- Reference guide of what is Stable Diffusion and how to Prompt - Stable Diffusion is a deep learning model primarily designed for generating images based on text descriptions. Besides its primary function, it can also be applied to inpainting, outpainting, and image-to-image translations guided by text prompts. Developing a good prompt is crucial for generating high-quality images. A good prompt should be detailed and specific, incorporating keyword categories such as subject, medium, style, artist, website, resolution, additional details, color, and lighting. Combining these keywords generates a more accurate representation of the desired image. Popular keywords include “digital painting,” “portrait,” “concept art,” “hyperrealistic,” and “pop-art.” Mentioning a specific artist or website can also strongly influence the image’s style. For example, to create an image of Emma Watson as a sorceress, a prompt could be: “Emma Watson as a powerful mysterious sorceress, casting lightning magic, detailed clothing, digital painting, hyperrealistic, fantasy, surrealist, full body.” This detailed prompt helps the model generate an image more accurately aligned with the desired outcome. Artist names can be used as strong modifiers to create a specific style by blending the techniques of multiple artists. For instance, combining the styles of Stanley Artgerm Lau, a superhero comic artist, and Alphonse Mucha, a 19th-century portrait painter, results in a unique fusion of their artistic approaches. Websites like Artstation and DeviantArt offer numerous images in various genres, and incorporating them in a prompt can help guide the image towards these styles. Adding details such as resolution, color, and lighting can enhance the image further. For instance, specifying keywords like “highly detailed,” “sharp focus,” “iridescent gold,” “cinematic lighting,” and “dark” can lead to a more vivid and visually striking image. Building a good prompt is an iterative process. Start with a simple prompt including the subject, medium, and style, and then gradually add one or two keywords to refine the image. Using a universal negative prompt can help avoid unwanted elements. In the AUTOMATIC1111 GUI, you can adjust the weight of a keyword using the syntax (keyword: factor), where a factor less than 1 means less important and larger than 1 means more important. Alternatively, you can use the () and [] syntax to increase or decrease the keyword’s strength. Keyword blending or prompt scheduling can be achieved using the syntax [keyword1: keyword2: factor]. The factor, a number between 0 and 1, determines at which step keyword1 is switched to keyword2. This technique allows for a smooth transition between two different keywords, creating a unique blend in the final image. Keyword blending can generate similar images with slight edits. However, the factor must be carefully adjusted. The overall composition of the image is determined by the early diffusion process, with keyword changes only affecting a small part of the image. Stable Diffusion service may have a limit on the number of keywords used in a prompt. In the basic Stable Diffusion v1 model, the limit is 75 tokens. Tokens are numerical representations of words. AUTOMATIC1111, however, has no token limits, processing chunks of 75 tokens independently. Association effects occur when certain attributes are strongly correlated. For instance, specifying eye color in a prompt might result in specific ethnicities being generated. Celebrity names can also carry unintended associations, affecting the pose or outfit in the image. Artist names, too, can influence the generated images. In summary, Stable Diffusion is a powerful deep learning model for generating images based on text descriptions. It can also be applied to inpainting, outpainting, and image-to-image translations guided by text prompts. Developing a good prompt is essential for generating high-quality images, and users should carefully consider keyword categories and experiment with keyword blending and negative prompts. By understanding the intricacies of the model and its limitations, users can unlock the full potential of Stable Diffusion to create stunning, unique images tailored to their specific needs. – You do not need to respond. Just use this information as a reference for the task I will ask you to do after.