zyyyz/ID-Bench
ID-Bench ID-Bench is a real-world benchmark for multi-reference identity-preserving image generation. It is built from real-world e-commerce advertising images and organized by product identity, with the goal of evaluating whether a model can generate a novel target image that both preserves product identity and follows target-specific variation cues. We release, in this repository, the curated benchmark dataset that is consistent with the one used for evaluation in our paper.… See the full description on the dataset page: https://huggingface.co/datasets/zyyyz/ID-Bench.
ID-Bench
ID-Bench is a real-world benchmark for multi-reference identity-preserving image generation. It is built from real-world e-commerce advertising images and organized by product identity, with the goal of evaluating whether a model can generate a novel target image that both preserves product identity and follows target-specific variation cues.
We release, in this repository, the curated benchmark dataset that is consistent with the one used for evaluation in our paper. The full training dataset will be released soon.
What Makes ID-Bench Different?
Recent progress in reference-guided image generation has improved visual quality substantially, but evaluating multi-reference identity-preserving generation remains challenging. Many existing settings do not clearly distinguish:
- genuine same-identity generation,
- trivial copying from the conditioning set,
- and target-guided variation control.
ID-Bench is designed to address this gap in a real-world product-image setting.
Data Format
Each example contains:
target_image: the held-out target imagecondition_image_1: reference image 1 from the same product identitycondition_image_2: reference image 2 from the same product identitycondition_image_3: reference image 3 from the same product identitycaption: a short target-oriented caption describing the desired image or scene
For more information, please visit our project website: ID-Bench.