Improving Progressive Generation with Decomposable Flow Matching
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.
685b6d7dd2ee4fac76521e5a
Moayed Haji-Ali
claimed_verified
2025-06-25T08:08:19.723Z
665aa3c1957e1e79ff031acb
/avatars/8fbe062bc8c635337991b5bf90c07052.svg
Moayed Haji Ali
user
mali6
685b6d7dd2ee4fac76521e5b
Willi Menapace
685b6d7dd2ee4fac76521e5c
Ivan Skorokhodov
685b6d7dd2ee4fac76521e5d
Arpit Sahni
685b6d7dd2ee4fac76521e5e
Sergey Tulyakov
685b6d7dd2ee4fac76521e5f
Vicente Ordonez
685b6d7dd2ee4fac76521e60
Aliaksandr Siarohin
Improving Progressive Generation with Decomposable Flow Matching
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.
Decomposable Flow Matching (DFM) framework enhances visual generation and video quality by applying Flow Matching at multiple scales without requiring complex multi-stage architectures.
diffusion models denoising Flow Matching Laplacian pyramid multi-scale representation progressive generation Imagenet-1k FDD scores finetuning FLUX training distribution convergence speed