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Improving Progressive Generation with Decomposable Flow Matching |
Snap Research
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Method
Decomposable Flow Matching (DFM): A generative model combining multiscale decomposition with Flow Matching. DFM progressively synthesizes different representation scales by generating coarse-structure scale first and incrementally refining it with finer scales.
DFM Architecture: Our framework (DFM) progressively synthesizes images by combining multiscale decomposition with Flow Matching. We modify the DiT architecture to use per-scale patchification and timestep-embedding layers while keeping the core DiT architecture untouched. DFM Training: We sample the stage count from a categorical distribution, then draw each stage flow-timestep from a logit-normal distribution biased toward lower noise in early stages, and finally train one DiT backbone to jointly predict all stage-wise velocities. DFM Inference: We denoise the coarsest stage first for T1 steps and activate next stages after the previous ones reach a predetermined per-stage noise threshold. This yields previewable outputs with earlier stages build global structure while later ones generate finer details. |
Results
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Across image and video generation, DFM outperforms the best-performing baselines, achieving the same Fréchet DINO Distance (FDD) of Flow Matching baselines with up to 2x less training compute. |
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Qualitative Results
Large-Scale Finetuning: Finetuning FLUX-dev with DFM (FLUX-DFM) achieves superior results than finetuning with standard full-finetuning (DFM-FT) for the same training compute.
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Training From Scratch for Image Generation: When trained from scratch on ImageNet-1k 512px, DFM achieves better quality than baselines using the same training resources.
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Training From Scratch for Video Generation: DFM is also suited for video generation, achieving better structural and visual quality than baselines when trained on the Kinetics-700 dataset with the same compute budget.
Ablations: We found that DFM benefits from more sampling steps in the coarse-structure stage and needs only a few in the high-frequency stage, and it stays largely insensitive to the choice of sampling per-stage noise threshold, especially at high CFG values.
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Citation
If you find this paper useful in your research, please consider citing our work:
@article{dfm,
title={Improving Progressive Generation with Decomposable Flow Matching},
author={Moayed Haji-Ali and Willi Menapace and Ivan Skorokhodov and Arpit Sahni and Sergey Tulyakov and
Vicente Ordonez and Aliaksandr Siarohin},
journal={arXiv preprint arXiv:2506.19839}
year={2025}}