FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance

Abstract

Recent advances in trajectory-controllable video generation have achieved remarkable progress. Previous methods mainly use adapter-based architectures for precise motion control along predefined trajectories. However, all these methods rely on a multi-step denoising process, leading to substantial time redundancy and computational overhead. While existing video distillation methods successfully distill multi-step generators into few-step, directly applying these approaches to trajectory-controllable video generation results in noticeable degradation in both video quality and trajectory accuracy. To bridge this gap, we introduce FlashMotion, a novel training framework designed for few-step trajectory-controllable video generation. We first train a trajectory adapter on a multi-step video generator for precise trajectory control. Then, we distill the generator into a few-step version to accelerate video generation. Finally, we finetune the adapter using a hybrid strategy that combines diffusion and adversarial objectives, aligning it with the few-step generator to produce high-quality, trajectory-accurate videos. For evaluation, we introduce FlashBench, a benchmark for long-sequence trajectory-controllable video generation that measures both video quality and trajectory accuracy across varying numbers of foreground objects. Experiments on two adapter architectures show that FlashMotion surpasses existing video distillation methods and previous multi-step models in both visual quality and trajectory consistency.

Figures (19)

• Figure 1: Illustration of the motivation and capabilities of FlashMotion. We define the SlowGenerator as the multi-step video model and the FastGenerator as its few-step distilled version. The SlowAdapter is trained with the SlowGenerator, while the FastAdapter is fine-tuned for the FastGenerator. (a) Using the SlowAdapter with SlowGenerator under few-step inference causes blurry outputs. (b) Applying the SlowAdapter to the FastGenerator degrades both quality and trajectory accuracy. (c) Finetuning the adapter with only diffusion loss still leads to blur artifacts. (d) Finetuning the adapter with existing distillation methods yields suboptimal quality and trajectory control. (e) FlashMotion achieves high-quality, accurate few-step trajectory-controllable video generation.
• Figure 2: Overview of FlashMotion training pipeline. FlashMotion is trained in three stages: (1) a SlowAdapter is first trained on the SlowGenerator with a diffusion loss; (2) a FastGenerator is distilled from the SlowGenerator under the supervision of a distribution matching yin2024onestep loss; and (3) the SlowAdapter is finetuned to align with the FastGenerator using a hybrid training strategy that combines adversarial and diffusion losses.
• Figure 3: (a) Architecture of FlashMotion. The trajectory adapter is finetuned upon the FastGenerator with a hybrid strategy that combines both diffusion and adversarial objectives. (b) Detailed illustration of our diffusion discriminator architecture. The discriminator adopts a DiT backbone cloned from the SlowGenerator, while several intermediate features from its DiT blocks are fed into an attention-based classifier to distinguish real videos from generated ones.
• Figure 4: Qualitative Comparisons results. FlashMotion demonstrates superior qualitative performance, outperforming both previous multi-step trajectory-controllable methods and few-step distillation baselines.
• Figure 5: Ablation studies on the FastAdapter training stage, diffusion loss, GAN loss, and the dynamic loss scaling strategy.