Generative AI in Fashion: Top 10 Use Cases & Examples

89% of all companies across different sectors are switching to digital technologies, and the generative AI in 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 has started affecting many elements of the fashion industry.

Here, we’ll explore the use cases and real-world examples of how generative AI is being leveraged to innovate in the fashion industry.

Generative AI tools for image & design generation

Before diving into specific applications of generative AI in the fashion industry, it’s important to understand how the technology creates original images and content that form the foundation of a design.

Using generative algorithms, AI can create unique, interesting images that merge computer-generated styling with human 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 can generate designs in the style of different artists and artistic genres, such as Monet, van Gogh, Cezanne and Ukiyo-e. (Source: ICCV 2017.)

ComfyUI is a free, open-source, node-based tool built on diffusion models like Stable Diffusion, enabling users to design and iterate generative flows. In the fashion world, it’s increasingly leveraged to automate creative tasks such as virtual clothing try‑on, outfit swapping, and fashion‐idea generation:

Projects like FLUX AI utilize multi‑node ComfyUI setups to create polished, realistic outfit changes in seconds, matching industry standards.

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 in fashion is capable of creating surprising and stylistic designs from a basic object. 

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

Design and Creative Applications

1. Pattern and Print Generation

Fashion designers traditionally spend considerable time creating original patterns and prints. Generative AI now assists in this process by producing novel designs based on specified parameters or style references.

Adidas has experimented with AI-generated shoe designs, using algorithms to create new colorways and patterns for existing silhouettes. The company’s FutureCraft initiative incorporates machine learning to generate design variations that might not occur through traditional human creativity alone.

Similarly, fashion tech company Stitch Fix uses generative models to create unique prints for its private label clothing. The system analyzes successful patterns from previous seasons and generates new variations that maintain aesthetic appeal while offering novelty.

The technology proves particularly useful for fast fashion retailers who need to produce high volumes of varied designs quickly. However, the output quality depends heavily on training data quality and often requires human refinement to achieve commercial viability.

Besides, you don’t need to be an exclusive fashion designer to create 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).²

Figure 4. Products generated by ClothingGAN.

2. Textile Design Innovation

Generative AI extends beyond surface patterns to textile structure design. Researchers at MIT have developed systems that can generate new fabric weave patterns by learning from existing textile databases. These AI-generated structures can then be physically produced using automated weaving equipment.

Fashion brand Unmade leverages generative design for creating customizable knitwear patterns. Their system allows customers to modify base designs through an interface, with AI generating the technical specifications needed for production. This approach bridges mass customization with manufacturing efficiency.

The technology also assists in sustainable textile development. AI models can generate fabric compositions that optimize for specific properties like durability, breathability, or biodegradability while maintaining aesthetic requirements.

3. Color Palette Development

Color selection significantly impacts consumer appeal and brand identity. Generative AI helps fashion companies develop color palettes by analyzing trend data, seasonal patterns, and consumer preferences.

Pantone, the color authority company, has explored AI tools for trend forecasting and palette generation. Their systems analyze social media imagery, runway shows, and cultural events to predict color trends and generate coordinated palettes for fashion brands.

Fashion forecasting agency WGSN uses generative models to create color combinations that align with predicted consumer preferences. The system considers factors like geographic location, demographic data, and seasonal variations to produce targeted color recommendations.

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.

Production and Manufacturing Applications

4. Size Grading and Pattern Adaptation

Traditional size grading requires skilled pattern makers to adapt designs across different sizes manually. Generative AI automates much of this process by learning how garments should fit different body types and generating appropriate pattern adjustments.

Hong Kong-based fashion technology company Tukatech has developed AI systems that can automatically grade patterns from a base size to full size ranges. The technology reduces grading time from hours to minutes while maintaining fit quality across sizes.

Fashion brand Reformation uses AI-assisted grading to ensure consistent fit across their size range. The system analyzes fit feedback from customers and adjusts grading rules to improve satisfaction with garment sizing.

5. Quality Control and Defect Detection

Manufacturing quality control traditionally relies on human inspection, which can be inconsistent and time-consuming. Computer vision models trained on garment defects can identify issues like stitching problems, fabric flaws, or construction errors automatically.

Chinese manufacturer TAL Apparel has implemented AI quality inspection systems across their facilities. The technology identifies defects in real-time during production, reducing waste and improving overall product quality. The system reportedly catches defects that human inspectors might miss while processing garments faster than manual inspection.

Similar systems are being adopted by manufacturers worldwide, with varying success rates depending on garment complexity and defect types. Simple defects like holes or stains are detected reliably, while subtle fitting issues remain challenging for current AI systems.

6. Production Planning and Demand Forecasting

Accurate demand prediction helps fashion brands optimize inventory and reduce waste. Generative AI models can analyze historical sales data, trend indicators, and external factors to predict demand for specific products.

Zara’s parent company, Inditex, uses AI models to forecast demand across its global retail network. The system considers factors like weather patterns, local events, and regional preferences to predict sales volumes for different products in different markets.

Fast fashion retailer H&M has implemented AI-driven demand forecasting to reduce inventory waste. Their system analyzes multiple data sources including social media trends, search patterns, and historical sales to predict which items will be popular in specific markets.

