A company uses Retrieval Augmented Generation with Amazon Bedrock and Stable Diffusion to create product images from text. Outputs are often generic and lack detail. Which change will most increase the specificity of the generated images?

Raising the classifier-free guidance (CFG) scale increases the generated image's adherence to the prompt. A higher CFG scale forces the model to follow the text prompt more strictly, leading to more specific and less generic outputs. Increasing the number of generation steps can improve image quality and detail, but doesn't directly increase adherence to the prompt's specificity. Using the MASKIMAGEBLACK option for the mask source is relevant for inpainting or outpainting tasks, not for improving the specificity of initial image generation from text. Increasing prompt strength is not a standard or effective parameter in most Stable Diffusion implementations for directly controlling prompt adherence; CFG scale is the primary control for this.