Please use this identifier to cite or link to this item:
http://dspace.azjhpc.org/xmlui/handle/123456789/266
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pakpoom, Mookdarsanit | - |
dc.contributor.author | Lawankorn, Mookdarsanit | - |
dc.date.accessioned | 2024-03-22T18:38:15Z | - |
dc.date.available | 2024-03-22T18:38:15Z | - |
dc.date.issued | 2023-12-01 | - |
dc.identifier.issn | 2616-6127 2617-4383 | - |
dc.identifier.uri | http://dspace.azjhpc.org/xmlui/handle/123456789/266 | - |
dc.description.abstract | Text-to-image (T2I) generation is a new area of large language models (LLMs), a type of prompt engineering involving inputting a textual description to generate an image. To shift a new paradigm of Thai natural language processing (Thai-NLP), this paper first presents state-of-the-art Thai Text-to-Image prompt engineering (TH-T2I) to translate Thai text into a semantic image according to the semantic Thai textual description. The pre-trained SCB-MT-EN-TH model is employed for Text-to-Text (T2T) translation. Moreover, the image generation is done according to a semantic text prompt by a stable diffusion model. The T2T is evaluated by Bi-lingual Evaluation Understudy (BLEU), while T2I is done by Inception and Frechet Inception Distance (FID). The images generated by TH-T2I were of high quality, as measured by Inception and FID. TH-T2I contributes to a T2I baseline model in Thai, preserving the Thai cultural language on digital heritage. | en_US |
dc.publisher | Azerbaijan Journal of High Performance Computing | en_US |
dc.subject | Text-to-Image Translation | en_US |
dc.subject | Image Generation | en_US |
dc.subject | Thai Prompt Engineering | en_US |
dc.subject | Stable Diffusion Model | en_US |
dc.title | Thai Text-to-Image Prompt Engineering by Pre-trained Large Language with Stable Diffusion Model | en_US |
dc.type | Article | en_US |
dc.source.journaltitle | Azerbaijan Journal of High Performance Computing | en_US |
dc.source.volume | 6 | en_US |
dc.source.issue | 1 | en_US |
dc.source.beginpage | 171 | en_US |
dc.source.endpage | 190 | en_US |
dc.source.numberofpages | 20 | en_US |
Appears in Collections: | Azerbaijan Journal of High Performance Computing |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
doi.org.10.32010.26166127.2023.6.2.171.190.pdf | 1.71 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.