Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be outdated, incorrect, or reflective of social biases present in the training data. Thus, there is a need to control these assumptions without requiring explicit user input or costly re-training. In this work, we aim to edit a given implicit assumption in a pre-trained diffusion model. Our Text-to-Image Model Editing method, TIME for short, receives a pair of inputs: a "source" under-specified prompt for which the model makes an implicit assumption (e.g., "a pack of roses"), and a "destination" prompt that describes the same setting, but with a specified desired attribute (e.g., "a pack of blue roses"). TIME then updates the model's cross-attention layers, as these layers assign visual meaning to textual tokens. We edit the projection matrices in these layers such that the source prompt is projected close to the destination prompt. Our method is highly efficient, as it modifies a mere 2.2% of the model's parameters in under one second. To evaluate model editing approaches, we introduce TIMED (TIME Dataset), containing 147 source and destination prompt pairs from various domains. Our experiments (using Stable Diffusion) show that TIME is successful in model editing, generalizes well for related prompts unseen during editing, and imposes minimal effect on unrelated generations.
Anchor Token Matching: Implicit Structure Locking for Training-free AR Image Editing
VCIP, College of Computer Science, Nankai University（VCIP，计算机科学学院，南开大学）；NKIARI, Shenzhen Futian（NKIARI，深圳福田）；Computer Vision Center, Universitat Autònoma de Barcelona（计算机视觉中心，巴塞罗那自治大学）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））
Very recently neural implicit rendering techniques have been rapidly evolved and shown great advantages in novel view synthesis and 3D scene reconstruction. However, existing neural rendering methods for editing purposes offer limited functionality, e.g., rigid transformation, or not applicable for fine-grained editing for general objects from daily lives. In this paper, we present a novel mesh-based representation by encoding the neural implicit field with disentangled geometry and texture codes on mesh vertices, which facilitates a set of editing functionalities, including mesh-guided geomet…
Understanding the Implicit User Intention via Reasoning with Large Language Model for Image Editing
Southern University of Science and Technology, Shenzhen, China（南方科技大学）；Johns Hopkins University, Baltimore, USA（约翰霍普金斯大学）；Pengcheng Laboratory, Shenzhen, China（鹏城实验室）
Diffusion models have shown remarkable capabilities in generating high quality and creative images conditioned on text. An interesting application of such models is structure preserving text guided image editing. Existing approaches rely on text conditioned diffusion models such as Stable Diffusion or Imagen and require compute intensive optimization of text embeddings or fine-tuning the model weights for text guided image editing. We explore text guided image editing with a Hybrid Diffusion Model (HDM) architecture similar to DALLE-2. Our architecture consists of a diffusion prior model that …
Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to edit an image based on a text query. Semantic image editing is an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image. Current editing methods based on diffusion models usually require to pr…
Peking University（北京大学）；The Hong Kong University of Science and Technology（香港科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Wrocław University of Science and Technology（沃拉夫大学科学与技术学院）；Poznań University of Technology（波兹南技术大学）；IDEAS Research Institute（IDEAS研究所）；Jagiellonian University（雅盖隆大学）
HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion
School of Computer Science & Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学国家大数据系统计算技术工程实验室）；Guangdong Provincial Key Laboratory of Intelligent Information Processing（广东省智能信息处理重点实验室）
MADiff: Text-Guided Fashion Image Editing with Mask Prediction and Attention-Enhanced Diffusion
Southeast University（东南大学）；Advanced Ocean Institute of Southeast University（东南大学先进海洋研究院）；Northwestern Polytechnical University（西北工业大学）；Binjiang Institute of Artificial Intelligence, ZJUT（浙江工业大学滨江人工智能研究院）；Alibaba Group（阿里巴巴集团）
University of Science and Technology of China（中国科学技术大学）；Microsoft Research Asia（微软亚洲研究院）
Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive approach will cause obvious fusing artifacts. We carefully analyze it and propose an information bottleneck and strong augmentations to avoid the trivial solution of directly copying and pasting the exemplar image. Meanwhile, to ensure the controllability of the editing process, we…
Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing
The University of Hong Kong（香港大学）；Shanghai AI Lab（上海人工智能实验室）；Ocean University of China（中国海洋大学）；Singapore Management University（新加坡管理大学）；South China University of Technology（华南理工大学）
MotionEditor: Editing Video Motion via Content-Aware Diffusion
Fudan University（复旦大学）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）；Microsoft Research Asia（微软亚洲研究院）；Carnegie Mellon University（卡内基梅隆大学）；Huya Inc.（虎牙公司）
