Unlearning Concepts from Text-to-Video Diffusion Models

• URL: http://arxiv.org/abs/2407.14209v1
• Date: Fri, 19 Jul 2024 11:15:02 GMT
• Title: Unlearning Concepts from Text-to-Video Diffusion Models
• Authors: Shiqi Liu, Yihua Tan,
• Abstract summary: We propose a novel concept-unlearning method by transferring the unlearning capability of the text encoder of text-to-image diffusion models to text-to-video diffusion models. Our method can unlearn copyrighted cartoon characters, artist styles, objects and people's facial characteristics.
• Score: 4.640452023364898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advancement of computer vision and natural language processing, text-to-video generation, enabled by text-to-video diffusion models, has become more prevalent. These models are trained using a large amount of data from the internet. However, the training data often contain copyrighted content, including cartoon character icons and artist styles, private portraits, and unsafe videos. Since filtering the data and retraining the model is challenging, methods for unlearning specific concepts from text-to-video diffusion models have been investigated. However, due to the high computational complexity and relative large optimization scale, there is little work on unlearning methods for text-to-video diffusion models. We propose a novel concept-unlearning method by transferring the unlearning capability of the text encoder of text-to-image diffusion models to text-to-video diffusion models. Specifically, the method optimizes the text encoder using few-shot unlearning, where several generated images are used. We then use the optimized text encoder in text-to-video diffusion models to generate videos. Our method costs low computation resources and has small optimization scale. We discuss the generated videos after unlearning a concept. The experiments demonstrates that our method can unlearn copyrighted cartoon characters, artist styles, objects and people's facial characteristics. Our method can unlearn a concept within about 100 seconds on an RTX 3070. Since there was no concept unlearning method for text-to-video diffusion models before, we make concept unlearning feasible and more accessible in the text-to-video domain.

Related papers

• Grid Diffusion Models for Text-to-Video Generation [2.531998650341267]
  Most existing video generation methods use either a 3D U-Net architecture that considers the temporal dimension or autoregressive generation. We propose a simple but effective novel grid diffusion for text-to-video generation without temporal dimension in architecture and a large text-video paired dataset. Our proposed method outperforms the existing methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2024-03-30T03:50:43Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• A Recipe for Scaling up Text-to-Video Generation with Text-free Videos [72.59262815400928]
  Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. We come up with a novel text-to-video generation framework, termed TF-T2V, which can directly learn with text-free videos.
  arXiv  Detail & Related papers  (2023-12-25T16:37:39Z)
• MEVG: Multi-event Video Generation with Text-to-Video Models [18.06640097064693]
  We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a pre-trained text-to-video generative model without a fine-tuning process. Our proposed method is superior to other video-generative models in terms of temporal coherency of content and semantics.
  arXiv  Detail & Related papers  (2023-12-07T06:53:25Z)
• Harvest Video Foundation Models via Efficient Post-Pretraining [67.30842563833185]
  We propose an efficient framework to harvest video foundation models from image ones. Our method is intuitively simple by randomly dropping input video patches and masking out input text during the post-pretraining procedure. Our method achieves state-of-the-art performances, which are comparable to some heavily pretrained video foundation models.
  arXiv  Detail & Related papers  (2023-10-30T14:06:16Z)
• LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation [44.220329202024494]
  We present a few-shot-based tuning framework, LAMP, which enables text-to-image diffusion model Learn A specific Motion Pattern with 816 videos on a single GPU. Specifically, we design a first-frame-conditioned pipeline that uses an off-the-shelf text-to-image model for content generation. To capture the features of temporal dimension, we expand the pretrained 2D convolution layers of the T2I model to our novel temporal-spatial motion learning layers.
  arXiv  Detail & Related papers  (2023-10-16T19:03:19Z)
• In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval [72.98185525653504]
  We propose a new setting, text-video retrieval with uncurated & unpaired data, that during training utilizes only text queries together with uncurated web videos. To improve generalization, we show that one model can be trained with multiple text styles. We evaluate our model on retrieval performance over multiple datasets to demonstrate the advantages of our style transfer framework.
  arXiv  Detail & Related papers  (2023-09-16T08:48:21Z)
• MagicVideo: Efficient Video Generation With Latent Diffusion Models [76.95903791630624]
  We present an efficient text-to-video generation framework based on latent diffusion models, termed MagicVideo. Due to a novel and efficient 3D U-Net design and modeling video distributions in a low-dimensional space, MagicVideo can synthesize video clips with 256x256 spatial resolution on a single GPU card. We conduct extensive experiments and demonstrate that MagicVideo can generate high-quality video clips with either realistic or imaginary content.
  arXiv  Detail & Related papers  (2022-11-20T16:40:31Z)
• Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval [70.30052749168013]
  Multi-channel video-language retrieval require models to understand information from different channels. contrastive multimodal models are shown to be highly effective at aligning entities in images/videos and text. There is not a clear way to quickly adapt these two lines to multi-channel video-language retrieval with limited data and resources.
  arXiv  Detail & Related papers  (2022-06-05T01:43:52Z)

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.