LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912149105803264 |
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| author | Song, Xue Cui, Jiequan Zhang, Hanwang Shi, Jiaxin Chen, Jingjing Zhang, Chi Jiang, Yu-Gang |
| author_facet | Song, Xue Cui, Jiequan Zhang, Hanwang Shi, Jiaxin Chen, Jingjing Zhang, Chi Jiang, Yu-Gang |
| contents | In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient specificity, and diverse interpretations of natural language, visual instructions can accurately reflect users' intent. Building on the success of LoRA in text-based image editing and generation, we dynamically learn an instruction-specific LoRA to encode the "change" in a before-after image pair, enhancing the interpretability and reusability of our model. Furthermore, generalizable models for image editing with visual instructions typically require quad data, i.e., a before-after image pair, along with query and target images. Due to the scarcity of such quad data, existing models are limited to a narrow range of visual instructions. To overcome this limitation, we introduce the LoRA Reverse optimization technique, enabling large-scale training with paired data alone. Extensive qualitative and quantitative experiments demonstrate that our model produces high-quality images that align with user intent and support a broad spectrum of real-world visual instructions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19156 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair Song, Xue Cui, Jiequan Zhang, Hanwang Shi, Jiaxin Chen, Jingjing Zhang, Chi Jiang, Yu-Gang Computer Vision and Pattern Recognition In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient specificity, and diverse interpretations of natural language, visual instructions can accurately reflect users' intent. Building on the success of LoRA in text-based image editing and generation, we dynamically learn an instruction-specific LoRA to encode the "change" in a before-after image pair, enhancing the interpretability and reusability of our model. Furthermore, generalizable models for image editing with visual instructions typically require quad data, i.e., a before-after image pair, along with query and target images. Due to the scarcity of such quad data, existing models are limited to a narrow range of visual instructions. To overcome this limitation, we introduce the LoRA Reverse optimization technique, enabling large-scale training with paired data alone. Extensive qualitative and quantitative experiments demonstrate that our model produces high-quality images that align with user intent and support a broad spectrum of real-world visual instructions. |
| title | LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.19156 |