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Controlnet
Guiding image generation with another image
With controlnet, you can guide the image generation process with another image. This is a great way to produce images with a consistent visual layout.
The standard controlnet used at glif is controlnet-canny. This is a controlnet variant that first tries to find edges in the reference image, and then uses those edges as guidance.

Note that not all Glif image models and APIs support controlnet - please see How to use controlnet on how to activate it.




Based on this reference image:





Based on this reference image:

Turn it on inside the Advanced section of the Image Spell:

Then put a URL to your reference image:

Make sure your URL resolves to (preferably) a
.jpg
or .png
image. Please note that imgur links might not resolve. Below are a couple of images where the strength of the
Conditioning
parameter is increased for the prompt an illustration of a cyborg
.The first image (
strength=0.0
) is always the control image. If almost no control scale is applied (0.1
), we get a base cyborg illustration. Moving to the right, the control image guides the image generation more and more.
SDXL
k_euler_a
sampler.
SDXL
unipc
sampler.
SD21
k_euler_a
sampler.
SD21
unipc
sampler.canny upper
and canny lower
refer to parameters of the Canny edge-detection algorithm used inside the model.- Canny Upper: Pixels with intensity gradients above this value are considered strong edges.
- Canny Lower: Pixels below this value are discarded. Those in between are considered weak edges and are kept only if connected to strong edges.
Here's an image to illustrate what happens:

Variation grid of the canny upper and lower thresholds.
- by moving to the right, we are increasing
canny_upper
, so less pixels are considered strong pixels (pixels that have a strong gradient). In the extreme case, only the edge of the ball is considered a strong gradient. (It is an abrupt change in pixel values.) - moving down, we are increasing
canny_lower
and here more and more pixels with weak gradients fall off. - in the upper left quadrant the most interesting stuff is happening: with certain mixes of lower and upper, we get a mix of strong pixels (strong gradients) and weak pixels (weak gradients) that get connected .
So a trick you can use: move both
canny upper
and canny lower
to a certain amount, e.g. 150 until you only have the most salient edges remain then move canny lower
down until you get sufficient detail. Last modified 2mo ago