Answer: Increase the classifier-free guidance (CFG) scale.

## Detailed Explanation To address the company's requirement of increasing specificity and detail in generated product images using Stable Diffusion within a RAG system on Amazon Bedrock, we need to analyze the available options based on how Stable Diffusion parameters function. ### Analysis of Options: **A: Increase the number of generation steps** - This parameter controls the number of denoising iterations during image generation. - While more steps can potentially improve image quality and reduce artifacts, they primarily affect the refinement of the generated image rather than its adherence to the text prompt. - Increasing steps doesn't directly address the core issue of the model not following the text description closely enough. **B: Use the MASK_IMAGE_BLACK mask source option** - This option relates to inpainting or image editing scenarios where parts of an image are masked and regenerated. - In the context described, the company is generating product images from text descriptions, not editing existing images. - This parameter is irrelevant to improving prompt adherence in text-to-image generation. **C: Increase the classifier-free guidance (CFG) scale** - **This is the optimal solution.** The CFG scale directly controls how strongly the model follows the text prompt versus generating more freely. - A higher CFG scale (typically 7-20 range) forces the model to pay more attention to the textual description, resulting in images that are more specific, detailed, and aligned with the prompt. - This parameter specifically addresses the problem of "random" and "lacking specific details" outputs by reducing randomness and increasing prompt adherence. - In Stable Diffusion implementations, including those accessible through Amazon Bedrock, adjusting the CFG scale is a standard method to improve prompt specificity. **D: Increase the prompt strength** - While this might seem relevant, "prompt strength" is not a standard parameter in Stable Diffusion's core text-to-image generation. - Some interfaces or implementations might use this terminology, but it typically refers to similar concepts as CFG scale or might be specific to certain implementations. - The established, documented parameter for controlling prompt adherence in Stable Diffusion is the CFG scale, making option C the more precise and reliable choice. ### Why Option C is Optimal: 1. **Direct Mechanism**: CFG scale operates by adjusting the balance between unconditional and conditional generation during the diffusion process, directly influencing how closely the output matches the text description. 2. **Proven Effectiveness**: In practice, increasing CFG scale consistently produces more detailed and specific images that better reflect the prompt's content. 3. **Addresses Core Issue**: The problem described (images being "too generic" and "missing precise details") is exactly what adjusting CFG scale is designed to solve. 4. **Standard Practice**: This is a well-documented approach in Stable Diffusion documentation and best practices for improving prompt adherence. ### Why Other Options Are Less Suitable: - **A** addresses image quality refinement rather than prompt specificity. - **B** is irrelevant to the text-to-image generation task described. - **D** uses non-standard terminology and is less precise than the established CFG scale parameter. Therefore, increasing the classifier-free guidance (CFG) scale is the most effective and appropriate solution to enhance the detail and specificity of generated product images in this RAG system.