Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A media company is using Vertex AI's Imagen model to generate images for marketing campaigns. They have a set of prompts that describe desired scenes, but the generated images often contain artifacts such as distorted faces or unnatural lighting. The team has tried varying the prompt wording but the issues persist. They are using the default parameters (no modifications). They have a budget for additional compute resources and want to improve image quality without switching to a more expensive model. The team has access to a small set of high-quality images in the same style as their target outputs. What should the team do?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fine-tune the Imagen model on the small set of high-quality images to improve output quality.
Fine-tuning the Imagen model on the small set of high-quality images allows the model to learn the desired style and reduce artifacts like distorted faces and unnatural lighting, improving output quality without switching to a more expensive model. Option A is incorrect because increasing the guidance scale may cause the model to overfit to the prompt and potentially introduce more artifacts rather than fix them, and the team already has issues with prompt adherence. Option B is incorrect because using more detailed prompts with negative prompts might help but the team already tried varying wording without success; the root cause is the model's lack of specific training on the desired quality, which fine-tuning directly addresses. Option D is incorrect because generating more images per prompt does not improve the per-image quality; it only increases the chance of finding a good one, and the team wants to improve overall image quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the guidance scale parameter to make the model follow prompts more closely.
Why it's wrong here
Higher guidance scale may reduce creativity but doesn't directly fix artifacts.
- ✗
Use a more detailed prompt style with negative prompts to avoid artifacts.
Why it's wrong here
Prompt engineering can help but may not fully eliminate artifacts; fine-tuning is more effective.
- ✓
Fine-tune the Imagen model on the small set of high-quality images to improve output quality.
Why this is correct
Fine-tuning adapts the model to produce images with fewer artifacts and desired style.
- ✗
Increase the number of images generated per prompt and manually select the best ones.
Why it's wrong here
More images increase chance of a good one but don't fix underlying quality issues.
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