Diffusion
Mental
Averages

Abstract

Can a diffusion model produce its own “mental average” of a concept—one that is as sharp and realistic as a typical sample? We introduce Diffusion Mental Averages (DMA), a model-centric answer to this question. While prior methods aim to average image collections, they produce blurry results when applied to diffusion samples from the same prompt. These data-centric techniques operate outside the model, ignoring the generative process. In contrast, DMA averages within the diffusion model’s semantic space, as discovered by recent studies. Since this space evolves across timesteps and lacks a direct decoder, we cast averaging as trajectory alignment: optimize multiple noise latents so their denoising trajectories progressively converge toward shared coarse-to-fine semantics, yielding a single sharp prototype. We extend our approach to multimodal concepts (e.g., dogs with many breeds) by clustering samples in semantically-rich spaces such as CLIP and applying Textual Inversion or LoRA to bridge CLIP clusters into diffusion space. This is, to our knowledge, the first approach that delivers consistent, realistic averages, even for abstract concepts, serving as a concrete visual summary and a lens into model biases and concept representation.

Approach

Given a concept prompt, we aim to synthesize an average image that captures the concept’s shared semantics under a probe diffusion model while preserving visual realism.

In diffusion models, semantic information is distributed along the denoising trajectory, evolving from coarse structure to fine details. This motivates performing averaging not in image space or within a single latent layer, but along the model’s denoising process itself. We therefore recast the problem as aligning multiple denoising trajectories so that they converge toward a shared semantic consensus.

Specifically, at each timestep, their h-space activations are averaged to form a semantic target, and each latent is optimized to match it before denoising to the next step. Repeating this process across timesteps aligns coarse-to-fine semantics, yielding a single "mental average" of the concept.