AI-Driven Fashion: Creating Unique Trends for GenZ with Stable Diffusion 3 and DreamBooth LoRA
Introduction
In an ever-changing fashion world, staying updated with the latest trends can be both a challenge and an exciting opportunity. The ability to predict and create trendy designs can help brands effectively meet the dynamic tastes of their audience. During a recent hackathon, I, from IIT Jodhpur, developed an AI-based solution to transform Myntra’s “Forward” section by generating trendy fashion designs specifically aimed at GenZ. This blog takes you through the journey of building this innovative project using advanced AI tools like Stable Diffusion 3, DreamBooth, and LoRA (Low-Rank Adaptation).
Project Overview
Objective
The main aim of this project was to improve Myntra’s “Forward” section by automating the creation of fashionable dress designs for GenZ users. We achieved this by fine-tuning a Stable Diffusion 3 model with DreamBooth LoRA, which allowed the AI to learn and generate designs from specific prompts.
Implementation Stages
Our project was divided into three main stages: Dataset Preparation, Model Fine-Tuning, and Inference & Deployment.
1. Dataset Preparation
Collecting the Data
The foundation of any AI model is its dataset. We started by gathering a wide collection of images and captions from Myntra’s “Forward” section. Each image was linked to a detailed text description, covering important attributes like color, style, length, and pattern. This allowed the model to learn the intricate details that connect with GenZ fashion trends.
Secure Image Identification with SHA-256 Hashing
To maintain dataset integrity, we used SHA-256 hashing to generate unique identifiers for each image, allowing efficient dataset management and avoiding duplicates. This helped keep the data quality high throughout the project.
Computing High-Dimensional Embeddings
Next, we computed high-dimensional embeddings for the image-text pairs. These embeddings acted as condensed representations of the data, capturing the key features needed for generating new designs. We used a pre-trained text encoder and image processing pipeline from the Stable Diffusion model to achieve this.
2. Model Fine-Tuning
Loading Stable Diffusion 3 and DreamBooth LoRA
Stable Diffusion 3 formed the base of our project, serving as a cutting-edge text-to-image generation model. DreamBooth, a method for fine-tuning models, and LoRA, which allows tuning with fewer parameters by focusing on low-rank adaptations, were used to customize the model specifically for generating fashion designs.
Configuring LoRA and Training the Model
The model was fine-tuned by adjusting the LoRA parameters, such as rank and alpha values, to optimize learning. Training involved iterating over the dataset while monitoring key metrics like loss and gradient accumulation. By the end of the training phase, our model was generating high-quality designs that reflected current trends and appealed to GenZ.
3. Inference and Deployment
Generating Fashion Designs
Once the model was fine-tuned, we moved to the inference phase, where the model was used to generate new fashion designs based on user prompts. The model’s ability to understand and creatively respond to these prompts was key in showing AI’s potential in fashion design.
Deployment with Gradio and Hugging Face
We deployed the model using Gradio, an open-source tool that makes creating web-based interfaces for machine learning models easy. Combined with Hugging Face, this setup provided a real-time interactive experience, where users could input their fashion preferences and receive AI-generated designs instantly. This demonstrated how the model could be integrated into Myntra’s platform to boost user engagement.
Technical Breakdown
Understanding the Execution Flow
The entire process began with collecting images from Myntra, applying SHA-256 hashing for secure identification. Images and captions were then transformed into embeddings using DreamBooth fine-tuning, which served as input for training the model with LoRA on Stable Diffusion 3. Finally, we deployed the model on Gradio for real-time user interaction.
Example Outputs
To show what our model could do, here are some examples:
- Input Prompt: “Khaki animal printed maxi dress,Shoulder straps,Sleeveless”
Output: The generated design closely matched the prompt, capturing the animal pattern, fit, and style.
- Input Prompt: “Black solid bodysuit,has a mandarin collar and long sleeves with zip clouser”
- Output: The AI created a design that effectively represented the solid bodysuit.
These examples highlight the model’s ability to convert complex fashion descriptions into attractive designs that match current trends.
Potential Impact
Though developed during a hackathon, this project has wider implications. Using AI to automate the fashion design process could significantly enhance the creative workflow for designers, reducing the time and effort needed to produce new collections and offering personalized shopping experiences. Integrating such technology into a platform like Myntra can help brands stay ahead of trends and serve their audiences better, especially the GenZ demographic.
Benefits to Myntra
- For Designers: Quick generation of diverse design options, making the creative process easier.
- For Users: Personalized, trendy fashion suggestions that enhance the shopping experience.
- For Myntra: Increased user engagement and potentially higher conversion rates, helping business growth.
Conclusion
Our project demonstrates how AI can transform the fashion industry. By fine-tuning Stable Diffusion 3 with DreamBooth and LoRA, we developed a system capable of generating high-quality, trend-aligned designs that cater to GenZ preferences. While this project was created during a hackathon, the techniques and models explored here have real-world applications that could change how fashion is designed and consumed.
Explore our GitHub repository for more details and see how these techniques can be applied to other creative fields.
