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Lanna Journal of Buddhist Studies and Cultures    Vol.7  No. 1 | January - December 2025 | 1

Creating  Digital Art  by AI Art Generators

Phra Athiwat Thammawatsiri, Phisit Kotsupho, Boonchuay Doojai, 

Phatcharabot Rittem

Mahachulalongkornrajavidyalaya University Chiang Mai

Phra Witawat Kochakan

Mahamamakut Buddhist University, Lanna campus

Email: athiwat.tham@mcu.ac.th

Abstract
In the era of rapid technological evolution, artificial intelligence (AI) 

has emerged as a transformative force in digital art creation. AI Art Generators 

powered primarily by Generative Adversarial Networks (GANs) and Diffusion 

Models enable users to produce complex, high-quality visual content by simply 

providing text prompts. Tools such as OpenAI’s DALL·E, Midjourney, Stable  

Diffusion, and Adobe Firefly illustrate this paradigm shift by bridging advanced 

algorithms with user-friendly interfaces that make creative production accessible 

to the general public.

This article explores the fundamental principles behind these  

systems, focusing on how GANs and Diffusion Models differ in architecture and 

image generation logic. Unlike traditional graphic design software, which relies 

heavily on the artist’s manual skills and iterative design processes, AI Art  

Generators automate tasks that were once labor-intensive. This automation 

democratizes artistic production but simultaneously raises critical questions 

about originality, authorship, and the role of human craftsmanship in the 

digital age.

The widespread adoption of AI-generated art has sparked global  

debates about intellectual property rights, ethical training datasets, and the 

potential misuse of artists’ works without consent. Recent lawsuits involving 



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Midjourney and Stability AI highlight the need for clearer legal frameworks that 

balance technological innovation with respect for creators’ rights. The paper argues 

that AI should not be viewed solely as a replacement for human creativity  

but rather as a powerful collaborator that expands the boundaries of visual  

expression.

By examining the technical foundations, creative implications, and 

ethical challenges surrounding AI Art Generators, this article contributes to an 

interdisciplinary understanding of how emerging technologies are reshaping  

artistic practice and cultural production. It calls for collaborative strategies 

among artists, developers, and policymakers to ensure that AI-driven creativity 

evolves responsibly and inclusively in the years to come.

Keywords :  AI Art Generators, Generative Adversarial Networks (GANs), 

                     Diffusion Models, Digital Art Ethics, Creative Collaboration

Introduction
The evolution of digital art has been deeply intertwined with the  

advancement of computing technology. In the 1980s and 1990s, artists began 

employing basic software such as Adobe Photoshop, CorelDRAW, and Microsoft 

Paint to produce two-dimensional graphics. These tools enabled digital painting, 

photo retouching, and layout design. The development of high-resolution  

monitors and graphic tablets significantly enhanced the realism and precision of 

digital artworks (Paul, 2015).

By the early 21st century, digital art transitioned into a new phase 

marked by three-dimensional (3D) animation, lighting simulation, and virtual 

reality (VR). Artists utilized software like Autodesk Maya, Blender, and Unity to 

render immersive environments. This era witnessed the emergence of  

interdisciplinary expressions—ranging from media art and interactive installations 

to data-driven visualizations (Lovejoy, 2004; Tribe & Jana, 2007).



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Despite such technological sophistication, digital artistry still required 

extensive manual skill. Artists had to master composition, color theory, and 

narrative design, resulting in a time-intensive creative process. However, a turning 

point arrived with the application of artificial intelligence (AI), particularly in fields 

like machine learning and computer vision. Technologies such as Generative 

Adversarial Networks (GANs) and Diffusion Models enabled machines to learn 

artistic patterns from massive datasets and generate original imagery based on 

simple text prompts (Goodfellow et al., 2014; Ramesh et al., 2021).

