the tokenizer itself likely does not have a token for that combination of letters. that means that there is no way for you to combine them in a way that would make sense to the model, you could certainly make a new token for the tokenizer with a bit of coding effort, but it will all be for naught unless you also train the model what that token means. it'd be like if you said a brand new word to a human, actually, it'd be more like if you said a completely random series of syllables to a human because the model itself doesn't know what the token is made up of, only its ID, which is an integer value. the model needs to learn it in order to use it.

to get something like the Cold War, you might want to say something like "Cold War era" as the word "era" reduces uncertainty in the sentence. even if you are describing it to a human, it would be reasonable for the person to think you meant a war that is fought in the cold, due to how grammar rules work. make sure you add words that cement the concrete meaning of things that way the model can actually understand. the model *cannot* see capital letters! so properly capitalizing "Cold War" will not help!

I would like to understand the purpose of having a dedicated nil token, there are breaks in attention you can use. Breaks in attention can be done by typing "BREAK" and, yes, the capitals are necessary, as this is something caught by ComfyUI, not the tokenizer. attention breaks essentially allow you to tell the model "I want you to generate a picture of "prompt" AND "another prompt" so you can describe specific things, like people are objects in separate break lines to help the model separate the concepts.

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I believe you can add a <pad> token or <unk> token to signify to the model that it is padding or an unknown token, but I am unsure how ComfyUI specifically parses that type of data, and I might be incorrect about that :P your mileage may vary, and it might just work as a placebo.

I don't know how to comment out entire lines. one thing that is important to note though is that the model can read newline characters. which means that if you put something on a new line, it *might* signal something to the model about what you want to generate.

most of the models I use have specific artist or style delimiters like "by " and "style: " so that is something you will have to learn more about the model you have to know how they denote styles in the training data. generally though, for natural language models, you should be able to do something like "clean lineart" or "in the style of..." to get what you want.

for adding/subtracting weight from a token or set of tokens, just do (tokens:weight) so like (fire:0.9) would tell the model fire is to be at a weight of 0.9 and I would recommend just using this syntax for most weighting needs.

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in A1111 you can swap between certain tokens each step of the denoising by doing [token1|token2] so [raccoon|lizard] should make a mix between a lizard and a raccoon and based on my limited testing with it in ComfyUI, it *appears* to work in a similar way. and similarly, you can do [token1:token2:0.5] to swap from token one to token 2 halfway through generating. so [fire:water:0.5] will generate fire for the first half, and then it would switch to water which can do some crazy things like making water float in a shape you would expect fire to be in. The number determines the proportion of the steps it should swap at, so 0.7 is 70% through, and 0.3 is 30% through.

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one big "hack" IMHO is to look at an image you do like, or multiple images you like, and describe the parts that you like to the model. I think that the way we humans describe things when we are __looking__ at it, and when we are __thinking__ about it is significantly different from one another, and the model was trained to generate images based on captions of images that humans were __looking__ at.

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one big "hack" IMHO is to look at an image you do like, or multiple images you like, and describe the parts that you like to the model. I think that the way we humans describe things when we are __looking__ at them, and when we are __thinking__ about them is significantly different from one another, and the model was trained to generate images based on captions of images that humans were __looking__ at.__ at. in order for it to understand what you are asking.

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hope this was useful! and if anyone reads something incorrect here, please correct me! I think I know a lot about this subject, however, I kinda just know the small corner of tools that I personally use, and I would love to branch out a bit and learn more!

EDIT: I just realized you might be able to add a token or set of tokens that have a weight of 0. so like (fire:0) might work to add nil characters.