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.
Why position inside the chunk matters
A key detail that many SD users overlook: CLIP does not treat all 75 tokens equally. The model’s attention mechanism gives earlier tokens more influence over the final image embedding. Tokens in the second chunk (positions 76–150 in extended mode) receive a noticeably weaker signal because they are processed in a separate pass and their embeddings are blended back in, rather than attending directly to the full context.
In practical terms, this means:
- A subject keyword at position 10 shapes the image far more than the same keyword at position 80.
- Style modifiers at position 70–74 are borderline and may or may not register clearly.
- Anything after position 75 is partially decoupled from your main subject.
This is why the classic advice is to put subject → key visual attributes → style → secondary details in that order, not alphabetically or by aesthetic preference.
Token cost of common prompt elements
Different parts of a typical SD prompt have very different token footprints. As rough guidance for planning:
| Prompt element | Approximate token cost |
|---|---|
| Single common word (e.g. “forest”) | 1 |
| Compound noun (e.g. “sunlit meadow”) | 2 |
| Artist reference with comma (e.g. “by Greg Rutkowski,“) | 4–5 |
| Attention weight like (keyword:1.3) | 3 |
| A long style phrase (“digital oil painting with dramatic rim lighting”) | 8–10 |
| A negative prompt replication of the above | Same as the positive count |
Prompts that rely heavily on artist references and multi-word style descriptors often hit 75 tokens faster than expected, because each comma, parenthesis, and number counts separately.
Extended mode and multi-chunk prompts
Some WebUIs (AUTOMATIC1111 with the Prompt editing extension, InvokeAI, others) support prompts longer than 75 tokens by processing multiple chunks and blending the embeddings. The 150-token mode in this analyzer reflects a two-chunk setup. In that setup:
- Chunk 1 (tokens 1–75): full influence
- Chunk 2 (tokens 76–150): reduced but real influence
- Anything beyond 150: varies by implementation, often discarded
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 are near the limit, trim until you are comfortably under to keep your strongest descriptors inside the first chunk.