[2605.02908] Memorization In Stable Diffusion Is Unexpectedly Driven by CLIP Embeddings
Skip to main content
Learn about arXiv becoming an independent nonprofit.
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
> cs > arXiv:2605.02908
Help | Advanced Search
All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text
Search
GO
quick links
Login
Help Pages
About
-->
Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.02908 (cs)
[Submitted on 6 Apr 2026]
Title:Memorization In Stable Diffusion Is Unexpectedly Driven by CLIP Embeddings
Authors:Bumjun Kim, Albert No
View a PDF of the paper titled Memorization In Stable Diffusion Is Unexpectedly Driven by CLIP Embeddings, by Bumjun Kim and 1 other authors
View PDF HTML (experimental)
Abstract:Understanding how textual embeddings contribute to memorization in text-to-image diffusion models is crucial for both interpretability and safety. This paper investigates an unexpected behavior of CLIP embeddings in Stable Diffusion, revealing that the model disproportionately relies on specific embeddings. We categorize input tokens as , , and with corresponding embeddings $\mathbf{v}^{\mathbf{sot}}, \mathbf{v}^{\mathbf{pr}}, \mathbf{v}^{\mathbf{eot}}, \mathbf{v}^{\mathbf{pad}}$. We discover that $\mathbf{v}^{\mathbf{pr}}$ contribute minimally to generation in memorized cases. In contrast, $\mathbf{v}^{\mathbf{pad}}$ strongly affect memorization due to their structural duplication of $\mathbf{v}^{\mathbf{eot}}$, the only embedding explicitly optimized during CLIP training. This duplication unintentionally amplifies the influence of $\mathbf{v}^{\mathbf{eot}}$, causing the model to over-rely on it, thereby driving memorization. Based on these observations, we propose two simple yet effective inference-time mitigation strategies: (1) Replacing the tokenizer's default from to the ! token before embedding, and masking the $\mathbf{v}^{\mathbf{eot}}$; (2) Partial masking of $\mathbf{v}^{\mathbf{pad}}$. Both suppress memorization without degrading quality, and are readily deployable without prior detection.
Comments: Accepted at CVPR 2026 Findings. Code is available at this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.02908 [cs.CV]
(or arXiv:2605.02908v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.02908
Focus to learn more
arXiv-issued DOI via DataCite
Submission history
From: Bumjun Kim [view email] [v1] Mon, 6 Apr 2026 13:04:37 UTC (15,546 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Memorization In Stable Diffusion Is Unexpectedly Driven by CLIP Embeddings, by Bumjun Kim and 1 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-05
Change to browse by:
cs cs.AI cs.LG
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
export BibTeX citation Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
About
Help
contact arXivClick here to contact arXiv Contact
subscribe to arXiv mailingsClick here to subscribe Subscribe
Copyright
Privacy Policy
Web Accessibility Assistance
arXiv Operational Status