Despite impressive results from recent text-to-image models like FLUX, visual and anatomical artifacts remain a significant hurdle for practical and professional use. Existing methods for artifact reduction, typically work in a post-hoc manner, consequently failing to intervene effectively during the core image formation process. Notably, current techniques require problematic and invasive modifications to the model weights, or depend on a computationally expensive and time-consuming process of regional refinement. To address these limitations, we propose DIAMOND, a training-free method that applies trajectory correction to mitigate artifacts during inference. By reconstructing an estimate of the clean sample at every step of the generative trajectory, DIAMOND actively steers the generation process away from latent states that lead to artifacts. Furthermore, we extend the proposed method to standard Diffusion Models, demonstrating that DIAMOND provides a robust, zero-shot path to high-fidelity, artifact-free image synthesis without the need for additional training or weight modifications in modern generative architectures. Code is available at https://gmum.github.io/DIAMOND/
Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience
Center for Machine Vision and Signal Analysis (CMVS), University of Oulu（奥卢大学机器视觉与信号分析中心（CMVS））；Division of Bioengineering, Graduate School of Engineering Science, Osaka University（大阪大学工程科学研究院生物工程系）；VTT Technical Research Center of Finland Ltd.（芬兰VTT技术研究中心有限公司）
