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12.2.1 Image Generation Roadmap: Prompt, Control, Review

Image generation is a workflow, not a single prompt. A useful result needs intent, prompt records, parameters, optional controls, candidate comparison, and review.

See the Pipeline First​

Image generation chapter learning flowchart

Stable Diffusion application mode selector

Stable Diffusion fine-tuning route selector

The first habit is to log what you asked for, which mode you used, which seed or parameters shaped the result, and what must be reviewed before export.

Build a Prompt Record​

import json

brief = {
    "topic": "RAG basics",
    "audience": "beginners",
    "style": "clean editorial cover",
}
prompt = f"{brief['style']} for {brief['topic']}, friendly visual metaphor for {brief['audience']}, clear layout"
record = {
    "mode": "text-to-image",
    "prompt": prompt,
    "negative_prompt": "blurry, watermark, unreadable text",
    "seed": 42,
    "review": ["legibility", "copyright", "brand safety"],
}

print(json.dumps(record, indent=2))

Expected output:

{
  "mode": "text-to-image",
  "prompt": "clean editorial cover for RAG basics, friendly visual metaphor for beginners, clear layout",
  "negative_prompt": "blurry, watermark, unreadable text",
  "seed": 42,
  "review": [
    "legibility",
    "copyright",
    "brand safety"
  ]
}

Image generation prompt record result map

If you cannot reproduce the prompt record, you cannot reliably improve the image.

Learn in This Order​

StepReadPractice Output
1Diffusion intuitionExplain noise, denoising, seed, and sampling
2Stable Diffusion partsMap text encoder, U-Net, VAE, and latent space
3Applications and controlCompare text-to-image, image-to-image, inpainting, ControlNet, LoRA

Pass Check​

You pass this chapter when you can write a prompt record, explain which generation mode you chose, save 3 candidate notes, and mark at least one review risk before export.