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How to Build your own AI Text-to-Image Generator
Build your own AI Text-to-Image Generator in Visual Studio Code Do you want to build your own AI Text-to-Image Generator in less than 15 minutes? Join me as I'll walk you through the process of building one using Stable Diffusion within Visual Studio Code! Prerequisites Before you start, ensure you have the following: Python 3.9 or higher. Hugging Face Account. Step 1: Set Up the Development Environment In your project directory, create a file named requirements.txt and add the following dependencies to the file: certifi==2022.9.14 charset-normalizer==2.1.1 colorama==0.4.5 customtkinter==4.6.1 darkdetect==0.7.1 diffusers==0.3.0 filelock==3.8.0 huggingface-hub==0.9.1 idna==3.4 importlib-metadata==4.12.0 numpy==1.23.3 packaging==21.3 Pillow==9.2.0 pyparsing==3.0.9 PyYAML==6.0 regex==2022.9.13 requests==2.28.1 tk==0.1.0 tokenizers==0.12.1 torch==1.12.1+cu113 torchaudio==0.12.1+cu113 torchvision==0.13.1+cu113 tqdm==4.64.1 transformers==4.22.1 typing_extensions==4.3.0 urllib3==1.26.12 zipp==3.8.1 To install the listed dependencies in the requirements.txt file, run the following command in your terminal: pip install -r requirements.txt Step 2: Configure Authentication In your project directory, create a file named authtoken.py and add the following code to the file: auth_token = "ACCESS TOKEN FROM HUGGING FACE" To obtain access token from Hugging Face, follow these steps: Log in to your Hugging Face account. Go to your profile settings and select Access Tokens Click on Create new token. Choose the token type as Read. Enter Token name and click Create token. Copy the generated token and replace ACCESS TOKEN FROM HUGGINGFACE in authtoken.py file with your token. Step 3: Develop the Application In your project directory, create a file named application.py and add the following code to the file: # Import the Tkinter library for GUI import tkinter as tk # Import the custom Tkinter library for enhanced widgets import customtkinter as ctk # Import PyTorch for handling tensors and model import torch # Import the Stable Diffusion Pipeline from diffusers library from diffusers import StableDiffusionPipeline # Import PIL for image handling from PIL import Image, ImageTk # Import the authentication token from a file from authtoken import auth_token # Initialize the main Tkinter application window app = tk.Tk() # Set the size of the window app.geometry("532x632") # Set the title of the window app.title("Text-to-Image Generator") # Set the appearance mode of customtkinter to dark ctk.set_appearance_mode("dark") # Create an entry widget for the prompt text input prompt = ctk.CTkEntry(height=40, width=512, text_font=("Arial", 20), text_color="black", fg_color="white") # Place the entry widget at coordinates (10, 10) prompt.place(x=10, y=10) # Create a label widget for displaying the generated image lmain = ctk.CTkLabel(height=512, width=512) # Place the label widget at coordinates (10, 110) lmain.place(x=10, y=110) # Define the model ID for Stable Diffusion modelid = "CompVis/stable-diffusion-v1-4" # Define the device to run the model on device = "cpu" # Load the Stable Diffusion model pipeline pipe = StableDiffusionPipeline.from_pretrained(modelid, revision="fp16", torch_dtype=torch.float32, use_auth_token=auth_token) # Move the pipeline to the specified device (CPU) pipe.to(device) # Define the function to generate the image from the prompt def generate(): # Disable gradient calculation for efficiency with torch.no_grad(): # Generate the image with guidance scale image = pipe(prompt.get(), guidance_scale=8.5)["sample"][0] # Convert the image to a PhotoImage for Tkinter img = ImageTk.PhotoImage(image) # Keep a reference to the image to prevent garbage collection lmain.image = img # Update the label widget with the new image lmain.configure(image=img) # Create a button widget to trigger the image generation trigger = ctk.CTkButton(height=40, width=120, text_font=("Arial", 20), text_color="white", fg_color="black", command=generate) # Set the text on the button to "Generate" trigger.configure(text="Generate") # Place the button at coordinates (206, 60) trigger.place(x=206, y=60) # Start the Tkinter main loop app.mainloop() To run the application, execute the following command in your terminal: python application.py This will launch the GUI where you can enter a text prompt and generate corresponding images by clicking the Generate button. Congratulations! You have successfully built an AI Text-to-Image Generator using Stable Diffusion in Visual Studio Code. Feel free to explore and enhance the application further by adding new features and improving the user interface. Happy coding!Understand the development lifecycle of a large language model (LLM) app
