【初心者必見！】AIイラストのプロンプトの仕組みと構文をわかりやすく解説（Stable Diffusion）

This paragraph delves into the intricacies of how prompts function within AI image generation systems, specifically Stable Diffusion. It explains that prompts are textual inputs that guide the AI to create desired images. The process involves encoding prompts into vectors that can be compared to images, which are initially just noise. Through a series of comparisons and refinements, the AI gradually removes noise that doesn't match the prompt, resulting in the final image. The paragraph also touches on the importance of prompt order, the use of breaks to divide processing chunks, and the concept of token similarity in refining the generated image.

This section discusses the tendency of prompts to mix within the generated image if there are similar elements present. It uses the example of generating an illustration of a woman with a black dress and red hair, versus one with a white dress and blonde hair, to illustrate how the colors can mix if the prompts are not properly separated. The paragraph explains how the use of breaks can help to prevent this mixing by dividing the prompts into separate processing units. It also covers the concept of repeating prompts and adjusting their weight to increase the likelihood of them being reflected in the image. The explanation extends to special syntax, including negative prompts, which can be used to exclude certain elements from the final image.

The final paragraph wraps up the discussion on prompts by summarizing the key points covered in the script. It reiterates that while the order of prompts theoretically doesn't matter, their effective use can be enhanced through the strategic use of breaks, repetition, and weight adjustments. The paragraph also cautions about the importance of understanding embeddings when dealing with prompts. It concludes by encouraging viewers to share any additional insights or questions in the comments section and looks forward to future discussions on the topic.