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
this https URL

基于大规模扩散生成模型的文本条件图像编辑引起了工业界和研究界的关注。现有方法大多是非参考编辑，用户只能提供源图像和文本提示，然而这限制了用户对编辑结果特征的控制。为增加用户的自由度，我们提出了一个名为特定参考条件的真实图像编辑任务，使用户能够提供参考图像进一步控制结果，例如用特定物体替换对象。为了实现这一目标，我们提出了一种名为 SpecRef 的快速基准方法。具体而言，我们设计了一个特定参考注意控制器来融合参考图像的特征，并采用掩码机制来防止编辑和非编辑区域的干扰。我们在典型的编辑任务上对 SpecRef 进行评估，并展示其能够达到令人满意的性能。源代码可在此 https URL 获取。