PRICE: Propagation-Risk-Informed Dynamic Diffusion Caching under Exact Compute Budgets

Diffusion caching accelerates sampling by reusing features across denoising steps. Given a target compute budget, a caching policy must decide which steps should use full computation and which can reuse the cache. Existing adaptive caching methods often make this decision based on local signals, such as the difference between adjacent steps. However, the goal of caching is to preserve the final output of full computation, and a small local error does not always lead to a small final error. Our single-step caching experiments show that similar local errors can have very different final effects depending on where they occur in the denoising process. Motivated by this observation, we propose PRICE (Propagation-Risk-Informed Caching under Exact budgets), a training-free method that estimates this final effect from the local discrepancy, solver step size, and downstream propagation. At each step, it compares the estimated risk with offline reference risks for the remaining steps and adjusts the threshold to the remaining reuse quota. On FLUX.1-dev, Qwen-Image, and Wan2.1, PRICE achieves 5.24x, 6.87x, and 3.49x acceleration, respectively, while better preserving full computation outputs than state-of-the-art caching baselines and maintaining competitive perceptual quality.