RectifiedHR: Enable Efficient High-Resolution Image Generation via
  Energy Rectification
Diffusion models have achieved remarkable advances in various image
generation tasks. However, their performance notably declines when generating
images at resolutions higher than those used during the training period.
Despite the existence of numerous methods for producing high-resolution images,
they either suffer from inefficiency or are hindered by complex operations. In
this paper, we propose RectifiedHR, an efficient and straightforward solution
for training-free high-resolution image generation. Specifically, we introduce
the noise refresh strategy, which theoretically only requires a few lines of
code to unlock the model's high-resolution generation ability and improve
efficiency. Additionally, we first observe the phenomenon of energy decay that
may cause image blurriness during the high-resolution image generation process.
To address this issue, we propose an Energy Rectification strategy, where
modifying the hyperparameters of the classifier-free guidance effectively
improves the generation performance. Our method is entirely training-free and
boasts a simple implementation logic. Through extensive comparisons with
numerous baseline methods, our RectifiedHR demonstrates superior effectiveness
and efficiency.
67c808936f68759bf37a8eb8
Zhen Yang
claimed_verified
2025-03-05T10:11:01.696Z
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Zhen Yang
user
YZCS
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Guibao Shen
admin_assigned
2025-03-05T11:20:13.342Z
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/avatars/a3cbcade6ea101e99f58641aa409fdfe.svg
Guibao SHEN
user
PaulSHEN1
67c808936f68759bf37a8eba
Liang Hou
claimed_verified
2025-07-01T10:16:22.812Z
64560a2aaaaf85a98fa9a4b9
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Liang Hou
user
lianghou
67c808936f68759bf37a8ebb
Mushui Liu
67c808936f68759bf37a8ebc
Luozhou Wang
admin_assigned
2025-03-05T11:35:12.288Z
6340333a733f9eef46913dc8
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luozhou wang
user
wileewang
67c808936f68759bf37a8ebd
Xin Tao
67c808936f68759bf37a8ebe
Pengfei Wan
claimed_verified
2025-03-17T14:42:45.111Z
662f93942510ef5735d7ad00
https://cdn-avatars.huggingface.co/v1/production/uploads/662f93942510ef5735d7ad00/ZIDIPm63sncIHFTT5b0uR.png
magicwpf
user
magicwpf
67c808936f68759bf37a8ebf
Di Zhang
admin_assigned
2025-03-05T11:35:23.909Z
644c8324f02250233d0d67d9
/avatars/feb39d281457c1750f3eada3c060a23e.svg
Di Zhang
user
dizhang
67c808936f68759bf37a8ec0
Ying-Cong Chen
RectifiedHR: Enable Efficient High-Resolution Image Generation via
  Energy Rectification
Diffusion models have achieved remarkable advances in various image
generation tasks. However, their performance notably declines when generating
images at resolutions higher than those used during the training period.
Despite the existence of numerous methods for producing high-resolution images,
they either suffer from inefficiency or are hindered by complex operations. In
this paper, we propose RectifiedHR, an efficient and straightforward solution
for training-free high-resolution image generation. Specifically, we introduce
the noise refresh strategy, which theoretically only requires a few lines of
code to unlock the model's high-resolution generation ability and improve
efficiency. Additionally, we first observe the phenomenon of energy decay that
may cause image blurriness during the high-resolution image generation process.
To address this issue, we propose an Energy Rectification strategy, where
modifying the hyperparameters of the classifier-free guidance effectively
improves the generation performance. Our method is entirely training-free and
boasts a simple implementation logic. Through extensive comparisons with
numerous baseline methods, our RectifiedHR demonstrates superior effectiveness
and efficiency.
RectifiedHR uses noise refresh and energy rectification strategies to improve high-resolution image generation efficiency and quality without additional training.
diffusion models noise refresh high-resolution image generation energy decay energy rectification classifier-free guidance