ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
Recent advancement in text-to-image models (e.g., Stable Diffusion) and
corresponding personalized technologies (e.g., DreamBooth and LoRA) enables
individuals to generate high-quality and imaginative images. However, they
often suffer from limitations when generating images with resolutions outside
of their trained domain. To overcome this limitation, we present the Resolution
Adapter (ResAdapter), a domain-consistent adapter designed for diffusion models
to generate images with unrestricted resolutions and aspect ratios. Unlike
other multi-resolution generation methods that process images of static
resolution with complex post-process operations, ResAdapter directly generates
images with the dynamical resolution. Especially, after learning a deep
understanding of pure resolution priors, ResAdapter trained on the general
dataset, generates resolution-free images with personalized diffusion models
while preserving their original style domain. Comprehensive experiments
demonstrate that ResAdapter with only 0.5M can process images with flexible
resolutions for arbitrary diffusion models. More extended experiments
demonstrate that ResAdapter is compatible with other modules (e.g., ControlNet,
IP-Adapter and LCM-LoRA) for image generation across a broad range of
resolutions, and can be integrated into other multi-resolution model (e.g.,
ElasticDiffusion) for efficiently generating higher-resolution images. Project
link is https://res-adapter.github.io
65e73b6bfc737133d7e13494
Jiaxiang Cheng
admin_assigned
2024-03-05T20:43:44.089Z
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Jiaxiang Cheng
user
jiaxiangc
65e73b6bfc737133d7e13495
Pan Xie
admin_assigned
2024-03-05T20:44:59.966Z
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Xie Pan
user
panxie
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Xin Xia
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2024-04-09T19:20:21.540Z
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XinXia
user
XiaXin-Aloys
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Jiashi Li
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2024-03-05T20:45:29.747Z
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Jiashi Li
user
Beginlner
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Jie Wu
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Yuxi Ren
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Huixia Li
claimed_verified
2024-03-21T08:29:09.326Z
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HXLee
user
HXLee
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Xuefeng Xiao
admin_assigned
2024-03-05T20:46:00.809Z
646b7f71df2609a541c1ab9f
/avatars/48b82e5fd9b06f41ff825507c36816cd.svg
Xuefeng Xiao
user
xiaoxuefeng
65e73b6bfc737133d7e1349c
Min Zheng
65e73b6bfc737133d7e1349d
Lean Fu
ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
Recent advancement in text-to-image models (e.g., Stable Diffusion) and
corresponding personalized technologies (e.g., DreamBooth and LoRA) enables
individuals to generate high-quality and imaginative images. However, they
often suffer from limitations when generating images with resolutions outside
of their trained domain. To overcome this limitation, we present the Resolution
Adapter (ResAdapter), a domain-consistent adapter designed for diffusion models
to generate images with unrestricted resolutions and aspect ratios. Unlike
other multi-resolution generation methods that process images of static
resolution with complex post-process operations, ResAdapter directly generates
images with the dynamical resolution. Especially, after learning a deep
understanding of pure resolution priors, ResAdapter trained on the general
dataset, generates resolution-free images with personalized diffusion models
while preserving their original style domain. Comprehensive experiments
demonstrate that ResAdapter with only 0.5M can process images with flexible
resolutions for arbitrary diffusion models. More extended experiments
demonstrate that ResAdapter is compatible with other modules (e.g., ControlNet,
IP-Adapter and LCM-LoRA) for image generation across a broad range of
resolutions, and can be integrated into other multi-resolution model (e.g.,
ElasticDiffusion) for efficiently generating higher-resolution images. Project
link is https://res-adapter.github.io
The Resolution Adapter (ResAdapter) enhances diffusion models to generate images in arbitrary resolutions and aspect ratios without extensive post-processing, preserving style consistency.
diffusion models Resolution Adapter ResAdapter resolution priors ControlNet IP-Adapter LCM-LoRA ElasticDiffusion