Text to image generation using Diffusion models
Shahryar A
Data Scientist
Data Analyst
ML Engineer
I implemented state-of-the-art Diffusion models to generate images from textual descriptions. Additionally, the project incorporates a Fair Face model to evaluate and ensure fairness in the generated images, particularly focusing on demographic attributes.
Key Components:
Text to Image Generation (Diffusion Models):
I employed Diffusion models, leveraging the PyTorch framework, to generate realistic images from textual descriptions. The models were trained on a diverse dataset containing pairs of textual descriptions and corresponding images. The diffusion process, involving the iterative addition of noise to an image, allowed for the generation of high-quality and coherent images.
Fair Face Model:
The Fair Face model was integrated to assess and mitigate biases in the generated images, with a specific focus on demographic attributes such as age, gender, and ethnicity. Adversarial training techniques were employed to minimize biases and ensure fair representation across various demographic groups.
Evaluation:
Quantitative Evaluation:
Quantitative measures, including Inception Score for image quality and predefined fairness metrics, were used to evaluate the performance of the models.
Qualitative Evaluation:
User studies were conducted to gather feedback on the perceived quality and fairness of the generated images.
Front End :
A user interface was implemented using Flask, providing users with the ability to input text and view generated images. Fairness metrics were also displayed for transparency and user understanding.
I am excited about the successful completion of this project, and it marks a significant achievement in the realm of text-to-image generation with a focus on fairness. The integration of Diffusion models and the Fair Face model ensures not only high-quality images but also a commitment to fairness in representation.
