MPC-Flow: Model Predictive Control Framework for Solving Inverse Problems with Flow-Based Generative Models
Researchers propose MPC-Flow, a new framework that uses model predictive control to guide flow-based generative models for solving inverse problems like image restoration without requiring training. The method breaks the computationally expensive trajectory optimization into smaller sub-problems, avoiding the need for backpropagation through the generative model. The approach demonstrates practical efficiency on tasks like inpainting, deblurring, and super-resolution, including scaling to large models like FLUX.2 on consumer hardware.
MPC-Flow addresses a key challenge in using flow-based generative models for inverse problems: efficiently guiding their dynamics during inference without expensive training or memory-intensive differentiation. The framework reformulates the optimal control problem as a sequence of smaller control sub-problems, enabling practical guidance at inference time. The authors provide theoretical analysis connecting MPC-Flow to the underlying optimal control objective and show how different algorithmic choices create a spectrum of guidance approaches, some avoiding backpropagation entirely. Evaluation on benchmark image restoration tasks—including linear (inpainting, deblurring) and non-linear (super-resolution) settings—demonstrates strong performance and scalability. Notably, the method successfully scales to state-of-the-art architectures, with demonstrations on the 32-billion-parameter FLUX.2 model running in quantized form on consumer hardware.
What's missing
The paper does not discuss computational time comparisons with other training-free guidance methods, nor does it provide detailed ablation studies isolating the contribution of specific algorithmic choices within the MPC framework. Additionally, limitations regarding failure modes or problem classes where MPC-Flow may underperform are not explicitly addressed in the abstract.
What different sources said
- arXiv cs.LGCenter
Solving Inverse Problems with Flow-based Models via Model Predictive Control
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