Core Concepts
Training-free diffusion guidance offers unique advantages but faces limitations that can be addressed with enhancement techniques.
Abstract
This content delves into the mechanisms, limitations, and enhancement techniques of training-free diffusion guidance. It explores the theoretical analysis supporting training-free guidance, its susceptibility to adversarial gradients, slower convergence rates compared to classifier guidance, and introduces techniques like random augmentation and adaptive gradient scheduling to overcome these limitations. The experiments evaluate the efficacy of these methods across various diffusion models such as CelebA-HQ, ImageNet, and human motion generation.
Directory:
- Abstract
- Training-free diffusion models are popular in various applications.
- Introduction
- Diffusion models' success in different domains.
- Classifier vs. Classifier-Free Guidance
- Comparison of classifier-based and classifier-free guidance approaches.
- Training-Free Diffusion Guidance
- Explanation of training-free guidance using off-the-shelf networks.
- Analysis of Training-Free Guidance
- Mechanisms and limitations of training-free guidance.
- Improving Training-Free Guidance
- Techniques like random augmentation and adaptive gradient scheduling.
- Experiments
- Evaluation of methods on CelebA-HQ, ImageNet, and human motion generation.
Stats
Adding additional control to pretrained diffusion models has become popular in computer vision, reinforcement learning, and AI for science.
Several studies have proposed training-free diffusion guidance using off-the-shelf networks pretrained on clean images.
Training-free methods are more susceptible to adversarial gradients and exhibit slower convergence rates compared to classifier guidance.
Quotes
"Adding additional control to pretrained diffusion models has become an increasingly popular research area."
"Training-free methods are more susceptible to adversarial gradients."