Addressing degeneracies in latent interpolation for diffusion models (2505.07481v1)

Abstract: There is an increasing interest in using image-generating diffusion models for deep data augmentation and image morphing. In this context, it is useful to interpolate between latents produced by inverting a set of input images, in order to generate new images representing some mixture of the inputs. We observe that such interpolation can easily lead to degenerate results when the number of inputs is large. We analyze the cause of this effect theoretically and experimentally, and suggest a suitable remedy. The suggested approach is a relatively simple normalization scheme that is easy to use whenever interpolation between latents is needed. We measure image quality using FID and CLIP embedding distance and show experimentally that baseline interpolation methods lead to a drop in quality metrics long before the degeneration issue is clearly visible. In contrast, our method significantly reduces the degeneration effect and leads to improved quality metrics also in non-degenerate situations.

• The paper investigates degeneracies encountered when interpolating latents in diffusion models, particularly with large sets of input images, identifying that naive methods lead to low image quality.
• The authors propose a channel-wise mean adjustment normalization strategy as a remedy, which significantly improves image quality metrics like FID and CLIP distance compared to baseline approaches.
• This research has practical implications for applications requiring interpolation across many images, such as data augmentation and image morphing, by improving output quality and downstream task performance.

Addressing Degeneracies in Latent Interpolation for Diffusion Models

The paper provides a comprehensive analysis of degeneracies encountered when interpolating latents in image-generating diffusion models, particularly when the number of input images is large. Diffusion models have widely been employed for applications such as image generation, deep data augmentation, and image morphing, thanks to their remarkable generative capabilities. However, interpolating between inverted image latents to generate new images can lead to undesirable degenerate outputs, particularly when these latents are aggregated from a larger set of inputs.

Key Findings and Contributions

The researchers have both theoretically and experimentally investigated the causes behind this degeneracy phenomenon in latent space interpolation. They identify that naive linear interpolation methods can lead to outputs with significantly low image quality, even before visual degeneration becomes evident. As a remedy, the authors propose a normalization scheme that addresses these degeneracies effectively. The proposed strategy involves implementing a channel-wise mean adjustment normalization that significantly reduces the occurrence of degenerate results and improves image quality metrics in both degenerate and non-degenerate instances.

An essential component of their evaluation of image quality was using Fréchet Inception Distance (FID) and CLIP embedding distance. Through these metrics, the work illustrates that prior baseline interpolation approaches fail to uphold quality as the number of inputs increases. Significantly, the research shows that a simple norm-based remedy is not sufficient; instead, mean adjustment combined with norm adjustment (either fixed normalization or normalization with interpolated norms) notably improves interpolation outcomes.

Theoretical Implications and Future Directions

This work contributes to the theoretical understanding of latent space dynamics in diffusion models. The detailed analysis of latent interpolation highlights the complex interplay between noise and bias in latent structures, critical for refining diffusion models further. Indeed, the researchers delve into how even minor biases can be amplified during the interpolation processes, leading to more substantial biases in output images as the quantity of input increases. These insights provide avenues for refining both the methodology and application of diffusion models.

Moving forward, this research prompts additional considerations for latent diffusion model applications, particularly those crossing over from image generation to applications requiring high fidelity and consistency across synthesized outputs — such as virtual reality environments and advanced graphics engines. Future work should explore extending this normalization approach to other architectures and tasks, potentially even beyond image synthesis, where latent space manipulation is key. Additionally, refining diffusion model inversion techniques could further complement these findings and lead to improved baseline latents.

Practical Implications

Practically speaking, the proposed solution has significant utility in scenarios requiring interpolation across large multitudes of images, such as in data augmentation and morphing tasks where diverse sets of inputs may provide more robust outputs. Improved image quality in such interpolations can directly influence downstream tasks such as classification and detection where synthetic datasets are utilized, facilitating better model training and generalizable performance.

In conclusion, the paper provides a valuable contribution to the field of image synthesis via diffusion models by addressing a specific but impactful issue of degeneration in latent interpolation. It not only provides a concrete solution but also lays out a framework for further exploration in ensuring high-quality outputs in extensive interpolative tasks.