SpecRef: A Fast Training-free Baseline of Specific Reference-Condition Real Image Editing

• URL: http://arxiv.org/abs/2401.03433v1
• Date: Sun, 7 Jan 2024 09:23:06 GMT
• Title: SpecRef: A Fast Training-free Baseline of Specific Reference-Condition Real Image Editing
• Authors: Songyan Chen, Jiancheng Huang
• Abstract summary: We propose a new task called Specific Reference Condition Real Image Editing. It allows user to provide a reference image to further control the outcome, such as replacing an object with a particular one. Specifically, we design a Specific Reference Attention Controller to incorporate features from the reference image, and adopt a mask mechanism to prevent interference between editing and non-editing regions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-conditional image editing based on large diffusion generative model has attracted the attention of both the industry and the research community. Most existing methods are non-reference editing, with the user only able to provide a source image and text prompt. However, it restricts user's control over the characteristics of editing outcome. To increase user freedom, we propose a new task called Specific Reference Condition Real Image Editing, which allows user to provide a reference image to further control the outcome, such as replacing an object with a particular one. To accomplish this, we propose a fast baseline method named SpecRef. Specifically, we design a Specific Reference Attention Controller to incorporate features from the reference image, and adopt a mask mechanism to prevent interference between editing and non-editing regions. We evaluate SpecRef on typical editing tasks and show that it can achieve satisfactory performance. The source code is available on https://github.com/jingjiqinggong/specp2p.

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