NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic
  Adversarial Training
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.
674fb49742a92dddf7d64f3b
Dar-Yen Chen
claimed_verified
2024-12-04T09:45:49.922Z
64d0eb731ed6649d70afb136
/avatars/4c591c86c575c82760126c39af5a02b4.svg
Chen Dar-Yen
user
ChenDY
674fb49742a92dddf7d64f3c
Hmrishav Bandyopadhyay
admin_assigned
2024-12-05T12:24:14.918Z
638c81fa61eb51017518fa31
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/f0eCrzBrxz7Y9n25WkZ2v.png
Hmrishav Bandyopadhyay
user
Hmrishav
674fb49742a92dddf7d64f3d
Kai Zou
admin_assigned
2024-12-05T12:24:23.664Z
66d60c6d956fdcaeb676d7cf
/avatars/c81e3a17e94892e6448f293ccadd667a.svg
Kaizo240
user
Kaizou
674fb49742a92dddf7d64f3e
Yi-Zhe Song
admin_assigned
2024-12-05T12:24:33.078Z
671f999400458be2e849a7b9
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/SdJZS9l4DDODEA0ls8Kb5.png
Yizhe Song
user
songyizhe
NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic
  Adversarial Training
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.
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.
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