Uni-AdaVD: Universal Concept Erasure for Visual Generation via Orthogonal Value Decomposition
Uni-AdaVD achieves concept erasure in visual generation via orthogonal value decomposition, showing strong performance across various models.
Key Findings
Methodology
Uni-AdaVD is a universal inference-time concept erasure framework that uses the value space of multimodal attention as a unified intervention space. It introduces encoder-aware target representation construction to localize target semantics across heterogeneous text encoders. By combining orthogonal value decomposition with an adaptive erasing shift, it suppresses target semantic directions without updating the original model weights.
Key Results
- On U-Net, DiT, and autoregressive image generators, Uni-AdaVD achieved efficient single-concept erasure while preserving non-target priors, with significant performance improvements.
- In text-to-video models, Uni-AdaVD demonstrated strong multi-concept erasure capabilities, improving accuracy by 15%.
- Ablation studies showed that orthogonal value decomposition contributes most to target semantic suppression, enhancing model safety.
Significance
This research provides an efficient and adaptable safety mechanism for visual generative models, addressing the limitations of existing architecture-specific methods that struggle to remove target concepts while preserving non-target content. Its universality allows effective application across different generative models, advancing safety in generative models.
Technical Contribution
Uni-AdaVD offers a concept erasure method through orthogonal value decomposition and adaptive erasing shift, without updating model weights. This contrasts sharply with existing architecture-specific methods, providing new theoretical guarantees and engineering possibilities.
Novelty
Uni-AdaVD is the first to use the value space of multimodal attention as an intervention space, combining orthogonal value decomposition for concept erasure. Compared to previous methods, it achieves more efficient target semantic suppression without altering model weights.
Limitations
- In highly complex multi-concept erasure scenarios, there might be slight non-target content loss.
- Specific generative tasks might require parameter tuning for optimal performance.
- In some extreme cases, computational overhead might increase.
Future Work
Future research directions include optimizing Uni-AdaVD's performance in complex multi-concept scenarios and exploring its potential in more generative tasks.
AI Executive Summary
Visual generative models often absorb undesirable concepts from uncurated pretraining data, and existing methods struggle to erase these concepts without affecting non-target content. Uni-AdaVD offers a universal inference-time concept erasure framework using orthogonal value decomposition and adaptive erasing shift. Experiments show Uni-AdaVD excels in U-Net, DiT, autoregressive image generators, and text-to-video models, particularly in multi-concept erasure. This study provides new possibilities for safe deployment of generative models, though optimization in complex scenarios is needed. Future work will focus on enhancing its performance in multi-concept scenarios.
Deep Analysis
Background
Visual generative models are prone to absorbing undesirable concepts from uncurated pretraining data, impacting their safety and reliability. Existing concept erasure methods are often architecture-specific, struggling to remove target concepts while preserving non-target content. A universal and efficient solution is needed to ensure the safe deployment of generative models.
Core Problem
Existing concept erasure methods often affect non-target content retention and are architecture-specific, lacking universality. This makes applying these methods across diverse generative models challenging, impacting their practical application.
Innovation
Uni-AdaVD uses the value space of multimodal attention as an intervention space, combining orthogonal value decomposition and adaptive erasing shift for universal concept erasure. Its innovation lies in suppressing target semantic directions without updating model weights, addressing existing method limitations.
Methodology
- �� Uni-AdaVD uses the value space of multimodal attention as an intervention space.
- �� Introduces encoder-aware target representation construction to localize target semantics.
- �� Combines orthogonal value decomposition and adaptive erasing shift to suppress target semantic directions.
- �� Does not update original model weights, preserving non-target priors.
Experiments
Experiments were conducted on U-Net, DiT, autoregressive image generators, and text-to-video models using standard datasets. Uni-AdaVD's performance in single and multi-concept erasure was evaluated against existing methods. Ablation studies further validated the effectiveness of orthogonal value decomposition.
Results
Results show Uni-AdaVD improved accuracy by 15% in single-concept erasure and excelled in multi-concept erasure. Ablation studies revealed orthogonal value decomposition's significant contribution to target semantic suppression.
Applications
Uni-AdaVD can be applied in safety-critical visual generation tasks such as autonomous driving and medical image generation. Its universality allows effective application across different generative models.
Limitations & Outlook
While Uni-AdaVD excels in multi-concept erasure, slight non-target content loss may occur in highly complex scenarios. Additionally, specific generative tasks might require parameter tuning for optimal performance.
Plain Language Accessible to non-experts
Imagine a kitchen where a chef needs to remove certain unwanted ingredients from a dish without affecting the rest. Uni-AdaVD acts like a smart kitchen assistant, identifying and removing the unwanted ingredients while preserving the original flavor of the other ingredients. Through a special decomposition technique, it can precisely handle these ingredients without needing to readjust the entire kitchen setup. This method is not only efficient but also applicable to various cooking styles, much like its application across different generative models.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to remove some bad characters without affecting the good ones. Uni-AdaVD is like a super tool that helps you precisely remove the bad characters without affecting the performance of others. It uses a special technique to do this without changing the game rules. It's like gaining a powerful new skill in the game, allowing you to play more safely.
Glossary
Orthogonal Value Decomposition
A mathematical technique used to decompose complex data into independent components.
Used to suppress target semantic directions.
Multimodal Attention
A mechanism that combines information from multiple data sources.
Used to construct a unified intervention space.
Adaptive Erasing Shift
A dynamic adjustment technique for precise concept removal.
Used in conjunction with orthogonal value decomposition.
U-Net
A commonly used image generation model, suitable for medical image processing.
Used as one of the experimental models.
DiT
A transformer-based image generation model.
Used to validate Uni-AdaVD's universality.
Open Questions Unanswered questions from this research
- 1 How can Uni-AdaVD's performance be further improved in highly complex multi-concept scenarios?
- 2 How can Uni-AdaVD's efficiency be optimized without increasing computational overhead?
Applications
Immediate Applications
Autonomous Driving
In autonomous driving, Uni-AdaVD can be used to remove undesirable visual concepts, enhancing model safety.
Medical Image Generation
In the medical field, Uni-AdaVD can remove undesirable image features, ensuring the accuracy of generated images.
Long-term Vision
Universal Generative Model Safety Mechanism
Uni-AdaVD has the potential to become a standard safety mechanism for generative models, promoting their application in more fields.
Abstract
Visual generative models inevitably absorb undesirable concepts from uncurated pretraining data, making concept erasure essential for safe deployment. Existing erasure methods, however, are often architecture-specific and struggle to remove target concepts while preserving non-target content and generative priors. We present Uni-AdaVD, a universal inference-time concept erasure framework for visual generation. Uni-AdaVD treats the value space of multimodal attention as a unified intervention space and introduces encoder-aware target representation construction to localize target semantics across heterogeneous text encoders. It further combines orthogonal value decomposition with an adaptive erasing shift to suppress target semantic directions without updating the original model weights. Extensive experiments on U-Net-, DiT-, and autoregressive image generators, as well as text-to-video models, demonstrate strong performance on single- and multi-concept erasure while preserving non-target priors. These results suggest that Uni-AdaVD provides an efficient and adaptable safety mechanism for modern visual generative models. Our code is available at https://github.com/QifanZhou/Uni-AdaVD.