21 lines
1.7 KiB
Markdown
21 lines
1.7 KiB
Markdown
My own version "from scratch" of a rescaled CFG. It isn't much but it's honest work.
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While this node is connected, this will turn your sampler's CFG scale into something else.
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This methods works by rescaling the CFG at each step by evaluating the potential average min/max values. Aiming at a desired output intensity.
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The base intensity has been arbitrarily chosen by me and your CFG scale will make this target vary.
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I have set the "central" CFG at 8. Meaning that at 4 you will aim at half of the desired range while at 16 it will be doubled. This makes it feel slightly like the usual.
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The CFG behavior during the sampling being automatically set for each channel makes it behave differently and therefores gives different outputs than the usual.
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From my observations by printing the results while testing, it seems to be going from around 16 at the beginning, to something like 4 near the middle and ends up near ~7.
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These values might have changed since I've done a thousand tests with different ways but that's to give you an idea, it's just me eyeballing the CLI's output.
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It makes the sampling generate overall better quality images. I get much less if not any artifacts anymore and my more creative prompts also tends to give more random, in a good way, different results.
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I attribute this more random yet positive behavior to the fact that it seems to be starting high and then since it becomes lower, it self-corrects and improvise, taking advantage of the sampling process a lot more.
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It is dead simple to use and made sampling more fun from my perspective :)
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You will find it in the model_patches category.
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TLDR: set your CFG at 8 to try it. No burned images and artifacts anymore. CFG is also a bit more sensitive because it's a proportion around 8.
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