# Elucidating The Design Space of Classifier-Guided Diffusion Generation

**Authors:** Jiajun Ma, Tianyang Hu, Wenjia Wang, Jiacheng Sun  
**arXiv:** [2310.11311](https://arxiv.org/abs/2310.11311)  
**Date:** 2023-10-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.11311v1/

## Key points

- Guidance plays a crucial role in conditional diffusion generation
- Existing guidance schemes have limitations
- Mainstream methods require additional training with labeled data
- Training-free methods offer flexibility but lack comparable performance
- The proposed approach leverages off-the-shelf classifiers in a training-free manner
- Pre-conditioning techniques effectively exploit pretrained classifiers for guiding diffusion generation
- Extensive experiments on ImageNet validate the effectiveness of the proposed method
- State-of-the-art diffusion models can be improved by up to 20% without significant increase in computational cost
- The approach holds potential for text-to-image generation tasks and can be readily scaled up
- The paper provides valuable insights into classifier-guided diffusion generation design space
- The code for implementing this method is available on GitHub for further exploration and experimentation.

## Summary

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In the field of conditional diffusion generation, guidance plays a crucial role in ensuring sample quality and controllability. However, existing guidance schemes have certain limitations. Mainstream methods like classifier guidance and classifier-free guidance require additional training with labeled data, which is time-consuming and cannot adapt to new conditions. On the other hand, training-free methods such as universal guidance offer more flexibility but fail to demonstrate comparable performance. To address these challenges, this work presents a comprehensive investigation into the design space of guiding diffusion generation. The authors propose leveraging off-the-shelf classifiers in a training-free manner to achieve significant performance improvements over existing schemes. By employing calibration as a general guideline, they introduce several pre-conditioning techniques that effectively exploit pretrained off-the-shelf classifiers for guiding diffusion generation. Extensive experiments conducted on ImageNet validate the proposed method's effectiveness. The results show that state-of-the-art diffusion models (DDPM, EDM, DiT) can be further improved by up to 20% using off-the-shelf classifiers without any significant increase in computational cost. With the availability of publicly accessible pretrained classifiers, this approach holds great potential and can be readily scaled up for text-to-image generation tasks. The paper provides valuable insights into the design space of classifier-guided diffusion generation. It addresses the limitations of existing guidance schemes by combining the advantages of both mainstream and training-free methods. The proposed approach not only enhances performance but also offers flexibility and adaptability to different conditions. The code for implementing this method is available on GitHub for further exploration and experimentation. Overall, this work contributes to advancing conditional diffusion generation by refining guidance schemes and improving sample quality while maintaining controllability. It opens up possibilities for future research in utilizing pretrained classifiers for various generative tasks beyond image generation.