PACT: Preserving Accurate Continuous Trajectories via Flow-Map Distillation

Large-scale Flow Matching models have set a new standard for image and video generation, but their reliance on many sequential inference steps makes them slow to deploy. While current distillation approaches like Distribution Matching Distillation (DMD) offer a way to accelerate this process, they often fail to preserve the teacher model's original data distribution, suffer from reduced visual diversity, and require highly complex training setups. To address this, we introduce PACT (Preserving Accurate Continuous Trajectories), a simple, stable, and compute-efficient framework that extends shortcut-path learning to pre-trained large-scale generators. By building an offline bank of the teacher's generation trajectories, we train a lightweight student model, using only Low-Rank Adaptation (LoRA) and new time-conditioning inputs, to predict the correct flow maps between any two visited states. This approach allows us to reuse offline data efficiently and avoids the need for auxiliary score models. We demonstrate PACT's scalability by distilling Stable Diffusion 3.5 Medium (2.5B parameters) and the LTX-2.3 video model (22B parameters) into highly efficient 4-step and 8-step generators. Our video student generates five-second audiovisual clips faster than real time, on a single GPU. Our distilled models achieve competitive visual quality while preserving the rich distributional diversity and exact seed-level behaviors of their teachers. Furthermore, PACT exhibits robust zero-shot generalization, successfully generating videos at resolutions and aspect ratios completely unseen during training.