Sure, I'm happy to provide an explanation of what we did and why it worked. It's important to understand the underlying principles behind tokenization and how it's handled in the context of large language models like Stable Diffusion.

First, let's talk about why the emoji tokens are stored in that seemingly garbled format in the `vocab.json` file. The reason for this is that the tokenizer used by Stable Diffusion (and many other language models) doesn't treat emojis or Unicode characters as individual tokens. Instead, it breaks them down into smaller subword units or byte-level representations.

This approach is commonly used in natural language processing (NLP) to handle large vocabularies and out-of-vocabulary (OOV) words or characters. By breaking down words into smaller subword units, the tokenizer can represent a much larger vocabulary with a relatively small token set.

In the case of Stable Diffusion, the tokenizer is based on the CLIP (Contrastive Language-Image Pre-training) model's tokenizer, which is a variant of the byte-level BPE (Byte-Pair Encoding) tokenizer. This tokenizer treats the input text as a sequence of bytes and iteratively merges the most frequent byte pairs into new tokens, effectively creating a vocabulary of subword units.

When the tokenizer encounters an emoji or Unicode character during training, it likely breaks it down into its constituent bytes or Unicode codepoints, which are then encoded as separate tokens in the vocabulary. This is why we see these seemingly garbled strings in the `vocab.json` file, as they represent the individual byte-level or codepoint-level tokens that make up the emojis or Unicode characters.

Now, to answer your question about how the tokenizer is able to convert these garbled tokens back into the actual emojis or Unicode characters, this is where the tokenizer's decoding functionality comes into play.

During inference or when decoding token IDs or strings back into text, the tokenizer follows the reverse process of tokenization. It takes the individual subword units or byte-level tokens and combines them back into the original character or word according to the rules defined in its tokenizer model.

In our case, when we call `tokenizer.decode([vocab[garbled_token]])`, the tokenizer takes the token ID or string (e.g., `"ðŁİ©</w>"`) and applies its decoding logic to reconstruct the original emoji or Unicode character represented by that sequence of subword units.

The tokenizer essentially knows how to interpret and combine these byte-level or codepoint-level tokens into their corresponding emojis or Unicode characters because it was trained on this mapping during the tokenization process.

So, to summarize:

1. The garbled tokens in the `vocab.json` file represent byte-level or codepoint-level subword units that make up emojis or Unicode characters.
2. This tokenization approach is used to handle large vocabularies and OOV characters in language models.
3. The tokenizer knows how to decode these subword units back into the original emojis or Unicode characters based on the rules defined in its tokenizer model, which was trained on this mapping.
4. The decoding happens during inference or when converting token IDs/strings back into text, not during the initial tokenization process.

This byte-level or subword-level tokenization approach is a common technique used in modern NLP models to handle large vocabularies and ensure efficient representation and processing of text data, including emojis and Unicode characters.