nodes/ChangeTheConstants-SeedVarianceEnhancer
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# SeedVarianceEnhancer v2.0
SeedVarianceEnhancer is a ComfyUI custom node intended to be used to add diversity to the outputs generated by Z-Image Turbo. It compensates for low seed variance, which is when generated images look very similar despite being generated with different seeds. It works by adding random noise to the embedding for the early generation steps.
SeedVarianceEnhancer is a ComfyUI custom node designed to add diversity to the outputs of Z-Image Turbo. It compensates for low seed variance, which is when generated images look very similar despite being generated with different seeds. It works by adding random noise to the embedding for the early generation steps.
## Installation:
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## Usage:
The node should be placed next in line after the positive prompt "CLIP Text Encode (Prompt)" node. The conditioning output should then connect to the sampler's positive prompt input. The default settings should work well with Z-Image turbo.
The node should be placed next in line after the positive prompt "CLIP Text Encode (Prompt)" node. The conditioning output should then connect to the sampler's positive prompt input. The default settings should work well with Z-Image Turbo. The node has been tested to work with the workflow from [https://github.com/comfyanonymous/ComfyUI_examples/tree/master/z_image](https://github.com/comfyanonymous/ComfyUI_examples/tree/master/z_image).
The randomize_percent setting determines what percentage of the embedding values will be randomly selected for modification with noise. The strength setting adjusts the scale of the noise. The seed value determines which values to modify and with what noise. By default, the noise_insert setting is set to "noise on beginning steps." By removing the noise before the end of the generation, the model will have time to pivot back towards prompt adherence. Steps_switchover_percent specifies what percentage of steps are processed before switching embeddings. "Noise on all steps" will reduce prompt adherence and text rendering significantly more so. For adjusting to a different step schedule, use this formula to calculate which percentage setting to use: (100/TOTALSTEPS) * STEPS - 1.
The randomize_percent setting determines what percentage of embedding values will be randomly selected for modification with noise. Strength adjusts the scale of the noise. The seed determines which values to modify and with what noise. Noise_insert is set to "noise on beginning steps" by default. By removing the noise before the end of the generation, the model will have time to pivot back towards prompt adherence. "Noise on all steps" will reduce prompt adherence and text rendering significantly. Steps_switchover_percent specifies what percentage of steps are processed before switching between noisy and original embeddings.
Strength values in the range of 15 to 30 and a randomize_percent of 50 are reasonable starting points for use with Z-Image Turbo. The steps_switchover_percent defaults to 20%. Some prompts will benefit from higher or lower settings. Longer more detailed prompts provide more tokens for SeedVarianceEnhancer to manipulate. Somewhat counterintuitively, use with more detailed prompts may result in more diverse outputs.
Strength values in the range of 15 to 30 and a randomize_percent of 50 are reasonable starting points for use with Z-Image Turbo. The steps_switchover_percent defaults to 20%. Use this formula to calculate which percentage setting to use: (100/TOTALSTEPS) * STEPS - 1. Some prompts will benefit from higher or lower settings. Longer more detailed prompts provide more embedding values for SeedVarianceEnhancer to manipulate. Somewhat counterintuitively, use with more detailed prompts may result in more diverse outputs.
## Advanced usage:
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SeedVarianceEnhancer nodes can be chained together. The noise from each node will add up. The node only uses the first embedding of a conditioning, so chaining to a node set to "noise at end steps" will not work.
Strength values can be set to negative numbers. Doing so inverts the noise added to the embedding.
Strength values can be set to negative values which will invert the noise added to the embedding.
## Use with other models:
SeedVarianceEnhancer should work with other models, but the strength value may need to be adjusted. The text encoder being used is a major factor. To assist with finding the optimal strength value, the node includes a feature that outputs statistics about the embedding. The standard deviation is usually within an order of magnitude of the optimal strength setting. When log_to_console is set to true, the node will print statistics about the embedding to the console.
SeedVarianceEnhancer should work with other models, but the strength value may need to be adjusted. Which text encoder is being used is a major factor. The node includes a feature that outputs statistics about the embedding to the console when log_to_console is set to true. The standard deviation is usually within an order of magnitude of the optimal strength setting.
## Results:
When used with Z-Image Turbo, the default settings will add a moderate amount of diversity at the cost of a some prompt adherence. Higher "randomize_percent" and "strength," as well as the use of "noise on all steps," will all worsen prompt adherence. The reduced prompt adherence may be desirable to users interested in generating highly diverse, strange, or unexpected images. Using very high settings offers the potential to discover unknown capabilities and knowledge within the model.
When used with Z-Image Turbo, the default settings will add a moderate amount of diversity at the cost of a some prompt adherence. Higher "randomize_percent" and "strength," as well as the use of "noise on all steps," will all substantially increasing deversity while worsening prompt adherence. The reduced prompt adherence may be desirable to users interested in generating highly diverse, strange, or unexpected images. Using very high settings offers the potential to discover unknown capabilities and knowledge within the model.
## Limitations:
SeedVarianceEnhancer does not properly handle all conditioning inputs, such as those containing multiple embeddings, so using it alongside other conditioning nodes may result in unexpected behavior.
## Change log:
v1.0 -> v2.0
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* changed category from conditioning to advanced/conditioning
* implemented the masking feature
## License
SeedVarianceEnhancer is released under the MIT No Attribution license.