# DiffFace: Diffusion-based Face Swapping with Facial Guidance Source: [DiffFace: Diffusion-based Face Swapping with Facial Guidance]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:54:03.791Z Authors: Kihong Kim, Yunho Kim, Seokju Cho, Junyoung Seo, Jisu Nam, Kychul Lee, Seungryong Kim, KwangHee Lee Published: 2022-12-27T02:51:46.000Z ## Abstract In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending. In specific, in the training process, the ID conditional DDPM is trained to generate face images with the desired identity. In the sampling process, we use the off-the-shelf facial expert models to make the model transfer source identity while preserving target attributes faithfully. During this process, to preserve the background of the target image and obtain the desired face swapping result, we additionally propose a target-preserving blending strategy. It helps our model to keep the attributes of the target face from noise while transferring the source facial identity. In addition, without any re-training, our model can flexibly apply additional facial guidance and adaptively control the ID-attributes trade-off to achieve the desired results. To the best of our knowledge, this is the first approach that applies the diffusion model in face swapping task. Compared with previous GAN-based approaches, by taking advantage of the diffusion model for the face swapping task, DiffFace achieves better benefits such as training stability, high fidelity, diversity of the samples, and controllability. Extensive experiments show that our DiffFace is comparable or superior to the state-of-the-art methods on several standard face swapping benchmarks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Face Swapping on CelebA (test)](): Face Swapping; CelebA (test); FID: 10.29. - [Face Swapping on FFHQ (test)](): Face Swapping; FFHQ (test); FID: 8.59. - [Face Swapping on FFHQ](): Face Swapping; FFHQ; FID: 8.59. ## Original sources and implementation [Original paper]() [PDF]()