Today, i want to find out, how to create / recreating images with ChatGPT.

Challenge: I found an image and want to recreate the image:

Write a Prompt that creates this Image

Create an image of a woman with long, sleek black hair and striking features. She should have a strong, confident posture with her arms crossed. She's wearing a simple, elegant black dress with a round neckline. The background is a modern, minimalist office with a hint of sophistication. The color palette should be neutral, with emphasis on shades of grey and black to match her attire. The lighting is soft and diffused, highlighting her from the front and casting a gentle shadow behind her. The overall aesthetic is chic and polished with a touch of corporate professionalism.

Write a Prompt that creates this Image

Create an image of a poised and professional woman with long, sleek black hair and prominent, striking features. She stands with a sense of authority, her arms confidently crossed in front of her. She is dressed in a simple yet sophisticated black dress with a modest round neckline. The setting is a contemporary, minimalist office space that exudes a refined elegance. The color scheme is a harmonious blend of neutral tones, dominated by greys and blacks that complement her attire. Soft, ambient lighting bathes the scene, casting a gentle illumination that accentuates her silhouette against the muted sophistication of the office backdrop. The composition should convey an atmosphere of chic professionalism and understated luxury.

Write a Prompt that creates this Image

Create an image of an elegant and professional woman with long, black hair and defined, striking features. She exhibits a posture of confidence and composure with her arms crossed. Her attire is a chic, form-fitting black dress with a modest neckline. The environment is a stylish, minimalist office setting with sophisticated furnishings and a neutral color scheme emphasizing grays and blacks. The scene is illuminated with soft, ambient lighting that casts a gentle glow on the woman, highlighting her assertive stance against the contemporary backdrop, evoking a sense of refined grace and corporate elegance.

Notes:

Mostly, after running the same prompt the second time, ChatGPT responded with something like that:

So, i ask ChatGPT nicely to do it again:

Create the image again, because each time the image looks different

Next Step: find the precise prompt

As we see, using the same prompt results in different images. So i asked ChatGPT how to create a prompt that produces the same image each time:

Ok, lets try:

Me

Using your suggestions, create a precise prompt that create this image. Use all 6 strategies in your prompt

ChatGPT

Create a digital portrait of a woman who embodies elegance and professionalism. She should have long, straight, jet-black hair with a glossy sheen, cascading down to mid-back. Her facial features are sharply defined, with high cheekbones, almond-shaped hazel eyes, meticulously groomed eyebrows, and full, neutral-colored lips. Her skin has a matte finish with a porcelain-like quality. Her stance is assertive; she stands straight with her shoulders back and arms crossed at the waist, showcasing a silver bracelet on her right wrist and a matching ring on her left middle finger. She wears a tailored, form-fitting black sheath dress with three-quarter sleeves and a boat neckline, ending just above the knees. The setting is an upscale, minimalist office: to her right is a steel-gray filing cabinet with a potted green succulent on top; to her left, a sleek, black office chair behind a dark walnut desk, on which rests a closed silver laptop and a white ceramic mug. The background comprises floor-to-ceiling windows with thin, vertical blinds casting soft shadows, and the walls are a light gray. The lighting is soft and directional, coming from the top left, creating a subtle highlight on her left cheek and a soft shadow to her right, enhancing her features without harshness. The overall color palette is monochrome, with a focus on blacks, whites, and grays, providing contrast against her black dress and the lighter office environment. The composition is centered and balanced, with the woman placed in the middle of the frame, allowing for an equal amount of office space on either side of her. The camera angle is at eye level, ensuring a direct and engaging connection with the viewer. The image should convey a narrative of confidence, sophistication, and modern professional elegance.

Wow, impresiv. Ok, here are the image created by this prompt:

The results looks more the same, but still lacking these “unique” factor.

So i asked ChatGPT again:

Ok, so we wont get 100% equal images. But being more precise, hopefully we will reach someting around 90 – 95%.

To achieve the most consistent results with an AI image generation model like DALL-E, you should craft a prompt that is detailed and specific, reducing the room for interpretation as much as possible. Here’s a general structure you can follow:

1. Subject Description: Start with a detailed description of the main subject, including physical characteristics, expressions, and attire.
   • Example: “Create an image of a woman in her 30s with shoulder-length curly brown hair, green eyes, wearing a navy blue blazer and white shirt.”
2. Posture and Expression: Clearly describe the subject’s posture, facial expression, and any gestures.
   • Example: “She has a friendly smile, direct eye contact with the viewer, and her hands are folded in her lap.”
3. Setting and Background: Detail the setting, including specific objects, colors, and the overall atmosphere.
   • Example: “She is seated on a red armchair in a well-lit room with bookshelves filled with colorful books in the background.”
4. Lighting and Shadows: Describe the lighting direction and intensity, as well as the resulting shadows.
   • Example: “There is soft lighting coming from the left, creating a gentle shadow on the right side of her face.”
5. Color Palette: Specify the colors you want to be dominant in the image.
   • Example: “The color palette is warm, with rich browns and creams, accentuating the wooden elements of the furniture and books.”
6. Camera Angle and Composition: Mention the perspective of the image, such as the angle and framing.
   • Example: “The viewpoint is at eye level, capturing the upper body of the woman, centered in the frame with equal space on both sides.”
7. Additional Details: Add any other specific details that you want to include.
   • Example: “She wears a silver pendant necklace and has a small mole on her right cheek.”

