Generative AI Image Generation
Image generation is one of the most visible capabilities of generative AI. A text prompt describing a scene, style, or concept produces an entirely new image that has never existed before. Tools like Stable Diffusion, DALL·E, and Midjourney have made this technology accessible to anyone — no drawing skills required.
How Image Generation Models Work
The dominant approach for modern image generation is the diffusion model. It works by learning to gradually remove noise from a random image until a clear image matching the prompt emerges.
Training Phase (what the model learns):
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Clean image of a cat
│ (add random noise gradually)
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[■▓▒░ noisy cat ░▒▓■]
│ (more noise)
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[■■■■ pure noise ■■■■]
Model learns: how to REVERSE this noise process step by step
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Generation Phase (creating a new image):
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Start with pure random noise
│ (model removes noise, guided by prompt)
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[partial shapes appear]
│ (more denoising steps)
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[rough image of a cat]
│ (final denoising)
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[sharp photorealistic cat image]
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The Role of Text in Image Generation
A text encoder (usually a CLIP model) converts the text prompt into a vector — a list of numbers that represents the meaning of the description. The diffusion model uses this vector to guide the denoising process toward an image that matches the prompt.
Prompt: "A golden retriever puppy sitting in autumn leaves, soft light"
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Text Encoder (CLIP)
│
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[0.8, 0.3, 0.9, 0.1 ... ] ← semantic vector
│
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Diffusion Model uses vector to guide image creation
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Final image: golden retriever puppy in autumn leaves
Key Image Generation Models
| Model | Creator | Key Feature |
|---|---|---|
| DALL·E 3 | OpenAI | Strong prompt adherence, integrated with ChatGPT |
| Stable Diffusion | Stability AI | Open-source, runs locally, highly customizable |
| Midjourney | Midjourney Inc. | Exceptional artistic quality and style control |
| Adobe Firefly | Adobe | Trained on licensed content, safe for commercial use |
| Imagen 3 | Google DeepMind | High photorealism and text rendering in images |
| Flux | Black Forest Labs | State-of-the-art open-source alternative to DALL·E |
Anatomy of an Effective Image Prompt
Image prompts follow a different pattern than text prompts. More descriptive and specific prompts produce better results.
WEAK PROMPT:
"A cat"
STRONG PROMPT:
"A tabby cat sitting on a wooden windowsill, warm golden afternoon light,
bokeh background of green garden, photorealistic, 85mm lens, high detail"
Elements of a strong image prompt:
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1. Subject → What is in the image? ("tabby cat")
2. Setting → Where and when? ("wooden windowsill, afternoon")
3. Lighting → Type and direction of light ("warm golden light")
4. Style → Photorealistic, cartoon, painting, sketch, etc.
5. Camera/Lens → For realistic photos ("85mm lens, bokeh")
6. Quality tags → ("high detail", "4K", "sharp focus")
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Image Generation Parameters
Guidance Scale (CFG Scale)
Controls how strictly the model follows the prompt:
- Low (1–4): Model is creative and less literal — may diverge from the prompt
- Medium (7–10): Good balance between prompt adherence and quality
- High (15–20): Very close to the prompt but may look over-processed
Steps (Sampling Steps)
The number of denoising steps the model takes. More steps = higher quality image but longer generation time.
- 20 steps: Fast, rough quality
- 50 steps: Good quality, standard use
- 100+ steps: Highest quality, slower
Seed
A seed is a random number that initializes the noise the model starts from. Using the same seed with the same prompt produces the same image every time — useful for reproducibility and iterative refinement.
Negative Prompts
Most image generation systems accept a negative prompt — a list of things to avoid in the generated image.
Prompt: "Portrait of a woman, professional headshot, studio lighting"
Negative Prompt: "blurry, low resolution, distorted hands, extra fingers,
watermark, overexposed, cartoonish"
Effect: The model actively avoids these qualities during generation.
ControlNet — Guiding Image Structure
ControlNet is an extension that lets users control the structure of generated images using reference inputs like edge maps, depth maps, or human pose skeletons. This allows precise control over composition without fully specifying every detail in text.
Reference Input (stick figure pose sketch)
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ControlNet + Diffusion Model
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Photorealistic image of a person in exactly that pose
Image-to-Image Generation
Beyond text-to-image, models also support image-to-image tasks:
- Inpainting: Fill in a masked area of an existing image with new content
- Outpainting: Extend an image beyond its original borders
- Style Transfer: Apply the artistic style of one image to another
- Upscaling: Increase image resolution while adding fine detail
Ethical Considerations in Image Generation
Image generation carries specific ethical responsibilities:
- Deepfakes: Realistic fake images of real people can be misused
- Copyright: Models trained on artist work without consent raise IP questions
- Misinformation: Fake but realistic images can spread false narratives
- Bias: Models trained on imbalanced data may stereotype certain groups
Responsible platforms add safety filters to prevent generation of harmful, illegal, or misleading content.
Image generation demonstrates how generative AI extends far beyond text. The next topic explores another sensory domain — audio and music generation.