PP-CFG: Polarity-Preserving Guidance for Controllable Few-Step Image and Video Generation

Few-step diffusion distillation reduces sampling cost but often weakens adjustable classifier-free guidance (CFG) and runtime negative prompting. We introduce PP-CFG, an architecture that keeps positive and negative text conditions separate and combines their attention responses with opposite signs within a shared visual backbone. Its Scale-Time Polarity Mixer refines each condition using the guidance scale and denoising timestep, allowing prompt influence to vary throughout generation. The student retains a continuous scale input and selectable negative prompts with one visual backbone pass per step. We evaluate PP-CFG on Wan2.1 for video generation with distribution matching distillation (DMD), and on Qwen-Image with both latent consistency model (LCM) distillation and DMD. PP-CFG achieves 81.46 VBench in four steps and lower video teacher–student distances across guidance scales than step-distilled DMD. On NegElement-1K, our benchmark of 1,000 matched image and video prompt cases, it achieves higher target-removal rates than the corresponding teachers. These experiments support the method's generality across attention architectures, generation modalities, and distillation objectives while retaining competitive generation quality.