Here’s how it would look put together:

“Create an image of a woman in her 30s with shoulder-length curly brown hair and green eyes, smiling friendly at the viewer, seated on a red armchair with hands folded in her lap. She is wearing a navy blue blazer and white shirt. The room is well-lit, with soft lighting coming from the left creating a gentle shadow on the right side of her face. The background is filled with bookshelves with colorful books. The color palette is warm, with rich browns and creams. The viewpoint is eye level, framing the woman in the center with equal space on both sides. She wears a silver pendant necklace and has a small mole on her right cheek.”

Even with this structure, slight variations can occur due to the stochastic nature of AI image generation. However, providing a highly structured and detailed prompt like the one above can significantly increase the consistency of the images produced.

Ok, let’s try i out

Roadmap

• Basics of Programming: Python is a great starting point due to its simplicity and extensive support in the AI field. Familiarize yourself with variables, data types, loops, conditionals, functions, and object-oriented programming. Resources: Codecademy, LeetCode, HackerRank.
• Understanding of AI and Machine Learning: Learn about foundational concepts like supervised learning, unsupervised learning, reinforcement learning, regression, classification, clustering, neural networks. Resources: Andrew Ng’s course on Coursera, “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron.
• Study Deep Learning: Deep learning is a special kind of machine learning involving artificial neural networks with several layers (“deep” structures). Study convolutional networks (used in image recognition), recurrent networks (used in sequence data), and the concept of deep reinforcement learning. Resources: Deep Learning Specialization by Andrew Ng on Coursera, “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville.
• Learn about Microsoft Azure: Azure is a cloud platform offering services like compute power, storage options, and AI models. Learn how to navigate the Azure portal, create/manage resources, and understand core services (Azure Compute, Azure Storage, Azure Data Services). Resources: Microsoft Learn, Azure documentation.
• Microsoft’s AI Services: Azure AI services include Azure Machine Learning for building, training, and deploying machine learning models, and Azure Cognitive Services for pre-trained AI services like vision, speech, and language processing. Resources: Microsoft Learn’s path for AI Engineer, Azure AI documentation.
• Azure OpenAI: Azure OpenAI offers access to powerful models like GPT-3 for tasks like text generation, translation, summarization, etc. Learn how to call these APIs from your application and how to use the results effectively. Resources: Azure OpenAI documentation, OpenAI’s GPT-3 playground.
• Projects: Practical projects could include building a chatbot using Azure Bot Service and QnA Maker, creating a text summarizer using GPT-3, or developing a computer vision application using Azure Cognitive Services.
• Stay Updated: AI is an ever-evolving field. Blogs like Towards Data Science, Medium, the Microsoft Azure blog, and arXiv for research papers can keep you updated. Webinars and online forums like AI Saturdays, AI Fest, and Stack Overflow also help.
• Certifications: Certifications like the Microsoft Certified: Azure AI Engineer Associate or Microsoft Certified: Azure Data Scientist Associate validate your skills and knowledge. They require a solid understanding of Azure services and machine learning concepts.
• Contribute to the Community: Sharing your knowledge and experience helps solidify your understanding and establishes your expertise. Write blogs or make YouTube videos explaining concepts, give talks at local meetups or conferences, contribute to open-source projects on GitHub, or answer questions on forums like Stack Overflow.

