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Found 6 papers, 1 papers with code
Direct Unlearning Optimization for Robust and Safe Text-to-Image Models
In this paper, we propose Direct Unlearning Optimization (DUO), a novel framework for removing Not Safe For Work (NSFW) content from T2I models while preserving their performance on unrelated topics.
Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment
Identifying the relevant input features that have a critical influence on the output results is indispensable for the development of explainable artificial intelligence (XAI).
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Upsample Guidance: Scale Up Diffusion Models without Training
We demonstrate that upsample guidance can be applied to various models, such as pixel-space, latent space, and video diffusion models.
Resolution Chromatography of Diffusion Models
In this paper, we introduce "resolution chromatography" that indicates the signal generation rate of each resolution, which is very helpful concept to mathematically explain this coarse-to-fine behavior in generation process, to understand the role of noise schedule, and to design time-dependent modulation.
Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry
Remarkably, our discovered local latent basis enables image editing capabilities by moving $\mathbf{x}_t$, the latent space of DMs, along the basis vector at specific timesteps.
Unsupervised Discovery of Semantic Latent Directions in Diffusion Models
Despite the success of diffusion models (DMs), we still lack a thorough understanding of their latent space.
