Enhancing Diversity in Conditional Diffusion Models through Condition-Annealed Sampling

Core Concepts

Conditional diffusion models can suffer from limited output diversity, especially when using high classifier-free guidance scales or trained on small datasets. The Condition-Annealed Diffusion Sampler (CADS) addresses this issue by annealing the conditioning signal during inference, leading to more diverse generations while maintaining high sample quality.

Abstract

The paper investigates the diversity and distribution coverage of conditional diffusion models. It is shown that these models can suffer from low output diversity when using high classifier-free guidance (CFG) scales or when trained on small datasets. Reducing the guidance scale or training on larger datasets only partially mitigates the issue. The authors introduce the Condition-Annealed Diffusion Sampler (CADS), a simple yet effective technique to amplify the diversity of diffusion models. During inference, CADS perturbs the conditioning signal using additive Gaussian noise combined with an annealing strategy. This approach breaks the statistical dependence on the conditioning signal during early inference, allowing more influence from the data distribution as a whole, and gradually restores that dependence during late inference. CADS can be integrated into all diffusion samplers without retraining the underlying model, and it provides minimal computational overhead. Extensive experiments demonstrate that CADS resolves the trade-off between diversity and quality in conditional diffusion models. CADS outperforms the standard DDPM sampling in several conditional generation tasks and sets a new state-of-the-art FID on class-conditional ImageNet generation at both 256x256 and 512x512 resolutions while utilizing higher guidance values.

Stats

The paper reports the following key metrics: FID (Fréchet Inception Distance): A measure of sample quality and diversity. Precision and Recall: Metrics for evaluating the diversity of generated samples. Mean Similarity Score (MSS) and Vendi Score: Metrics for measuring the similarity among generated samples.

Quotes

"Sampling with low classifier-free guidance degrades image quality (Ho & Salimans, 2022; Dhariwal & Nichol, 2021), and collecting a larger dataset may not be feasible in all domains." "Our primary contribution lies in establishing a connection between the conditioning signal and the emergence of low-diversity outputs." "CADS can be readily integrated into any sampler, and it significantly diversifies the generations, as demonstrated in Section 4."

Key Insights Distilled From

CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling

by Seyedmorteza... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2310.17347.pdf

Deeper Inquiries

How can the condition annealing strategy in CADS be extended to handle more complex conditioning signals, such as segmentation maps or dense spatial semantics?

In order to extend the condition annealing strategy in CADS to handle more complex conditioning signals like segmentation maps or dense spatial semantics, several modifications and enhancements can be considered: Adaptive Noise Addition: Instead of applying a fixed annealing schedule and noise scale, the strategy can be adapted to dynamically adjust the amount of noise added based on the complexity of the conditioning signal. This adaptive approach can ensure that the noise level is sufficient to promote diversity while not overwhelming the conditioning information. Multi-Stage Annealing: For complex conditioning signals, a multi-stage annealing process can be implemented. This involves gradually increasing the noise level in different stages of the inference process, allowing the model to explore a wider range of variations while still maintaining alignment with the conditioning signal. Conditional Rescaling: Rescaling the conditioning vector back to its original mean and standard deviation may not be sufficient for highly complex conditioning signals. Customized rescaling techniques that take into account the specific characteristics of the conditioning data can help maintain the alignment and relevance of the conditioning information throughout the sampling process. Hierarchical Conditioning: For segmentation maps or dense spatial semantics, a hierarchical conditioning approach can be beneficial. By introducing noise at different levels of the hierarchy, the model can capture both global and local features, leading to more diverse and contextually relevant generations. Attention Mechanisms: Integrating attention mechanisms into the annealing process can help the model focus on different parts of the conditioning signal at different stages of sampling. This can enhance the model's ability to generate diverse outputs while respecting the intricate details of the conditioning information.

How might this technique be combined with other recent advancements in diffusion models to further enhance the overall performance?

Combining CADS with other recent advancements in diffusion models can lead to further improvements in performance: Dynamic Guidance Mechanisms: Integrating CADS with dynamic guidance mechanisms can provide a comprehensive approach to balancing quality and diversity in generated samples. By dynamically adjusting the guidance weight based on the sampling stage and the complexity of the conditioning signal, the model can achieve optimal results. Efficient Sampling Techniques: Pairing CADS with efficient sampling techniques, such as truncated sampling or progressive sampling, can enhance the overall sampling efficiency without compromising diversity. These techniques can help the model explore the data distribution more effectively and generate high-quality, diverse samples. Latent Space Exploration: Leveraging CADS in combination with techniques for latent space exploration can enable the model to discover novel variations and generate more diverse outputs. By encouraging exploration in the latent space while maintaining alignment with the conditioning signal, the model can produce unique and realistic samples. Transfer Learning Strategies: Applying transfer learning strategies in conjunction with CADS can further enhance the model's performance. By fine-tuning pretrained models with CADS on specific tasks or datasets, the model can adapt to new conditions and generate diverse, high-quality outputs tailored to the target domain. Ensemble Methods: Utilizing ensemble methods by combining multiple diffusion models trained with CADS can improve the robustness and diversity of generated samples. By aggregating outputs from different models, the ensemble can capture a broader range of variations and produce more diverse and realistic generations.