| 6.8085 \| 8.8727 \| 8.0311 \|
	\| RePlan-Qwen \| 0.5164 \| 0.9010 \| 0.7419 \| 7.6963 \| 6.3391 \| 7.6542 \| 7.2298 \|
	\| Nano Banana \| 1.1868 \| 0.8860 \| 0.7020 \| 8.8256 \| 8.3552 \| 8.0839 \| 8.4167 \|
	\| Qwen-Image-Lightning \| 0.6073 \| 0.8920 \| 0.7680 \| 8.5188 \| 8.1941 \| 8.1977 \| 8.2738 \|
	\| Edit-R1-Qwen \| 0.4430 \| 0.9046 \| 0.7597 \| 8.5834 \| 8.3314 \| 8.1542 \| 8.3565 \|
	\| Edit-R1-Flux \| 0.1550 \| 0.9195 \| 0.7966 \| 8.7015 \| 8.0226 \| 9.0142 \| 8.5798 \|
	\| GPT-Image-1 High \| 0.3315 \| 0.9080 \| 0.7800 \| 9.0134 \| 8.5598 \| 8.4077 \| 8.6300 \|
	\| Qwen-Image-Edit-2511 \| 1.0682 \| 0.8728 \| 0.7080 \| 8.4578 \| 8.2467 \| 7.5958 \| 8.1003 \|
	\| Qwen-Image-Edit-2511 + SFT + Cyclic Reward \| 0.2080 \| 0.9245 \| 0.7990 \| 8.8866 \| 8.3373 \| 8.8923 \| 8.7055 \|
	\| Flux.1-Kontext-dev \| 0.1490 \| 0.9210 \| 0.8110 \| 8.7111 \| 7.9506 \| 8.9096 \| 8.5240 \|
	\| Flux.1-Kontext-dev + SFT + Cyclic Reward \| 0.1204 \| 0.9224 \| 0.8115 \| 8.7996 \| 7.9901 \| 9.0740 \| 8.6216 \|

	---

	## Usage



	```python
	from datasets import load_dataset

	ds = load_dataset("FractalAIResearch/ProductConsistencyBenchmark")
	row = ds[0]
	img, instr = row["image"], row["edit_instruction"]
	```

	---

	## License

	This dataset is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)

	---

	## 📖 Citation

	```bibtex
	@misc{khanna2026productconsistencyimprovingproductidentity,
	title={ProductConsistency: Improving Product Identity Preservation in Instruction-Based Image Editing via SFT and RL},
	author={Mukund Khanna and Raj Singh Yadav and Kunal Singh},
	year={2026},
	eprint={2606.19103},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url={https://arxiv.org/abs/2606.19103},
	}
	```