generative ai

Generative Ai

This article primarily focusses on Computer Vision and Diffusion models. Real World Applications Video/Image Restoration Take an old video or photo that is low quality or blurred and improve it using DL. Image Editing and Synthesis using text commands: “Make my smile wider” - Text suggested edits “segment image of guy wearing blue shirt and brown pants from an image” Text to speech Synthesis Here is a good summary of TTS algorithms from aiSummer School Speech to text OpenAI’s whisper Audio Generation Riffusion Code synthesis Generating Fakes (Photo’s, Videos, Personas) This is bread and butter for Generative algorithms ML Applications Text guided image generation also referred as Classifier Guidance In-Painting: This refers to the process of filling in missing or corrupted parts of an image or video with plausible content....

Third part tutorial for density generation using diffusion models

Denoising Diffusion Models Part 3: Generating Characters and numbers with Diffusion Models

Notebook Github Link Colab EMINST Denoising and Conditional generation Colab EMNIST Introduction We have introduced most of the concepts in the previous two blogs. In this blog post, we will see how the concepts translate to code. If you want to check out the earlier posts, you can find them here, diffusion model intro 1, and diffusion model intro 2. EMNIST dataset Extended-MNIST dataset, as the name suggests, is an extension of the popular MNIST dataset....

Colab tutorial for class conditioned diffusion models

Denoising Diffusion Models Part 2: Improving Diffusion Models

Code for this blog post: Notebook Github Link Colab Predicting Error and Score Function Error / Score Prediction Classifier free Guidance and other improvements Advanced concepts Topics to cover We have done most of the heavy-lifting in Part 1 of this series on Diffusion Models. To be able to use them well in practice, we may need to make some more improvements. That’s what we will do. Time step embedding and concatenation/fusion to the input data....

First part tutorial for density generation using diffusion models

Denoising Diffusion Models Part 1: Estimating True Distribution

Code for this blog post: Notebook Github Link Colab Basic: Predicting Original Distribution Vanilla Implementation The best way to learn is by writing the maths in your notebook alongside the tutorial, or by implementing the code alongside the notebooks. What are Denoising Diffusion Models? Denoising Diffusion Models, commonly referred to as “Diffusion models”, are a class of generative models based on the Variational Auto Encoder (VAE) architecture. These models are called likelihood-based models because they assign a high likelihood to the observed data samples $p(X)$....

Diffusion Model Jupyter and Colab Notebooks

The code accompanying the tutorials on denoising diffusion models. Notebook Description GitHub Link Colab Basic: Predicting Original Distribution Introduces Diffusion model concepts with PyTorch Vanilla Implementation Predicting Error and Score Function Diffusion models while predicting error with PyTorch Error / Score Prediction Classifier free Guidance and other improvements Diffusion models with Time Step Embeddings, Classifier Free Guidance, and time step striding to improve sampling from a diffusion model Advanced concepts EMINST Denoising and Conditional generation Working on EMNIST data Colab EMNIST If you have suggestions, please feel free to contribute to GitHub Repo....