Before understanding how to work with prompt flow, let's explore the development lifecycle of a Large Language Model (LLM) application. The lifecycle consists of the following stages: Initialization: Define the use case and design the solution. Experimentation: Develop a flow and test with a small dataset. Evaluation and refinement: Assess the flow with a larger dataset. Production: Deploy and monitor the flow and application. During both evaluation and refinement, and production, you might find that your solution needs to be improved. You can revert back to experimentation during which you develop your flow continuously, until you're satisfied with the results. Let's explore each of these phases in more detail. Initialization Imagine you want to design and develop an LLM application to classify news articles. Before you start creating anything, you need to define what categories you want as output. You need to understand what a typical news article looks like, how you present the article as input to your application, and how the application generates the desired output. In other words, during initialization you: Define the objective Collect a sample dataset Build a basic prompt Design the flow To design, develop, and test an LLM application, you need a sample dataset that serves as the input. A sample dataset is a small representative subset of the data you eventually expect to parse as input to your LLM application. When collecting or creating the sample dataset, you should ensure diversity in the data to cover various scenarios and edge cases. You should also remove any privacy sensitive information from the dataset to avoid any vulnerabilities. Experimentation You collected a sample dataset of news articles, and decided on which categories you want the articles to be classified into. You designed a flow that takes a news article as input, and uses an LLM to classify the article. To test whether your flow generates the expected output, you run it against your sample dataset. The experimentation phase is an iterative process during which you (1) run the flow against a sample dataset. You then (2) evaluate the prompt's performance. If you're (3) satisfied with the result, you can move on to evaluation and refinement. If you think there's room for improvement, you can (4) modify the flow by changing the prompt or flow itself. Evaluation and refinement When you're satisfied with the output of the flow that classifies news articles, based on the sample dataset, you can assess the flow's performance against a larger dataset. By testing the flow on a larger dataset, you can evaluate how well the LLM application generalizes to new data. During evaluation, you can identify potential bottlenecks or areas for optimization or refinement. When you edit your flow, you should first run it against a smaller dataset before running it again against a larger dataset. Testing your flow with a smaller dataset allows you to more quickly respond to any issues. Once your LLM application appears to be robust and reliable in handling various scenarios, you can decide to move the LLM application to production. Production Finally, your news article classification application is ready for production. During production, you: Optimize the flow that classifies incoming articles for efficiency and effectiveness. Deploy your flow to an endpoint. When you call the endpoint, the flow is triggered to run and the desired output is generated. Monitor the performance of your solution by collecting usage data and end-user feedback. By understanding how the application performs, you can improve the flow whenever necessary. Explore the complete development lifecycle Now that you understand each stage of the development lifecycle of an LLM application, you can explore the complete overview:What is Convolutional Neural Network — CNN (Deep Learning)
Convolutional Neural Networks (CNNs) are a type of deep learning neural network architecture that is particularly well suited to image classification and object recognition tasks. A CNN works by transforming an input image into a feature map, which is then processed through multiple convolutional and pooling layers to produce a predicted output. Convolutional Neural Network — CNN architecture In this blog post, we will explore the basics of CNNs, including how they work, their architecture, and how they can be used for a wide range of computer vision tasks. We will also provide examples of some real-world applications of CNNs, and outline some of the benefits and limitations of this deep-learning architecture. Working of Convolutional Neural Network: A convolutional neural network starts by taking an input image, which is then transformed into a feature map through a series of convolutional and pooling layers. The convolutional layer applies a set of filters to the input image, each filter producing a feature map that highlights a specific aspect of the input image. The pooling layer then downsamples the feature map to reduce its size, while retaining the most important information. The feature map produced by the convolutional layer is then passed through multiple additional convolutional and pooling layers, each layer learning increasingly complex features of the input image. The final output of the network is a predicted class label or probability score for each class, depending on the task. The architecture of Convolutional Neural Network: A typical CNN architecture is made up of three main components: the input layer, the hidden layers, and the output layer. The input layer receives the input image and passes it to the hidden layers, which are made up of multiple convolutional and pooling