Importantly, local research has explored how these techniques are 

practically adapted in Southeast Asia. Athiwat Thammawatsiri, Phisit Kotsupho, and 

Phatcharabot Rittem (2024) demonstrated how GANs and AI art generators can be 

used to preserve cultural identity and innovate contemporary Buddhist art 

The emergence of AI art generators including OpenAI’s DALL·E,  

Midjourney, Stable Diffusion, and Adobe Firefly has revolutionized how digital 

artworks are conceptualized and produced. These tools can rapidly create  

complex images based on user-provided prompts, thereby democratizing access 

to creative production and reducing the time and technical skill previously  

required.

DALL·E, first launched by OpenAI in 2021 and later refined into DALL·E 

3 (available via ChatGPT Plus and Bing), uses transformer-based architecture and 

introduces prompt comprehension features with content filtering and  

watermarking (OpenAI, 2023). Midjourney, released as an open beta in 2022, 

quickly gained popularity on Discord for its distinctive visual style. It has since 

evolved to version 7 as of April 2025. In contrast, Stable Diffusion, built on  

open-source principles and trained on LAION-5B, enables local image generation 

and fine-tuned control over inpainting and outpainting processes (Stability AI, 

2023). Adobe Firefly emphasizes legal safety by relying exclusively on licensed 

Adobe Stock and public domain content, offering creators image protection 

systems (Adobe, 2024).



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While AI art tools unlock new possibilities, they also introduce  

unresolved questions. First, there remains a gap in understanding the artistic and 

contextual precision of AI-generated imagery. While technical aspects of GANs 

and diffusion models are well-documented, less is known about how well these 

tools replicate the aesthetic sensitivity and interpretive depth of human artistry 

(Elgammal et al., 2017; McCormack et al., 2019).

Second, ethical and legal concerns have surfaced, particularly around 

copyright infringement and unauthorized data usage. In 2023–2025, Midjourney 

and Stability AI faced lawsuits over the use of artists’ works without consent, 

highlighting the urgent need for global regulatory frameworks (AP News, 2023). 

Additionally, fears persist that AI-generated art may devalue craftsmanship and 

endanger traditional creative professions especially for emerging artists raising 

philosophical questions about authorship, originality, and creative intention in 

the AI era.

This article aims to address these gaps by providing a comprehensive 

overview of AI art generators: their underlying technologies, artistic implications, 

ethical debates, and future trajectories. It seeks to bridge the divide between 

scientific insight, aesthetic theory, and social impact in the context of AI-driven 

creativity.

AI Art Generators: Concepts and Technologies
In addition, next-generation AI Art Generators have integrated prompt 

engineering techniques that allow users to specify image details more precisely, 

including color palettes, artistic composition, and visual style. They also leverage 

Natural Language Processing (NLP) to interpret symbolic and contextual  

meanings within the user’s text prompts. This capability enables AI Art  

Generators to go beyond simple image replication and produce new, highly 

customized artworks that reflect contemporary concepts and individual user 

identity (Ramesh et al., 2021).



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1. How AI Art Generators Work: Generative Adversarial Networks 

(GANs) and Diffusion Models

AI Art Generators apply Artificial Intelligence (AI) and Deep Learning 

to create original digital artworks. A key approach is the Generative Adversarial 

Network (GAN), which consists of two main parts: the Generator and the  

Discriminator (Goodfellow et al., 2014). The Generator produces new images 

from random noise that mimic real data, while the Discriminator distinguishes 

whether an image is real or fake. Through this adversarial training, both parts 

improve over time, enabling the Generator to produce highly realistic, complex 

outputs. GANs are widely used to generate hyper-realistic portraits, contemporary 

digital art, and experimental graphic designs.

Another popular technique is the Diffusion Model, which works by 

gradually adding noise to an image until its details are completely erased, then 

training the model to learn how to remove the noise step by step in reverse, 

producing a clear image according to a given text prompt (Ramesh et al., 2022). 

Notable tools such as DALL·E and Stable Diffusion demonstrate this method by 

turning short text descriptions into unique, highly detailed images.

