title: UniFL: Improve Stable Diffusion via Unified Feedback Learning
summary: 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.
paper_authors._id: 6614b38ce2ba01441b6951ce
paper_authors.name: Jiacheng Zhang
paper_authors._id: 6614b38ce2ba01441b6951cf
paper_authors.name: Jie Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-09T19:47:42.965Z
paper_authors._id: 6381c5d63680a7cf34e08ca9
paper_authors.avatarUrl: /avatars/731467e2d80d0ae163c4a00a9e3ff9e5.svg
paper_authors.fullname: wujie10558@gmail.com
paper_authors.type: user
paper_authors.user: wujie10
paper_authors._id: 6614b38ce2ba01441b6951d0
paper_authors.name: Yuxi Ren
paper_authors._id: 6614b38ce2ba01441b6951d1
paper_authors.name: Xin Xia
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-17T10:28:28.358Z
paper_authors._id: 63089a78ff78e2aead8d10e7
paper_authors.avatarUrl: /avatars/f326fd08abee7e31599a78923be30003.svg
paper_authors.fullname: XinXia
paper_authors.type: user
paper_authors.user: XiaXin-Aloys
paper_authors._id: 6614b38ce2ba01441b6951d2
paper_authors.name: Huafeng Kuang
paper_authors._id: 6614b38ce2ba01441b6951d3
paper_authors.name: Pan Xie
paper_authors._id: 6614b38ce2ba01441b6951d4
paper_authors.name: Jiashi Li
paper_authors._id: 6614b38ce2ba01441b6951d5
paper_authors.name: Xuefeng Xiao
paper_authors._id: 6614b38ce2ba01441b6951d6
paper_authors.name: Weilin Huang
paper_authors._id: 6614b38ce2ba01441b6951d7
paper_authors.name: Min Zheng
paper_authors._id: 6614b38ce2ba01441b6951d8
paper_authors.name: Lean Fu
paper_authors._id: 6614b38ce2ba01441b6951d9
paper_authors.name: Guanbin Li
paper_title: UniFL: Improve Stable Diffusion via Unified Feedback Learning
paper_summary: 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.
paper_ai_keywords: diffusion models feedback learning perceptual feedback learning decoupled feedback learning adversarial feedback learning ImageReward LCM SDXL Turbo Lora ControlNet AnimateDiff