Adversarial Score Distillation: When score distillation meets GAN
(2312.00739)Abstract
Existing score distillation methods are sensitive to classifier-free guidance (CFG) scale: manifested as over-smoothness or instability at small CFG scales, while over-saturation at large ones. To explain and analyze these issues, we revisit the derivation of Score Distillation Sampling (SDS) and decipher existing score distillation with the Wasserstein Generative Adversarial Network (WGAN) paradigm. With the WGAN paradigm, we find that existing score distillation either employs a fixed sub-optimal discriminator or conducts incomplete discriminator optimization, resulting in the scale-sensitive issue. We propose the Adversarial Score Distillation (ASD), which maintains an optimizable discriminator and updates it using the complete optimization objective. Experiments show that the proposed ASD performs favorably in 2D distillation and text-to-3D tasks against existing methods. Furthermore, to explore the generalization ability of our WGAN paradigm, we extend ASD to the image editing task, which achieves competitive results. The project page and code are at https://github.com/2y7c3/ASD.
Overview
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Adversarial Score Distillation (ASD) combines score distillation with Generative Adversarial Networks (GANs) to improve stability and quality in AI.
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ASD employs an optimizable discriminator with WGAN optimization to address issues present in traditional score distillation methods.
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Experiments show ASD outperforms previous methods in 2D distillation and text-to-3D tasks, enhancing quality, stability, and diversity.
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ASD is applied to image editing with high fidelity, showcasing its robustness and versatility in content creation.
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The study highlights the potential for using pretrained diffusion models for various tasks and suggests future work should focus on improving computational efficiency.
Understanding Adversarial Score Distillation
The Interplay of Distillation and GANs
Recent developments in AI have introduced a method known as Adversarial Score Distillation (ASD), which integrates the concept of score distillation with Generative Adversarial Networks (GAN). Traditional score distillation techniques have been sensitive to the scale associated with classifier-free guidance, leading to issues like over-smoothness or instability at small scales, and over-saturation at larger ones. ASD addresses these scale-sensitive issues by employing an optimizable discriminator updated using a complete WGAN optimization objective.
The Innovations of ASD
ASD proposes a novel framework where a discriminator is implementable by combining diffusion models and textual-inversion embedding or alternative methods that allow it to be optimizable. This discriminator optimization uses losses derived from WGAN, which provides significant improvements over past methods that employed a fixed sub-optimal discriminator or an incomplete optimization objective.
The experiments conducted demonstrate that ASD yields favorable outcomes in tasks like 2D distillation and text-to-3D tasks, showing improvements over existing score distillation methods in terms of quality, stability, and diversity.
Exploring Image Editing
Further exploration of ASD's capability is evident in its application to image editing tasks. The approach was able to produce competitive results, managing to carry out intricate tasks like simplifying images, refining details, and replacing objects within an image contextually and with high fidelity. This extension of ASD to image editing illustrates its versatility and robust nature, in using the established WGAN paradigm.
Contributions and Implications
This advancement in score distillation methodology has broader implications, shedding light on the fundamental connections between score distillation and GANs. It opens up avenues for leveraging pretrained diffusion models for diverse downstream tasks without the necessity for task-specific model redesigns or extensive fine-tuning datasets.
Future Directions
While ASD shows promise, it currently shares a similar computational demand to previous variants like VSD. Future work may enhance its efficiency through advancements in optimization strategies, possibly leading to even faster and more refined distillation processes.
In summary, ASD marks a significant step in the realm of AI-driven content creation, potentially leading to better quality, consistency, and versatility in future applications.
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