Improving Progressive Generation with Decomposable Flow Matching
Authors: name: Moayed Haji-Ali, name: Willi Menapace, name: Ivan Skorokhodov, name: Arpit Sahni, name: Sergey Tulyakov, name: Vicente Ordonez, name: Aliaksandr Siarohin

Generating high-dimensional visual modalities is a computationally intensive
task. A common solution is progressive generation, where the outputs are
synthesized in a coarse-to-fine spectral autoregressive manner. While diffusion
models benefit from the coarse-to-fine nature of denoising, explicit
multi-stage architectures are rarely adopted. These architectures have
increased the complexity of the overall approach, introducing the need for a
custom diffusion formulation, decomposition-dependent stage transitions,
add-hoc samplers, or a model cascade. Our contribution, Decomposable Flow
Matching (DFM), is a simple and effective framework for the progressive
generation of visual media. DFM applies Flow Matching independently at each
level of a user-defined multi-scale representation (such as Laplacian pyramid).
As shown by our experiments, our approach improves visual quality for both
images and videos, featuring superior results compared to prior multistage
frameworks. On Imagenet-1k 512px, DFM achieves 35.2% improvements in FDD scores
over the base architecture and 26.4% over the best-performing baseline, under
the same training compute. When applied to finetuning of large models, such as
FLUX, DFM shows faster convergence speed to the training distribution.
Crucially, all these advantages are achieved with a single model, architectural
simplicity, and minimal modifications to existing training pipelines.