# Evaluation of StyleGAN-CLIP Models in Text-to-Image Generation of Faces
## 5 Results and Discussion
Evaluation of different models on the basis of human ratings: Figure 4 shows the distributions per model for human ratings of images generated by all five image generation models. Statistical analysis (post hoc HSD-Tukey test) identifies StyleGAN2 as the best model, whose mean aggregate ratings are significantly higher than all other models and is followed by both StyleGAN3 and StyleGAN2-dist. The difference between StyleGAN2 and StyleGAN3 is mainly from Q2 (picture realism) since both models perform equally on Q1 (alignment of image to text). Interestingly, StyleGAN2-ada generates the most realistic images (statistically different to all other models). However, the same model fails in Q1 (alignment with text). This failure can be attributed to the StyleGAN2-ada training process, which uses Adaptive Discriminator Augmentation (ADA), and prioritises image realism over latent space structure. ADA introduces heavy image augmentations to the discriminator to stabilise training on limited data, which distorts (or modifies) the feedback the generator receives. As a result, the generator learns to produce photorealistic images  
Figure 2: Scatter plots and linear relationships: (**a**) between human rating for Q1 and CLIP score (r = 0.065, \(p\) = 0.005), (**b**,**c**) between human rating for Q2 and FID (r = \(-\)0.005, \(p\) = 0.822) and CMMD (r = \(-\)0.264, \(p\) = 0.0) scores, respectively. Each point represents one of the description–picture items rated in the human evaluation survey. The line is the line of best fit for the relationship between items along both variables of interest. but develops a less disentangled and less semantically consistent latent space. Since the text-to-image method in use benefits from a clean and interpretable latent space to align CLIP text embeddings with meaningful visual features, the warped latent structure of StyleGAN2-ADA does not help the optimisation method to converge to an acceptable text-image alignment. In contrast, StyleGAN2 and StyleGAN3 preserve better latent geometry, enabling more faithful text-to-image alignment when gradient descent is used to search for a latent space that produces an image which matches the description. Furthermore, StyleGAN1 performs better than styleGAN2-ada in Q1, although it performs extremely badly in Q2. In addition, the Q3 ratings alone align with the aggregated ratings, perhaps with the exception of StyleGAN2-ada. The HSD-Tukey tests yield the same cluster groupings for both Q3 and the aggregated scores. Finally, we conclude that, overall, according to human judgments, StyleGAN2 is the best model for generating still images. Evaluation of editing models on the basis of human ratings: Figure 5 shows the distributions per model for human ratings of images generated by the StyleGAN2 and StyleGAN3 models in a single step (i.e two cascaded sentences at input) and in two steps, i.e., first generating an image with the first sentence using StyleGAN2/3 models and then editing the image using the respective editing models. From the plots, the StyleGAN2-edit model has the highest mean, followed by the StyleGAN2-CLIPinvertor and StyleGAN3-edit models. A post hoc HSD-Tukey test reveals that the ratings for the StyleGAN3-edit are significantly less than for both the StyleGAN2 editing models. However, there is no significant difference between the StyleGAN2-edit and the StyleGAN2-CLIPinvertor models. We also note that these results follow the same trends observed in the analysis of the single-step models. Comparison of editing and non-editing models: We are interested in knowing whether the editing models perform better than the single-step generation models on the basis of human evaluation. With reference to Figure 5, the StyleGAN2-edit model's mean rating is the highest. However, a post hoc HSD-Tukey test does not support the hypothesis that the editing model performed any better than the single-step generation models. In addition, the StyleGAN3-edit model yielded a mean rating that is significantly lower than the mean ratings of all the other models.