Introduction

In a world where fashion trends change rapidly, staying ahead of the curve is both a challenge and an opportunity. The ability to predict and generate trendy designs can empower brands to cater to their audience’s evolving tastes more effectively. During a recent hackathon, our team, SheCodes from IIT Jodhpur, developed an AI-powered solution aimed at revolutionizing the "Forward" section of Myntra by generating trendy fashion designs targeted at GenZ. This blog will take you through the journey of creating this innovative project using state-of-the-art AI techniques, including Stable Diffusion 3, DreamBooth, and LoRA (Low-Rank Adaptation).

Project Overview

Objective

The project’s primary goal was to enhance Myntra's "Forward" section by automating the generation of fashionable dress designs tailored for GenZ users. We achieved this by fine-tuning a Stable Diffusion 3 model with DreamBooth LoRA, allowing the AI to learn and generate designs based on specific text prompts.

Team Composition

The project was executed by SheCodes, a team of three dedicated members:

• Aditi Baheti
• Aayushi Bhimani
• Ritu Singh

Implementation Stages

Our project was divided into three major stages: Dataset Preparation, Model Fine-Tuning, and Inference & Deployment.

1. Dataset Preparation

Collecting the Data

The foundation of any AI model lies in the quality of its dataset. We began by collecting a diverse set of images and captions from Myntra's "Forward" section. Each image was paired with a detailed text description, capturing essential attributes such as color, style, length, and pattern. This ensured that the model could learn the intricate details of fashion trends that resonate with GenZ.

Secure Image Identification with SHA-256 Hashing

To manage and maintain the integrity of our dataset, we employed SHA-256 hashing. This cryptographic technique provided a unique identifier for each image, enabling us to handle large datasets efficiently and avoid duplicate entries. By ensuring the uniqueness of each image, we could maintain a high standard of data quality throughout the project.

Computing High-Dimensional Embeddings

The next step involved computing high-dimensional embeddings for the image-text pairs in our dataset. These embeddings serve as a condensed representation of the data, capturing the most important features that the model would later use to generate new designs. This was achieved using a pre-trained text encoder and image processing pipeline from the Stable Diffusion model.

2. Model Fine-Tuning

Loading Stable Diffusion 3 and DreamBooth LoRA

Stable Diffusion 3, a state-of-the-art text-to-image generative model, formed the backbone of our project. We leveraged DreamBooth, a fine-tuning technique that allows the model to learn specific tasks, and LoRA, which enables fine-tuning with fewer parameters by focusing on low-rank adaptations. This combination allowed us to tailor the model specifically for generating fashion designs based on our curated dataset.

Configuring LoRA and Training the Model

The model was fine-tuned by adjusting the LoRA parameters, such as the rank and alpha values, to optimize learning. This involved iteratively training the model on our dataset while monitoring key metrics like loss and gradient accumulation. By the end of the training phase, the model was adept at generating high-quality fashion designs that reflected current trends and resonated with GenZ preferences.

3. Inference and Deployment

Generating Fashion Designs

With the model fine-tuned, we moved on to the inference phase, where the model was tasked with generating new fashion designs based on user-provided prompts. The ability of the model to interpret and creatively respond to these prompts was key to demonstrating the potential of AI in fashion design.

Deployment with Gradio and Hugging Face

For deployment, we chose Gradio, an open-source tool that makes it easy to create web-based interfaces for machine learning models. Integrated with Hugging Face, this setup allowed us to create a real-time, interactive experience where users could input their fashion preferences and receive AI-generated designs instantly. This deployment showcased how the model could be integrated into Myntra's platform to enhance user engagement.

Technical Breakdown

Understanding the Execution Flow

The overall execution of the project can be visualized in the flowchart provided. The process begins with collecting images from Myntra and applying SHA-256 hashing for secure identification. The images and captions are then transformed into embeddings using the DreamBooth fine-tuning method. These embeddings serve as the foundation for training the model, which is then fine-tuned using LoRA with Stable Diffusion 3. Finally, the model is deployed on Gradio, allowing users to generate fashion designs based on their prompts.

Example Outputs

To better understand the capabilities of our model, consider the following examples:

• Input Prompt: "Blue, white floral print tiered fit & flare dress, above the knee length, Square neck, Short, puffed sleeves."
  • Output: The model generates a dress design that closely matches the description, capturing the floral pattern, fit, and style as described.

• Input Prompt: "Beige-colored & black regular wrap top, Animal printed, V-neck, three-quarter, regular sleeves."
  • Output: The AI outputs a design that mirrors the input description, including the wrap style, animal print, and sleeve length.

These examples highlight the model's ability to interpret complex fashion descriptions and translate them into visually appealing designs that align with current trends.

Potential Impact

While this project was developed within the scope of a hackathon, its implications are far-reaching. The ability to automate fashion design using AI could significantly enhance the creative process for designers, reduce the time and effort required to produce new collections, and offer personalized shopping experiences for users. By integrating this technology into a platform like Myntra, brands can stay ahead of trends and cater more effectively to their audience, particularly the GenZ demographic.

Benefits to Myntra

• For Designers: Quick generation of diverse design options, reducing the time and effort required for the creative process.
• For Users: Personalized, trendy fashion suggestions that enhance the shopping experience.
• For Myntra: Increased user engagement and potentially higher conversion rates, contributing to overall business growth.

Conclusion

Our project demonstrates the potential of AI to revolutionize the fashion industry. By fine-tuning a state-of-the-art diffusion model with DreamBooth and LoRA, we were able to create a system capable of generating high-quality, trend-aligned fashion designs tailored to the preferences of GenZ. While the project was developed for a hackathon, the techniques and models we explored have real-world applications that could transform how fashion is designed and consumed.

We invite you to explore our GitHub repository for more details and to see how these techniques can be applied to other creative fields.