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---
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license: mit
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language:
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- en
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base_model:
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- stable-diffusion-v1-5/stable-diffusion-v1-5
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datasets:
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- timbrooks/instructpix2pix-clip-filtered
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- Aleksandar/Top-Bench-X
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---
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# EditCLIP: Representation Learning for Image Editing |
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[](https://arxiv.org/abs/2503.20318) |
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[](https://qianwangx.github.io/EditCLIP/) |
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[](https://github.com/QianWangX/EditCLIP) |
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[](https://iccv2025.thecvf.com/) |
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## 💡 Abstract |
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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. |
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## 📊 Benchmark |
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We evaluate EditCLIP using **Top-Bench-X**, a benchmark for image editing evaluation: |
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- **Dataset:** Top-Bench-X |
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- **Link:** https://huggingface.co/datasets/Aleksandar/Top-Bench-X |
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## 🌟 Citation |
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```bibtex |
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@inproceedings{wang2025editclip, |
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title={EditCLIP: Representation Learning for Image Editing}, |
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author={Wang, Qian and Cveji{\'c}, Aleksandar and Eldesokey, Abdelrahman and Wonka, Peter}, |
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booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, |
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pages={15960--15970}, |
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year={2025} |
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} |
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``` |
