TEXT 2 IMAGE AI Art Generators:
How to create AI Art, options available, text prompt data, & more…
4. AI Writing Assistants / Brainstorming Tools
8. Let’s Solve a Problem / Create a Solution
#1: Introduction - What is Text 2 Image AI?
Text to Image AI is a type of machine learning program that takes a simple user input of words/ideas, then outputs an image.
#2: Txt2Img Generator List
Midjourney - Independent research lab offering AI that creates images from textual descriptions. One of the most popular and simple to use, accessible via discord. Free trial available, $10/mo or $30/mo subscription options available.
Stable Diffusion - Stable Diffusion is a machine learning, text-to-image model. Open source, several options available for running SD (You’ll need a computer that has a NVIDIA GPU with at least 4GB VRAM & At least 10GB of space on Hard drive):
https://github.com/bentoml/stable-diffusion-bentoml
https://news.ycombinator.com/item?id=32642255
https://www.reddit.com/r/StableDiffusion/comments/wuyu2u/how_do_i_run_stable_diffusion_and_sharing_faqs/
https://www.reddit.com/r/StableDiffusion/comments/x9wy67/how_powerful_a_pc_is_needed_to_run_stable/
https://www.howtogeek.com/830179/how-to-run-stable-diffusion-on-your-pc-to-generate-ai-images/
https://huggingface.co/CompVis/stable-diffusion
https://huggingface.co/spaces/stabilityai/stable-diffusion
Dalle-2 - Waitlist available. Not fully released to the public yet. Machine learning model developed by OpenAI to generate digital images from natural language descriptions; uses a modified version of GPT-3 to generate images.
Craiyon - Craiyon, formerly DALL·E mini, is an AI model that can draw images from any text prompt; use it online free!
Want access to 20+ more Txt2Art AI Tools? Click here to view the GBU for more.
Want help setting up SD or MJ? Click here to schedule a consult.
Click here to generate AI art FREE with no set up, no log-ins, no code.
#3: Txt2Img Prompt Guide
The Official Midjourney User Guide: https://midjourney.gitbook.io/docs/web-app
#4: AI Writing Assistants / Brainstorming Tools
Whether you need help thinking of creative prompts or you’re writing an entire essay and want assistance from AI tools, here are a few good options to explore:
InkForAll - “Writing Tools to Help You Write Better, Convert Faster & Rank Higher” (5000 words free! )
https://blog.inkforall.com/what-is-ai-writing
Rytr - “The best, all-in-one writing platform. Tired of dealing with gazillion apps in your writing workflow? Rytr provides powerful features to manage everything from one place.” (10K Characters free!)
Copy.AI - “Experience the full power of an AI content generator that delivers premium results in seconds.” (2000 words free!)
Want access to 20+ more AI Writing Tools? Click here to view the GBU.
Click here to check out PROMPTER for Txt2Img prompt assistance.
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Section #5 Section #6 Section #7 Section #8
#5: What is Machine Learning?
*(Section #5, #6, & #7 are all quotes from excellent articles found on IBM’s website, links included below. I figured attempting to explain ML to you made less sense than simply letting the experts speak. If you have any questions I can assist you with, I’d love to hear from you, you’ll find a link to contact me below.)*
From IBM :
“Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase. They will be required to help identify the most relevant business questions and the data to answer them.
Machine learning algorithms are typically created using frameworks that accelerate solution development, such as TensorFlow and PyTorch.”
Read more: https://www.ibm.com/cloud/learn/machine-learning
“UC Berkeley (link resides outside IBM) breaks out the learning system of a machine learning algorithm into three main parts:
Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.
Machine learning models fall into three primary categories:
Supervised machine learning
Supervised learning, also known as supervised machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately. This occurs as part of the cross validation process to ensure that the model avoids overfitting or underfitting. Supervised learning helps organizations solve a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox.
Unsupervised machine learning
Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. This method’s ability to discover similarities and differences in information make it ideal for exploratory data analysis, cross-selling strategies, customer segmentation, and image and pattern recognition. It’s also used to reduce the number of features in a model through the process of dimensionality reduction.
Semi-supervised learning
Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of not having enough labeled data for a supervised learning algorithm. It also helps if it’s too costly to label enough data.
Learn more: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning
Reinforcement machine learning
Reinforcement machine learning is a machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.
Learn more: https://developer.ibm.com/articles/cc-models-machine-learning/#reinforcement-learning
Common machine learning algorithms:
Neural networks: Neural networks simulate the way the human brain works, with a huge number of linked processing nodes. Neural networks are good at recognizing patterns and play an important role in applications including natural language translation, image recognition, speech recognition, and image creation.
Linear regression: This algorithm is used to predict numerical values, based on a linear relationship between different values.
Logistic regression: This supervised learning algorithm makes predictions for categorical response variables, such as“yes/no” answers to questions.
Clustering: Using unsupervised learning, clustering algorithms can identify patterns in data so that it can be grouped. Computers can help data scientists by identifying differences between data items that humans have overlooked.
Decision trees: Decision trees can be used for both predicting numerical values (regression) and classifying data into categories. Decision trees use a branching sequence of linked decisions that can be represented with a tree diagram. One of the advantages of decision trees is that they are easy to validate and audit, unlike the black box of the neural network.
Random forests: In a random forest, the machine learning algorithm predicts a value or category by combining the results from a number of decision trees.”
#6: What are Neural Networks?
From IBM :
“Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, neural networks is actually a sub-field of machine learning, and deep learning is a sub-field of neural networks.
Neural networks, or artificial neural networks (ANNs), are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network by that node. The “deep” in deep learning is just referring to the number of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the input and the output—can be considered a deep learning algorithm or a deep neural network. A neural network that only has three layers is just a basic neural network.”
#7: What is Deep Learning?
From IBM :
“The way in which deep learning and machine learning differ is in how each algorithm learns. "Deep" machine learning can use labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. Deep learning can ingest unstructured data in its raw form (e.g., text or images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman notes in this MIT lecture (01:08:05) (link resides outside IBM).
Deep learning and neural networks are credited with accelerating progress in areas such as computer vision, natural language processing, and speech recognition.”
Learn more : https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks
Interested in learning more about AI, ML, & More? Click here to access the Master-List with hundreds of curated AI Tools & Resources.
#8: Let’s Solve a Problem / Create a Solution
Interested in Txt 2 Art & AI in general? I’d love to hear from you if there’s a project you want to work on.
Whether you want to get some AI Art done @ low cost or you want to pursue an education or career in Machine Learning, I’d love to hear from you!
Using the resources available at ExploreAI.online, we can begin to extend ourselves by increasing the number of tasks we can do without thinking about them.
Let’s seek to know why things work whenever possible; let’s can explore our interests and seek to understand the things that improve lives.
Let’s learn to love to learn, and learn deeply, so that we are always capable of rebuilding by using First Principles Thinking if those things that improve our lives should fade from their current form.
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ALL of the images you see in this post were generated using AI tools. (excluding the social media logos.)
This article was written the “old fashioned way” (with fingers, a brain, and a keyboard), but AI writing assistants were used to brainstorm and elaborate upon ideas.
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