Consistency distillation methods have demonstrated significant success in accelerating generative tasks of diffusion models. However, since previous consistency distillation methods use simple and straightforward strategies in selecting target timesteps, they usually struggle with blurs and detail losses in generated images. To address these limitations, we introduce Target-Driven Distillation (TDD), which (1) adopts a delicate selection strategy of target timesteps, increasing the training efficiency; (2) utilizes decoupled guidances during training, making TDD open to post-tuning on guidance scale during inference periods; (3) can be optionally equipped with non-equidistant sampling and x0 clipping, enabling a more flexible and accurate way for image sampling. Experiments verify that TDD achieves state-of-the-art performance in few-step generation, offering a better choice among consistency distillation models.

Trajectory distillation based on consistency models (CMs) provides an effective framework for accelerating diffusion models by reducing inference steps. However, we find that existing CMs degrade style similarity and compromise aesthetic quality in stylization tasks-especially when handling image-to-image or video-to-video transformations that start denoising from partially noised inputs. The core limitation stems from existing methods enforcing initial-step alignment between the probability flow ODE (PF-ODE) trajectories of student models and their imperfect teacher models. This partial alignment strategy inevitably fails to guarantee full trajectory consistency, thereby compromising the overall generation quality. To address this issue, we propose Single Trajectory Distillation (STD), a training framework initiated from partial noise states. To counteract the additional time overhead introduced by STD, we design a trajectory bank that pre-stores intermediate states of the teacher model's PF-ODE trajectories, effectively offsetting the computational cost during student model training. This mechanism ensures STD maintains equivalent training efficiency compared to conventional consistency models. Furthermore, we incorporate an asymmetric adversarial loss to explicitly enhance style consistency and perceptual quality in generated outputs. Extensive experiments on image and video stylization demonstrate that our method surpasses existing acceleration models in terms of style similarity and aesthetic evaluations. Our code and results are available on the project page: https://single-trajectory-distillation.github.io/.