layers. The output layer provides the predicted class label or probability scores for each class. The hidden layers are the most important part of a CNN, and the number of hidden layers and the number of filters in each layer can be adjusted to optimize the network’s performance. A common architecture for a CNN is to have multiple convolutional layers, followed by one or more pooling layers, and then a fully connected layer that provides the final output. Applications of Convolutional Neural Network: CNNs have a wide range of applications in computer vision, including image classification, object detection, semantic segmentation, and style transfer. Image classification: Image classification is the task of assigning a class label to an input image. CNNs can be trained on large datasets of labeled images to learn the relationships between the image pixels and the class labels, and then applied to new, unseen images to make a prediction. Object detection: Object detection is the task of identifying objects of a specific class in an input image and marking their locations. This can be useful for applications such as security and surveillance, where it is important to detect and track objects in real time. Semantic segmentation: Semantic segmentation is the task of assigning a class label to each pixel in an input image, producing a segmented image that can be used for further analysis. This can be useful for applications such as medical image analysis, where it is important to segment specific structures in an image for further analysis. Style transfer: Style transfer is the task of transferring the style of one image to another image while preserving the content of the target image. This can be useful for applications such as art and design, where it is desired to create an image that combines the content of one image with the style of another. Layers of Convolutional neural network: The layers of a Convolutional Neural Network (CNN) can be broadly classified into the following categories: Convolutional Layer: The convolutional layer is responsible for extracting features from the input image. It performs a convolution operation on the input image, where a filter or kernel is applied to the image to identify and extract specific features. Convolutional Layer Pooling Layer: The pooling layer is responsible for reducing the spatial dimensions of the feature maps produced by the convolutional layer. It performs a down-sampling operation to reduce the size of the feature maps and reduce computational complexity. MaxPooling Layer Activation Layer: The activation layer applies a non-linear activation function, such as the ReLU function, to the output of the pooling layer. This function helps to introduce non-linearity into the model, allowing it to learn more complex representations of the input data. Activation Layer Fully Connected Layer: The fully connected layer is a traditional neural network layer that connects all the neurons in the previous layer to all the neurons in the next layer. This layer is responsible for combining the features learned by the convolutional and pooling layers to make a prediction. Fully Connected Layer Normalization Layer: The normalization layer performs normalization operations, such as batch normalization or layer normalization, to ensure that the activations of each layer are well-conditioned and prevent overfitting. Dropout Layer: The dropout layer is used to prevent overfitting by randomly dropping out neurons during training. This helps to ensure that the model does not memorize the training data but instead generalizes to new, unseen data. Dense Layer: After the convolutional and pooling layers have extracted features from the input image, the dense layer can then be used to combine those features and make a final prediction. In a CNN, the dense layer is usually the final layer and is used to produce the output predictions. The activations from the previous layers are flattened and passed as inputs to the dense layer, which performs a weighted sum of the inputs and applies an activation function to produce the final output. Dense layer Benefits of Convolutional Neural Network: Feature extraction: CNNs are capable of automatically extracting relevant features from an input image, reducing the need for manual feature engineering. Spatial invariance: CNNs can recognize objects in an image regardless of their location, size, or orientation, making them well-suited to object recognition tasks. Robust to noise: CNNs can often handle noisy or cluttered images, making them useful for real-world applications where image quality may be variable. Transfer learning: CNNs can leverage pre-trained models, reducing the amount of data and computational resources required to train a new model. Performance: CNNs have demonstrated state-of-the-art performance on a range of computer vision tasks, including image classification, object detection, and semantic segmentation. Limitations of Convolutional Neural Network: Computational cost: Training a deep CNN can be computationally expensive, requiring significant amounts of data and computational resources. Overfitting: Deep CNNs are prone to overfitting, especially when trained on small datasets, where the model