EraseFlow: GFlowNet-Driven Concept Unlearning in Stable Diffusion
Published:Dec 31, 2025 09:06
•1 min read
•Zenn SD
Analysis
This article reviews the EraseFlow paper, focusing on concept unlearning in Stable Diffusion using GFlowNets. The approach aims to provide a more controlled and efficient method for removing specific concepts from generative models, addressing a growing need for responsible AI development. The mention of NSFW content highlights the ethical considerations involved in concept unlearning.
Key Takeaways
- •The article discusses the EraseFlow paper presented at NeurIPS 2025.
- •EraseFlow uses GFlowNets for concept unlearning in Stable Diffusion.
- •The review acknowledges the increasing complexity and importance of concept unlearning research.
Reference
“画像生成モデルもだいぶ進化を成し遂げており, それに伴って概念消去（unlearningに仮に分類しておきます）の研究も段々広く行われるようになってきました.”