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A plethora of text-guided image editing methods have recently been developedby leveraging the impressive capabilities of large-scale diffusion-basedgenerative models such as Imagen and Stable Diffusion. A standardizedevaluation protocol, however, does not exist to compare methods acrossdifferent types of fine-grained edits. To address this gap, we introduceEditVal, a standardized benchmark for quantitatively evaluating text-guidedimage editing methods. EditVal consists of a curated dataset of images, a setof editable attributes for each image drawn from 13 possible edit types, and anautomated evaluation pipeline that uses pre-trained vision-language models toassess the fidelity of generated images for each edit type. We use EditVal tobenchmark 8 cutting-edge diffusion-based editing methods including SINE, Imagicand Instruct-Pix2Pix. We complement this with a large-scale human study wherewe show that EditVall's automated evaluation pipeline is strongly correlatedwith human-preferences for the edit types we considered. From both the humanstudy and automated evaluation, we find that: (i) Instruct-Pix2Pix, Null-Textand SINE are the top-performing methods averaged across different edit types,however {\it only} Instruct-Pix2Pix and Null-Text are able to preserve originalimage properties; (ii) Most of the editing methods fail at edits involvingspatial operations (e.g., changing the position of an object). (iii) There isno `winner' method which ranks the best individually across a range ofdifferent edit types. We hope that our benchmark can pave the way to developingmore reliable text-guided image editing tools in the future. We will publiclyrelease EditVal, and all associated code and human-study templates to supportthese research directions in https://deep-ml-research.github.io/editval/.

EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods