Generative AI in Fashion and Retail: Visualizing Products and Trends
Fashion is a dynamic and ever-evolving industry driven by creativity and the pursuit of the next big trend. Designers constantly seek innovative ways to explore new concepts and bring their visions to life.
⚠️ To see more images generated for this article, click on this link.
In recent years, generative artificial intelligence (AI) has emerged as a powerful tool that promises to revolutionize the creative industry, enabling rapid prototyping, concept exploration, and creativity augmentation. This article delves into generative AI and its applications in fashion trend exploration.
What is Generative AI?
Generative AI refers to a class of machine learning techniques that enable computers to generate new content, such as images, music, or text, that is indistinguishable from human-created content. It relies on neural networks trained on vast data to learn patterns and generate new, original outputs.
In fashion, generative AI models can be trained on diverse clothing items, patterns, and styles datasets to create unique and novel fashion designs.
Traditionally, fashion designers would go through a lengthy and iterative process of sketching, fabric sourcing, and physical prototyping to bring their ideas to life. Generative AI offers an exciting alternative by allowing designers to prototype and visualize their concepts in a digital environment rapidly.
By inputting design parameters and styles into generative models, designers can generate numerous variations of garments within seconds. This speeds up the design process, reduces costs, and provides a quick experimentation and iteration platform.
Exploring the “Low Rise Waist” Trend
2023 is the year when the low-rise waist jeans will make their way back. Recently, I embarked on a journey to explore the resurgence of this trend using generative AI. I wanted to test the computer’s ability to generate a range of outfits with little to no boundaries.
I employed ControlNet Reference and prompt engineering techniques to refine further and guide the output of the generative model. ControlNet Reference is a method that involves providing reference images to guide the process. By selecting only one specific fashion image that exemplified the low-rise trend, I could steer the generative model toward producing designs aligned with my vision.
Prompt engineering, on the other hand, involves carefully crafting textual prompts to elicit desired outputs from the generative model. By experimenting with various prompts, I could influence the generated outfits' style, color palette, lighting, etc. This level of control allowed me to actively participate in the generative process and fine-tune the results to align with my creative vision.
I started with the “wrong” prompt, which generated the images below:
After some adjustments, the model started to “get” what I wanted, generating the following 12 images:
But I was still not happy about the lightning, so after a few adjustments, here’s what the model came up with:
Generative AI: a wild horse
Contrary to popular belief, generative models do not operate in isolation. They require careful fine-tuning and the expertise of a human professional to ensure that the generated results align with the desired outcomes.
Without this human touch, the model can easily veer off course and produce outcomes that deviate significantly from the intended vision. Therefore, it is crucial to have an experienced professional working with these generated images, ensuring that they adhere to the brand’s values and maintain the desired aesthetic.
Talking about aesthetics, what if we would like to merge the low-rise waist concept with a collection based on the clouds? As you can see, generative AI is flexible enough to allow you to experiment with little cost:
As you can see, this was a very quick and “free” experiment. Can you imagine how many exciting possibilities Generative AI can open for fashion designers to explore and experiment with trends using a more structured approach?
By inputting keywords or style preferences, generative models can produce a plethora of concept images that embody the desired aesthetic. These AI-generated images provide inspiration and a starting point for designers to refine their ideas, encouraging innovative thinking and experimentation.
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