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What are the downsides of using text-to-image Gen AI?

Dahlia Arnold

Aug 15, 2023

The Downsides of Making Art with Text-to-Image Generative AI

The emergence of text-to-image generative AI has introduced a transformative dimension to the world of artistic creation. This innovation empowers users to conjure images simply by providing textual prompts. However, while its potential for reshaping artistic endeavors is palpable, the technology also carries certain drawbacks that warrant scrutiny.

A foremost challenge inherent to text-to-image generative AI lies in the aspect of control. This AI operates on an expansive repository of images, often producing outputs that resemble those within its training dataset. For artists seeking to forge distinctive and innovative creations, this inherent similarity can prove restrictive.

Another facet that demands consideration is the time factor. Text-to-image generative AI necessitates processing the textual input and crafting the corresponding image, which can consume several seconds, or even minutes. Such time-intensive generation may prove irksome for artists desiring swift image creation.

Beyond the temporal constraints, there is a financial dimension to ponder. The computational demands of training AI models on vast image datasets entail considerable costs. This expense may render the technology financially inaccessible for individual artists.

Given these quandaries, is there an alternative avenue to harness AI in image creation?

Enter the realm of generative adversarial networks (GANs). This approach harnesses AI's potential to generate authentic images through a competitive mechanism. A pair of GANs, comprising a generator and a discriminator, engage in a contest where the generator produces images while the discriminator discerns fake from authentic images. Over iterations, the generator evolves to craft images that progressively deceive the discriminator.

While GANs demand more significant training investment compared to text-to-image generative AI, they offer enhanced control and flexibility. Artists can furnish the GAN with a set of reference images, enabling it to generate images aligned with the training corpus. This facet empowers artists to foster truly original and distinctive outputs.

Additionally, the expediency of GANs merits acknowledgment. With the ability to produce images within seconds, GANs outpace the text-to-image generative AI in terms of efficiency, catering to artists seeking swifter creation.

In summation, the advent of text-to-image generative AI heralds a transformative tool for artistic expression. Yet, it carries its share of challenges, including output control, time-intensive generation, and cost implications. GANs present an alternative avenue, offering artists greater command, flexibility, and speed in image creation. In the evolving landscape of AI-driven artistry, the quest for the optimal approach continues.

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