SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910036168540160 |
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| author | Ge, Xingtong Zhang, Xin Xu, Tongda Zhang, Yi Zhang, Xinjie Wang, Yan Zhang, Jun |
| author_facet | Ge, Xingtong Zhang, Xin Xu, Tongda Zhang, Yi Zhang, Xinjie Wang, Yan Zhang, Jun |
| contents | The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from convergence difficulties on large-scale flow-based text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze the issues when applying vanilla DMD on large-scale models. Then, to overcome the scalability challenge, we propose implicit distribution alignment (IDA) to constrain the divergence between the generator and the fake distribution. Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep denoising importance from the teacher model. With IDA alone, DMD converges for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1 dev. Together with a scaled VFM-based discriminator, our final model, dubbed \textbf{SenseFlow}, achieves superior performance in distillation for both diffusion based text-to-image models such as SDXL, and flow-matching models such as SD 3.5 Large and FLUX.1 dev. The source code is available at \href{https://github.com/XingtongGe/SenseFlow}{https://github.com/XingtongGe/SenseFlow} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00523 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation Ge, Xingtong Zhang, Xin Xu, Tongda Zhang, Yi Zhang, Xinjie Wang, Yan Zhang, Jun Computer Vision and Pattern Recognition The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from convergence difficulties on large-scale flow-based text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze the issues when applying vanilla DMD on large-scale models. Then, to overcome the scalability challenge, we propose implicit distribution alignment (IDA) to constrain the divergence between the generator and the fake distribution. Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep denoising importance from the teacher model. With IDA alone, DMD converges for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1 dev. Together with a scaled VFM-based discriminator, our final model, dubbed \textbf{SenseFlow}, achieves superior performance in distillation for both diffusion based text-to-image models such as SDXL, and flow-matching models such as SD 3.5 Large and FLUX.1 dev. The source code is available at \href{https://github.com/XingtongGe/SenseFlow}{https://github.com/XingtongGe/SenseFlow} |
| title | SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.00523 |