In this paper, we explore the use of CLIP (Contrastive Language-Image Pre-training) latents for hierarchical text-conditional image generation. The proposed method, called HClipT, leverages the power of CLIP to generate images that are not only visually plausible but also semantically consistent with the given text description. Our approach builds upon existing work in text-to-image synthesis and incorporates a hierarchical framework to improve the quality and diversity of generated images.
Methodology
To create HClipT, we first train a CLIP model on a large dataset of text-image pairs. We then use this pre-trained model to generate images for a given text description through a hierarchical text-conditional image generation process. The hierarchy is established by using a sequence of latent spaces, each capturing a different level of abstraction in the image generation task. This allows HClipT to generate images that are not only visually plausible but also semantically consistent with the given text description.
Experiments
We evaluate HClipT on several benchmark datasets and compare it to existing state-of-the-art methods. Our results show that HClipT outperforms these methods in terms of both image quality and diversity, as measured by the Frechet Inception Distance (FID) score. We also perform a series of ablation studies to analyze the contributions of different components in HClipT, providing insights into the effectiveness of our proposed method.
Conclusion
In this paper, we presented HClipT, a novel approach for hierarchical text-conditional image generation using CLIP latents. Our method leverages the power of CLIP to generate images that are both visually plausible and semantically consistent with the given text description. By establishing a hierarchical framework for text-to-image synthesis, HClipT is able to generate a wider range of images that are more diverse and of higher quality than previous methods. We demonstrate the effectiveness of HClipT through experiments on several benchmark datasets and show that it outperforms existing state-of-the-art methods in terms of both image quality and diversity.
