Utilizing stable diffusion and fine-tuning models in advertising production and logo creation: An application of text-to-image technology

Research Article
Open access

Utilizing stable diffusion and fine-tuning models in advertising production and logo creation: An application of text-to-image technology

Chenyang Wang 1*
  • 1 Jeme Tienyow Honors College, Beijing Jiaotong University, Beijing, 100091, China    
  • *corresponding author 20221020@bjtu.edu.cn
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/32/20230180
ACE Vol.32
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-289-3
ISBN (Online): 978-1-83558-290-9

Abstract

This article delves into the implementation of text-to-image technology, taking advantage of stable diffusion and fine-tuning models, in the realms of advertising production and logo design. The conventional methods of production often encounter difficulties concerning cost, time constraints, and the task of locating suitable imagery. The solution suggested herein offers a more efficient and cost-effective alternative, enabling the generation of superior images and logos. The applied methodology is built around stable diffusion techniques, which employ variational autoencoders alongside diffusion models, yielding images based on textual prompts. In addition, the process is further refined by the application of fine-tuning models and adaptation processes using a Low-Rank Adaptation approach, which enhances the image generation procedure significantly. The Stable Diffusion Web User Interface offers an intuitive platform for users to navigate through various modes and settings. This strategy not only simplifies the production processes, but also decreases resource requirements, while providing ample flexibility and versatility in terms of image and logo creation. Results clearly illustrate the efficacy of the technique in producing appealing advertisements and logos. However, it is important to note some practical considerations, such as the quality of the final output and limitations inherent in text generation. Despite these potential hurdles, the use of artificial intelligence-generated content presents vast potential for transforming the advertising sector and digital content creation as a whole.

Keywords:

stable diffusion, text-to-image technology, advertisement production, latent diffusion models

Wang,C. (2024). Utilizing stable diffusion and fine-tuning models in advertising production and logo creation: An application of text-to-image technology. Applied and Computational Engineering,32,36-43.
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References

[1]. Vahid, H., & Esmae’li, S. (2012). The power behind images: Advertisement discourse in focus. International Journal of Linguistics, 4(4), 36-51.

[2]. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10 684-10 695).

[3]. Hu, E. J., Shen, Y., Wallis, P., et al. (2021). Lora: Low-rank adaptation of large language models. arXiv preprint. arXiv:2106.09685.

[4]. Valipour, M., Rezagholizadeh, M., Kobyzev, I., & Ghodsi, A. (2022). Dylora: Parameter efficient tuning of pre-trained models using dynamic search-free low-rank adaptation. arXiv preprint arXiv:2210.07558.

[5]. Rost, M., & Andreasson, S. (2023). Stable Walk: An interactive environment for exploring Stable Diffusion outputs.

[6]. Pieters, R., Wedel, M., & Zhang, J. (2007). Optimal feature advertising design under competitive clutter. Management Science, 53(11), 1815-1828.

[7]. Malik, M. E., Ghafoor, M. M., Iqbal, H. K., et al. (2013). Impact of brand image and advertisement on consumer buying behavior. World Applied Sciences Journal, 23(1), 117-122.

[8]. Wu, J., Gan, W., Chen, Z., et al. (2023). AI-generated content (aigc): A survey. arXiv preprint. arXiv:2304.06632.

[9]. Harrer, S. (2023). Attention is not all you need: The complicated case of ethically using large language models in healthcare and medicine. EBioMedicine, 90.

[10]. Gozalo-Brizuela, R., & Garrido-Merchán, E. C. (2023). A survey of Generative AI Applications. arXiv preprint. arXiv:2306.02781.

Cite this article

Wang,C. (2024). Utilizing stable diffusion and fine-tuning models in advertising production and logo creation: An application of text-to-image technology. Applied and Computational Engineering,32,36-43.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-289-3(Print) / 978-1-83558-290-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.32
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Vahid, H., & Esmae’li, S. (2012). The power behind images: Advertisement discourse in focus. International Journal of Linguistics, 4(4), 36-51.

[2]. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10 684-10 695).

[3]. Hu, E. J., Shen, Y., Wallis, P., et al. (2021). Lora: Low-rank adaptation of large language models. arXiv preprint. arXiv:2106.09685.

[4]. Valipour, M., Rezagholizadeh, M., Kobyzev, I., & Ghodsi, A. (2022). Dylora: Parameter efficient tuning of pre-trained models using dynamic search-free low-rank adaptation. arXiv preprint arXiv:2210.07558.

[5]. Rost, M., & Andreasson, S. (2023). Stable Walk: An interactive environment for exploring Stable Diffusion outputs.

[6]. Pieters, R., Wedel, M., & Zhang, J. (2007). Optimal feature advertising design under competitive clutter. Management Science, 53(11), 1815-1828.

[7]. Malik, M. E., Ghafoor, M. M., Iqbal, H. K., et al. (2013). Impact of brand image and advertisement on consumer buying behavior. World Applied Sciences Journal, 23(1), 117-122.

[8]. Wu, J., Gan, W., Chen, Z., et al. (2023). AI-generated content (aigc): A survey. arXiv preprint. arXiv:2304.06632.

[9]. Harrer, S. (2023). Attention is not all you need: The complicated case of ethically using large language models in healthcare and medicine. EBioMedicine, 90.

[10]. Gozalo-Brizuela, R., & Garrido-Merchán, E. C. (2023). A survey of Generative AI Applications. arXiv preprint. arXiv:2306.02781.

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