may memorize the training data rather than generalize to new, unseen data. Lack of interpretability: CNNs are considered to be a “black box” model, making it difficult to understand why a particular prediction was made. Limited to grid-like structures: CNNs are limited to grid-like structures and cannot handle irregular shapes or non-grid-like data structures. Conclusion: In conclusion, Convolutional Neural Networks (CNNs) is a powerful deep learning architecture well-suited to image classification and object recognition tasks. With its ability to automatically extract relevant features, handle noisy images, and leverage pre-trained models, CNNs have demonstrated state-of-the-art performance on a range of computer vision tasks. However, they also have their limitations, including a high computational cost, overfitting, a lack of interpretability, and a limited ability to handle irregular shapes. Nevertheless, CNNs remain a popular choice for many computer vision tasks and are likely to continue to be a key area of research and development in the coming years.Fundamentals of machine learning
Machine learning is in many ways the intersection of two disciplines - data science and software engineering. The goal of machine learning is to use data to create a predictive model that can be incorporated into a software application or service. To achieve this goal requires collaboration between data scientists who explore and prepare the data before using it to train a machine learning model, and software developers who integrate the models into applications where they're used to predict new data values (a process known as inferencing). What is machine learning? Machine learning has its origins in statistics and mathematical modeling of data. The fundamental idea of machine learning is to use data from past observations to predict unknown outcomes or values. For example: The proprietor of an ice cream store might use an app that combines historical sales and weather records to predict how many ice creams they're likely to sell on a given day, based on the weather forecast. A doctor might use clinical data from past patients to run automated tests that predict whether a new patient is at risk from diabetes based on factors like weight, blood glucose level, and other measurements. A researcher in the Antarctic might use past observations automate the identification of different penguin species (such as Adelie, Gentoo, or Chinstrap) based on measurements of a bird's flippers, bill, and other physical attributes. Machine learning as a function Because machine learning is based on mathematics and statistics, it's common to think about machine learning models in mathematical terms. Fundamentally, a machine learning model is a software application that encapsulates a function to calculate an output value based on one or more input values. The process of defining that function is known as training. After the function has been defined, you can use it to predict new values in a process called inferencing. Let's explore the steps involved in training and inferencing. The training data consists of past observations. In most cases, the observations include the observed attributes or features of the thing being observed, and the known value of the thing you want to train a model to predict (known as the label). In mathematical terms, you'll often see the features referred to using the shorthand variable name x, and the label referred to as y. Usually, an observation consists of multiple feature values, so x is actually a vector (an array with multiple values), like this: [x1,x2,x3,...]. To make this clearer, let's consider the examples described previously: In the ice cream sales scenario, our goal is to train a model that can predict the number of ice cream sales based on the weather. The weather measurements for the day (temperature, rainfall, windspeed, and so on) would be the features (x), and the number of ice creams sold on each day would be the label (y). In the medical scenario, the goal is to predict whether or not a patient is at risk of diabetes based on their clinical measurements. The patient's measurements (weight, blood glucose level, and so on) are the features (x), and the likelihood of diabetes (for example, 1 for at risk, 0 for not at risk) is the label (y). In the Antarctic research scenario, we want to predict the species of a penguin based on its physical attributes. The key measurements of the penguin (length of its flippers, width of its bill, and so on) are the features (x), and the species (for example, 0 for Adelie, 1 for Gentoo, or 2 for Chinstrap) is the label (y). An algorithm is applied to the data to try to determine a relationship between the features and the label, and generalize that relationship as a calculation that can be performed on x to calculate y. The specific algorithm used depends on the kind of predictive problem you're trying to solve (more about this later), but the basic principle is to try to fit a function to the data, in which the values of the features can be used to calculate the label. The result of the algorithm is a model that encapsulates the calculation derived by the algorithm as a function - let's call it f. In mathematical