Diffusion Model Concept Removal by Neuron Pruning
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Solution Overview
Problem
Diffusion models generate images with undesired concepts such as nudity, violence, copyrighted content, and biases, posing risks to artists' livelihoods and societal harm, and existing methods to mitigate these risks are computationally expensive or vulnerable to adversarial attacks.
Innovation Solution
A method to modify a pre-trained diffusion model by identifying and modifying specific weight neurons responsible for undesired concepts using text prompts, without retraining, by calculating importance scores and applying a binary mask to prune these neurons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If diffusion models are trained on large-scale web-scraped datasets, then image generation capability is improved, but undesired content such as nudity, violence, and biases are introduced
Solution Approach 1:
The patent extracts and removes specific undesired concepts from the diffusion model by identifying and modifying the responsible neurons. Instead of retraining the entire model on filtered datasets, the method locates specific neurons that generate undesired content and selectively modifies them, thereby removing harmful factors while preserving the overall image generation capability.
Solution Approach 2:
The patent applies local quality by treating different parts of the model differently. Rather than uniformly filtering all training data or applying global constraints, the method identifies specific neurons responsible for undesired concepts and modifies only those local regions. This allows the model to maintain high-quality image generation in most areas while removing harmful content in specific localized regions.
2Reliability
If concept editing methods are used to remove undesired prompts, then robustness to some attacks is improved, but vulnerability to adversarial attacks based on textual inversion increases
Solution Approach 1:
The patent segments the concept removal process into distinct phases: identification of undesired concepts, localization of responsible neurons, and selective modification. By dividing the process and applying different techniques at each stage (including both neuron identification and weight modification), the method achieves more comprehensive protection against various adversarial attacks compared to single-method approaches.
3Object-affected harmful factors
If fine-tuning is applied to remove undesired content, then content safety is improved, but computational cost increases significantly
Solution Approach 1:
The patent extracts only the essential components needed for concept removal - specifically the neurons responsible for undesired concepts - rather than performing comprehensive fine-tuning of the entire model. This extraction approach identifies and modifies only the critical elements that generate harmful content, dramatically reducing computational cost while maintaining content safety.
Solution Approach 2:
The patent applies partial action by performing concept removal on a selective basis rather than进行全面 fine-tuning. Instead of adjusting all model parameters, the method focuses only on the specific neurons that contribute to undesired content generation, applying just enough modification to achieve safety without the excessive computational burden of full fine-tuning.
4Ease of manufacture
If token blacklisting or resteering is used for concept erasure, then implementation simplicity is improved, but effectiveness against adversarial attacks decreases
Solution Approach 1:
The patent segments the defense mechanism into multiple layers: neuron identification based on activation patterns, weight modification of identified neurons, and optional aggregation across multiple concepts. This segmented approach maintains relative implementation simplicity while significantly improving effectiveness against adversarial attacks compared to single-layer token blacklisting methods.
Data Source
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AI summary
Broadly speaking, embodiments of the present techniques provide a method for modifying a pre-trained diffusion model used for image generation to remove undesired concepts from being included in images generated by the model. Advantageously, the present techniques enable a pre-trained diffusion model to be modified for a user or set of users to remove certain undesired concepts from being included in images generated by the model. This may reduce the risk that the model generates images including undesirable concepts, such as nudity, violence, copyrighted images or styles, or bias (such as gender or racial bias).