Growth Inhibitors for Suppressing Inappropriate Image Concepts in Diffusion Models
- URL: http://arxiv.org/abs/2408.01014v2
- Date: Mon, 17 Feb 2025 08:34:38 GMT
- Title: Growth Inhibitors for Suppressing Inappropriate Image Concepts in Diffusion Models
- Authors: Die Chen, Zhiwen Li, Mingyuan Fan, Cen Chen, Wenmeng Zhou, Yanhao Wang, Yaliang Li,
- Abstract summary: Text-to-image diffusion models inadvertently learn inappropriate concepts from vast and unfiltered training data.
Our method effectively captures the manifestation of subtle words at the image level, enabling direct and efficient erasure of target concepts.
- Score: 35.2881940850787
- License:
- Abstract: Despite their remarkable image generation capabilities, text-to-image diffusion models inadvertently learn inappropriate concepts from vast and unfiltered training data, which leads to various ethical and business risks. Specifically, model-generated images may exhibit not safe for work (NSFW) content and style copyright infringements. The prompts that result in these problems often do not include explicit unsafe words; instead, they contain obscure and associative terms, which are referred to as implicit unsafe prompts. Existing approaches directly fine-tune models under textual guidance to alter the cognition of the diffusion model, thereby erasing inappropriate concepts. This not only requires concept-specific fine-tuning but may also incur catastrophic forgetting. To address these issues, we explore the representation of inappropriate concepts in the image space and guide them towards more suitable ones by injecting growth inhibitors, which are tailored based on the identified features related to inappropriate concepts during the diffusion process. Additionally, due to the varying degrees and scopes of inappropriate concepts, we train an adapter to infer the corresponding suppression scale during the injection process. Our method effectively captures the manifestation of subtle words at the image level, enabling direct and efficient erasure of target concepts without the need for fine-tuning. Through extensive experimentation, we demonstrate that our approach achieves superior erasure results with little effect on other concepts while preserving image quality and semantics.
Related papers
- Continuous Concepts Removal in Text-to-image Diffusion Models [27.262721132177845]
Concerns have been raised about the potential for text-to-image models to create content that infringes on copyrights or depicts disturbing subject matter.
We propose a novel approach called CCRT that includes a designed knowledge distillation paradigm.
It constrains the text-image alignment behavior during the continuous concept removal process by using a set of text prompts.
arXiv Detail & Related papers (2024-11-30T20:40:10Z) - Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models [76.39651111467832]
We introduce Reliable and Efficient Concept Erasure (RECE), a novel approach that modifies the model in 3 seconds without necessitating additional fine-tuning.
To mitigate inappropriate content potentially represented by derived embeddings, RECE aligns them with harmless concepts in cross-attention layers.
The derivation and erasure of new representation embeddings are conducted iteratively to achieve a thorough erasure of inappropriate concepts.
arXiv Detail & Related papers (2024-07-17T08:04:28Z) - Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion [51.931083971448885]
We propose a framework named Human Feedback Inversion (HFI), where human feedback on model-generated images is condensed into textual tokens guiding the mitigation or removal of problematic images.
Our experimental results demonstrate our framework significantly reduces objectionable content generation while preserving image quality, contributing to the ethical deployment of AI in the public sphere.
arXiv Detail & Related papers (2024-07-17T05:21:41Z) - Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models [58.74606272936636]
Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts.
The models could be exploited for malicious purposes, such as generating images with violence or nudity, or creating unauthorized portraits of public figures in inappropriate contexts.
concept removal methods have been proposed to modify diffusion models to prevent the generation of malicious and unwanted concepts.
arXiv Detail & Related papers (2024-06-21T03:58:44Z) - Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models [58.065255696601604]
We use compositional property of diffusion models, which allows to leverage multiple prompts in a single image generation.
We argue that it is essential to consider all possible approaches to image generation with diffusion models that can be employed by an adversary.
arXiv Detail & Related papers (2024-04-21T16:35:16Z) - Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation [36.93643249463899]
A risk with diffusion-based models is the potential generation of inappropriate content, such as biased or harmful images.
Previous work interprets vectors in an interpretable latent space of diffusion models as semantic concepts.
We propose a novel self-supervised approach to find interpretable latent directions for a given concept.
arXiv Detail & Related papers (2023-11-28T20:40:45Z) - Implicit Concept Removal of Diffusion Models [92.55152501707995]
Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images.
We present the Geom-Erasing, a novel concept removal method based on the geometric-driven control.
arXiv Detail & Related papers (2023-10-09T17:13:10Z) - Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion
Models [63.20512617502273]
We propose a method called SDD to prevent problematic content generation in text-to-image diffusion models.
Our method eliminates a much greater proportion of harmful content from the generated images without degrading the overall image quality.
arXiv Detail & Related papers (2023-07-12T07:48:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.