Iterating and Refining

The Iteration Mindset

🔄 Quick Recall: In the previous lesson, we learned composition techniques—rule of thirds, camera angles, depth of field, and framing. Now we’ll learn the workflow for refining images until they match your vision.

Your first generation is almost never your final image. Professional AI image creators typically go through 5-15 iterations before getting the result they want.

By the end of this lesson, you’ll have a systematic workflow for refining AI-generated images from “close enough” to “exactly right.”

The Refinement Workflow

Step 1: Start Broad

Begin with a complete prompt covering your key components. Don’t worry about perfection—get the general direction right.

Initial prompt: “A cozy bookstore interior, warm lighting, wooden shelves filled with books, reading nook with armchair, watercolor illustration style”

Step 2: Generate Multiple Variations

Most platforms generate 4 images per prompt. Examine all of them for:

• Which has the best overall composition?
• Which captures the mood closest to your vision?
• Which has the most interesting details?

Step 3: Identify What to Change

Look at your best result and diagnose what needs adjustment:

Step 4: Change One Thing at a Time

This is the critical discipline. If you change everything at once, you can’t tell what worked.

Iteration 1: Original prompt Iteration 2: Changed lighting only — “warm candlelight” instead of “warm lighting” Iteration 3: Added composition — “shallow depth of field, focus on the armchair” Iteration 4: Refined style — “detailed watercolor illustration with visible brushstrokes”

✅ Quick Check: Your image has good composition and style but the colors feel too cold. What single change would you make to the prompt?

Negative Prompts

Telling the AI what NOT to include is often as important as telling it what to include.

Common Negative Prompts

For photorealism:

Negative: cartoon, illustration, painting, anime, drawing, text, watermark, blurry, low quality, deformed

For clean illustrations:

Negative: photorealistic, photograph, noisy, grainy, text, watermark, busy background

For portraits:

Negative: extra fingers, deformed hands, blurry face, cross-eyed, extra limbs, bad anatomy

Platform-Specific Usage

• Midjourney: Use --no flag: --no text, watermark, blurry
• Stable Diffusion: Separate negative prompt field
• DALL-E: Include in main prompt: “without text or watermarks”

Image-to-Image Refinement

When text prompts alone can’t achieve what you want, image-to-image (img2img) uses an existing image as a starting point.

Use Cases

Style transfer: Take a photograph and apply a painting style Detail refinement: Take a rough generation and add more detail Variation creation: Generate variations of a successful image Composition guidance: Use a sketch or reference to guide the AI’s layout

Strength/Denoising Parameter

Most img2img tools have a “strength” or “denoising” slider:

• Low (0.2-0.4): Keeps close to the original image. Good for subtle changes.
• Medium (0.5-0.7): Significant changes while maintaining core structure. Good for style transfer.
• High (0.8-1.0): Nearly complete regeneration guided loosely by the original. Good for dramatic reimagining.

Seed Control

Every image generation uses a random seed number. Fixing the seed while changing the prompt produces variations of the same basic composition.

Why this matters:

• Found a great composition but want different lighting? Same seed, different prompt.
• Want to compare two styles directly? Same seed, different style words.
• Need to reproduce an exact image? Save the seed number.

In Midjourney: Use --seed [number] In Stable Diffusion: Set seed in the interface In DALL-E: Not directly controllable

The Professional Workflow

Here’s how experienced AI image creators work:

Phase 1: Concept (5-10 generations)

• Broad prompts exploring the general idea
• Different styles, compositions, angles
• Goal: find the right direction

Phase 2: Refinement (10-20 generations)

• Narrow prompts based on Phase 1 favorites
• Adjust individual components
• Lock seed when composition is right
• Goal: get each element working

Phase 3: Polish (5-10 generations)

• Fine-tuning details
• Negative prompts to remove artifacts
• img2img for specific fixes
• Upscaling for final resolution
• Goal: publication-ready image

Upscaling

AI-generated images often need higher resolution for print or large displays.

In-platform upscaling:

• Midjourney: Built-in upscaling (click U1-U4)
• DALL-E: Generate at higher resolution settings

External upscaling:

• Topaz Gigapixel AI
• Real-ESRGAN (free, open source)
• Magnific AI

Upscaling should be the last step—after all creative decisions are finalized.

Common Iteration Pitfalls

Prompt creep. Adding more and more words with each iteration until the prompt is 200 words long. AI models have attention limits—longer isn’t always better.

Abandoning too soon. Giving up after 3 generations. Most great images take 10+ iterations.

Random changes. Changing multiple things at once makes it impossible to learn what works.

Forgetting what worked. Not saving successful prompts and seeds. Keep a prompt journal.

Try It Yourself

Take a prompt you’ve written in previous lessons. Run through the full iteration workflow:

1. Generate 4 initial images
2. Pick the best one and identify what needs improvement
3. Make 3 iterations, changing one thing each time
4. Try a negative prompt to remove unwanted elements
5. Compare your final image to your first generation

Key Takeaways

• First generations are starting points, not final images—expect 5-15 iterations
• Change one component at a time to understand what produces each effect
• Negative prompts are essential for avoiding common artifacts and unwanted elements
• Image-to-image refinement bridges the gap when text prompts alone aren’t enough
• Seed control enables direct comparisons between prompt variations
• Keep a prompt journal to remember what worked

Up Next

In Lesson 6: Platform-Specific Techniques, you’ll learn the unique features, parameters, and best practices for DALL-E, Midjourney, and Stable Diffusion.