![]() Style or ArtistsĪnother part of the template that can heavily impact the outcome of the generated image is the style or the artist. For many subjects, that art form will be photography. If the art form is not specified in the prompt, the generative models will usually choose one it has seen the most during training. For more inspiration, you can have a look at. There are various other art forms like stickers and tattoos. lighting (golden hour, studio lighting, natural lighting, etc.).camera settings (fast shutter speed, macro, fish-eye, motion blur, etc.),.film type (black & white, polaroid, 35mm, etc.),.E.g., for photography, you can become very specific by defining details like : Prompt: “CCTV still of a cat wearing sunglasses” (Image made by the author with DreamStudio)Īs you can see, you can even define the specific medium for each art form. photography: studio photography, polaroid, camera phone, etc.The form of art is a crucial part of the prompt. While Midjourney has a special command for cases like this ( -no), you can bypass this issue by avoiding negative phrasing and instead positively phrasing your prompt. Prompts containing negative words like “not”, “but”, “except”, and “without” are difficult for the text-to-image generative models to understand. Parameters: E.g., in Midjourney, you can suffix any part of a prompt with ::weight to give it a weight (e.g.I have also seen prompts repeating the subject in different languages or using emojis. Repetition: Repeating the subject by phrasing it differently can impact its weighting.Order: Tokens near the front of a prompt are weighted more heavily than the tokens in the back of a prompt.If you want to give a specific subject a heavier weight, there are various ways to do so. Prompt: “twelve cats wearing sunglasses” (Image made by the author with DreamStudio) Weights What do you want to see? While this might be the most straightforward, it is also the most difficult regarding the amount of detail you want to provide. The most important part of a prompt is the subject. However, the direct impact of tokenization is not always clear. For prompt engineering, you can use commas ( ,), pipes ( |), or double colons ( ::) as hard separators. Tokenization in the context of prompt engineering describes the separation of a text into smaller units (tokens). We will get to each part in the following sections. Template and TokenizationĪ prompt usually follows the following template (adjusted from ). That means whether you capitalize your text does not impact the generated image therefore, you can write your prompt in lowercase. While it might not work as well as English prompts, you can use it for enhancement (see section Repetition).Īlso, e.g., Midjourney is not case-sensitive. However, as you can see, both the image generated with a Japanese prompt as well as the image generated with an emoji only prompt fail to produce a pair of sunglasses on the cat. Prompt: “□□” (Image made by the author with DreamStudio) The images will be produced with DreamStudio (GUI for Stable Diffusion) with the default settings and a fixed seed of 42 to generate similar-looking images for comparison.įor more inspiration on prompt engineering, you can have a look at, which is a collection of prompts and their resulting images produced with Stable Diffusion. We will use the base prompt “a cat wearing a pair of sunglasses” similarly to. Therefore, not all tips might apply to the specific generative model you are using. This is a general guide, and there are differences between DALL♾2, Stable Diffusion, and Midjourney. This article will give you a quick guide to prompt engineering before you waste all your free trial credits. Users are incentivized to use as few prompts as possible. Because DALL♾2, the Midjourney Discord server, and StabilityAI’s DreamStudio have a credit-based pricing model, users are incentivized to use as few prompts as possible to get an image they like. With the release of Stable Diffusion, Midjourney, and DALL♾2, people have been saying that prompt engineering could become a new profession. If you have already played around with a text-to-image generative model, you know how difficult it is to produce an image you like.
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