Diffusion Model-Based Image Editing: A Survey
CoRR(2024)
摘要
Denoising diffusion models have emerged as a powerful tool for various image
generation and editing tasks, facilitating the synthesis of visual content in
an unconditional or input-conditional manner. The core idea behind them is
learning to reverse the process of gradually adding noise to images, allowing
them to generate high-quality samples from a complex distribution. In this
survey, we provide an exhaustive overview of existing methods using diffusion
models for image editing, covering both theoretical and practical aspects in
the field. We delve into a thorough analysis and categorization of these works
from multiple perspectives, including learning strategies, user-input
conditions, and the array of specific editing tasks that can be accomplished.
In addition, we pay special attention to image inpainting and outpainting, and
explore both earlier traditional context-driven and current multimodal
conditional methods, offering a comprehensive analysis of their methodologies.
To further evaluate the performance of text-guided image editing algorithms, we
propose a systematic benchmark, EditEval, featuring an innovative metric, LMM
Score. Finally, we address current limitations and envision some potential
directions for future research. The accompanying repository is released at
https://github.com/SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods.
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