3 Generative Models Explained in Azure

Key Takeaways

This demonstration covers three model types. Generative adversarial networks, GANs, diffusion, and auto reggressive. You'll see how each works and what problems they solve for generating text, numbers, and images. First, we'll review generative adversarial networks, or GANs for short. Unlike the other models where we'll use pre-built versions, we'll train again from scratch using Azure machine learning. In the Azure portal, you'll see the main dashboard. Under Azure services, select create a resource. Then search for machine learning and select it from the results. From the results page, under Azure Machine Learning, select create Azure Machine Learning in the drop- down box. Under the basics tab in the Azure Machine Learning screen, fill out this form. Select your subscription and resource group. Give your workspace a unique name. We'll use GAN-ML-workspace. Select the region closest to you. Finally, select the review plus create button at the lower left of the screen. Once validation completes, select create in the lower left corner. As you can see, our deployment is now in progress. After waiting a few seconds, you'll see that your deployment is complete. Select go to resource. From the resource page, select the launch studio to open the Azure Machine Learning Studio. In the Azure Machine Learning Studio, go to the notebooks tab on the left under the authoring section. Before we write any code into a notebook, we're going to create compute. To do so, select the create compute button. Name your compute GAN-P compute. Keep virtual machine type as CPU. Select standard E4DS. Then select review and create and create. This will take several minutes to set up. As you can see by the green dot, our compute is now running. In a notebook, we can create models programmatically. We have a notebook ready to go. So, we'll paste it here and run it to train our model. If you would like to run your own version of a GAN training notebook, we will have it linked in the download section. The output is newly generated samples that resemble the training data, typically visualized as grids of images that become more realistic as training progresses. for example, sharper faces or clearer objects or as batches of synthetic data points that match the source distribution. Next, we'll review diffusion models. Back on your main Azure portal page, open an instance of Azure AI Foundry. With an Azure AI Foundry or Foundry Hub resource open, launch the AI Foundry. Select on your project, then select go to project. With the Azure AI Foundry open, select models and endpoints in the left navigation to open the model catalog. Select deploy model and then deploy base model from the drop down. To find a diffusion model, open the inference tasks dropdown and search for text to image. Diffusion models generate new outputs by gradually denoising from pure noise over many steps, usually giving higher fidelity and broader coverage, but with slower inference and less acceleration. Within the text to image filtered results, scroll down until you see stable image core. Select this radio button and select confirm. Then select continue and subscribe and deploy. You can change this deployment name to your own custom name. Once satisfied, select deploy. This and any other models you deploy are tied to your subscription, which is tied to billing. Your subscription should be in the closest region to you to reduce cost and latency. With your model now deployed, select open and playground in the top left. Select your new deployment from the drop down. Enter in the prompt. create an image of an elephant in a small pool and then select generate. As you can see, almost as if magic, we now have a new picture of an elephant standing in a small pool. Let's try one more. Add create a house that has 10,000 square ft of space as well as a twocar garage and select generate. As you can see, diffusion models are very good at following our directions and rendering new images out of noise. Now we'll review auto reggressive models for time series forecasting. Return to the model catalog by selecting models and endpoints. Under deploy model, select deploy base model again and search for timegen and select time gen 1. Select confirm then continue and then subscribe and deploy. Unlike GANs or diffusion which are designed to generate independent samples that resemble a data distribution which is great for image and synthetic data, auto reggressive forecasting predicts the next step condition on priorly observed steps. This makes it ideal for a time series where order seasonality and lag dependencies matter. We prefer auto reggressive models here because they natively handle temporal structure, yield rolling, multi-step forecast, and let you incorporate external signals while maintaining coherence over time. Once ready, select deploy. Once deployed, you'll see the target URI and key under endpoint on the right. Select the copy icon on the right. Go back to your Azure machine learning notebook tab and create a new file. We'll name ours auto reggressive. Then select create. As with our GAN, we'll use sample code to connect to our Azure timegen model that we deployed previously. If you would like to do an example of this on your own, download the auto reggressive practice folder and open the forecasting notebook within it. You'll need to create a virtual environment and copy the target URI and key into your endpoint URL and API key variables. In this case, we'll copy the endpoint URL and API keys into their appropriate places in our code. You can run this code and see what happens when you connect to an Azure timegen model. In the case of our downloadable example, you will see that it loads the air passengers data set which shows monthly international airline passengers in Australia between the years of 1949 and 1960. And time gen 1 will generate a six-month forecast for 1961. You've now successfully worked with generative adversarial networks, diffusion models, and auto reggressive models.