Diagnosing and fixing artifacts in generated images involves identifying patterns caused by model architecture, training data, or optimization processes. Common artifacts include checkerboard patterns, blurry textures, color banding, and unnatural distortions. To diagnose, start by inspecting output samples for recurring visual anomalies. For example, checkerboard patterns often stem from transposed convolution layers in models like GANs, while blurriness might indicate insufficient model capacity or improper loss function weighting. Tools like activation maps or gradient analysis can help pinpoint layers contributing to the issue. Comparing artifacts across different training stages (early vs. late epochs) can also reveal whether the problem arises from convergence instability or architectural flaws.
To fix artifacts, adjust the model architecture or training pipeline based on the diagnosed cause. For checkerboard patterns, replace transposed convolutions with upsampling layers followed by standard convolutions to reduce grid-like artifacts. If blurriness persists, experiment with loss functions: combining L1 loss with a perceptual loss (using a pre-trained VGG network) often improves texture detail. Color banding, caused by limited output bit-depth or normalization issues, can be mitigated by using higher-precision data formats (e.g., 16-bit PNG) or adjusting the model’s final activation layer (e.g., using tanh instead of sigmoid for broader dynamic range). For distortions in specific regions, data augmentation (e.g., random crops, rotations) can help the model generalize better to edge cases.
Validation and iteration are critical. Use metrics like Fréchet Inception Distance (FID) to quantify improvements, but also visually inspect samples. Tools like TensorBoard or custom visualization scripts can track artifact frequency during training. For example, if color inconsistencies persist, check the training dataset for imbalances (e.g., overrepresented hues) and apply histogram equalization or dataset resampling. Hyperparameters like learning rate, batch size, and gradient penalty coefficients (in GANs) should be tuned systematically—tools like Optuna or grid search scripts help automate this. In one case, reducing the batch size from 64 to 16 eliminated “ghosting” artifacts by allowing finer gradient updates. Always validate fixes across multiple training runs to ensure robustness.