Core Viewpoint - The article presents a novel approach called Single Trajectory Distillation (STD) aimed at enhancing the efficiency and quality of image and video style transfer tasks within the AIGC domain, addressing issues related to style consistency and aesthetic quality in existing models [2][3][49]. Group 1: Introduction to STD - The authors from the Dynamic-X-Lab focus on advancing technologies in image generation and video animation, utilizing high-quality generative models [2]. - The existing consistency models face challenges in maintaining style similarity and aesthetic quality, particularly in image-to-image and video-to-video transformations [2][3]. Group 2: Mechanism of STD - STD introduces a training framework that starts from a partially noisy state, addressing the inefficiencies of traditional methods [3]. - A Trajectory Bank is designed to store intermediate states from the teacher model's PF-ODE trajectory, reducing the computational burden during student model training [3][11]. - An Asymmetric Adversarial Loss is incorporated to significantly enhance the style consistency and perceptual quality of the generated results [4][11]. Group 3: Experimental Results - Extensive experiments demonstrate that STD outperforms existing accelerated diffusion models in terms of style similarity and aesthetic evaluation [5][33]. - In comparative tests, STD achieved a CSD score of 0.503 and an aesthetic score of 4.815, surpassing other methods [30][33]. Group 4: Ablation Studies - Ablation studies confirm that the use of the Trajectory Bank mitigates the additional training time introduced by STD, while both STD and the Asymmetric Adversarial Loss significantly improve style similarity and aesthetic scores [36][37]. - The results indicate that the strength of the Asymmetric Adversarial Loss correlates positively with image quality, reducing noise and enhancing contrast [38]. Group 5: Scalability and Future Applications - The STD method is posited to be applicable to other tasks involving partial noise-based image and video editing, such as inpainting, showcasing superior results compared to traditional methods [47][49].