Technologies in AI Image Generation for Fashion

The intersection of AI and fashion has brought about transformative tools for showcasing garments, enhancing customer experiences, and streamlining production workflows. 

Below, we explore some different methods used to generate AI images along with their pros and cons.

1. Try-On Technology

This method leverages inpainting techniques to “paste” or overlay a garment onto a photograph of an existing model. By analyzing the contours and features of the input image, the AI adjusts the garment to fit the model’s pose and body shape.

Pros:

• Speed: Results can be generated quickly, making it ideal for high-volume use cases.

• Single Input Image: Requires only one image to perform the try-on.

• No need for photoshoot: Works from flatlays, hence allowing brands to skip the physical photoshoot process altogether.

Cons:

• Product Accuracy: Struggles with intricate garment details, such as complicated prints or textures, which may not be faithfully reproduced.

• Resolution Limitations: Outputs are often lower in resolution, which may limit their use for high-end marketing.

2. LoRA-Based Training

This method involves training a specialized neural network using LoRA (Low-Rank Adaptation) or similar techniques. The model learns to generate realistic images of the garment in various poses, lighting conditions, and contexts.

Pros:

• Highest Image Quality: Delivers high-resolution, photorealistic outputs suitable for premium campaigns.

• Flexibility: Allows the garment to be visualized in diverse poses and lighting scenarios, enhancing creative possibilities.

Cons:

• Training Requirements: Requires 4-10 images of the garment, increasing the preparation workload.

• Time-Consuming: Training can take 20 minutes to an hour, which may be inefficient for fast-paced projects.

• Cost: Computationally expensive, making it less accessible for smaller brands.

3. Background Swap or Model Swap

This technique uses foundation models in conjunction with controlnet or inpainting capabilities to modify the background or swap the model while preserving the garment’s original fit and appearance. This is ideal for enhancing photoshoot versatility without reshooting.

Pros:

• Product Accuracy: Ensures that the garment’s features remain intact and true-to-life.

• Single Input Image: Requires only one well-shot image of the garment to perform transformations.

Cons:

• Fit Model or Mannequin Dependency: The initial image must be taken on a fit model or mannequin, limiting the flexibility of sourcing base images.

• Limited Pose Variations: The garment’s pose remains static or changes minimally, reducing creative freedom.

• Immersion Limitations: May produce a “sticker-like” effect, where the garment looks less natural or integrated with the model or background.

• Shear Garments and Netted Outfits: This technique does not work with semi-transparent garments where the outfit needs to be partially changed.

Key Considerations for Adoption

Fashion brands aiming to integrate these technologies must assess their priorities. For quick previews or online retail, try-on technology may suffice. High-end campaigns demanding creative flexibility will benefit from LoRA-based training, despite its higher costs. Background and model swapping offers a middle ground, ideal for brands with existing imagery looking to enhance versatility without heavy investments.

By understanding these methods and their nuances, fashion businesses can leverage AI to elevate their visual content, optimize workflows, and better engage with their customers.