NCA-GENM Question 200
Single answerYou are tasked with building a text-to-image generation system using CLIP and a diffusion model. During the training process, how does CLIP contribute to improving the quality of the generated images?
- A
CLIP provides a similarity score between the generated image and the text prompt to guide the diffusion model during training.
- B
CLIP directly generates pixel-level details for the image based on the text prompt.
- C
CLIP preprocesses the text prompt into a format that can be directly used by the diffusion model.
- D
CLIP adjusts the noise schedule in the diffusion model to enhance realism in the generated images.
Show answer and explanation
Correct answer: A
Explanation
CLIP plays a critical role in text-to-image generation by providing a semantic similarity score between the generated image and the input text prompt. This feedback allows the diffusion model to iteratively improve the alignment between the generated images and the desired textual descriptions. CLIP does not directly generate image details, preprocess text prompts, or modify the diffusion noise schedule.
- A. Correct.
Correct. CLIP is used to evaluate the alignment between the generated image and the text prompt by providing a similarity score. This score is then used as feedback to optimize the diffusion model during training.
- B. Incorrect.
Incorrect. CLIP does not generate pixel-level details; it evaluates the semantic alignment between text and images.
- C. Incorrect.
Incorrect. CLIP does not preprocess the text prompt into a format for the diffusion model. The text prompt is typically processed by a tokenizer before being input into the system.
- D. Incorrect.
Incorrect. CLIP does not adjust the noise schedule in the diffusion model. The noise schedule is a distinct component of the diffusion process, unrelated to CLIP’s functionality.