Technologies and Approaches in AI Image Generation
Written by:
Igor Gorovyy
DevOps Engineer Lead & Senior Solutions Architect
LinkedIn
Introduction
As part of preparing a demo for colleagues, I wrote a short article on how I use generative AI in my creative work. A significant part of my life is dedicated to wood carving. Many people have seen beautiful works framed in elegant borders in museums or exhibitions. When I experience a "creative block," I turn to AI for ideas and inspiration. Of course, it would be better to travel and study the works of old masters, but life is complicated right now, and travel isn't always an option. Moreover, my professional interests are closely tied to technology, so I take the opportunity to combine my two hobbies.
AI-powered image generation is one of the most exciting and rapidly evolving fields in modern technology. A key player in this domain is Stable Diffusion—a neural network model capable of creating highly realistic and stylized images based on textual descriptions.
In this article, we will explore the core technologies behind AI image generation, the principles of their operation, and the step-by-step process of generating images.
Core Technologies
To produce high-quality images, a number of libraries and frameworks are utilized:
- Torch (PyTorch) – A powerful machine learning framework widely used for neural networks, including generative models.
- Diffusers – A library for working with diffusion-based models, enabling the creation, training, and optimization of image generation models.
- Transformers – A Hugging Face library used for text processing and model architecture management.
- PIL (Pillow) – A library for image processing and saving.
- CUDA – A technology from NVIDIA that enables GPU acceleration for faster computation.
How Stable Diffusion Works
Stable Diffusion is based on diffusion processes and operates through the following steps:
- Random Noise Initialization – Initially, the model starts with a randomly generated noisy image.
- Step-by-Step Denoising – The neural network gradually modifies the noise to match the provided text description (prompt).
- Result Optimization – The more inference steps used, the more detailed and realistic the final image becomes.
- Filtering and Enhancement – Methods for improving quality, color correction, and texture refinement are applied.
Crafting Text Prompts
Positive Prompt
A positive prompt defines what should be depicted. This is a key element of generation that influences the final result. For example:
A classical oil painting of a distinguished lady, 18th-century style, dark background, Durer lighting, realistic, old canvas texture
This prompt specifies:
- Style: oil painting
- Time period: 18th-century
- Atmosphere: dark background
- Inspiration source: Durer lighting
- Level of detail: realistic
- Texture: old canvas texture
Negative Prompt
A negative prompt defines what elements should be avoided during generation. For example:
deformed, distorted, disfigured, bad anatomy, changed face, different face, extra limbs, extra fingers, extra features, duplicate, multiple faces, blurry, bad art, cartoon, anime, sketchy
This prompt helps avoid:
- Anatomical distortions
- Excessive detailing
- Rendering errors
- Unwanted artistic styles like cartoon or anime
Generation Settings Parameters
1. num_inference_steps (Number of Generation Steps)
Determines how many iterations will be performed to refine the image. A higher value results in better detail but increases generation time.
- Example:
200
2. guidance_scale (Prompt Adherence Scale)
Controls how strictly the model should follow the prompt. Higher values enforce stricter adherence to instructions but may limit creative variations.
- Example:
9.0
3. height and width (Image Size)
Defines the resolution of the generated image. Higher values require more computational resources.
- Example:
1024x1024
These parameters allow fine-tuning of the generation process, ensuring the best alignment with your requirements.
Results
Conclusion
Generative models like Stable Diffusion unlock new possibilities in digital art, design, and visualization. They allow users to create high-quality images from textual descriptions, eliminating the need for manual drawing or complex modeling. With GPU acceleration, this process takes only seconds, making the technology accessible and convenient for a wide range of users.
