Text to image generation using Diffusion models by Shahryar AText to image generation using Diffusion models by Shahryar A

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.

# Sample code for generating images from text using a diffusion model

import torch

from diffusion_model import DiffusionModel



# Load pre-trained diffusion model

diffusion_model = DiffusionModel.load_model('diffusion_model.pth')



# Generate image from text

text_description = "A serene landscape with mountains and a lake."

generated_image = diffusion_model.generate_image(text_description)



# Sample code for fairness assessment using Fair Face model

import torch

from fair_face_model import FairFaceModel



# Load pre-trained Fair Face model

fair_face_model = FairFaceModel.load_model('fair_face_model.pth')



# Assess fairness of a generated image

generated_image = load_generated_image('generated_image.png')

fairness_score = fair_face_model.measure_fairness(generated_image)



# Print fairness score

print(f"Fairness Score: {fairness_score}")

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Posted Dec 28, 2023

Implemented state-of-the-art Diffusion models to generate images from textual descriptions. Additionally, the project incorporates a Fair Face model to evaluate

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