title: Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models
summary: Text-to-image diffusion models can generate diverse, high-fidelity images
  based on user-provided text prompts. Recent research has extended these models
  to support text-guided image editing. While text guidance is an intuitive
  editing interface for users, it often fails to ensure the precise concept
  conveyed by users. To address this issue, we propose Custom-Edit, in which we
  (i) customize a diffusion model with a few reference images and then (ii)
  perform text-guided editing. Our key discovery is that customizing only
  language-relevant parameters with augmented prompts improves reference
  similarity significantly while maintaining source similarity. Moreover, we
  provide our recipe for each customization and editing process. We compare
  popular customization methods and validate our findings on two editing methods
  using various datasets.
paper_authors._id: 647015269ba7aeaacb27c080
paper_authors.name: Jooyoung Choi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-26T13:57:32.536Z
paper_authors._id: 62f9e54defd2de85492a4dde
paper_authors.avatarUrl: /avatars/9925de0d02b2f2ed03cbafda44fd7de4.svg
paper_authors.fullname: Jooyoung Choi
paper_authors.type: user
paper_authors.user: jychoi
paper_authors._id: 647015269ba7aeaacb27c081
paper_authors.name: Yunjey Choi
paper_authors._id: 647015269ba7aeaacb27c082
paper_authors.name: Yunji Kim
paper_authors._id: 647015269ba7aeaacb27c083
paper_authors.name: Junho Kim
paper_authors._id: 647015269ba7aeaacb27c084
paper_authors.name: Sungroh Yoon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-02T07:18:30.976Z
paper_authors._id: 64c99967d33e6a7f82c35f0b
paper_authors.avatarUrl: /avatars/70e77eb570d12e11c2807e10cd4d926d.svg
paper_authors.fullname: Sungroh Yoon
paper_authors.type: user
paper_authors.user: sryoon
paper_title: Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models
paper_summary: Text-to-image diffusion models can generate diverse, high-fidelity images
  based on user-provided text prompts. Recent research has extended these models
  to support text-guided image editing. While text guidance is an intuitive
  editing interface for users, it often fails to ensure the precise concept
  conveyed by users. To address this issue, we propose Custom-Edit, in which we
  (i) customize a diffusion model with a few reference images and then (ii)
  perform text-guided editing. Our key discovery is that customizing only
  language-relevant parameters with augmented prompts improves reference
  similarity significantly while maintaining source similarity. Moreover, we
  provide our recipe for each customization and editing process. We compare
  popular customization methods and validate our findings on two editing methods
  using various datasets.
paper_ai_keywords: text-to-image diffusion models high-fidelity images text-guided image editing concept Custom-Edit reference images language-relevant parameters augmented prompts reference similarity source similarity