NCA-GENM Question 202
Select 3You are training a text-to-image diffusion model using CLIP. During the process, you notice that the model struggles to align the generated images with the provided English text prompts. Which approach would most likely improve the alignment between text and image outputs?
- A
Fine-tune the CLIP model on a domain-specific dataset relevant to your application.
- B
Increase the number of diffusion steps in the training process to enhance image quality.
- C
Add a discriminator network to the training process to better evaluate text-image alignment.
- D
Use larger text prompts with more descriptive details to provide better guidance.
- E
Replace CLIP with a pre-trained convolutional neural network (CNN) for text-image alignment.
Show answer and explanation
Correct answers: A, B, D
Explanation
Improving text-image alignment in a text-to-image diffusion model often involves enhancing the capabilities of the CLIP model (e.g., fine-tuning), improving the image generation process (e.g., increasing diffusion steps), or providing better guidance through descriptive prompts. These approaches directly address the alignment issue, while other methods like adding a discriminator or replacing CLIP with a CNN are not suitable for this specific context.
- A. Correct.
Fine-tuning the CLIP model on a domain-specific dataset improves its ability to understand text-image relationships for your specific use case, enhancing alignment.
- B. Correct.
Increasing the number of diffusion steps can improve the quality of the generated images, which may indirectly improve text-image alignment by creating more visually coherent outputs.
- C. Incorrect.
Adding a discriminator network is not standard in text-to-image diffusion models and does not directly address alignment issues. It is more commonly used in GANs.
- D. Correct.
Using more descriptive text prompts provides better guidance to the model, helping it generate images that align more closely with the text.
- E. Incorrect.
Replacing CLIP with a CNN is not recommended as CLIP is specifically designed for text-image understanding, whereas standard CNNs are not optimized for this purpose.