Understanding of AI and Machine Learning

• Mathematical Foundations: Brush up on your knowledge of Linear Algebra, Calculus, Probability, and Statistics. These are essential for understanding machine learning algorithms.
• Programming Skills: Python is the most used language in the field of machine learning. Familiarize yourself with Python, its libraries like NumPy, Pandas, and Matplotlib, and an IDE such as Jupyter Notebook.
• Data Preprocessing: Learn how to clean and preprocess data, handle missing data, and perform feature scaling. Understand the importance of data visualization for exploring your dataset.
• Supervised Learning: Start with simple linear regression and then move on to multiple linear regression, logistic regression, k-nearest neighbors, support vector machines, and decision trees. Understand the concept of overfitting and underfitting, and techniques to combat them like regularization.
• Unsupervised Learning: Learn about clustering techniques like K-means and hierarchical clustering. Understand dimensionality reduction techniques like Principal Component Analysis (PCA).
• Advanced Topics: Once you’re comfortable with the basics, move onto more advanced topics like ensemble methods, neural networks, and deep learning. Learn about different types of neural networks like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers.
• Tools and Libraries for Machine Learning: Get hands-on experience with Scikit-Learn, TensorFlow, and Keras. Learn how to build, train, and evaluate models with these libraries.
• Validation and Evaluation Metrics: Understand validation techniques like k-fold cross-validation. Learn about different evaluation metrics like accuracy, precision, recall, F1 score, ROC AUC, mean squared error, etc.
• Special Topics: Study special topics like natural language processing, reinforcement learning, generative adversarial networks, transfer learning, etc.
• Projects: Apply your knowledge to real-world projects. This could be anything from predicting house prices to building a chatbot.
• Stay Updated and Keep Learning: Machine learning is a rapidly evolving field. Read research papers, follow relevant blogs, participate in Kaggle competitions, and take online courses to keep your knowledge up-to-date.

Deep Learning

• Mathematical Foundations: Make sure you have a good understanding of Linear Algebra, Calculus, Probability, and Statistics. These are crucial for understanding how deep learning algorithms work.
• Programming Skills: Python is the most common language in the field of deep learning. Make sure you’re comfortable with it. Libraries like NumPy and Matplotlib will also be useful.
• Understanding of Machine Learning: Before delving into deep learning, you should have a solid understanding of basic machine learning concepts and algorithms. This includes concepts like overfitting, underfitting, bias-variance tradeoff, and understanding of algorithms like linear regression, logistic regression, and more.
• Introduction to Neural Networks: Start with understanding what neural networks are and how they work. Learn about the architecture of neural networks, activation functions, and the backpropagation algorithm for training neural networks.
• Frameworks for Deep Learning: Get hands-on experience with deep learning frameworks like TensorFlow and PyTorch. Learn how to define, train, and evaluate neural networks with these libraries.
• Convolutional Neural Networks (CNNs): These are used primarily for image processing, object detection, and recognition tasks. Understand the architecture of CNNs and concepts like convolutional layers, pooling layers, and filters.
• Recurrent Neural Networks (RNNs): Used for sequence data like time series and natural language. Learn about the structure of RNNs, the problem of long-term dependencies, and solutions like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units).
• Advanced Topics in Deep Learning: Explore advanced topics like autoencoders, generative adversarial networks (GANs), transfer learning, reinforcement learning, etc.
• Deep Learning for Natural Language Processing: Understand how deep learning is used in NLP. Learn about Word2Vec, GloVe, RNNs, LSTMs, GRUs, and Transformer models like BERT.
• Projects: Apply your knowledge to real-world projects. This could be anything from image recognition tasks, text generation, sentiment analysis, etc.
• Stay Updated and Keep Learning: Deep learning is a rapidly evolving field. Follow the latest research papers, participate in online courses and challenges like those on Kaggle.

Book on Machine Learning

Introduction to Machine Learning

• Definition and Importance
• Applications of Machine Learning
• Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning

Mathematical Foundations

• Linear Algebra
• Calculus
• Probability and Statistics
• Exercises and Sample Problems

Python for Machine Learning

• Python Basics
• Python Libraries: NumPy, Pandas, Matplotlib
• Exercises: Python Coding Problems

Data Preprocessing

• Importing Data
• Cleaning Data
• Feature Engineering
• Data Visualization
• Exercises: Data Cleaning and Visualization

Supervised Learning

• Regression Analysis
  • Simple Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
• Classification Algorithms
  • Decision Trees
  • K-Nearest Neighbors
  • Support Vector Machines
• Exercises: Implementing Supervised Learning Algorithms

Unsupervised Learning

• Clustering
  • K-Means Clustering
  • Hierarchical Clustering
• Dimensionality Reduction
  • Principal Component Analysis
• Exercises: Implementing Unsupervised Learning Algorithms

Evaluating Machine Learning Models

• Overfitting and Underfitting
• Bias-Variance Tradeoff
• Cross-Validation
• Performance Metrics
• Exercises: Evaluating Models

Neural Networks and Deep Learning

• Introduction to Neural Networks
• Backpropagation
• Convolutional Neural Networks
• Recurrent Neural Networks
• Exercises: Implementing Neural Networks

Advanced Topics

• Ensemble Methods
• Reinforcement Learning
• Natural Language Processing
• Transfer Learning
• Generative Adversarial Networks
• Exercises: Advanced Topics Projects

Case Studies in Machine Learning

• Case Study 1: Predictive Analytics
• Case Study 2: Image Recognition
• Case Study 3: Natural Language Processing

Future Trends in Machine Learning

Appendix

References and Further Reading

Glossary of Machine Learning Terms

Generate a futuristic version of the Mona Lisa

Chat GPT / Dall-E

Leonardo AI