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About this paper
Haohang Xu, Longyu Chen, Yichen Zhang, Shuangrui Ding, Zhipeng Zhang
MSF introduces a diffusion model framework that employs multi-scale latent factorization with base-residual decomposition, achieving enhanced image generation quality and substantial computational efficiency. This approach yields FID scores of 2.08 on ImageNet 256x256 and 2.47 on ImageNet 512x512, while demonstrating 4x to 11x faster sampling speeds compared to the DiT baseline.
Key insights
- Residual information, representing high-frequency details, is easier to model and requires significantly fewer sampling steps than full-resolution signals.
- Decomposing the generative task into base-residual components across multiple scales allows for inherent architectural efficiency gains.
- Classifier-Free Guidance (CFG) can be optionally omitted for higher-resolution residual generation without major quality degradation, providing further sampling speedup.
Summary by alphaXiv.
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