notation: y = f(x) Now that the training phase is complete, the trained model can be used for inferencing. The model is essentially a software program that encapsulates the function produced by the training process. You can input a set of feature values, and receive as an output a prediction of the corresponding label. Because the output from the model is a prediction that was calculated by the function, and not an observed value, you'll often see the output from the function shown as ŷ (which is rather delightfully verbalized as "y-hat"). Types of machine learning There are multiple types of machine learning, and you must apply the appropriate type depending on what you're trying to predict. A breakdown of common types of machine learning is shown in the following diagram. Supervised machine learning Supervised machine learning is a general term for machine learning algorithms in which the training data includes both feature values and known label values. Supervised machine learning is used to train models by determining a relationship between the features and labels in past observations, so that unknown labels can be predicted for features in future cases. Regression Regression is a form of supervised machine learning in which the label predicted by the model is a numeric value. For example: The number of ice creams sold on a given day, based on the temperature, rainfall, and windspeed. The selling price of a property based on its size in square feet, the number of bedrooms it contains, and socio-economic metrics for its location. The fuel efficiency (in miles-per-gallon) of a car based on its engine size, weight, width, height, and length. Classification Classification is a form of supervised machine learning in which the label represents a categorization, or class. There are two common classification scenarios. Binary classification In binary classification, the label determines whether the observed item is (or isn't) an instance of a specific class. Or put another way, binary classification models predict one of two mutually exclusive outcomes. For example: Whether a patient is at risk for diabetes based on clinical metrics like weight, age, blood glucose level, and so on. Whether a bank customer will default on a loan based on income, credit history, age, and other factors. Whether a mailing list customer will respond positively to a marketing offer based on demographic attributes and past purchases. In all of these examples, the model predicts a binary true/false or positive/negative prediction for a single possible class. Multiclass classification Multiclass classification extends binary classification to predict a label that represents one of multiple possible classes. For example, The species of a penguin (Adelie, Gentoo, or Chinstrap) based on its physical measurements. The genre of a movie (comedy, horror, romance, adventure, or science fiction) based on its cast, director, and budget. In most scenarios that involve a known set of multiple classes, multiclass classification is used to predict mutually exclusive labels. For example, a penguin can't be both a Gentoo and an Adelie. However, there are also some algorithms that you can use to train multilabel classification models, in which there may be more than one valid label for a single observation. For example, a movie could potentially be categorized as both science fiction and comedy. Unsupervised machine learning Unsupervised machine learning involves training models using data that consists only of feature values without any known labels. Unsupervised machine learning algorithms determine relationships between the features of the observations in the training data. Clustering The most common form of unsupervised machine learning is clustering. A clustering algorithm identifies similarities between observations based on their features, and groups them into discrete clusters. For example: Group similar flowers based on their size, number of leaves, and number of petals. Identify groups of similar customers based on demographic attributes and purchasing behavior. In some ways, clustering is similar to multiclass classification; in that it categorizes observations into discrete groups. The difference is that when using classification, you already know the classes to which the observations in the training data belong; so the algorithm works by determining the relationship between the features and the known classification label. In clustering, there's no previously known cluster label and the algorithm groups the data observations based purely on similarity of features. In some cases, clustering is used to determine the set of classes that exist before training a classification model. For example, you might use clustering to segment your customers into groups, and then analyze those groups to identify and categorize different classes of customer (high value - low volume, frequent small purchaser, and so on). You could then use your categorizations to label the observations in your clustering results and use the labeled data to train a classification model that predicts to which customer category a new customer might belong.Best Azure Certifications For 2024
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