FreeU: Free Lunch in Diffusion U-Net
In this paper, we uncover the untapped potential of diffusion U-Net, which
serves as a "free lunch" that substantially improves the generation quality on
the fly. We initially investigate the key contributions of the U-Net
architecture to the denoising process and identify that its main backbone
primarily contributes to denoising, whereas its skip connections mainly
introduce high-frequency features into the decoder module, causing the network
to overlook the backbone semantics. Capitalizing on this discovery, we propose
a simple yet effective method-termed "FreeU" - that enhances generation quality
without additional training or finetuning. Our key insight is to strategically
re-weight the contributions sourced from the U-Net's skip connections and
backbone feature maps, to leverage the strengths of both components of the
U-Net architecture. Promising results on image and video generation tasks
demonstrate that our FreeU can be readily integrated to existing diffusion
models, e.g., Stable Diffusion, DreamBooth, ModelScope, Rerender and ReVersion,
to improve the generation quality with only a few lines of code. All you need
is to adjust two scaling factors during inference. Project page:
https://chenyangsi.top/FreeU/.
650bcd0dd4a0852d3c827c73
Chenyang Si
admin_assigned
2023-09-21T09:32:08.312Z
635f8ed47c05eb9f59963d3a
https://cdn-avatars.huggingface.co/v1/production/uploads/635f8ed47c05eb9f59963d3a/uQf4p9N9pSaFy87Wg9v4k.jpeg
ChenyangSi
user
ChenyangSi
650bcd0dd4a0852d3c827c74
Ziqi Huang
claimed_verified
2023-09-21T07:22:35.208Z
60efe7fa0d920bc7805cada5
https://cdn-avatars.huggingface.co/v1/production/uploads/60efe7fa0d920bc7805cada5/2LBrJBjSCOP5ilZIpWLHl.png
Ziqi Huang
user
Ziqi
650bcd0dd4a0852d3c827c75
Yuming Jiang
admin_assigned
2023-09-21T09:32:28.591Z
629c95b7a5d6f5fe10e6ed45
/avatars/c9a5f0e7fae3a079d26db266e9ff90a3.svg
Yuming Jiang
user
yumingj
650bcd0dd4a0852d3c827c76
Ziwei Liu
admin_assigned
2023-09-21T09:32:36.944Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
FreeU: Free Lunch in Diffusion U-Net
In this paper, we uncover the untapped potential of diffusion U-Net, which
serves as a "free lunch" that substantially improves the generation quality on
the fly. We initially investigate the key contributions of the U-Net
architecture to the denoising process and identify that its main backbone
primarily contributes to denoising, whereas its skip connections mainly
introduce high-frequency features into the decoder module, causing the network
to overlook the backbone semantics. Capitalizing on this discovery, we propose
a simple yet effective method-termed "FreeU" - that enhances generation quality
without additional training or finetuning. Our key insight is to strategically
re-weight the contributions sourced from the U-Net's skip connections and
backbone feature maps, to leverage the strengths of both components of the
U-Net architecture. Promising results on image and video generation tasks
demonstrate that our FreeU can be readily integrated to existing diffusion
models, e.g., Stable Diffusion, DreamBooth, ModelScope, Rerender and ReVersion,
to improve the generation quality with only a few lines of code. All you need
is to adjust two scaling factors during inference. Project page:
https://chenyangsi.top/FreeU/.
A method called FreeU improves diffusion U-Net models' generation quality by re-weighting skip connections and backbone features without additional training.
diffusion U-Net U-Net denoising skip connections backbone free lunch FreeU image generation video generation Stable Diffusion DreamBooth ModelScope Rerender ReVersion