| ---
|
| license: mit
|
| language:
|
| - en
|
| base_model:
|
| - stable-diffusion-v1-5/stable-diffusion-v1-5
|
| datasets:
|
| - timbrooks/instructpix2pix-clip-filtered
|
| - Aleksandar/Top-Bench-X
|
| ---
|
| # EditCLIP: Representation Learning for Image Editing |
| [](https://arxiv.org/abs/2503.20318) |
| [](https://qianwangx.github.io/EditCLIP/) |
| [](https://github.com/QianWangX/EditCLIP) |
| [](https://iccv2025.thecvf.com/) |
|
|
| ## 💡 Abstract |
|
|
| We introduce EditCLIP, a novel representation-learning approach for image editing. Our method learns a unified representation of edits by jointly encoding an input image and its edited counterpart, effectively capturing their transformation. To evaluate its effectiveness, we employ EditCLIP to solve two tasks: exemplar-based image editing and automated edit evaluation. In exemplar-based image editing, we replace text-based instructions in InstructPix2Pix with EditCLIP embeddings computed from a reference exemplar image pair. Experiments demonstrate that our approach outperforms state-of-the-art methods while being more efficient and versatile. For automated evaluation, EditCLIP assesses image edits by measuring the similarity between the EditCLIP embedding of a given image pair and either a textual editing instruction or the EditCLIP embedding of another reference image pair. Experiments show that EditCLIP aligns more closely with human judgments than existing CLIP-based metrics, providing a reliable measure of edit quality and structural preservation. |
|
|
| ## 📊 Benchmark |
| We evaluate EditCLIP using **Top-Bench-X**, a benchmark for image editing evaluation: |
| - **Dataset:** Top-Bench-X |
| - **Link:** https://huggingface.co/datasets/Aleksandar/Top-Bench-X |
|
|
|
|
| ## 🌟 Citation |
| ```bibtex |
| @inproceedings{wang2025editclip, |
| title={EditCLIP: Representation Learning for Image Editing}, |
| author={Wang, Qian and Cveji{\'c}, Aleksandar and Eldesokey, Abdelrahman and Wonka, Peter}, |
| booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, |
| pages={15960--15970}, |
| year={2025} |
| } |
| ``` |
