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…

Increasing the number of generation steps generally improves image quality or detail by allowing more iterations of the diffusion process, but it doesn't directly increase the *adherence* to the prompt's specificity if the CFG scale is low.

Using the MASK_IMAGE_BLACK mask source option is related to inpainting or outpainting, where parts of an image are masked for modification, and is not relevant to increasing prompt specificity for initial image generation.

The classifier-free guidance (CFG) scale parameter controls how much the image generation process adheres to the input prompt versus allowing the model more creative freedom. Increasing the CFG scale will make the generated images more closely follow the text description, thereby increasing their specificity and reducing randomness.

Increasing the prompt strength (often implicitly tied to CFG or a separate parameter in some models) without specifying it as CFG scale is less precise. In Stable Diffusion, CFG scale is the direct parameter for prompt adherence.