CLIP is All You Need for Human-like Semantic Representations in Stable Diffusion

The research paper titled 'CLIP is All You Need for Human-like Semantic Representations in Stable Diffusion' explores the capabilities of latent diffusion models, particularly Stable Diffusion, in generating images from text. The study reveals that while Stable Diffusion achieves state-of-the-art results in text-to-image generation, its semantic understanding is primarily derived from the CLIP model's text encoding rather than the diffusion process itself. By employing regression layers to probe the internal representations of Stable Diffusion, the researchers found that specific semantic attributes exhibit varying decoding accuracies, indicating that some attributes are represented more effectively than others. Furthermore, the study notes that during the inverse diffusion process, distinguishing between attributes becomes increasingly challenging. This research underscores the pivotal role of CLIP in enhancing the semantic representation of object attributes, suggesting that advancem…