Constraints

Constraints do influence the trained image without using CLIP. E.g., the image mean can be trained to be above or below a specific threshold.

Constraints only affect the pixels that are processed by the transforms of the target.

Here's a list of all available constraints:

• blur: Adds the difference between the image and a blurred version to the training loss.
• border: Adds a border with a specific size and color to the training loss.
• contrast: Pushes the contrast above or below a threshold value.
• edge_mean: Adds the difference between the current image and and an edge-detected version to the training loss.
• mean: Pushes the image color mean above or below a threshold value.
• noise: Adds the difference between the current image and a noisy image to the training loss.
• normalize: Adds image normalization to the training loss.
• saturation: Pushes the saturation above or below a threshold value.
• std: Pushes the standard deviation above or below a threshold value.