4-7 Prompt Edit | Keyword Blending Guide

This guide explains advanced prompting techniques for better control over image generation on SeaArt.AI, applicable to all models except Flux, which uses a different system.

You have learned how to write basic prompt with weights on ⁠Complete Prompting Guide. This guide will teach you advanced prompting techniques to have more control over image generation. Effects can show difference for each model but these rules are applies to all models work on SeaArt.AI. Only Flux doesn't work with these tools because it has different prompt understanding system.

Before Start

• Term keyword can be any word in your prompt. Keyword can be more than one word in some syntax.

• Term factor is value of sampling steps. Scales between 1 and highest sampling step.

• Term factor% is percentage value of sampling steps. Can't be higher that 1 (100%) and scales between 0-1.

[keyword1:keyword2:factor] Syntax

Generation starts with keyword1 and changes to keyword2 after factor step.

• Lower values sometimes will not have visible effect because it has small part of generation.

• Order matters, first keyword1 is your starting term while keyword2 is secondary part of generation.

Example

portrait of a girl, [smiling:frown:0.7] face with 20 step generation will be generated as:

Step 1-14 portrait of a girl, smiling face

Step 15-20 portrait of a girl, frown face

Below You Can See Difference

Images were generated with same settings with 20 steps. factor% changed with 0.1, 0.3, 0.5, 0.7, 0.9

As you can see first image has more angry face because only 2 step generated with smiling while last image has smiling face because 18 step generated with smiling and changed into frown at last 2 steps.

[keyword:factor] Syntax

Generation starts without keyword and adds keyword to prompt after factor step.

• Higher values will not have visible effect because main composition of image will be generated already and AI cannot make this type significant changes.

Example

portrait of a girl, green eyes, [pink hair:7] with 20 step generation will be generated as:

Step 1-7 portrait of a girl, green eyes,

Step 8-20 portrait of a girl, green eyes, pink hair

Below You Can See Difference

Images were generated with same settings with 20 steps. Term factor is changed with 3, 7, 10, 13, 17

As you can see, there is no pinkish hair in the third and later images because AI already generated main composition and couldn't make changes even we add pink hair later. White color was totally luck, it could be any color includes pink.

[keyword::factor] Syntax

Generation starts with keyword and removes keyword from prompt after factor step.

• Higher values will not have significant differences because main composition will be generated with keyword already.

Example

portrait of a girl, [ginger::3], black dress with 20 step generation will be generated as:

Step 1-3 portrait of a girl, ginger, black dress

Step 4-20 portrait of a girl, , black dress

Below You Can See Difference

Images were generated with same settings with 20 steps. Term factor is changed with 3, 5, 7, 10, 13

As you can see there is no significant difference after third image because main composition was already generated when we remove keyword from prompt. Blonde hair was totally luck, it could be any hair color includes ginger.

[keyword1|keyword2] Syntax

This syntax will swap keywords on each step. keyword can be more than two, you can add keyword as much as sampling steps but it's not recommended.

• Lower keyword count equals to better results.

Example

portrait of a girl, [blonde|ginger|black] hair with 20 step generation will be generated as:

Step 1-4-7-10-13-16-19 portrait of a girl, [blonde|ginger|black] hair

Step 2-5-8-11-14-17-20 portrait of a girl, [blonde|ginger|black] hair

Step 3-6-9-12-15-18 portrait of a girl, [blonde|ginger|black] hair

Below You Can See Examples

1. portrait of a girl, [blonde|ginger|black] hair

2. [frog|bird|cat]

keyword1 (keyword2) Syntax

Slash can be used to use actual parantheses in prompt without weight. Some tags include parantheses () and this syntax help you to use them.

Example

mona (genshin impact) will be tokenized as a mona with 1x weight and genshin impact with 1.1x weight.

mona \(genshin impact\) will be tokenized as itself, without weighting and with actual parantheses.

Below You Can See Example

(masterpiece, best quality), mona \(genshin impact\)