Prompt weighting            Prompt weighting provides a way to emphasize or de-emphasize certain parts of a prompt, allowing for more control over the generated image. A prompt can include several concepts, which gets turned into contextualized text embeddings. The embeddings are used by the model to condition its cross-attention layers to generate an image (read the Stable Diffusion blog post to learn more about how it works). Prompt weighting works by increasing or decreasing the scale of the text embedding vector that corresponds to its concept in the prompt because you may not necessarily want the model to focus on all concepts equally. The easiest way to prepare the prompt-weighted embeddings is to use Compel, a text prompt-weighting and blending library. Once you have the prompt-weighted embeddings, you can pass them to any pipeline that has a prompt_embeds (and optionally negative_prompt_embeds) parameter, such as StableDiffusionPipeline, StableDiffusionControlNetPipeline, and StableDiffusionXLPipeline. If your favorite pipeline doesn’t have a prompt_embeds parameter, please open an issue so we can add it! This guide will show you how to weight and blend your prompts with Compel in 🤗 Diffusers. Before you begin, make sure you have the latest version of Compel installed:   Copied # uncomment to install in Colab