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Each project is presented as a Jupyter notebook and includes detailed explanations, code examples, and visualizations to help you understand how GANs and Diffusion Models work and how to use them.\n\n## 🚀 Getting started\n\nTo get started, you'll need to install the dependencies listed in `requirements.txt`. You can do this by running:\n\n```pip install -r requirements.txt```\n\nOnce you've installed the dependencies, you can run the Jupyter notebooks in the notebooks directory. Each notebook includes step-by-step instructions and code examples to run and experiment with.\n\n## 📝 Projects\n\n### GAN\n\n**Project 1: Basic GAN**\n\nIn this project, you'll learn the basics of GANs and build a simple GAN that generates images of handwritten digits. You'll also learn how to evaluate the performance of your GAN and how to generate new images.\n\n**Project 2: Conditional GAN**\n\nIn this project, you'll learn how to build a conditional GAN that generates images of animals based on their species. You'll also learn how to use a pretrained classifier to guide the generation process and improve the quality of the generated images.\n\n**Project 3: Wasserstein GAN**\n\nIn this project, you'll learn about Wasserstein GANs, a variant of GANs that use a different loss function to train the generator and discriminator. You'll build a Wasserstein GAN that generates images of faces and compare its performance to a traditional GAN.\n\n**Project 4: StyleGAN**\n\nIn this project, you'll learn about StyleGAN, a state-of-the-art GAN architecture that can generate high-quality images with fine-grained control over the style and appearance. You'll build a StyleGAN that generates images of landscapes and experiment with different styles and settings.\n\n### Diffusion\n\n* [**Introduction to 🤗 Diffusers**](notebooks/introduction_to_diffusers.ipynb)\n  \n    In this notebook, a diffusion model is trained to generate images of cute butterflies 🦋. This process will cover the core components of the 🤗 Diffusers library, laying a solid foundation for more advanced applications.[]()\n\n**Project 6: Diffusion with Conditioning**\n\nIn this project, you'll learn how to condition diffusion models on additional inputs to guide the generation process. You'll build a conditioned diffusion model to generate images based on specific conditions or constraints.\n\n**Project 7: Advanced Diffusion Techniques**\n\nIn this project, you'll explore advanced techniques in diffusion models, including enhancements and modifications to improve the quality and efficiency of the generated images. You'll implement and experiment with state-of-the-art diffusion methods.\n\n## 📝 Contributing\n\nIf you find a bug or have a suggestion for a new project, please open an issue or submit a pull request. We welcome contributions from the community and are happy to help newcomers get started.\n\n## 📄 License\n\nThis repository is licensed under the MIT License. See the [LICENSE]() file for more information.\n\n## 🙏 Acknowledgments\n\nWe would like to thank the authors of the papers and tutorials that inspired this collection, as well as the open-source contributors who made this work possible.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsanikamal%2Fgan-diffusion-atoz","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsanikamal%2Fgan-diffusion-atoz","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsanikamal%2Fgan-diffusion-atoz/lists"}