Methodologies for Diffusion Model Interpretability: A Systematic Review
Abstract
Diffusion generative models have gained rapid traction since 2020 due to their expressiveness and highquality outputs. Explaining and interpreting these models is essential for enabling further improvements and fostering trustworthiness. This systematic review identifies and analyzes interpretability methods applied to diffusion models across domains, highlighting key trends, outlining strategies, and identifying emerging research directions. We screened 1,489 papers published between 2020–2025 across IEEE, Scopus, DBLP, arXiv, and Elicit, and included 81 studies that met predefined criteria. Most methods target latent space analysis (n= 35), followed by data attribution (n= 16) and denoising dynamics (n= 14). Image generation and text-to-image synthesis dominate application areas (n= 73), with limited coverage in robotics, audio, and neuroscience (n= 8). This review offers a structured taxonomy, quantifies interpretability research trends, and identifies domain–specific and architectural gaps. Supplementary material and processing code are available at [GitHub link].
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 73 participants or records.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.