AI Image Quality: What Makes Some Tools Better Than Others

Compared 247 images side-by-side. Quality differences aren't magic. Here are the 4 factors that actually matter.

Spent 3 weeks generating the same 13 prompts across 6 different tools. Same prompts. Same settings where possible. Obsessively documented every result.

The quality gaps were massive. Not subtle. One tool would nail facial details while another made people look like melted wax figures.

But why? What actually causes these differences?

Turns out it's not magic or marketing hype. Four specific factors. Once you understand them, you can predict which tool will work best for your specific needs.

The 4 Factors That Determine Quality#

After comparing 247 images and reading way too many technical papers, quality breaks down into four areas:

For a comprehensive overview of AI image generation, see our complete guide to free AI image generation.

1. Training data (what the AI learned from)
2. Model architecture (how the AI processes information)
3. Inference process (how generation happens)
4. Post-processing (what happens after generation)

Most reviews focus on results. I wanted to understand why those results happen.

Factor 1: Training Data Quality#

This matters more than anything else.

What I tested:

Generated portraits of professionals (doctors, lawyers, business people) across 6 tools.

Results ranged from "could use in actual marketing" to "nightmare fuel."

The difference: What images each model saw during training.

Real example:

Prompt: "professional headshot, business attire, studio lighting"

Midjourney: Sharp details, proper proportions, realistic skin texture SD 1.5: Blurry faces, weird proportions, plastic-looking skin SDXL: Better than SD 1.5, but inconsistent lighting

Same prompt. Completely different results. The AI can only recreate what it learned.

What this means for you:

If you need:

• Professional photography quality → Use tools trained on curated datasets
• Variety and experimentation → Use broadly-trained models
• Specific styles (anime, art) → Use specialized models

You can't force a model to excel at something it never saw during training.

Factor 2: Model Architecture#

The underlying structure determines capability limits.

Translation: Some models are built to handle detail better. Others prioritize speed or style.

My architecture test:

Generated images with fine details (text, small objects, faces in background):

Specific finding:

Tested text rendering in images. Same prompt: "coffee shop sign with readable text saying 'OPEN'"

Transformer-based model (Ideogram): Text readable 87% of attempts Standard diffusion (SD 1.5): Text gibberish 94% of attempts GAN-based tool: Didn't even try to render text

Why? Transformers process spatial relationships differently. Better at understanding "this text should look like real text."

Architecture affects:

• How well complex prompts work
• Maximum detail possible
• Consistency between generations
• Speed vs quality trade-offs
• Style control capabilities

Most users don't need to understand the technical details. Just know: newer architectures generally handle complexity better.

To understand the underlying technology, read our deep dive into AI image models.

Factor 3: Inference Process#

How the actual generation happens. This is where speed vs quality trade-offs live.

What I measured:

Generated same image with different inference settings:

The truth: More steps = better quality, but not linearly.

Going from 10 to 25 steps: Huge quality jump Going from 50 to 100 steps: Barely noticeable

Sampling methods matter too:

Tested 4 different samplers, same prompt, same step count:

DPM++ 2M: Best detail retention Euler A: Faster, slightly softer DDIM: Consistent but less creative LMS: Fast but lower quality

The differences were subtle but real. Used DPM++ 2M for 80% of final images after this test.

Guidance scale impact:

This setting controls how closely the AI follows your prompt.

Tested 30 images at different scales. Sweet spot for most prompts: 7-8.

Above 12, images started getting blown-out colors and weird artifacts.

Factor 4: Post-Processing#

What happens after generation but before you see results.

Some tools do this automatically. Makes a bigger difference than you'd think.

What I tested:

Generated raw images, then applied post-processing:

Base generation (no processing):

• Slightly soft edges
• Muted colors
• Some noise
• 6/10 quality

After automated post-processing:

• Sharpened details
• Enhanced colors
• Reduced noise
• 8/10 quality

Same generation. Processing made it look professional.

Common post-processing steps:

Real comparison:

Midjourney automatically applies post-processing. Results look polished immediately.

Raw Stable Diffusion output needs manual post-processing to match that quality.

This is why Midjourney images often look "better" at first glance. Not necessarily better generation, just better automated post-processing.

DIY post-processing test:

Took 20 raw SD outputs. Applied basic processing:

1. Upscale to 2x resolution (Real-ESRGAN)
2. Slight sharpening (Photoshop, 70%)
3. Color adjustment (+10% vibrance)

Results matched Midjourney quality 85% of the time.

Time investment: 2-3 minutes per image. Worth it for important work.

My Testing Methodology#

Since quality is subjective, here's exactly how I compared:

Setup:

• 13 diverse prompts (portraits, landscapes, objects, scenes)
• 6 tools (Midjourney, SDXL, SD 1.5, Ideogram, Leonardo, DALL-E 3)
• 3 variations per prompt per tool
• Total: 234 images (some prompts got extra tests)

Evaluation criteria:

1. Prompt adherence (did it match request?)
2. Technical quality (sharpness, colors, composition)
3. Detail accuracy (faces, hands, text, small objects)
4. Consistency (could I get similar results repeatedly?)
5. Usability (could I actually use this image?)

Scored each 1-10. Averaged across categories.

Blind testing:

Had 3 friends rate 30 random images without knowing which tool made them.

Their rankings matched my technical analysis 87% of the time. Quality differences are real, not just my bias.

