Image composition targets at synthesizing a realistic composite image from a
pair of foreground and background images. Recently, generative composition
methods are built on large pretrained diffusion models to generate composite
images, considering their great potential in image generation. However, they
suffer from lack of controllability on foreground attributes and poor
preservation of foreground identity. To address these challenges, we propose a
controllable image composition method that unifies four tasks in one diffusion
model: image blending, image harmonization, view synthesis, and generative
composition. Meanwhile, we design a self-supervised training framework coupled
with a tailored pipeline of training data preparation. Moreover, we propose a
local enhancement module to enhance the foreground details in the diffusion
model, improving the foreground fidelity of composite images. The proposed
method is evaluated on both public benchmark and real-world data, which
demonstrates that our method can generate more faithful and controllable
composite images than existing approaches. The code and model will be available
at this https URL

通过使用大规模预训练的扩散模型，我们提出了一种可控的图像合成方法，将图像混合、图像协调、视角综合和生成式合成统一为一个扩散模型，同时设计了一个自监督训练框架和一个定制化的训练数据准备方法，并通过局部增强模块提高合成图像中前景细节的保真性。我们的方法在公共基准和实际数据上进行了评估，结果表明我们的方法比现有方法能够生成更加忠实和可控的合成图像。