Summary of Gradient-free Classifier Guidance For Diffusion Model Sampling, by Rahul Shenoy et al.
Gradient-Free Classifier Guidance for Diffusion Model Sampling
by Rahul Shenoy, Zhihong Pan, Kaushik Balakrishnan, Qisen Cheng, Yongmoon Jeon, Heejune Yang, Jaewon Kim
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method combines the benefits of classifier guidance and classifier-free guidance by using a pre-trained classifier solely in inference mode. This Gradient-free Classifier Guidance (GFCG) approach efficiently generates high-fidelity images while preserving diversity, with applications in class-conditioned and text-to-image generation diffusion models. The GFCG method achieves state-of-the-art results on the ImageNet 512 dataset, setting a new record for _{} (23.09) while maintaining a high classification Precision of 94.3%. This is compared to Autoguidance (ATG), which achieves 90.2% Precision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Guided sampling methods in image generation using diffusion models have shown great promise, but each has its limitations. The proposed Gradient-free Classifier Guidance (GFCG) method combines the benefits of classifier guidance and classifier-free guidance without using gradient descent. This efficient approach generates high-fidelity images while preserving diversity. The results show that GFCG consistently improves class prediction accuracy on both class-conditioned and text-to-image generation diffusion models. |
Keywords
» Artificial intelligence » Classification » Gradient descent » Image generation » Inference » Precision
