here are my exercises for learning about Deffusion models and their usage!
This repository is dedicated to exploring diffusion models, a class of probabilistic models used primarily for generative tasks in machine learning. The projects within this repository showcase the implementation and analysis of diffusion models, focusing on different datasets and applications. Each project is organized into separate folders, containing Jupyter Notebooks that detail the steps taken, along with the datasets used.
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Project 1: Image Generation with Diffusion Models
- Jupyter Notebook: Implements a diffusion model for generating high-quality images. The notebook covers the model architecture, training process, and evaluation of generated images.
- Dataset: Contains image datasets used for training and testing the diffusion model.
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Project 2: Text-to-Image Synthesis
- Jupyter Notebook: Explores the application of diffusion models for text-to-image synthesis, where descriptive text is used to generate corresponding images.
- Dataset: Includes paired text and image data used to train the model for generating images based on textual descriptions.
Each project is equipped with the necessary code and datasets, making it easy to replicate the experiments and gain insights into the capabilities of diffusion models.