NCA-GENM Question 175
Select 4You are tasked with designing a generative AI system for creating high-quality, contextually relevant images based on text prompts. The system must use a U-Net architecture for image generation, integrate a text-to-image model like CLIP for understanding prompts, and apply prompt engineering for precise outcomes. Additionally, you need to leverage NVIDIA SDKs for optimized deployment. Which combination of steps would best achieve this goal?
- A
Use a U-Net architecture as the core model for generating images and train it on a dataset of paired images and captions.
- B
Integrate CLIP to encode text prompts into latent representations that guide the U-Net during image generation.
- C
Apply prompt engineering to improve the text inputs by using descriptive language and explicit contextual detail.
- D
Use NVIDIA Triton™ Inference Server for deploying the trained model to provide real-time image generation capabilities.
- E
Replace the U-Net architecture with a GAN to simplify the deployment process and avoid the need for prompt engineering.
Show answer and explanation
Correct answers: A, B, C, D
Explanation
To design a system for generative image tasks based on text prompts, using a U-Net architecture for image generation and CLIP for text-to-image alignment is a robust approach. Prompt engineering enhances the quality of results by refining inputs, while NVIDIA Triton™ Inference Server ensures efficient deployment and real-time performance. Replacing the U-Net with a GAN contradicts the specified requirements and does not inherently simplify the workflow.
- A. Correct.
Correct: U-Net is a suitable architecture for generative image tasks, particularly when paired with a dataset of images and captions for training.
- B. Correct.
Correct: CLIP can encode textual prompts into latent representations that inform the U-Net's image generation process, ensuring the outputs align with the input prompts.
- C. Correct.
Correct: Prompt engineering is critical to refining text inputs for better control over the generative model's output, especially in text-to-image tasks.
- D. Correct.
Correct: NVIDIA Triton™ Inference Server is an optimized deployment solution for models, enabling real-time inference for complex generative AI workflows.
- E. Incorrect.
Incorrect: While GANs are another type of generative model, replacing the U-Net with a GAN would not necessarily simplify deployment or eliminate the need for prompt engineering. The question specifies using a U-Net architecture.