Memorization In Stable Diffusion Is Unexpectedly Driven by CLIP Embeddings
Stable Diffusion's memorization is driven by CLIP embeddings, which is crucial for interpretability and safety, and this paper reveals the model's disproportionate reliance on specific embeddings
- Investigate the role of CLIP embeddings in Stable Diffusion using arXiv paper 2605.02908v1
- Analyze the categorization of input tokens and their corresponding embeddings
- Apply the findings to improve the interpretability and safety of text-to-image diffusion models
- Configure the model to reduce its reliance on specific embeddings
- Test the updated model to evaluate its performance and safety
AI researchers and engineers working on text-to-image diffusion models, such as Stable Diffusion, can benefit from understanding how CLIP embeddings contribute to memorization, to improve model interpretability and safety
💡 CLIP embeddings play a significant role in memorization in Stable Diffusion, and understanding this is essential for improving model interpretability and safety
💡 CLIP embeddings drive memorization in Stable Diffusion, revealing a crucial aspect of model interpretability and safety #AI #StableDiffusion
Key Takeaways
Stable Diffusion's memorization is driven by CLIP embeddings, which is crucial for interpretability and safety, and this paper reveals the model's disproportionate reliance on specific embeddings
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Abstract:
arXiv:2605.02908v1 Announce Type: cross 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}}, \mathb
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