AID: Attention Interpolation of Text-to-Image Diffusion (2403.17924v3)

Abstract: Conditional diffusion models can create unseen images in various settings, aiding image interpolation. Interpolation in latent spaces is well-studied, but interpolation with specific conditions like text or poses is less understood. Simple approaches, such as linear interpolation in the space of conditions, often result in images that lack consistency, smoothness, and fidelity. To that end, we introduce a novel training-free technique named Attention Interpolation via Diffusion (AID). Our key contributions include 1) proposing an inner/outer interpolated attention layer; 2) fusing the interpolated attention with self-attention to boost fidelity; and 3) applying beta distribution to selection to increase smoothness. We also present a variant, Prompt-guided Attention Interpolation via Diffusion (PAID), that considers interpolation as a condition-dependent generative process. This method enables the creation of new images with greater consistency, smoothness, and efficiency, and offers control over the exact path of interpolation. Our approach demonstrates effectiveness for conceptual and spatial interpolation. Code and demo are available at https://github.com/QY-H00/attention-interpolation-diffusion.

• The paper introduces a novel AID methodology that differentiates between inner and outer attention interpolation to refine text-to-image diffusion.
• It employs a Beta prior for dynamic path selection, enhancing the role of denoising steps in generating coherent visual content.
• Results demonstrate that AID effectively balances concept fusion and spatial layout, paving the way for advanced creative applications.

Analysis of Attention Interpolation in Text-to-Image Diffusion Models

This paper presents a detailed exposition and analysis concerning the intricacies of attention interpolation mechanisms, specifically within text-to-image diffusion models. The research introduces a novel set of methodologies called AID (Attention Interpolation Diffusion), which essentially deciphers and articulates the effects of two distinct strategies of attention interpolation: inner interpolated attention and outer interpolated attention. The problem of effective interpolation between text conditions in diffusion-based text-to-image synthesis frameworks is addressed, where understanding the ability to interpolate and synthesize diverse concepts is central.

Key Concepts and Methods

1. Inner vs. Outer Attention Interpolation: The paper delineates the mathematical structures underlying inner and outer interpolated attention. The inner attention interpolation employs a consistent attention map, creating a fusion between source key-value pairs. In contrast, outer interpolation alternates the attention maps and processes keys and values separately. This dichotomy explains the tendencies in generated content, with AID-I gravitating towards concept fusion and AID-O more towards spatial composition.
2. Beta Prior Selection for Interpolation Sequence: Using a Beta prior, the paper advocates for a dynamic selection of interpolation paths, correlating with denoising steps. Notably, setting hyperparameters $\alpha = T$ and $\beta = T$ yields a smooth interpolation feed, a choice empirically validated through Bayesian Optimization.
3. Denoising and Warm-up Steps: The paper examines the interplay between denoising operations and initial warm-up steps, inferring that early denoising domineers image content determination, whereas subsequent steps refine content details. This insight is leveraged to frame spatial layout in early stages and proceed with self-generation in later stages.

Implications and Numerical Outcomes

The qualitative outcomes showcased throughout the paper underscore the potential of AID methods to synthesize coherent multi-concept images and smoothly interpolate between divergent artistic styles or animal forms. Figures provide evidence of the method’s ability to create perceptually consistent and aesthetically appealing interpolations.

Practical implications of this research are manifold, notably influencing fields involving creative content generation, including graphic design and digital art. The AID framework could become pivotal in achieving a fine balance between fidelity to text prompts and creativity in visual outcomes.

Future Directions

The paper opens several avenues for future exploration. Integrating more complex textual embeddings or context-aware semantic guides might further enhance model expressiveness and control. Moreover, expanding these methodologies to video generation or real-time applications could significantly broaden their utility.

In conclusion, this paper presents substantial contributions to the understanding and implementation of attention interpolation within text-to-image diffusion models, setting a foundation that may redefine how interpolated concept visualization is approached within computational contexts. Such methodologies have the potential to substantially elevate model performance, contributing to more nuanced and effective content synthesis.