Generation of Synthetic Data for Sustainable Fashion Using a Diffusion Model

The fashion industry is a significant contributor to greenhouse gas emissions and textile waste, prompting the need for sustainable practices. This thesis explores the use of diffusion models for generating synthetic data to enhance datasets used in machine learning, specifically focusing on second-hand fashion. Diffusion models, known for their ability to create high-quality images, offer potential solutions to the imbalance and quality issues in existing datasets. The study investigates how image generation and editing through diffusion models can improve datasets, the effectiveness of different prompting strategies, and the performance of synthetic data in machine learning models compared to real data. The methodology involves using the Kandinsky 2.2 inpainting model to generate and edit images, followed by manual and automated classification to evaluate image quality. Experiments demonstrate that diffusion models can plausibly improve dataset quality by adding and removing damage in images, although fully automating this process remains challenging. The results indicate that augmenting the datasets with synthetic images can potentially enhance the performance of the model, although the variability of the results suggests the need for further research. This thesis contributes to the field of sustainable fashion by proposing innovative methods for dataset augmentation using state-of-the-art generative models, aiming to support the development of efficient and automated sorting processes in the textile industry.