Consumer Experience and Personalization

7. Virtual Try-On Technology

Online fashion shopping faces the challenge of fit uncertainty. Generative AI creates virtual try-on experiences that show how garments would appear on individual customers using their photos or body measurements.

Sephora’s Virtual Artist app uses generative models to show how makeup products would look on users’ faces. While primarily focused on cosmetics, the technology demonstrates the potential for similar applications in fashion accessories and clothing.

Snapchat’s new augmented reality (AR) features allow real-fit try-on from major fashion brands

Fashion retailer ASOS has tested virtual fitting rooms that generate images of how clothes would fit customers based on their uploaded photos and measurements. Early implementations showed promise but faced challenges with accuracy and user adoption.

Startup companies like Zeekit (acquired by Walmart) have developed specialized virtual try-on technology for fashion e-commerce. Their system generates realistic images of customers wearing different garments, though adoption varies by product category and customer demographic.

8. Personalized Product Recommendations

Generative AI in fashion can create personalized product recommendations that go beyond traditional collaborative filtering. These systems generate suggestions based on individual style preferences, body type, lifestyle factors, and purchase history.

Stitch Fix has built its business model around AI-powered personalization. Their algorithms analyze customer preferences, feedback, and styling outcomes to generate personalized clothing selections. The system continuously learns from customer responses to improve future recommendations.

Amazon’s fashion recommendations use generative models to suggest complete outfits rather than individual items. The system considers how different pieces work together and generates coordinated looks based on customer preferences and seasonal trends.

9. Style Transfer and Customization

Generative AI in fashion enables customers to modify existing designs or create new ones based on their preferences. Style transfer algorithms can apply the aesthetic of one garment to another, creating personalized variations.

Fashion brand Eon has developed a platform where customers can modify existing designs using AI tools. Users can adjust colors, patterns, and styling details, with the system generating production-ready specifications for customized garments.

Nike has experimented with AI-powered customization tools that allow customers to generate unique designs for shoes and apparel. The system combines customer inputs with design constraints to create feasible products that can be manufactured.

Marketing and Brand Applications

10. Content Generation for Social Media

Fashion brands require constant content creation for social media marketing. Generative AI in fashion can create product photography, model imagery, and marketing copy to support digital marketing efforts.

Fashion brand Levi’s has used AI-generated models in their marketing campaigns to show diversity in body types and ethnicities. The technology allows brands to create more inclusive imagery without the costs associated with traditional photo shoots.

Online retailer Boohoo has experimented with AI-generated product photography that shows clothing in different settings and on various model types. This approach reduces photography costs while providing more diverse imagery for their e-commerce platform.

11. Trend Analysis and Forecasting

Understanding fashion trends requires analyzing vast amounts of visual and textual data from multiple sources. Generative AI can process this information and generate trend reports and predictions.

Fashion forecasting company Heuritech uses AI to analyze social media images and identify trending styles, colors, and silhouettes. Their system can predict which trends will become mainstream based on early adoption patterns observed online.

Trend forecasting agency Fashion Snoops uses generative models to create visual mood boards and trend presentations based on data analysis. The technology helps translate data insights into actionable design directions for fashion brands.

12. Dynamic Pricing and Inventory Optimization

Generative AI in fashion models can simulate different pricing scenarios and predict their impact on sales and inventory levels. This capability helps fashion retailers optimize pricing strategies across different markets and seasons.

Fashion retailer Nordstrom uses AI models to optimize pricing across their inventory. The system considers factors like competitor pricing, inventory levels, seasonality, and customer demand to suggest optimal price points for different products.

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:

Creative Authenticity Concerns

The fashion industry values originality and creative expression. Some designers and brands worry that AI-generated designs may lack the human creativity and cultural understanding that drives fashion innovation.

Several high-profile cases have emerged where AI-generated designs closely resembled existing works, raising questions about originality and intellectual property. Fashion brands must balance AI efficiency with maintaining creative integrity and brand identity.

Technical Accuracy and Quality Control

Current generative AI in fashion systems often produces outputs that require human refinement. Fashion applications demand high accuracy in areas like fit, drape, and technical specifications, where errors can result in unwearable products.

Virtual try-on technologies still struggle with accurately representing how fabrics drape on different body types. The technology works better for structured garments than flowing fabrics, limiting its applicability across all fashion categories.

Consumer Acceptance and Trust

Consumer adoption of AI-driven fashion features varies significantly. While some customers appreciate personalized recommendations and virtual try-on capabilities, others prefer traditional shopping experiences.

Data privacy concerns also affect consumers willingness to share personal information needed for AI personalization. Fashion brands must balance personalization capabilities with customer privacy expectations.

Integration with Existing Workflows

Fashion companies often operate with established design and production processes. Integrating generative AI in fashion requires significant changes to workflows and may face resistance from employees accustomed to traditional methods.

Training staff to use AI tools effectively requires investment in education and change management. Companies report varying success rates depending on how well they manage this transition process.

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
• Generative AI in Retail: 7 Use Cases, Examples & Benefits
• Generative AI in Manufacturing Industry: 5 Use Cases & 5 Benefits

External Links

• 1. State of Fashion Technology Report 2022 | McKinsey. McKinsey & Company
• 2. “How to Build an AI Fashion Designer | by Fathy Rashad.” Towards Data Science. Accessed 25 December 2022.