By combining GANs and Diffusion Models, AI Art Generators have  

become powerful tools for expanding the boundaries of digital creativity. They 

allow artists to merge human imagination with AI’s computational power to 

create art beyond traditional limits (Goodfellow et al., 2014; Ramesh et al., 2022).

2. The Underlying Technologies

The underlying technologies behind AI Art Generators combine 

cutting-edge innovations that enable systems to learn, generate, and process 

complex data to produce digital artworks with high accuracy. A fundamental 

pillar is the Deep Neural Network (DNN), which mimics the structure of the 

human brain using layers of artificial neurons capable of recognizing patterns in 

images, sounds, and texts (LeCun et al., 2015). The key advantage is the ability 

to learn directly from massive datasets through repeated training without relying 

on fixed rules.



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Another critical enabler is parallel processing with Graphics Processing 

Units (GPUs), which significantly accelerates model training and large-scale data 

processing (Raina et al., 2009). Today, cloud computing services and  

specialized hardware such as Tensor Processing Units (TPUs) play a major role in 

pushing the limits of next-generation models.

Additionally, advanced algorithms like the Attention Mechanism and 

Transformer Architecture have become the backbone of modern AI art systems 

such as DALL·E or Stable Diffusion, which can accurately link text prompts to 

images in flexible, creative ways (Vaswani et al., 2017).

In summary, the technologies behind AI Art Generators are not just 

about models alone but rather an integrated system of advanced neural  

networks, high-performance computing, and smart algorithms that enable  

limitless possibilities for contemporary digital art (LeCun et al., 2015; Vaswani et 

al., 2017).

3. Difference from Traditional Graphic Software

AI Art Generators differ significantly from traditional graphic software 

like Photoshop or Illustrator. Instead of manual pixel-level control, they rely on 

AI models trained on large datasets to learn visual patterns (Elgammal, 2019). 

Subsequently, these systems generate new images based on text prompts or 

image inputs, eliminating the need for detailed manual configuration. This 

AI-driven approach lowers the barrier for non-experts to produce complex  

artworks. In contrast, traditional graphic software requires users to specify every 

line, color, and composition detail, making creation heavily dependent on user 

skill (Mazzone & Elgammal, 2019).

The Impact of AI Art Generators on Digital Art

AI Art Generators have significantly transformed the digital art  

landscape. They enable people without deep artistic or design skills to create 

unique artworks by converting text prompts or data patterns into images within 



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seconds (McCormack et al., 2019). This shift democratizes art production and 

opens up new markets such as NFT art and interactive media (Elgammal, 2019).

However, this technology also raises questions about authorship and 

artistic authenticity. When works are generated by AI, it becomes debatable 

who should be considered the true creator, and the risk of duplication or  

unauthorized reproduction increases (Mazzone & Elgammal, 2019). While many 

traditional artists see AI as a tool rather than an artist in itself, this ethical debate 

will likely remain central to the evolution of digital art.

1. The Transformation of Artistic Creation Processes

The rise of AI Art Generators has transformed the process of artistic 

creation. Where traditional art relies heavily on manual skills and detailed  

composition, artists today can generate ideas and instruct AI systems through text 

or input data instead (Elgammal, 2019). This means artists no longer need to draw 

every line or mix every color by hand but can focus more on conceptual design 

and selecting outputs from AI quickly and flexibly (McCormack et al., 2019).

Moreover, the creation process has shifted toward iteration,  

experimentation, and modification. AI models can generate multiple image 

variations within seconds, allowing artists to test new ideas without the long 

production time required by traditional methods (Colton et al., 2015). This opens 

up a new dimension for art-making beyond the physical studio, extending into the 

digital realm and online communities where artists and AI co-create continuously.

2. Expanding Opportunities for Emerging Artists

The emergence of AI Art Generators has opened doors for emerging 

artists to create artworks more easily without requiring advanced technical or 

manual skills typical of traditional art systems. AI tools allow new artists to  

experiment with ideas and produce works quickly, lowering material costs and 

barriers to entry. Additionally, digital platforms such as NFT marketplaces enable 

them to share and sell their works globally (Mazzone & Elgammal, 2019). As a 

result, emerging artists can generate income and gain recognition without fully 

relying on conventional galleries or agencies. This trend decentralizes art  



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production and broadens creative opportunities for newcomers (McCormack et 

al., 2019).

