UIP2P: Unsupervised Instruction-based Image Editing via Cycle Edit Consistency

• URL: http://arxiv.org/abs/2412.15216v1
• Date: Thu, 19 Dec 2024 18:59:58 GMT
• Title: UIP2P: Unsupervised Instruction-based Image Editing via Cycle Edit Consistency
• Authors: Enis Simsar, Alessio Tonioni, Yongqin Xian, Thomas Hofmann, Federico Tombari,
• Abstract summary: We propose an unsupervised model for instruction-based image editing that eliminates the need for ground-truth edited images during training. Our method addresses these challenges by introducing a novel editing mechanism called Cycle Edit Consistency ( CEC) CEC applies forward and backward edits in one training step and enforces consistency in image and attention spaces.
• Score: 69.33072075580483
• License:
• Abstract: We propose an unsupervised model for instruction-based image editing that eliminates the need for ground-truth edited images during training. Existing supervised methods depend on datasets containing triplets of input image, edited image, and edit instruction. These are generated by either existing editing methods or human-annotations, which introduce biases and limit their generalization ability. Our method addresses these challenges by introducing a novel editing mechanism called Cycle Edit Consistency (CEC), which applies forward and backward edits in one training step and enforces consistency in image and attention spaces. This allows us to bypass the need for ground-truth edited images and unlock training for the first time on datasets comprising either real image-caption pairs or image-caption-edit triplets. We empirically show that our unsupervised technique performs better across a broader range of edits with high fidelity and precision. By eliminating the need for pre-existing datasets of triplets, reducing biases associated with supervised methods, and proposing CEC, our work represents a significant advancement in unblocking scaling of instruction-based image editing.

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