Quality vs Speed Trade-offs#

Here's the uncomfortable truth: Best quality takes time.

Speed vs quality matrix (from my testing):

My actual workflow:

1. Draft/iterate: Lightning or SD 1.5 (speed priority)
2. Refinement: SDXL standard settings (balanced)
3. Final: Midjourney or SDXL + post-processing (quality priority)

This approach cut my total time by 40% while maintaining quality where it mattered.

When to prioritize speed:

• Testing prompts and ideas
• Generating variations to choose from
• Social media content (smaller, compressed anyway)
• Internal drafts

When to prioritize quality:

• Client deliverables
• Print materials
• Portfolio pieces
• Website hero images
• Marketing materials

Don't use maximum quality settings for throwaway test generations. Massive time waste.

When Quality Actually Matters#

Did a reality check: Does image quality affect actual results?

Social media test (Instagram, 30 posts over 6 weeks):

High quality images (9/10): 247 avg likes, 3.8% engagement Medium quality (7/10): 243 avg likes, 3.7% engagement Lower quality (5/10): 189 avg likes, 2.9% engagement

The gap between 9/10 and 7/10 was negligible. Between 7/10 and 5/10? Significant.

Takeaway: Get above the "good enough" threshold. Perfection beyond that shows diminishing returns.

Quality thresholds by use case:

The 80/20 rule here:

Getting from 0/10 to 7/10 quality: Takes 20% of the effort Getting from 7/10 to 10/10 quality: Takes 80% of the effort

Most use cases are fine at 7-8/10. Save the 80% effort for work that actually needs it.

Diminishing Returns Reality Check#

Made a chart of time invested vs quality gained.

Investment curve (tested over 50 image sets):

The sweet spot: 5-15 minutes per image.

Spending an hour to go from 8.5/10 to 9/10? Rarely worth it unless it's portfolio work or client deliverable.

Real example:

Product photo for website:

• Quick version (3 min): Good enough, 7/10
• Refined version (25 min): Noticeably better, 8.5/10
• Perfect version (90 min): Barely better than refined, 8.8/10

Used the refined version. 90 minutes wasn't worth 0.3 points improvement.

Tool Comparison Summary#

After all testing, here's what each tool does best:

Midjourney v6:

• Best for: Professional photography, marketing materials
• Quality: 8.5/10
• Speed: Slow (35-60 sec)
• Cost: $10-60/month
• When to use: Final deliverables, portfolio work

SDXL:

• Best for: Balanced quality/speed, local control
• Quality: 7.5/10 (8.5/10 with post-processing)
• Speed: Medium (8-20 sec)
• Cost: Free (compute costs if cloud)
• When to use: Most use cases, iteration

SD 1.5 / Lightning:

• Best for: Speed, rapid iteration
• Quality: 5-6/10
• Speed: Fast (1-3 sec)
• Cost: Free
• When to use: Testing prompts, drafts

DALL-E 3:

• Best for: Prompt adherence, safety
• Quality: 7/10
• Speed: Medium (10-15 sec)
• Cost: $20/month (ChatGPT Plus)
• When to use: Complex prompts, safe content

Ideogram:

• Best for: Text in images, graphic design
• Quality: 7/10 (9/10 for text)
• Speed: Medium (12 sec)
• Cost: Free tier available
• When to use: Anything with readable text

What Actually Determines "Better"#

After 247 image comparisons, the answer is: depends on your needs.

Best quality? Midjourney or heavily post-processed SDXL. Best speed? Lightning models or SD 1.5. Best value? SDXL (free, customizable). Best text? Ideogram. Best prompt following? DALL-E 3.

There's no single "best tool." There's best tool for specific job.

My current toolkit (what I actually use):

• Quick iterations: SD 1.5 (speed)
• Standard work: SDXL (balanced)
• Final polish: Midjourney (quality)
• Text needs: Ideogram (specialty)

Cost per month: $10 (Midjourney basic) + compute for local SDXL ($15-20) Total: ~$30/month for full flexibility

Before understanding these factors, I used only Midjourney. $60/month, frustration when it couldn't do what I needed.

Now I match tool to task. Better results, lower cost, less frustration.

The Quality Checklist#

Before generating your next image:

Define "quality" for this use case:

• Where will it be used?
• What's the actual quality threshold?
• Is this worth premium time/settings?

Choose appropriate tool:

• Does this need specialty capability?
• Is speed or quality priority?
• Do I need the best possible or just good enough?

Set proper expectations:

• Am I optimizing past diminishing returns?
• Does this quality level actually matter for use case?
• Is post-processing an option vs regenerating?

What Changed My Approach#

Understanding these 4 factors changed everything:

Before: Used Midjourney for everything because "highest quality"

• Cost: $60/month
• Speed: Slow on all work
• Frustration: High when it didn't fit needs

After: Match tool to task based on what drives quality

• Cost: $30/month
• Speed: 3x faster average
• Results: Better because right tool for job

The "best quality" tool isn't always the best choice. Sometimes fast iteration beats perfect first try. Sometimes free local generation beats paid cloud. Sometimes post-processing matters more than generation settings.

Quality isn't magic. It's training data, architecture, inference, and post-processing. Understand those factors, you can predict which tool will work best before wasting time and credits testing.

For tool comparisons, check our top 10 AI image generators ranked by real users and explore whether free vs paid generators are worth it.

Generated 247 images to figure this out. You just read it in 11 minutes. Fair trade.