When working with AI-generated images, visual artifacts including deformed facial structures, duplicated body parts, pixelated surfaces, or disproportionate scaling can be frustrating and undermine the quality of the output. These issues commonly arise due to inherent constraints of the neural architecture, vague input cues, or suboptimal parameter tuning.

To effectively troubleshoot distorted features in AI-generated images, start by examining your prompt. Vague or ambiguous descriptions often lead the model to fill in gaps with incorrect assumptions. Be specific about anatomy, posture, lighting, and style. For example, instead of saying "a person," try "a woman with symmetrical facial features, standing upright, wearing a blue dress, soft daylight from the left." Precise language guides the AI toward more accurate interpretations.

Next, consider the model you are using. Different models have varying strengths and weaknesses. Some models perform well on animals but distort architectural elements. Research which models are best suited for your use case—many open-source and commercial platforms offer specialized variants for human figures, architecture, or fantasy art. Switching to a more appropriate model can instantly reduce distortions. Also ensure that you are using the current iteration, because developers regularly resolve persistent anomalies.

Adjusting generation parameters is another critical step. More denoising steps can sharpen features yet exaggerate glitches in unstable configurations. Reducing classifier-free guidance minimizes hallucinations while preserving intent. If the image appears distorted beyond recognition, reduce the strength of the prompt influence. Conversely, if features are too generic, increase it slightly while monitoring for overfitting. Most tools allow you to control the sampling depth; extending beyond 40 steps typically refines fine details and reduces visual noise in intricate compositions.

Pay attention to resolution settings. Producing images below target size and recruiter engagement than those without scaling up degrades spatial accuracy. Whenever possible, create at native resolution to preserve integrity. If you must upscale, employ AI-enhanced super-resolution models like LATTE or 4x-UltraSharp. They retain edge definition and reduce pixelation.

If distortions persist, try using anti-prompts. These allow you to ban specific visual errors. For instance, adding "deformed hands, extra fingers, asymmetrical eyes, blurry face" to your negative prompt can significantly reduce common anomalies. Negative instructions act as corrective signals that steer the model away from known pitfalls.

Another effective technique is to create a batch of options to identify the cleanest result. Use seed values to recreate and tweak minor changes. This method helps isolate whether the issue is due to chance-based artifacts or systemic errors in input design.

Lastly, post-processing can help. Use basic editing tools to manually correct minor issues like smoothing skin tones, fixing eye alignment, or adjusting lighting. While not a substitute for a well-generated image, it can rescue images that are nearly perfect. Always remember that AI-generated images are probabilistic outputs, not precise renderings. Some level of imperfection is normal, but with methodical troubleshooting, you can dramatically improve consistency and realism in your outputs.