EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders

arXiv:2609.03629 · cs.CV, cs.AI · Submitted 2026-09-03 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-03

Updated: 2026-09-08

Code: https://github.com/HiDream-ai/EraseSAE

Terminology

• Diffusion Model — 36× in this paper · explained in 8 episodes
• Diffusion Models — 33× in this paper · explained in 6 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Pruning — 4× in this paper · explained in 4 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Granularity Mismatch — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Adversarial Attacks — 2× in this paper · explained in 1 episode
• Adversarial Training — 2× in this paper · explained in 1 episode
• Entanglement — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Mask Generation — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Machine Unlearning — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sparse Autoencoders (SAEs) — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• BatchTopK Sparse Autoencoders
• HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer
• HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer
• Adam: A Method for Stochastic Optimization
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Pruning for Robust Concept Erasing in Diffusion Models
• T2VUnlearning: A Concept Erasing Method for Text-to-Video Diffusion Models

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