A generative image pipeline uses CLIP to evaluate whether candidate images produced by a diffusion model match a given text prompt. Which property of CLIP makes it suitable for this guidance role?
- ACLIP is a pixel-level decoder that reconstructs images token by token from a text sequence.
- BCLIP is a variational autoencoder that compresses images into a latent distribution conditioned on text.
- CCLIP uses autoregressive cross-attention to generate image patches in the order determined by the text prompt.
- DCLIP projects both images and text into a shared embedding space where cosine similarity measures semantic alignment. Correct
Why A is wrong: CLIP contains no decoder and produces no pixels; it is a dual-encoder trained to score text-image similarity, not to generate or reconstruct images.
Why B is wrong: A variational autoencoder uses encoder-decoder structure with a probabilistic latent space; CLIP uses contrastive learning across two modality-specific encoders with no variational bottleneck.
Why C is wrong: Autoregressive patch generation describes models such as image GPT; CLIP generates no patches and does not process modalities sequentially in a generative manner.
Why D is correct: Contrastive training aligns image and text embeddings so that a matched pair scores high similarity, enabling CLIP to act as a differentiable semantic signal for guiding a separate generator.