{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/doubly-abductive-counterfactual-inference-for","title":"Doubly Abductive Counterfactual Inference for Text-based Image Editing","arxiv_id":"2403.02981","date":"2024-03-05","proceeding":"CVPR 2024 1","authors":["Xue Song","Jiequan Cui","Hanwang Zhang","Jingjing Chen","Richang Hong","Yu-Gang Jiang"],"abstract":"We study text-based image editing (TBIE) of a single image by counterfactual inference because it is an elegant formulation to precisely address the requirement: the edited image should retain the fidelity of the original one. Through the lens of the formulation, we find that the crux of TBIE is that existing techniques hardly achieve a good trade-off between editability and fidelity, mainly due to the overfitting of the single-image fine-tuning. To this end, we propose a Doubly Abductive Counterfactual inference framework (DAC). We first parameterize an exogenous variable as a UNet LoRA, whose abduction can encode all the image details. Second, we abduct another exogenous variable parameterized by a text encoder LoRA, which recovers the lost editability caused by the overfitted first abduction. Thanks to the second abduction, which exclusively encodes the visual transition from post-edit to pre-edit, its inversion -- subtracting the LoRA -- effectively reverts pre-edit back to post-edit, thereby accomplishing the edit. Through extensive experiments, our DAC achieves a good trade-off between editability and fidelity. Thus, we can support a wide spectrum of user editing intents, including addition, removal, manipulation, replacement, style transfer, and facial change, which are extensively validated in both qualitative and quantitative evaluations. Codes are in https://github.com/xuesong39/DAC.","url_abs":"https://arxiv.org/abs/2403.02981v2","url_pdf":"https://arxiv.org/pdf/2403.02981v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"doubly-abductive-counterfactual-inference-for","repo_url":"https://github.com/xuesong39/dac","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"counterfactual-inference","task_name":"Counterfactual Inference"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"text-based-image-editing","task_name":"Text-based Image Editing"},{"task_slug":null,"task_name":"counterfactual"}],"methods":[{"method_slug":"dac","method_name":"DAC"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2403.02981","atlas_url":"https://app.syntology.ai/?focus=2403.02981","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.02981"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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