3. Reducing Technical Skill Barriers

One major advantage of AI Art Generators is their ability to reduce 

the technical skill barriers that have traditionally limited access to digital art 

creation. In the past, producing high-quality digital art required strong drawing, 

design, and specialized software skills. Today, artists and general users can  

generate artworks simply by typing a prompt or idea into an AI system, which 

then processes and creates new images automatically (McCormack et al., 2019). 

This empowers newcomers or those without design backgrounds to experiment 

freely, shortens the learning curve for complex tools, and opens up creative 

opportunities for everyone (Mazzone & Elgammal, 2019).

Copyright and Ethical Issues
Although AI Art Generators expand the creative possibilities of digital 

art, they also introduce complex legal and ethical challenges. A major topic of 

debate is ownership rights over AI-generated works. Many AI models are trained 

on vast datasets that often include copyrighted photos, paintings, or designs 

(Elgammal, 2019). Using these works without consent to train AI models may 

unintentionally infringe on the intellectual property rights of original creators 

(McCormack et al., 2019). At present, many jurisdictions do not clearly define 

whether the rights belong to the artist providing the input, the model developer, 

or the platform owner (Vincent, 2020).

Ethical considerations add another layer of complexity. The 

automation of image creation by AI challenges the value of originality and  

craftsmanship that traditionally define art (Mazzone & Elgammal, 2019). Some 

fear that automated production could marginalize professional artists or flood 

the market with mass-produced, soulless works. Furthermore, AI can be misused 

to produce deepfake images or sensitive content that violates privacy or  

manipulates facts, raising serious legal and moral concerns (Vincent, 2020).



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To address these risks, scholars and artists advocate for shared  

ethical standards, such as source attribution, digital provenance certification, 

clear disclosure of the AI model used, and proper credit to original creators 

whose works contributed to training (Elgammal, 2019; McCormack et al., 2019). 

Therefore, copyright and ethics are central issues that must be resolved for AI 

art to grow responsibly and fairly in the creative industries.

Future Trends: Advancing AI Art Generators, Human–AI Collabora-

tion, and Impacts  

on Art Education
1. Advancing AI Art Generators

AI Art Generators will continue to improve, especially through  

next-generation models like Diffusion Models and Generative Adversarial Networks 

(GANs) combined with fine-tuning and style transfer techniques (Ramesh et al., 

2022). These advancements will allow artists to generate highly detailed,  

custom-styled works. In the future, integration with AR/VR will enable immersive 

art experiences that bridge the digital and physical realms (Goodfellow et al., 

2014).

2. Human–AI Collaboration

Another major trend is human–AI collaboration. Artists increasingly use 

AI to generate drafts, experiment with colors, or create complex compositions, 

which they then refine and interpret to produce unique pieces (Elgammal, 2019). 

This shifts AI’s role from a mere tool to a creative partner, expanding the artist’s 

conceptual process without replacing their human insight (McCormack et al., 

2019).

3. Impacts on Art Education and Skills

Art education will need to evolve to keep pace. Future students will 

learn traditional techniques alongside prompt engineering, AI ethics, and digital 

copyright awareness (Vincent et al., 2021). New-generation artists will act as 



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Creative Directors who guide AI’s output rather than manually creating every 

detail. This hybrid skill set—combining data handling, narrative design, and  

system thinking—will prepare artists to create contemporary works that resonate 

in a fast-changing digital world.

Conclusion

The rapid advancement of AI Art Generators represents a significant 

milestone in the ongoing evolution of digital art. By integrating powerful machine 

learning models such as Generative Adversarial Networks (GANs) and Diffusion 

Models, these systems have made it possible for anyone—regardless of  

traditional artistic training—to create highly detailed, compelling visuals using 

simple text prompts. This paradigm shift has not only democratized creative 

production but has also fundamentally transformed the roles of artists,  

designers, and creative industries more broadly.

