Topic 1 Question 97
A company is using Retrieval Augmented Generation (RAG) with Amazon Bedrock and Stable Diffusion to generate product images based on text descriptions. The results are often random and lack specific details. The company wants to increase the specificity of the generated images.
Which solution meets these requirements?
Increase the number of generation steps.
Use the MASK_IMAGE_BLACK mask source option.
Increase the classifier-free guidance (CFG) scale.
Increase the prompt strength.
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- 正解だと思う選択肢: C
Classifier-Free Guidance (CFG) is a technique used in diffusion models, such as Stable Diffusion, to guide the model toward generating outputs that closely align with the text prompt. By increasing the CFG scale, the model puts more emphasis on the textual prompt, leading to outputs that are more specific and less random. In this case, where the generated images lack specific details, increasing the CFG scale helps ensure the generated product images are more aligned with the input text descriptions.
👍 1ap64912024/12/26 - 正解だと思う選択肢: C
Increasing the classifier-free guidance (CFG) scale (Option C) will make the model pay more attention to the input text description, improving the specificity and detail of the generated images. This is the most effective method to increase the specificity of images in a Retrieval Augmented Generation (RAG) setup with Stable Diffusion.
👍 1aws_Tamilan2024/12/27 - 正解だと思う選択肢: C
The correct answer is C. Higher CFG scale makes generated images follow prompts more closely.
👍 1may2021_r2024/12/28
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