Generative AI Fashion Industry: Use Cases & Examples in 2024

89% of all companies across different sectors are switching to digital technologies and the fashion industry is not an exception. McKinsey reports that in 2021, fashion brands and companies invested approximately 1.7% of their income in emerging technologies. Moreover, they estimate the figure will rise between 3.0% and 3.5% by 2030.¹

Blockchain technology, non-fungible tokens (NFTs), and AI technology are digital technologies that are implemented in the fashion industry. On the other hand, generative AI is relatively new; yet it started affecting many elements of the fashion industry.

This article explains the importance of generative AI and top 5 generative AI fashion use cases with case studies.

What is generative AI?

Generative AI refers to a class of machine learning algorithms designed to generate new, original content based on a set of input data.

Generative AI is used for a variety of tasks, including generating text, images, music, codes, and even entire websites. AI-driven generative adversarial networks (GANs), a type of generative AI, can perform creative tasks that were once thought to be unique to humans. These powerful machine learning models can create realistic images, videos, and voice outputs.

Why is generative AI important for the fashion industry?

Generative AI is important for the fashion industry as it brings many benefits. It can improve customer satisfaction and allow online retailers to bring generative products to market faster and more cost-effectively by:

• Diversifying and personalizing fashion designs
• Increasing the representation of all body types with generated models 
• Creating automated digital experience in online shopping

In the fashion retail industry, where both aesthetics and consumer pleasure are important factors in fashion design and speed and novelty are crucial, generative adversarial network (GAN) offers an efficient way to generate new product designs at a low cost. Watch the video below to see the generative ability of GANs in use.

Generative AI tools for image & design generation

Before explaining the specific use cases of generative AI in the fashion industry, it is good to know how it generates creative images and other contents constitutive of a design. 

By utilizing generative algorithms, AI can create unique and interesting images that merge computer-generated styling with human-driven creativity. The artwork created by generative AI in this way offers an entirely new approach to creating visual art. It can tap into generative elements and generate infinite variations of the same image. 

Figure 1. The cycleGAN algorithm is able to generate designs in the style of different artists and artistic genres, such as Monet, van Gogh, Cezanne and Ukiyo-e. (Source: ICCV 2017.)²

With generative AI, the artist’s creativity is no longer limited by limitations such as cost or resources. Rather, it allows various professionals like graphic and fashion designers to craft truly innovative or fusion works of art at the click of a button. In Figure 1 above, you can see how it is able to produce creative, stylistic, and unique outputs from the same input. Since the fashion industry relies on these three elements (creativity, style, uniqueness), generative AI is a perfect match for its purposes. 

Most AI-generated images are nearly impossible to differentiate from real ones. When participants in a study were unaware that generative AI technology had been used, they tended to perceive the images generated by GANs as more novel than the original images.³

Another famous generative AI tool, DALL-E, can create a wide range of images, including: 

• Photorealistic images 
• Abstract patterns
• Stylized illustrations. 

It has been demonstrated to be capable of generating highly creative and novel images that go beyond what it was explicitly trained on. Some examples from its realistic and artistic generations:

Figure 2. Entering “An Apple” will get a series of photorealistic apple images.

Figure 3. Adding modifier “by Magritte” dramatically changes the entire character of the prompt.

Here again, you can see how generative AI is capable of creating surprising and stylistic designs from a basic object. 

5 use cases of generative AI in the fashion industry with example cases

1. Creative Designing for Fashion Designers

With its great ability to generate new images and content, generative AI can assist fashion designers in the creative design process by developing new ideas or helping to refine and optimize existing designs with the latest trends. This can be done through a variety of techniques, including:

• Generative design: Generative AI can create entirely new fashion designs based on specified constraints and parameters, such as the desired aesthetic, materials, and target market.
• Style transfer: Generative AI can be used to apply the style of one design to another, allowing designers to create variations on existing designs or combine elements from different sources.

Besides, you don’t need to be an exclusive fashion designer for creating new designs. An ML engineer specializing in generative arts, Fathy Rashad, created his own generative cloth designer ClothingGAN by using StyleGan and GANSpace (see the figure below).⁴

Figure 4. Products generated by ClothingGAN.

2. Turning Sketches into Color Images

Generative AI benefits the fashion industry as it can also transform sketches into fully colored images. Generative AI allows designers and artists to experience their vision in real-time with minimal effort (see Figure 4). With this technology, they can save valuable time and resources while being able to experiment without difficulty.

Figure 5. Image of a black-white sketch turned into photo-like color image by pix2pix.⁵

Additionally, generative AI can help limit human error, such as errors in color-matching and patterns. It can also enable fashion brands to become more creative, leveraging the ability to analyze numerous sketch-to-color combinations and generate multiple variations for review.

For example, Khroma is a tool that allows a trained algorithm to create genuine and personalized color palettes. Similarly, Colormind enables preparing creative color palettes based on preferred samples from movies, photographs, artworks, etc.

