Image Generation Settings
Search for our available models here to learn more about them.
Please use our default settings for optimal results if you're a beginner.
Image generation engines can often generate disproportionate body parts, extra limbs or fingers, strange textures etc. Use negative prompting to avoid disfiguration or for creative outputs like avoiding certain colour schemes, elements or styles.
By default, each run produces one output per Model.
An increase in output quality is comparable to a gradual progression in any drawing process that begins with a draft version and ends with a finished product.
At lower values the image will effectively be random. The standard value is between 6-8. Values that are too high can also distort the image.
Face Repositioning Settings
How big should the face look?
Where would you like to place the face in the scene?
![](data:image/png;base64,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)