Leading examples such as DALL·E, Midjourney, Stable Diffusion, and 

Adobe Firefly illustrate the diverse technological paths this field has taken. 

DALL·E’s transformer-based architecture demonstrates how diffusion models 

can generate contextually accurate images that align closely with textual  

descriptions. Midjourney, with its community-driven evolution, emphasizes  

stylistic experimentation and user collaboration. Stable Diffusion stands out for 

its open-source model, which empowers users to run and fine-tune AI image 

generation locally, raising both innovation and legal questions. Meanwhile,  

Adobe Firefly highlights the importance of copyright-safe AI training data and 

responsible usage by leveraging licensed assets from Adobe Stock and public 

domain collections.

However, despite their immense potential, AI Art Generators pose 

unresolved challenges that require urgent attention. Concerns about copyright 

infringement, the unauthorized use of artists’ works for training data, and the lack 

of clear international legal frameworks highlight significant gaps that the creative 



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community, policymakers, and technology developers must address. Equally 

important are philosophical questions surrounding authorship and originality: if 

an AI can autonomously generate images that rival human-made art, how should 

society recognize the human element in the final output?

This article argues that rather than viewing AI as a replacement for 

human creativity, it should be approached as a powerful creative collaborator. 

When used responsibly, AI Art Generators can expand the boundaries of artistic 

expression, accelerate ideation, and inspire new forms of hybrid human–ma-

chine creativity. Yet, achieving this balance requires robust ethical standards, 

transparent data practices, and meaningful legal safeguards to protect artists’ 

intellectual property and ensure fair recognition of their contributions.

Ultimately, the future of AI-driven art depends on a collaborative  

effort between artists, researchers, developers, and policymakers to establish 

frameworks that encourage innovation while upholding artistic integrity. As AI 

continues to redefine how art is imagined and produced, embracing it as a 

co-creator—rather than a competitor—will be key to shaping a more inclusive 

and ethically grounded digital art landscape.

Recommendations
1. Develop clear ethical guidelines for AI training datasets.

2. Create international legal frameworks for authorship and copyright.

3. Promote AI literacy and prompt engineering in art education.

4. Implement transparency and attribution for AI-generated works.

5. Encourage collaboration among artists, developers, and policymak-

ers.



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References
Adobe. (2024). What is Adobe Firefly?. Adobe. https://www.adobe.com/sensei/

generative-ai/firefly.html

AP News. (2023, January 17). Artists file lawsuit against AI image generators. AP 

News. Retrieved from https://apnews.com/article/technology-law-

suits-copyright-artificial-intelligence-bf67b5e5b70e6e384961af1d-

fe6560e0

Simon Colton, John Charnley, Alison Pease. (2015). Computational creativity 

theory: The FACE and IDEA models. Proceedings of the 2nd Interna-

tional Conference on Computational Creativity. Retrieved from 

https://computationalcreativity.net/iccc2015/

Ahmed Elgammal. (2019). AI is blurring the definition of artist. American Scien-

tist, 107(1), 18–21. 

Ahmed Elgammal. Bingchen Liu. Mohamed Elhoseiny. Marian Mazzone. (2017). 

CAN: Creative Adversarial Networks, generating “art” by learning about 

styles and deviating from style norms. arXiv preprint arXiv:1706.07068. 

Retrieved from https://arxiv.org/abs/1706.07068

Ian J. Goodfellow, Jean Pouget-Abadie , Mehdi Mirza, Bing Xu, David Warde-Far-

ley, Sherjil Ozair† , Aaron Courville, Yoshua Bengio. (2014). Generative 

adversarial nets. arXiv preprint arXiv:1406.2661. Retrieved from 

https://arxiv.org/pdf/1406.2661

Yann LeCun, Yoshua Bengio, Geoffrey Hinton. (2015). Deep learning. Nature, 

521(7553), 436–444. Retrieved from https://www.nature.com/articles/

nature14539

Margot Lovejoy. (2004). Digital currents: Art in the electronic age. Routledge. 

