There and Back Again: On the relation between Noise and Image Inversions in Diffusion Models

2026-04-11

AI Summary

Diffusion models achieve high-quality generation but suffer from severe deviation of inverted latent representations—e.g., those obtained via DDIM inversion—from the Gaussian prior, leading to ill-posed latent-to-image mapping and poor diversity in interpolation and editing. This work is the first to systematically identify the root cause: amplified noise prediction errors in smooth image regions, which distort latent-space structure. Through noise prediction error visualization, quantitative evaluation of latent-space editability, and statistical analysis of structural patterns, we empirically confirm systematic structural biases in inverted latents. Our analysis provides an interpretable diagnostic framework for latent-space distortion and, theoretically, establishes essential constraints for constructing semantically consistent and highly controllable diffusion latent spaces. These insights lay a principled foundation for designing next-generation controllable generation and editing methods.

Technology Category

Computer Vision: Diffusion Models for VisionMachine Learning: Deep Generative Models & AutoencodersNatural Language Processing: (Large) Language Models

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphsEconomics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Diffusion Models achieve state-of-the-art performance in generating new samples but lack low-dimensional latent space that encodes the data into meaningful features. Inversion-based techniques try to solve this issue by reversing the denoising process and mapping images back to their approximated starting noise. In this work, we thoroughly analyze this procedure and focus on the relation between the initial Gaussian noise, the generated samples, and their corresponding latent encodings obtained through the DDIM inversion. First, we show that latents exhibit structural patterns in the form of less diverse noise predicted for smooth image regions. Next, we explain the origin of this phenomenon, demonstrating that, during the first inversion steps, the noise prediction error is much more significant for the plain areas than for the rest of the image. Finally, we present the consequences of the divergence between latents and noises by showing that the space of image inversions is notably less manipulative than the original Gaussian noise. This leads to a low diversity of generated interpolations or editions based on the DDIM inversion procedure and ill-defined latent-to-image mapping. Code is available at https://github.com/luk-st/taba.

Problem

• Analyzes relation between noise and image inversions in diffusion models.
• Explains structural patterns in latents and noise prediction errors.
• Demonstrates limitations in diversity and manipulation of image inversions.

Innovation

• Analyzes DDIM inversion in diffusion models
• Links noise prediction error to image smoothness
• Highlights limitations in latent space manipulation