title: NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic
    Adversarial Training
summary: We introduce NitroFusion, a fundamentally different approach to single-step
  diffusion that achieves high-quality generation through a dynamic adversarial
  framework. While one-step methods offer dramatic speed advantages, they
  typically suffer from quality degradation compared to their multi-step
  counterparts. Just as a panel of art critics provides comprehensive feedback by
  specializing in different aspects like composition, color, and technique, our
  approach maintains a large pool of specialized discriminator heads that
  collectively guide the generation process. Each discriminator group develops
  expertise in specific quality aspects at different noise levels, providing
  diverse feedback that enables high-fidelity one-step generation. Our framework
  combines: (i) a dynamic discriminator pool with specialized discriminator
  groups to improve generation quality, (ii) strategic refresh mechanisms to
  prevent discriminator overfitting, and (iii) global-local discriminator heads
  for multi-scale quality assessment, and unconditional/conditional training for
  balanced generation. Additionally, our framework uniquely supports flexible
  deployment through bottom-up refinement, allowing users to dynamically choose
  between 1-4 denoising steps with the same model for direct quality-speed
  trade-offs. Through comprehensive experiments, we demonstrate that NitroFusion
  significantly outperforms existing single-step methods across multiple
  evaluation metrics, particularly excelling in preserving fine details and
  global consistency.
paper_authors._id: 674fb49742a92dddf7d64f3b
paper_authors.name: Dar-Yen Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-04T09:45:49.922Z
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paper_authors.avatarUrl: /avatars/4c591c86c575c82760126c39af5a02b4.svg
paper_authors.fullname: Chen Dar-Yen
paper_authors.type: user
paper_authors.user: ChenDY
paper_authors._id: 674fb49742a92dddf7d64f3c
paper_authors.name: Hmrishav Bandyopadhyay
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-05T12:24:14.918Z
paper_authors._id: 638c81fa61eb51017518fa31
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/f0eCrzBrxz7Y9n25WkZ2v.png
paper_authors.fullname: Hmrishav Bandyopadhyay
paper_authors.type: user
paper_authors.user: Hmrishav
paper_authors._id: 674fb49742a92dddf7d64f3d
paper_authors.name: Kai Zou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-05T12:24:23.664Z
paper_authors._id: 66d60c6d956fdcaeb676d7cf
paper_authors.avatarUrl: /avatars/c81e3a17e94892e6448f293ccadd667a.svg
paper_authors.fullname: Kaizo240
paper_authors.type: user
paper_authors.user: Kaizou
paper_authors._id: 674fb49742a92dddf7d64f3e
paper_authors.name: Yi-Zhe Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-05T12:24:33.078Z
paper_authors._id: 671f999400458be2e849a7b9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/SdJZS9l4DDODEA0ls8Kb5.png
paper_authors.fullname: Yizhe Song
paper_authors.type: user
paper_authors.user: songyizhe
paper_title: NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic
    Adversarial Training
paper_summary: We introduce NitroFusion, a fundamentally different approach to single-step
  diffusion that achieves high-quality generation through a dynamic adversarial
  framework. While one-step methods offer dramatic speed advantages, they
  typically suffer from quality degradation compared to their multi-step
  counterparts. Just as a panel of art critics provides comprehensive feedback by
  specializing in different aspects like composition, color, and technique, our
  approach maintains a large pool of specialized discriminator heads that
  collectively guide the generation process. Each discriminator group develops
  expertise in specific quality aspects at different noise levels, providing
  diverse feedback that enables high-fidelity one-step generation. Our framework
  combines: (i) a dynamic discriminator pool with specialized discriminator
  groups to improve generation quality, (ii) strategic refresh mechanisms to
  prevent discriminator overfitting, and (iii) global-local discriminator heads
  for multi-scale quality assessment, and unconditional/conditional training for
  balanced generation. Additionally, our framework uniquely supports flexible
  deployment through bottom-up refinement, allowing users to dynamically choose
  between 1-4 denoising steps with the same model for direct quality-speed
  trade-offs. Through comprehensive experiments, we demonstrate that NitroFusion
  significantly outperforms existing single-step methods across multiple
  evaluation metrics, particularly excelling in preserving fine details and
  global consistency.
paper_ai_summary: NitroFusion uses a dynamic adversarial framework with specialized discriminator groups for high-quality, high-fidelity single-step image generation with flexible quality-speed trade-offs.
paper_ai_keywords: diffusion dynamic adversarial framework discriminator heads noise levels quality aspects discriminator overfitting global-local discriminator heads unconditional/conditional training bottom-up refinement denoising steps fine details global consistency