Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models (2312.04410v1)

Abstract: Recently, diffusion models have made remarkable progress in text-to-image (T2I) generation, synthesizing images with high fidelity and diverse contents. Despite this advancement, latent space smoothness within diffusion models remains largely unexplored. Smooth latent spaces ensure that a perturbation on an input latent corresponds to a steady change in the output image. This property proves beneficial in downstream tasks, including image interpolation, inversion, and editing. In this work, we expose the non-smoothness of diffusion latent spaces by observing noticeable visual fluctuations resulting from minor latent variations. To tackle this issue, we propose Smooth Diffusion, a new category of diffusion models that can be simultaneously high-performing and smooth. Specifically, we introduce Step-wise Variation Regularization to enforce the proportion between the variations of an arbitrary input latent and that of the output image is a constant at any diffusion training step. In addition, we devise an interpolation standard deviation (ISTD) metric to effectively assess the latent space smoothness of a diffusion model. Extensive quantitative and qualitative experiments demonstrate that Smooth Diffusion stands out as a more desirable solution not only in T2I generation but also across various downstream tasks. Smooth Diffusion is implemented as a plug-and-play Smooth-LoRA to work with various community models. Code is available at https://github.com/SHI-Labs/Smooth-Diffusion.

• The paper introduces Smooth Diffusion with Step-wise Variation Regularization to enhance latent space smoothness in diffusion models.
• It proposes a novel ISTD metric to quantitatively assess smoothness, ensuring predictable changes in output with controlled latent variations.
• Experimental results show improved image inversion, reconstruction, and editing, offering robust advancements for generative tasks.

Overview of "Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models"

The paper "Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models" presents an innovative approach aimed at enhancing the latent space smoothness of diffusion models, particularly focusing on generative tasks like text-to-image (T2I) synthesis. The authors introduce the concept of Smooth Diffusion, which utilizes Step-wise Variation Regularization to regulate the variation between inputs and outputs, aiming to ensure more controlled and consistent transformations.

Motivation and Problems Addressed

Diffusion models have gained significant traction for their ability to produce high-fidelity images from text prompts. However, the latent space in these models often lacks smoothness, resulting in unpredictable and non-continuous outputs during image interpolation, inversion, and editing tasks. This lack of smoothness presents challenges for consistently generating visually coherent transitions and accurately reconstructing or editing images.

Methodology

The authors propose Smooth Diffusion, focusing primarily on improving the latent space smoothness without compromising model performance. To this end, they introduce Step-wise Variation Regularization, a technique designed to ensure that changes in latent space (input) predictably yield proportional changes in output images.

Key Components:

• Step-wise Variation Regularization: This regularization is structured to work during training by imposing a constraint that maintains a constant ratio between input variations and output variations, effectively smoothing the latent space.
• Interpolation Standard Deviation Metric (ISTD): A novel metric to quantitatively assess latent space smoothness by measuring the consistency of transformations across interpolated latent inputs.

Experimental Evaluation

The paper provides extensive experimental analysis demonstrating the efficacy of Smooth Diffusion in several areas:

1. Latent Space Interpolation: Outstanding reductions in ISTD values are reported, indicating substantial improvements in smoothness over baselines like Stable Diffusion.
2. Image Inversion and Reconstruction: The model exhibits enhanced reconstruction capabilities, which are particularly evident when using inversion techniques like DDIM and Null-text inversion. This highlights the potential for smoother latent spaces to improve downstream image reconstruction tasks.
3. Image Editing: Both text-based and drag-based image editing are explored, with Smooth Diffusion showing superior performance in maintaining unedited content while accurately applying desired edits. This includes competitive results against various state-of-the-art methodologies.

Implications and Future Directions

Enhancing the smoothness of the latent spaces in diffusion models has broad implications for AI generative tasks. It could lead to more reliable and efficient image generation tools, enabling advances in applications like video generation, where continuity and smoothness are crucial.

The introduction of Smooth Diffusion represents a meaningful step in generative AI research, providing a foundation for further exploration and development. Future work might focus on integrating these techniques with other generative frameworks or applying them to multi-modal tasks, expanding the versatility and robustness of generative models.

In conclusion, this paper contributes to the refinement of diffusion models by tackling a previously under-explored domain, thereby enhancing the quality and applicability of generative AI technologies in more demanding applications.