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An embedding is basically an embedded txt2img macro. It works completely on the txt2img layer and the description should be the opposite of how you normally create a description for a model (for example).

From personal experience, you may need to break this up into multiple embeddings. If you are all right with that, then describe every detail in the image except what you want. The embedding needs to infer (learn from what is not being described) what is missing from the images and it will recreate what is missing using txt2img.

Examples

  1. A close up photo of a woman's ear. (AI will focus on the ears and see you did not describe the shiny things hanging off them. So will learn to recreate …

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@minienglish1
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@Ackerlight
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@minienglish1
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@andupotorac
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@andupotorac
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Answer selected by runner22k
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