Text to Image Generation: A Literature Review Focus on the Diffusion Model
This paper reviews the progress in text-to-image generation, which enables the creation of images from textual descriptions. This technology holds promise across various fields, including creative arts, gaming, and healthcare. The main approaches in this area are Generative Adversarial Networks (GAN...
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| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/04/itmconf_iwadi2024_02037.pdf |
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| _version_ | 1849772518710181888 |
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| author | Zhou Jingxi |
| author_facet | Zhou Jingxi |
| author_sort | Zhou Jingxi |
| collection | DOAJ |
| description | This paper reviews the progress in text-to-image generation, which enables the creation of images from textual descriptions. This technology holds promise across various fields, including creative arts, gaming, and healthcare. The main approaches in this area are Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DM). While GANs initially made significant advancements in realistic image generation, they faced issues with stability and diversity. VAEs introduced a probabilistic approach, allowing for diverse outputs but often at the cost of image quality. The development of DM, like Stable Diffusion, Imagen, and DALL-E 2, has addressed many limitations, producing high-quality, coherent images through iterative denoising. DM stands out for its stability and ability to generate detailed, semantically accurate images. This review explores the strengths and limitations of each approach, with an emphasis on the advantages of DM. It also discusses future directions, including improving efficiency, enhancing multimodal capabilities, and reducing data requirements to make these models more accessible and versatile for various applications. |
| format | Article |
| id | doaj-art-93c946832b6c4d2ab3314afb67a9f1df |
| institution | DOAJ |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-93c946832b6c4d2ab3314afb67a9f1df2025-08-20T03:02:18ZengEDP SciencesITM Web of Conferences2271-20972025-01-01730203710.1051/itmconf/20257302037itmconf_iwadi2024_02037Text to Image Generation: A Literature Review Focus on the Diffusion ModelZhou Jingxi0Beijing No. 80 High SchoolThis paper reviews the progress in text-to-image generation, which enables the creation of images from textual descriptions. This technology holds promise across various fields, including creative arts, gaming, and healthcare. The main approaches in this area are Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DM). While GANs initially made significant advancements in realistic image generation, they faced issues with stability and diversity. VAEs introduced a probabilistic approach, allowing for diverse outputs but often at the cost of image quality. The development of DM, like Stable Diffusion, Imagen, and DALL-E 2, has addressed many limitations, producing high-quality, coherent images through iterative denoising. DM stands out for its stability and ability to generate detailed, semantically accurate images. This review explores the strengths and limitations of each approach, with an emphasis on the advantages of DM. It also discusses future directions, including improving efficiency, enhancing multimodal capabilities, and reducing data requirements to make these models more accessible and versatile for various applications.https://www.itm-conferences.org/articles/itmconf/pdf/2025/04/itmconf_iwadi2024_02037.pdf |
| spellingShingle | Zhou Jingxi Text to Image Generation: A Literature Review Focus on the Diffusion Model ITM Web of Conferences |
| title | Text to Image Generation: A Literature Review Focus on the Diffusion Model |
| title_full | Text to Image Generation: A Literature Review Focus on the Diffusion Model |
| title_fullStr | Text to Image Generation: A Literature Review Focus on the Diffusion Model |
| title_full_unstemmed | Text to Image Generation: A Literature Review Focus on the Diffusion Model |
| title_short | Text to Image Generation: A Literature Review Focus on the Diffusion Model |
| title_sort | text to image generation a literature review focus on the diffusion model |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/04/itmconf_iwadi2024_02037.pdf |
| work_keys_str_mv | AT zhoujingxi texttoimagegenerationaliteraturereviewfocusonthediffusionmodel |