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@@ -33,4 +33,8 @@ The "no captions" versions have learned to conform entirely on a zero prompt sta
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  Adding the "noise" variation to the "no captions" normalized weights, results in a higher response and potency to very short or brief prompts based on random tokens being planted throughout the caption at various locations. This bleeds additional information into the model slowly while still allowing it to converge more rapidly without conforming to the non-noise hard-commit encoding memorization alternative.
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  ### Code
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- The model code is present in model.py. Inference code will be available in the long-winded article.
 
 
 
 
 
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  Adding the "noise" variation to the "no captions" normalized weights, results in a higher response and potency to very short or brief prompts based on random tokens being planted throughout the caption at various locations. This bleeds additional information into the model slowly while still allowing it to converge more rapidly without conforming to the non-noise hard-commit encoding memorization alternative.
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  ### Code
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+ The model code is present in model.py. Inference code will be available in the long-winded article.
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+ The SDXL shunt inference code is present in the comfyui release which is hosted on the github.
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+ The training notebook is available now as I just pushed it.