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 limitations in the underlying model, improper prompting, or misconfigured generation settings.

To effectively troubleshoot distorted features in AI-generated images, start by examining your prompt. Ambiguous wording triggers the system to hallucinate unrealistic elements. Be specific about physiological details, stance, light source direction, and visual aesthetic. For example, instead of saying "a person," try "a young adult with even eye spacing, standing naturally, in a flowing red gown, diffused window light from above." Clear, detailed prompts steer the model toward faithful execution.

Next, consider the model you are using. Each AI architecture has distinct learned biases. Some models excel at portraits but struggle with hands or complex clothing. Research which models are best suited for your use case—a range of models are optimized specifically for realistic humans, cityscapes, or mythical creatures. Upgrading to a targeted model dramatically improves anatomical fidelity. Also ensure that deliver quality on par with—and sometimes exceeding—traditional photography you are using the current iteration, because developers regularly resolve persistent anomalies.

Adjusting generation parameters is another critical step. High sampling steps can improve detail but may also amplify noise if the model is not stable. Toning down prompt adherence keeps outputs grounded and avoids excessive stylization. If the image appears distorted beyond recognition, reduce the strength of the prompt influence. Conversely, if features are lacking specificity, increase it slightly while monitoring for overfitting. Most tools allow you to control the denoising iterations; stepping up from 30 to 80 frequently improves structural integrity, especially in crowded or detailed environments.

Pay attention to resolution settings. Generating low-resolution images and then upscaling them can stretch and blur details. Whenever possible, create at native resolution to preserve integrity. If you must upscale, employ AI-enhanced super-resolution models like LATTE or 4x-UltraSharp. These tools preserve structure and minimize artifacts.

If distortions persist, try using exclusion keywords. These allow you to ban specific visual errors. For instance, adding "twisted fingers, fused toes, mismatched irises, smeared facial features" to your negative prompt can greatly diminish typical generation failures. Many models are trained to avoid these phrases, making negative prompts a powerful tool for refinement.

Another effective technique is to produce several outputs and pick the most natural one. Use consistent seeds to isolate subtle variations. This method helps isolate whether the issue is due to randomness or structural flaws in the model or prompt.

Lastly, post-processing can help. Use light editing software to correct imperfections such as uneven complexion, misaligned pupils, or inconsistent highlights. While not a substitute for a well-generated image, it can restore functionality to flawed outputs. Always remember that machine-made visuals are statistical approximations, not photorealistic captures. Minor flaws are expected, yet systematic refinement leads to significantly more reliable and lifelike results.