Why is there a 75-token limit?

CLIP, the text encoder used by Stable Diffusion 1.x and SDXL, processes text in fixed chunks of 77 tokens — 75 usable plus a start and end token. Anything beyond the first chunk is handled in a separate pass that's stitched together, which weakens how strongly later tokens influence the image.

Is this an exact token count?

It's a close estimate, not the exact CLIP BPE count. The real CLIP tokenizer uses byte-pair encoding with a fixed vocabulary, which can't run fully in the browser without shipping a large vocab file. This analyzer approximates it well enough to tell you whether you're comfortably under, near, or over the limit.

What does "extended" mode mean?

Many WebUIs automatically split long prompts into multiple 75-token chunks and blend them, effectively giving you 150 or more tokens. The 150 mode reflects that, but remember tokens past the first chunk influence the image less, so front-load what matters most.

Does word order matter?

Yes. Earlier tokens generally have more influence on the result, and tokens that overflow into a later chunk have the least. Put your subject and most important descriptors first, and push style and minor details toward the end.

Prompt Token Length Analyzer

Prompt token length analyzer

Stable Diffusion’s text encoder, CLIP, reads your prompt in fixed chunks of 77 tokens (75 usable). Go past that and the extra words spill into a second pass that influences the image far less — so a bloated prompt quietly wastes its most important descriptors. This analyzer estimates your token count and tells you whether you’re safe, near the edge, or overflowing.

How it works

CLIP tokenizes text with byte-pair encoding: common words are one token, rarer or longer words split into several, and punctuation counts too. This tool approximates that process in your browser — it can’t ship CLIP’s full vocabulary, so the count is a close estimate rather than an exact match. It then compares your estimate against the 75-token limit (or 150 in extended mode) and flags any tokens that would overflow into the next chunk.

Tips and notes

Front-load the important stuff. Subject and key descriptors first; style and minor details last. Overflow tokens have the least influence.
Cut filler. Words like “very”, “a”, “the” and repeated adjectives eat tokens without changing the image much.
Mind the punctuation. Commas and parentheses are tokens too — a heavily weighted prompt can hit the limit faster than the word count suggests.
Treat it as a guide. When you’re near the limit, trim until you’re comfortably under to keep your strongest descriptors inside the first chunk.