By implementing such tools, generative AI can also help to reduce the need for physical samples, saving time and resources.

3. Generating Representative Fashion Models

Using generative AI to create a diversity of fashion models can help fashion companies to better serve a wide range of customers and showcase their products in a more realistic and accurate way. A Cambridge University research shows that, when Dove’s advertising campaign featuring women of various skin tones and body types increased sales by 600% in two months.⁶

For being representative for all human body types, it can be used to create a diversity of fashion models in a virtual world in several ways:

• Virtual try-on: Generative AI can create virtual representations of fashion products that can be superimposed onto images of people, allowing customers to “try on” clothes virtually. These virtual models can be customized to represent a wide range of body types, colors and sizes, allowing customers to see how the clothes would look on them specifically.
• 3D rendering: Generative AI can create 3D models of fashion products that can be rotated and viewed from different angles. These models can be customized to represent a wide range of body types, colors, and sizes, allowing designers to see how the clothes would look on different body models.

Japanese tech company DataGrid used GANS technology to create models that can change bodily. You can watch the video released by the company showing a multitude of generated models:

Lalaland is another tech startup that makes hyper realistic virtual fashion models driven by generative AI for use on e-commerce platforms. It works by creating model avatars, uploading the images of garments, styling the product, and then downloading output images. 

4. Marketing & Trend Analysis for Fashion Brands

AI-powered generative models allow companies to speed up and improve their trend forecasting and marketing analytics capabilities. As a result, companies stay ahead of trends while meeting the customers’ future needs more effectively.

It can help trend analysis by:

• Bringing together a variety of techniques, such as machine learning and probabilistic programming. These techniques allow for powerful generative models that consider the customer desires in the fashion business.
• Generating deeply personalized options for specific consumer desires that go beyond what traditional analytics and customer demand algorithms can do.

It also improves marketing capabilities by:

• Utilizing data analysis, natural language processing and machine learning to create a highly tailored and personalized product range for the target audience
• Designing emails, website pages, captions, and ads that are tailored to a specific person’s interests and preferences in order to engage them
• Plotting creative and authentic marketing and ad content that are likely to storm search results

5. Protecting Data Privacy of Consumers

The fashion industry can utilize generative AI to improve consumer data privacy. The generative AI algorithms allows fashion companies to generate new designs while keeping customer data private. With synthetic datasets that generative AI produces, companies are able to create unique patterns and automated data analytics while protecting customers’ details, such as:

• Contact information 
• Banking information 
• Purchase history 
• Preferences 
• More from third parties 

It safeguards individuals’ financial security and provides organizations with valuable insights into their target market without invading people’s privacy. This way, generative AI offers a way for fashion brands to revolutionize their business strategy in a secure manner.

Challenges of generative AI for the fashion industry

The biggest challenge of generative AI for creative sectors such as the fashion industry can be the ambiguities around the copyright of AI-generated work. Using generative AI in the fashion industry can lead to some problems such as:

• Disclaiming the uniqueness, originality, or copyright eligibility of the generated designs or other fashion materials
• Ownership problems about whether the fashion designer or the programmer of the AI deserves the authorship rights of the generated work
• Misuse of such tools for unethical marketing strategies
• Risk of diminishing human creativity in the fashion industry and leading to unemployment

For more on the challenges of generative AI, you can check our articles on the copyright and ethical concerns around generative AI.

Further reading

If you are interested in the generative AI creations, applications, and tools in general, you should also check these articles:

• AI Text Generation: Top 12 Use Cases & 2 Case Studies
• Top 70+ Generative AI Applications / Use Cases
• Top 35 Generative AI Tools by Category (Text, Image…)
• Generative AI in Retail: 7 Use Cases, Examples & Benefits
• Generative AI in Manufacturing Industry: 5 Use Cases & 5 Benefits

If you have questions or need help regarding generative AI, feel free to reach out:

External Links

• 1. “State of Fashion Technology Report 2022.” McKinsey, 2 May 2022. Accessed 25 December 2022.
• 2. “Unpaired image-to-image translation using cycle-consistent adversarial networks.” ICCV 2017, Zhu, J.Y., Park, T., Isola, P., Efros, A.A.
• 3. “Artificial intelligence in the fashion industry | by Research Features.” Medium. Accessed 25 December 2022.
• 4. “How to Build an AI Fashion Designer | by Fathy Rashad.” Towards Data Science. Accessed 25 December 2022.
• 5. “Generative Models as Fashion Designers.” Artificial Intelligence for Fashion, 125–139. Luce, L. (2018). doi:10.1007/978-1-4842-3931-5_8 .
• 6. “Generative Deep Learning in Fashion: The Pinnacles, Perils And Why We’ll Be Synthesising Supermodels.” The Interline, 27 January 2022. Accessed 3 January 2024.