SeedVarianceEnhancer is a ComfyUI node that adds randomness to the text embedding, resulting in more diverse, and sometimes surprising image outputs. It is optimized for use with Z-Image Turbo.You can now find the project on GitHub:<a target="_blank" rel="ugc" href="https://github.com/ChangeTheConstants/SeedVarianceEnhancer">https://github.com/ChangeTheConstants/SeedVarianceEnhancer</a>As impressed as I was with the release of Z-Image Turbo, I was frustrated by the low diversity of its outputs. So I created SeedVarianceEnhancer as a hack to add a bit of chaos into the generation process. It adds random noise to the text embedding, altering the model's understanding of the prompt. When used with the default settings, you should get much greater variety in outputs without having to manually change the prompt. If you want to have some fun, turn up the settings and you will get outputs you wouldn't even think to ask for. SeedVarianceEnhancer trades prompt adherence for diversity. Some outputs will be failures, but if you generate numerous outputs with different seeds, you may find you like many of them more than what you would have gotten without SeedVarianceEnhancer.Just place the SeedVarianceEnhancer node between the positive prompt and the sampler.The default settings should add noticeable diversity without much compromise in prompt adherence. Strength settings in the range from 15 to 30 while leaving the other settings default is a good place to start experimenting. There are also a few other settings for tweaking. Randomize_percent controls how much of the embedding is altered by noise. By default, noise will only be used in the beginning steps of generation. The noise_insert setting also lets you choose to use the noisy embedding for the entire generation process or just the end. The steps_switchover_percent adjust the timing of the switch between noisy and original embeddings if noise_insert is not set to all steps.The default settings generally work well, but some prompts will benefit greatly from modified settings. There are cases in which a prompt will generate very broken images with the default settings, but will also yield great results with modified setting. Experimentation may be required to achieve good results. Try decreasing steps_switchover_percent to 10% while also increasing strength to 40.<ul><li>Version 2.0 introduces two new features. A new masking feature helps protect important portions of the prompt from the added randomness. Reference the demonstration slides and the readme. The second new feature prints to the console statistical information about the embedding to assist in finding good strength values when using other models.</li><li>Version v2.1 improves compatibility with models that use the umt5-xxl text encoder, such as Wan. Feel free to report any successes or failures here or in the GitHub discussions section. I have done only limited testing and I don't yet know if users are finding the node useful with other models.</li><li>Version v2.2 is the variations upon variations update. Chaining nodes now works better, allowing you to add additional randomness on top of a previously generated image. It is also now possible to add text to the end of prompts without completely changing the image. Visit the GitHub page for a short video demonstrating these techniques. v2.2 will not reproduce images made with v2.1 unless you add 1 billion to strength, which triggers a reversion to old behavior.</li></ul>For those that are interested, more detailed technical information about usage is within the readme included in the zip file.SeedVarianceEnhancer will work for other models, but the optimal settings for those models may be very different. Enable log_to_console to see suggested strength values.Installation:Extract the contents of the zip file and place the SeedVarianceEnhancer directory in your ComfyUI/custom_nodes directory. Launch or restart ComfyUI. You will find the SeedVarianceEnhancer inside the advanced/conditioning node group.

SeedVarianceEnhancer (optimized for Z-Image Turbo)

For most use cases, version 2.0 will not offer improved performance over the initial release. It does include a new masking feature designed to protect parts of the prompt from the added randomness. There is also a new statistical analysis feature that should help find working strength values when using the node with other models. Check the README.md file for a more detailed explanation.v1.0 -&gt; v2.0* implemented the masking feature* cleaned up code structure* added input validity checks* widened strength value limits, and increased precision* revised tooltip texts* added logging to console toggle control* added statistical analysis of embedding tensor feature* added "disabled" setting to noise_insert control.* changed category from conditioning to advanced/conditioning

Files with SHA256: 4a28f7e9e1775ea8dbfc7e6447f3dafa69c54db271be03c84d1b1c5cc6361825