v1.0.0 - DiffusionModel

Release Notes for v1.0.0

We are excited to announce the release of version 1.0.0 of our Deep Learning Car Image Generator. This release marks the culmination of our efforts to develop an unconditional image generation model based on the paper "Denoising Diffusion Probabilistic Models" by Jonathan Ho, Ajay Jain, and Pieter Abbeel (2020). The application uses a Deep Learning model to generate new car images from scratch, employing a diffusion process to progressively refine random noise into realistic car images. Leveraging state-of-the-art techniques from recent research, our model ensures diverse image generation, with quality similar to the training data.

Key Features

• Unconditional Image Generation: Generate novel car images without any specific input conditions.
• Diffusion Process Implementation: Core image generation is based on the diffusion probabilistic model as described in the reference paper.
• Custom Neural Network Architecture: A neural network designed to optimize the image generation process, featuring:
  • Layer Normalization and Activation: Efficiently normalizes layers and incorporates ReLU activation functions.
  • Downsampling and Upsampling Blocks: Multi-scale processing through downsampling (left path) and upsampling (right path) blocks.
  • MLP Integration: Incorporates a Multi-Layer Perceptron (MLP) for feature extraction and transformation within the model.