Editorialquality assurance
Troubleshooting the Most Common AI Image Artifacts
Diagnose and correct blur, anatomy errors, typography issues, and lighting inconsistencies with targeted prompt and model adjustments.
Aiden BrooksApr 29, 20241 min readUpdated 5/1/2024
troubleshootingimage qualityqa
Troubleshooting the Most Common AI Image Artifacts
Even mature diffusion models can struggle with hands, text, or motion. Use this checklist to quickly stabilize renders before assets hit review.
Artifact Diagnostic Table
| Issue | Likely Cause | Quick Fix |
|---|---|---|
| Blurry textures | Low step count or aggressive denoise strength | Increase steps to 30+, add detail emphasis to prompt |
| Extra fingers | High guidance scale amplifying noise | Lower CFG scale and add negative prompt: extra digits |
| Warped text | Model not trained on typography | Switch to typography-tuned model or overpaint via inpainting |
| Mismatched lighting | Prompt lacks lighting direction | Describe key light, fill light, and environment reflections |
Debugging Workflow
- Identify artifact type and log it in your QA tracker.
- Adjust prompt variables or swap schedulers, then regenerate with the same seed to isolate impact.
- Use localized inpainting for final touchups rather than rerunning entire scenes.
Teams that document artifacts and fixes see iteration time drop by 35% after the first sprint.
— MultiMind QA Cohort Study, 2024
References
- [1] Runway. "How to Fix Common GenAI Mistakes". https://runwayml.com/blog
- [2] Hugging Face. "Diffusers Troubleshooting Guide". https://huggingface.co/docs/diffusers
- [3] NVIDIA. "Improving Generative Image Quality". https://developer.nvidia.com/blog
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