Otherwise known as Textual Inversions, embeddings are a bit different than the usual additions to a comfy workflow. Instead of a node, they are added directly to a prompt. This is due to the nature of an embedding, they are specialized models which provide a specific meaning to a token.
Wait, tokens?
While prompts are human readable, they are not read in a human fashion by stable diffusion. They are translated into _tokens_, and stable diffusion assigns meaning to them according to associations in the model (including lora, which come before the prompts).
Tokens are placed in an prompt with the keyword `embedding:` followed by the embedding's filename. For example, a popular _negative_ embedding is EasyNegative.pt.
` embedding:EasyNegative`
There is no space between `embedding:` and `EasyNegative`, it is all one "word." The filename extension (`.pt` in this case) can be left off if desired.
Embeddings can be placed in positive or negative prompts, as desired. Much like lora, trigger words aren't strictly neceessary, but can add additional "weight" to the prompt.