Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning

Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedules, limiting inference procedures from adapting to the data. We introduce Equilibrium Forcing (EqF), a simplified objective for video denoising generative models without noise level conditioning. EqF turns inference into a gradient-based optimization problem with modular training- and inference-time designs that decouple learning the denoising field from sampling. By adapting the structure of the gradient flow during sampling to propose data-dependent step sizes, EqF improves video quality and consistency on challenging autoregressive video generation benchmarks. Extensive analysis elucidates exactly how removing the noise level conditioning enables EqF's data-dependent inference properties to surpass the performance of standard noise level-conditional denoising video methods. Project page: https://anonequilibriumforcing.github.io/