FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free
  Scale Fusion
Visual diffusion models achieve remarkable progress, yet they are typically
trained at limited resolutions due to the lack of high-resolution data and
constrained computation resources, hampering their ability to generate
high-fidelity images or videos at higher resolutions. Recent efforts have
explored tuning-free strategies to exhibit the untapped potential
higher-resolution visual generation of pre-trained models. However, these
methods are still prone to producing low-quality visual content with repetitive
patterns. The key obstacle lies in the inevitable increase in high-frequency
information when the model generates visual content exceeding its training
resolution, leading to undesirable repetitive patterns deriving from the
accumulated errors. To tackle this challenge, we propose FreeScale, a
tuning-free inference paradigm to enable higher-resolution visual generation
via scale fusion. Specifically, FreeScale processes information from different
receptive scales and then fuses it by extracting desired frequency components.
Extensive experiments validate the superiority of our paradigm in extending the
capabilities of higher-resolution visual generation for both image and video
models. Notably, compared with the previous best-performing method, FreeScale
unlocks the generation of 8k-resolution images for the first time.
675f980c4d67dd90e85ecc6c
Haonan Qiu
admin_assigned
2024-12-16T10:14:47.092Z
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Haonan Qiu
user
MoonQiu
675f980c4d67dd90e85ecc6d
Shiwei Zhang
admin_assigned
2024-12-16T10:15:06.661Z
63a1200045edac9f7508bae9
/avatars/e84f0b045f32c5b8b4da43458650b925.svg
Shiwei Zhang
user
StevenZhang
675f980c4d67dd90e85ecc6e
Yujie Wei
admin_assigned
2024-12-16T10:16:14.731Z
637f70d6fab5db9101c3dfc8
https://cdn-avatars.huggingface.co/v1/production/uploads/637f70d6fab5db9101c3dfc8/NgkYNXWLDavLbrnCby2Fl.jpeg
Yujie Wei
user
weilllllls
675f980c4d67dd90e85ecc6f
Ruihang Chu
admin_assigned
2024-12-16T10:15:58.665Z
642e3bcb958faf258a40e89c
/avatars/213501def37dc53032cee17e37fcc4c1.svg
Ruihang Chu
user
Ruihang
675f980c4d67dd90e85ecc70
Hangjie Yuan
admin_assigned
2024-12-16T10:15:52.570Z
649d54b314afbb10ce2a9eeb
/avatars/15c325d8c2273ff63569f23015e98486.svg
Hangjie Yuan
user
JacobYuan
675f980c4d67dd90e85ecc71
Xiang Wang
admin_assigned
2024-12-16T10:15:46.971Z
65fca775fa59bdf4737b1a84
/avatars/a161b510bde8f57e7686cbb0b4aa6a52.svg
Xiang Wang
user
xiangwang1223
675f980c4d67dd90e85ecc72
Yingya Zhang
675f980c4d67dd90e85ecc73
Ziwei Liu
admin_assigned
2024-12-16T10:15:16.895Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free
  Scale Fusion
Visual diffusion models achieve remarkable progress, yet they are typically
trained at limited resolutions due to the lack of high-resolution data and
constrained computation resources, hampering their ability to generate
high-fidelity images or videos at higher resolutions. Recent efforts have
explored tuning-free strategies to exhibit the untapped potential
higher-resolution visual generation of pre-trained models. However, these
methods are still prone to producing low-quality visual content with repetitive
patterns. The key obstacle lies in the inevitable increase in high-frequency
information when the model generates visual content exceeding its training
resolution, leading to undesirable repetitive patterns deriving from the
accumulated errors. To tackle this challenge, we propose FreeScale, a
tuning-free inference paradigm to enable higher-resolution visual generation
via scale fusion. Specifically, FreeScale processes information from different
receptive scales and then fuses it by extracting desired frequency components.
Extensive experiments validate the superiority of our paradigm in extending the
capabilities of higher-resolution visual generation for both image and video
models. Notably, compared with the previous best-performing method, FreeScale
unlocks the generation of 8k-resolution images for the first time.
FreeScale, a tuning-free inference paradigm, enables high-resolution visual generation by fusing information from different scales, overcoming the limitations of existing methods.
visual diffusion models tuning-free strategies high-resolution visual generation pre-trained models repetitive patterns high-frequency information scale fusion receptive scales desired frequency components