Wildcard Divide

ComfyUI custom node that specifies wildcard prompts for multiple regions

The above workflow is docs/example.json.

Wildcard Divide Node

This node incorporates the syntax of Impact Pack Wildcards while introducing additional syntactical features.

Weighted Child Selection

You can assign selection weights to options by prefixing them with a numerical value. This number determines the likelihood of that particular option being chosen.

hair:
  - 4, blonde
  - 5, black
  - 1, red

In this example, invoking __hair__ will result in "blonde" being selected with a probability of 4/(4+5+1) = 4/10 = 0.4. When a numerical prefix is omitted, a default weight of 1 is assumed.

This weighted selection mechanism is functionally equivalent to the following syntax in Impact Pack Wildcards:

hair:
  - {4::blonde|5::black|1::red}

Pattern-based Selection

Lines that start with / are pattern matchers. These are selected when the pattern matches the previously generated prompt. Here's an example:

outfit:
  - blouse, skirt, __legs__
  - shirt, pants, __legs__
  - swimsuit, __legs__
legs:
  - /skirt/ stockings
  - /pants/ socks
  - bare feet

Here's how the pattern matching works:

1. When __outfit__ expands to blouse, skirt (1/3 probability):

   • __legs__ will only consider options matching /skirt/ plus unmatched options
   • Therefore, it will randomly choose between stockings or bare feet

2. When __outfit__ expands to shirt, pants (1/3 probability):

   • __legs__ will only consider options matching /pants/ plus unmatched options
   • Therefore, it will randomly choose between socks or bare feet

3. When __outfit__ expands to swimsuit (1/3 probability):

   • Since no patterns match, only unmatched options are considered
   • Therefore, it will select bare feet

Note: Options without any pattern matcher (like bare feet in this example) are always included as candidates, regardless of the previous prompt.

Pattern Alternatives

When a line includes !, the text after ! will be selected when the pattern does not match the prompt. For example:

outfit:
  - blouse, skirt
  - dress
  - swimsuit
legs:
  - /swimsuit/ bare feet ! stockings

In this example, stockings will be selected when the outfit doesn't contain swimsuit (i.e., when blouse, skirt or dress is selected). Conversely, if swimsuit is selected, bare feet will be chosen.

Exclusive Pattern Matching

When a pattern ends with =, it becomes an exclusive pattern that will remove all non-matching options from consideration. For example:

outfit:
  - blouse, skirt
  - dress
  - swimsuit
legs:
  - /skirt/= stockings
  - bare feet

In this example:

• When __outfit__ selects blouse, skirt (1/3 probability):

  • The /skirt/= pattern matches
  • Due to the = suffix, all non-matching options (in this case, bare feet) are excluded
  • Therefore, stockings will be selected with 100% probability

• When __outfit__ selects either dress or swimsuit:

  • The /skirt/= pattern doesn't match
  • Only the non-pattern option bare feet remains available
  • Therefore, bare feet will be selected with 100% probability

Pattern Matching with Conditional Exclusion

The =~ suffix creates a sophisticated pattern matching rule that combines conditional exclusion with fallback behavior. When a pattern ends with =~, it implements the following logic:

outfit:
  - blouse, skirt
  - dress
  - swimsuit
legs:
  - /skirt/=~ stockings
  - bare feet
  - socks

This operates in two distinct modes:

1. When Pattern Matches: If __outfit__ contains skirt (probability: 1/3):

   • The /skirt/=~ pattern activates
   • All non-matching options (bare feet, socks) are excluded
   • stockings is selected with 100% probability

2. When Pattern Fails: If "skirt" is not present in __outfit__:

   • The pattern-matched option (stockings) remains in the candidate pool
   • All options become eligible for selection
   • Random selection occurs between stockings, bare feet, and socks

This mechanism provides a elegant way to enforce specific combinations while maintaining flexibility when conditions aren't met.

Split region

You can use [SEP] to divide an image into different regions. Each [SEP] divides the image into n equal parts.

scene: 2girls [SEP] blonde hair [SEP] black hair

For example, if written as above, 2girls would be applied to the entire image, blonde hair to the left half of the image, and black hair to the right half.

Split Direction

You can specify the orientation of the split using the opt:horizontal and opt:vertical options.

scene:
  - opt:horizontal 2girls [SEP] blonde hair [SEP] black hair
  - opt:vertical sky [SEP] blue sky [SEP] red sky

This syntax allows for precise control over image segmentation:

1. Horizontal Split (Left to Right): If the first option is selected, the image is divided horizontally. In this case:

   • 2girls applies to the entire image
   • blonde hair is applied to the left half
   • black hair is applied to the right half

2. Vertical Split (Top to Bottom): If the second option is chosen, the image is segmented vertically:

   • sky is applied across the entire image
   • blue sky affects the top half
   • red sky influences the bottom half

Image Size Specification

You can define the dimensions of the output image using the opt:widthxheight syntax. This feature allows for dynamic image size adjustment based on the selected option.

scene:
  - opt:1216x832 2girls [SEP] blonde hair [SEP] black hair
  - opt:832x1216 sky [SEP] blue sky [SEP] red sky

In this example, selecting the second option would result in an image with dimensions of 832x1216 pixels.

To implement this functionality, ensure that you connect the width and height outputs to the empty latent image node in your workflow. This connection enables the dynamic resizing of the output based on the specified dimensions.