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Papers

Text-to-image Diffusion Models in Generative AI: A Survey

2023-03-14 · Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, In So Kweon, Junmo Kim

This survey reviews the progress of diffusion models in generating images from text, ~\textit{i.e.} text-to-image diffusion models. As a self-contained work, this survey starts with a brief introduction of how diffusion models work for image synthesis, followed by the background for text-conditioned image synthesis. Based on that, we present an organized review of pioneering methods and their improvements on text-to-image generation. We further summarize applications beyond image generation, such as text-guided generation for various modalities like videos, and text-guided image editing. Beyond the progress made so far, we discuss existing challenges and promising future directions.

📄 PDF Abstract BibTeX arXiv:2303.07909

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Tasks

Image GenerationSurveytext-guided-generationtext-guided-image-editingText to Image GenerationText-to-Image Generation

Methods 이 논문이 사용한 방법론

Diffusion Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound…

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