# High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning Source: [High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning]() Collection index: [llms.txt]() Data updated: 2026-03-20T05:00:04.947Z Authors: Dailan He, Xiahong Wang, Shulun Wang, Guanglu Song, Bingqi Ma, Hao Shao, Yu Liu, Hongsheng Li Published: 2025-03-28T06:50:17.000Z ## Abstract Face swapping aims to seamlessly transfer a source facial identity onto a target while preserving target attributes such as pose and expression. Diffusion models, known for their superior generative capabilities, have recently shown promise in advancing face-swapping quality. This paper addresses two key challenges in diffusion-based face swapping: the prioritized preservation of identity over target attributes and the inherent conflict between identity and attribute conditioning. To tackle these issues, we introduce an identity-constrained attribute-tuning framework for face swapping that first ensures identity preservation and then fine-tunes for attribute alignment, achieved through a decoupled condition injection. We further enhance fidelity by incorporating identity and adversarial losses in a post-training refinement stage. Our proposed identity-constrained diffusion-based face-swapping model outperforms existing methods in both qualitative and quantitative evaluations, demonstrating superior identity similarity and attribute consistency, achieving a new state-of-the-art performance in high-fidelity face swapping. ## 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 FFHQ](): Face Swapping; FFHQ; FID: 3.61. ## Original sources and implementation [Original paper]() [PDF]()