Retrieved from https://www.routledge.com/Digital-Currents-Art-in-

the-Electronic-Age/Lovejoy/p/book/9780415307819?srsltid=Afm-

BOorzHyzOZ55iOp6B41RFmtD7uVwWb0Ve98v88A0hlr2-ubXTZCud

Marian Mazzone, Ahmed Elgammal. (2019). Art, creativity, and the potential of 

artificial intelligence. Arts, 8(1), 26. Retrieved from https://www.mdpi.



Mahachulalongkornrajavidyalaya Universit, Chiang Mai Campus

Lanna Journal of Buddhist Studies and Cultures    Vol.7  No. 1 | January - December 2025 | 13

com/2076-0752/8/1/26

Jon McCormack, Toby Gifford, Patrick Hutchings. (2019). Autonomy, authentic-

ity, authorship and intention in computer generated art. Retrieved 

from https://link.springer.com/chapter/10.1007/978-3-030-16667-0_3

OpenAI. (2023). DALL·E 3 system card. OpenAI. Retrieved from https://openai.

com/dall-e-3

Christiane Paul. (2015). Digital art (3rd ed.). Thames & Hudson.

Phra Athiwat Thammawatsiri, Phisit Kotsupho, Phatcharabot Rittem, Phra Witawat 

Kochakan, Taviz Tatnormjit, Lipikorn Makaew. The Digital Arts Creation 

in the Modern World’s Innovation. Lanna Journal of Buddhist Stud-

ies and Cultures. 6(1), 2024.

Rajat Raina, Anand Madhavan, Andrew Y. Ng. (2009). Large-scale deep unsuper-

vised learning using graphics processors. Proceedings of the 26th 

Annual International Conference on Machine Learning, 873–880. 

https://robotics.stanford.edu/~ang/papers/icml09-LargeScaleUnsu-

pervisedDeepLearningGPU.pdf

Aditya Ramesh, P., Prafulla Dhariwal, Alex Nichol. (2022). Hierarchical text-con-

ditional image generation with CLIP latents. arXiv preprint arXiv: 

2204.06125. Retrieved from https://3dvar.com/Ramesh2022Hierarchi-

cal.pdf

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Rad-

ford, Mark Chen, Ilya Sutskever . (2021). Zero-shot text-to-image 

generation. Proceedings of Machine Learning Research. https://pro-

ceedings.mlr.press/v139/ramesh21a/ramesh21a.pdf

Stability AI. (2023). Stable Diffusion public release. Stability ai.  Retrieved from 

https://stability.ai/news/stable-diffusion-public-release

Rebecca Umbach, Nicola Henry. (2024). Non-consensual synthetic intimate 

imagery: prevalence, attitudes, and knowledge in 10 countries. 

arXiv preprint arXiv: 2402.01721. Retrieved from https://dl.acm.org/



Mahachulalongkornrajavidyalaya Universit, Chiang Mai Campus

Lanna Journal of Buddhist Studies and Cultures    Vol.7  No. 1 | January - December 2025 |14

doi/full/10.1145/3613904.3642382

University of Nevada, Reno. (2023). How are deepfakes dangerous?. Retrieved 

from https://www.unr.edu/nevada-today/news/2023/atp-deepfakes 

arxiv.orgunr.edu

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan 

N Gomez, Łukasz Kaiser, Illia Polosukhin. (2017). Attention is all you 

need. Advances in Neural Information Processing Systems, 30. Re-

trieved from https://proceedings.neurips.cc/paper/2017/

file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

James Vincent. (2018). A porn company promises to insert customers into 

scenes using deepfakes. The Verge. Retrieved from https://www.

theverge.com/2018/8/21/17763278/deepfake-porn-cus-

tom-clips-naughty-america theverge.com

Vincent, J., Elgammal, A., & McCormack, J. (2021). The future of AI and art ed-

ucation. Computational Creativity Bulletin, 5(1), 12–18.


