UniFL: Improve Stable Diffusion via Unified Feedback Learning
Diffusion models have revolutionized the field of image generation, leading
to the proliferation of high-quality models and diverse downstream
applications. However, despite these significant advancements, the current
competitive solutions still suffer from several limitations, including inferior
visual quality, a lack of aesthetic appeal, and inefficient inference, without
a comprehensive solution in sight. To address these challenges, we present
UniFL, a unified framework that leverages feedback learning to enhance
diffusion models comprehensively. UniFL stands out as a universal, effective,
and generalizable solution applicable to various diffusion models, such as
SD1.5 and SDXL. Notably, UniFL incorporates three key components: perceptual
feedback learning, which enhances visual quality; decoupled feedback learning,
which improves aesthetic appeal; and adversarial feedback learning, which
optimizes inference speed. In-depth experiments and extensive user studies
validate the superior performance of our proposed method in enhancing both the
quality of generated models and their acceleration. For instance, UniFL
surpasses ImageReward by 17% user preference in terms of generation quality and
outperforms LCM and SDXL Turbo by 57% and 20% in 4-step inference. Moreover, we
have verified the efficacy of our approach in downstream tasks, including Lora,
ControlNet, and AnimateDiff.
6614b38ce2ba01441b6951ce
Jiacheng Zhang
6614b38ce2ba01441b6951cf
Jie Wu
claimed_verified
2024-04-09T19:47:42.965Z
6381c5d63680a7cf34e08ca9
/avatars/731467e2d80d0ae163c4a00a9e3ff9e5.svg
wujie10558@gmail.com
user
wujie10
6614b38ce2ba01441b6951d0
Yuxi Ren
6614b38ce2ba01441b6951d1
Xin Xia
claimed_verified
2025-01-17T10:28:28.358Z
63089a78ff78e2aead8d10e7
/avatars/f326fd08abee7e31599a78923be30003.svg
XinXia
user
XiaXin-Aloys
6614b38ce2ba01441b6951d2
Huafeng Kuang
6614b38ce2ba01441b6951d3
Pan Xie
6614b38ce2ba01441b6951d4
Jiashi Li
6614b38ce2ba01441b6951d5
Xuefeng Xiao
6614b38ce2ba01441b6951d6
Weilin Huang
6614b38ce2ba01441b6951d7
Min Zheng
6614b38ce2ba01441b6951d8
Lean Fu
6614b38ce2ba01441b6951d9
Guanbin Li
UniFL: Improve Stable Diffusion via Unified Feedback Learning
Diffusion models have revolutionized the field of image generation, leading
to the proliferation of high-quality models and diverse downstream
applications. However, despite these significant advancements, the current
competitive solutions still suffer from several limitations, including inferior
visual quality, a lack of aesthetic appeal, and inefficient inference, without
a comprehensive solution in sight. To address these challenges, we present
UniFL, a unified framework that leverages feedback learning to enhance
diffusion models comprehensively. UniFL stands out as a universal, effective,
and generalizable solution applicable to various diffusion models, such as
SD1.5 and SDXL. Notably, UniFL incorporates three key components: perceptual
feedback learning, which enhances visual quality; decoupled feedback learning,
which improves aesthetic appeal; and adversarial feedback learning, which
optimizes inference speed. In-depth experiments and extensive user studies
validate the superior performance of our proposed method in enhancing both the
quality of generated models and their acceleration. For instance, UniFL
surpasses ImageReward by 17% user preference in terms of generation quality and
outperforms LCM and SDXL Turbo by 57% and 20% in 4-step inference. Moreover, we
have verified the efficacy of our approach in downstream tasks, including Lora,
ControlNet, and AnimateDiff.
UniFL enhances diffusion models via feedback learning, improving visual quality, aesthetic appeal, and inference speed across various applications.
diffusion models UniFL perceptual feedback learning decoupled feedback learning adversarial feedback learning ImageReward LCM SDXL Turbo Lora ControlNet AnimateDiff