T2VUnlearning: A Concept Erasing Method for Text-to-Video Diffusion Models

• URL: http://arxiv.org/abs/2505.17550v2
• Date: Fri, 30 May 2025 06:11:29 GMT
• Title: T2VUnlearning: A Concept Erasing Method for Text-to-Video Diffusion Models
• Authors: Xiaoyu Ye, Songjie Cheng, Yongtao Wang, Yajiao Xiong, Yishen Li,
• Abstract summary: We propose a robust and precise unlearning method for text-to-video (T2V) models.<n>To achieve precise unlearning, we incorporate a localization and a preservation regularization to preserve the model's ability to generate non-target concepts.<n>Our method effectively erases a specific concept while preserving the model's generation capability for all other concepts, outperforming existing methods.
• Score: 5.876360170606312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in text-to-video (T2V) diffusion models have significantly enhanced the quality of generated videos. However, their ability to produce explicit or harmful content raises concerns about misuse and potential rights violations. Inspired by the success of unlearning techniques in erasing undesirable concepts from text-to-image (T2I) models, we extend unlearning to T2V models and propose a robust and precise unlearning method. Specifically, we adopt negatively-guided velocity prediction fine-tuning and enhance it with prompt augmentation to ensure robustness against LLM-refined prompts. To achieve precise unlearning, we incorporate a localization and a preservation regularization to preserve the model's ability to generate non-target concepts. Extensive experiments demonstrate that our method effectively erases a specific concept while preserving the model's generation capability for all other concepts, outperforming existing methods. We provide the unlearned models in \href{https://github.com/VDIGPKU/T2VUnlearning.git}{https://github.com/VDIGPKU/T2VUnlearning.git}.

Related papers

• EraseAnything++: Enabling Concept Erasure in Rectified Flow Transformers Leveraging Multi-Object Optimization [18.80236205171204]
  EraseAnything++ is a unified framework for concept erasure in both image and video diffusion models.<n>Our method anchors erasure on key visual representations and propagates it consistently across spatial and temporal dimensions.<n>In the video setting, we further enhance consistency through an anchor-and-propagate mechanism that initializes erasure on reference frames and enforces it throughout subsequent transformer layers.
  arXiv  Detail & Related papers  (2026-03-01T08:13:05Z)
• Learning from Next-Frame Prediction: Autoregressive Video Modeling Encodes Effective Representations [53.91818843831925]
  We propose NExT-Vid, a novel autoregressive visual generative pretraining framework.<n>We introduce a context-isolated autoregressive predictor to decouple semantic representation from target decoding.<n>Through context-isolated flow-matching pretraining, our approach achieves strong representations.
  arXiv  Detail & Related papers  (2025-12-24T07:07:08Z)
• CGCE: Classifier-Guided Concept Erasure in Generative Models [53.7410000675294]
  Concept erasure has been developed to remove undesirable concepts from pre-trained models.<n>Existing methods remain vulnerable to adversarial attacks that can regenerate the erased content.<n>We introduce an efficient plug-and-play framework that provides robust concept erasure for diverse generative models.
  arXiv  Detail & Related papers  (2025-11-08T05:38:18Z)
• Sculpting Memory: Multi-Concept Forgetting in Diffusion Models via Dynamic Mask and Concept-Aware Optimization [20.783312940122297]
  Text-to-image (T2I) diffusion models have achieved remarkable success in generating high-quality images from textual prompts.<n>However, their ability to store vast amounts of knowledge raises concerns in scenarios where selective forgetting is necessary.<n>We propose textbfDynamic Mask coupled with Concept-Aware Loss, a novel unlearning framework designed for multi-concept forgetting.
  arXiv  Detail & Related papers  (2025-04-12T01:38:58Z)
• TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models [53.937498564603054]
  Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images.<n>To mitigate risk, concept erasure methods are studied to facilitate the model to unlearn specific concepts.<n>We propose TRCE, using a two-stage concept erasure strategy to achieve an effective trade-off between reliable erasure and knowledge preservation.
  arXiv  Detail & Related papers  (2025-03-10T14:37:53Z)
• SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders [4.013156524547073]
  Diffusion models can inadvertently generate harmful or undesirable content.<n>Recent machine unlearning approaches offer potential solutions but often lack transparency.<n>We introduce SAeUron, a novel method leveraging features learned by sparse autoencoders to remove unwanted concepts.
  arXiv  Detail & Related papers  (2025-01-29T23:29:47Z)
• ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancement [49.513401043490305]
  This work explores the continual general pre-training of text-to-video models.<n>We break this task into two key aspects: increasing model capacity and improving semantic understanding.<n>For semantic understanding, we propose a method that leverages large language models as advanced text encoders.
  arXiv  Detail & Related papers  (2024-12-25T18:58:07Z)
• Boosting Alignment for Post-Unlearning Text-to-Image Generative Models [55.82190434534429]
  Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.<n>This often inadvertently leads to the generation of harmful or inappropriate content and raises copyright concerns.<n>We propose a framework that seeks an optimal model update at each unlearning iteration, ensuring monotonic improvement on both objectives.
  arXiv  Detail & Related papers  (2024-12-09T21:36:10Z)
• Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models [17.91181821887101]
  We show that fine-tuning a text-to-image diffusion model on seemingly unrelated images can cause it to "relearn" concepts that were previously "unlearned"<n>Our findings underscore the fragility of composing incremental model updates, and raise serious new concerns about current approaches to ensuring the safety and alignment of text-to-image diffusion models.
  arXiv  Detail & Related papers  (2024-10-10T16:10:27Z)
• HARIVO: Harnessing Text-to-Image Models for Video Generation [45.63338167699105]
  We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models. Key innovations include novel loss functions for temporal smoothness and a mitigating gradient sampling technique. Our method, built on the frozen StableDiffusion model, simplifies training processes and allows for seamless integration with off-the-shelf models like ControlNet and DreamBooth.
  arXiv  Detail & Related papers  (2024-10-10T09:47:39Z)
• VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide [48.22321420680046]
  VideoGuide is a novel framework that enhances the temporal consistency of pretrained text-to-video (T2V) models.<n>It improves temporal quality by interpolating the guiding model's denoised samples into the sampling model's denoising process.<n>The proposed method brings about significant improvement in temporal consistency and image fidelity.
  arXiv  Detail & Related papers  (2024-10-06T05:46:17Z)
• Score Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Models [63.43422118066493]
  Machine unlearning (MU) is a crucial foundation for developing safe, secure, and trustworthy GenAI models.<n>Traditional MU methods often rely on stringent assumptions and require access to real data.<n>This paper introduces Score Forgetting Distillation (SFD), an innovative MU approach that promotes the forgetting of undesirable information in diffusion models.
  arXiv  Detail & Related papers  (2024-09-17T14:12:50Z)
• 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)
• Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation [72.90144343056227]
  We explore the visual representations produced from a pre-trained text-to-video (T2V) diffusion model for video understanding tasks. We introduce a novel framework, termed "VD-IT", tailored with dedicatedly designed components built upon a fixed T2V model. Our VD-IT achieves highly competitive results, surpassing many existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-18T17:59:58Z)
• Learning without Forgetting for Vision-Language Models [86.53237963364754]
  Class-Incremental Learning (CIL) or continual learning is a desired capability in the real world.<n>Recent advances in Vision-Language Models (VLM) have shown promising capabilities in learning generalizable representations.<n>We propose PROjectiOn Fusion (PROOF) that enables VLMs to learn without forgetting.
  arXiv  Detail & Related papers  (2023-05-30T17:59:32Z)

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.