Here's a skill that separates beginners from experienced practitioners: the ability to look at a generated image and diagnose what went wrong — and more importantly, whether it's a prompt problem, a model limitation, or a parameter issue. Different root causes require different fixes.
Think of it like being a doctor: a headache could be caused by dehydration (simple fix), a migraine (different treatment), or something more serious (structural problem). Prescribing the right treatment requires the right diagnosis.
The Diagnostic Framework
1. Is it a prompt problem?
Symptoms: The image is well-rendered but doesn't match what you wanted. Wrong subject, wrong mood, wrong composition, wrong style. The model clearly "heard" something different from what you intended.
- Fix: Rephrase the prompt. Be more specific about what you want. Remove ambiguous terms.
- Test: Try 3-4 different prompt phrasings with the same seed. If the same problem persists across all phrasings, it's probably not a prompt problem.
"Apple on a table" → Got an Apple computer on a table
"A fresh red Fuji apple on a wooden cutting board, kitchen setting, food photography"
2. Is it a model limitation?
Symptoms: The image has specific, recurring quality issues regardless of prompt changes — distorted hands, illegible text, wrong finger count, objects merging into each other. The model is generating its best attempt but the task is beyond its reliable capability.
- Fix: Use a different model, use inpainting to fix specific regions, or accept the limitation and work around it.
- Test: Try the same concept across multiple models. If all models struggle, it's a fundamental limitation of current technology. If only your model struggles, consider switching.
3. Is it a parameter problem?
Symptoms: Colors are oversaturated or washed out. Image looks plasticky/over-refined or too rough/noisy. Composition is okay but the "feel" is wrong.
- Fix: Adjust guidance scale (lower for more natural, higher for more prompt-adherent), sampling steps (more for refinement, fewer for speed), or try a different sampler.
- Test: Keep the same prompt and seed, vary one parameter at a time. You'll quickly see what each parameter does.
4. Is it a seed problem?
Symptoms: Sometimes the prompt works great, sometimes it doesn't. The quality is inconsistent across generations.
- Fix: Generate more variations (batch generation). Not every seed produces a winner — that's expected.
- Test: Generate 8-16 images with the same prompt but different seeds. If some are great and some are bad, it's just natural variation — curate the good ones.
Common Failure Modes and Their Causes
| What You See | Likely Cause | Fix |
|---|---|---|
| Wrong subject / style | Prompt ambiguity | Rephrase with specifics |
| Distorted hands/fingers | Model limitation | Inpaint or use specialized model |
| Garbled text in image | Model limitation | Use FLUX (better text) or add text in post |
| Oversaturated / plasticky | Guidance too high | Lower CFG scale to 5-7 |
| Doesn't match prompt at all | Guidance too low or prompt conflict | Raise CFG or check for conflicts |
| Too generic / boring | Underspecified prompt | Add lighting, camera, specific details |
| Muddled / confused composition | Overspecified prompt | Simplify to 3-4 key elements |
| Good sometimes, bad sometimes | Natural seed variation | Batch generate and curate |
- Diagnose failures into four categories: prompt problem, model limitation, parameter issue, or seed variation.
- Each category requires a different fix — rewriting the prompt isn't always the answer.
- Consistent failures across seeds = prompt or model issue. Inconsistent results = seed variation (batch generate more).
- Systematic diagnosis is faster than random prompt iteration.