ComfyUI-Advanced-Latent-Control

This custom node helps to transform latent in different ways.

Custom Nodes

Latent mirror

This node can flip latent and merge original and flipped version

Input:

• latent

Fields:

• direction – can be vertically, horizontally or both
• multiplier – multiply latent by specified number

Output:

• latent

Usage:

Latent shift

This node can shift latent along x and y axes

Input:

• latent

Fields:

• x_shift – a number between -1 and 1 that indicates how much the latent should be shifted
• y_shift – a number between -1 and 1 that indicates how much the latent should be shifted

Output:

• latent

Usage:

TSampler with transforms (Latent Control)

This node can multiply, mirror and shift latent during generation

Input:
exactly matches the base KSampler

Fields:

• base KSampler fields
• start_mirror_at – a number between 0 and 1 that indicates at what point the sampler will start mirroring
• stop_mirror_at – a number between 0 and 1 that indicates at what point the sampler will stop mirroring
• mirror_mode – can be replace or combine. replace will replace the latent with the transformed one, combine will add the original and the transformed latent and divide by 2
• mirror_direction – can be none, vertically, horizontally, both, 90 degree rotation or 180 degree rotation
• start_shift_at – a number between 0 and 1 that indicates at what point the sampler will start shifting
• stop_shift_at – a number between 0 and 1 that indicates at what point the sampler will stop shifting
• shift_mode – can be replace or combine. replace will replace the latent with the transformed one, combine will add the original and the transformed latent and divide by 2
• x_shift – a number between -1 and 1 that indicates how much the latent should be shifted
• y_shift – a number between -1 and 1 that indicates how much the latent should be shifted
• start_multiplier_at – a number between 0 and 1 that indicates at what point the sampler will start multiplying
• stop_multiplier_at – a number between 0 and 1 that indicates at what point the sampler will stop multiplying
• multiplier_mode – can be replace or combine. replace will replace the latent with the transformed one, combine will add the original and the transformed latent
• multiplier – multiply latent by specified number

Output: exactly matches the base KSampler

Usage:
You also can use those params together

TSampler (Latent Control)

This node allows to combine a lot of transforms with different parameters

Input:

• base KSampler fields
• transform_optional – field that can take output from one of those nodes: Mirror transform, Shift transform, Multiply transform or Combine transforms

Fields: exactly matches the base KSampler

Output: exactly matches the base KSampler

Usage:

Multiply, Mirror and Shift transform nodes parameters exactly match the corresponding KSampler with transforms (Latent Control) parameters

There are two new transform nodes:

• Latent add
• Latent interpolate

They work exactly the same as LatentAdd and LatentBlend nodes from standard node pack, but also, can multiply result by specified number.

Offset

You can apply specific offset for transform nodes.

Fields:

• process_every – a number that indicates which steps will be processed
• offset – a number that indicates offset for previous parameter. For example: if process_every is 4 and offset is 0, sampler apply transformation with this pattern: 0 0 0 1. This pattern will repeat again and again. If offset is 2, pattern will be 0 1 0 0, if -1 – 1 0 0 0.
• mode – can be process_every or skip_every. For example, with skip_every previous pattern (0 0 0 1) turn into this: 1 1 1 0

Output:

• offset

Usage:

You can combine different offsets to achieve interesting patterns. For example:
0 0 0 1 and 0 0 1 give this pattern 0 0 1 1 0 1 0 1 1 0 0 1

One time nodes

Each transform node has own one-time version. They allow to make one transform action at specified step.

Usage:

Latent normalize

Input
exactly matches the VAE Decode node

Output

• latent

When you multiply latent by negative or big positive (bigger than 2) number and paste this latent in sampler, you can see that the image will be generated very poorly. This is because stable diffusion cannot work with such set of numbers (meaning the numbers contained in latent).

But you can prevent this behavior by sequential decode and encode latent using vae. Node Latent normalize make this process easier.

This node also change some results even if output without this node looks good.

And it very slightly changes results from latent, which have not been modified.