workflow-templates-broken-zap-2026-06-08/site/knowledge/concepts/embeddings.md

Textual Embeddings

Textual embeddings are learned text representations that encode specific concepts, styles, or objects into the CLIP text encoder's vocabulary. These tiny files (~10–100 KB) effectively add new "words" to your prompt vocabulary, letting you reference complex visual concepts — a particular art style, a specific character, or a set of undesirable artifacts — with a single token. Because they operate at the text-encoding level, embeddings integrate seamlessly with your existing prompts and require no changes to the model itself.

How It Works in Hanzo Studio

• Key nodes: CLIPTextEncode — reference embeddings directly in your prompt text using the syntax embedding:name_of_embedding
• Typical workflow pattern: Place embedding files in HanzoStudio/models/embeddings/ → type embedding:name_of_embedding inside your positive or negative prompt in a CLIPTextEncode node → connect to sampler as usual

Key Settings

• Prompt weighting: Embeddings have no dedicated strength slider, but you can adjust their influence with prompt weighting syntax, e.g., (embedding:name_of_embedding:1.2) to increase strength or (embedding:name_of_embedding:0.6) to soften it
• Placement: Add embeddings to the negative prompt to suppress unwanted features, or to the positive prompt to invoke a learned concept

Tips

• Embeddings are commonly used in negative prompts (e.g., embedding:EasyNegative, embedding:bad-hands-5) to reduce common artifacts like malformed hands or distorted faces
• Make sure the embedding matches your base model version — an SD 1.5 embedding will not work correctly with an SDXL checkpoint
• You can combine multiple embeddings with regular text in the same prompt for fine-grained control