| import torch
|
| import torch.nn.functional as F
|
|
|
| class Mahiro:
|
| @classmethod
|
| def INPUT_TYPES(s):
|
| return {"required": {"model": ("MODEL",),
|
| }}
|
| RETURN_TYPES = ("MODEL",)
|
| RETURN_NAMES = ("patched_model",)
|
| FUNCTION = "patch"
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| CATEGORY = "_for_testing"
|
| DESCRIPTION = "Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt."
|
| def patch(self, model):
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| m = model.clone()
|
| def mahiro_normd(args):
|
| scale: float = args['cond_scale']
|
| cond_p: torch.Tensor = args['cond_denoised']
|
| uncond_p: torch.Tensor = args['uncond_denoised']
|
|
|
| leap = cond_p * scale
|
|
|
| u_leap = uncond_p * scale
|
| cfg = args["denoised"]
|
| merge = (leap + cfg) / 2
|
| normu = torch.sqrt(u_leap.abs()) * u_leap.sign()
|
| normm = torch.sqrt(merge.abs()) * merge.sign()
|
| sim = F.cosine_similarity(normu, normm).mean()
|
| simsc = 2 * (sim+1)
|
| wm = (simsc*cfg + (4-simsc)*leap) / 4
|
| return wm
|
| m.set_model_sampler_post_cfg_function(mahiro_normd)
|
| return (m, )
|
|
|
| NODE_CLASS_MAPPINGS = {
|
| "Mahiro": Mahiro
|
| }
|
|
|
| NODE_DISPLAY_NAME_MAPPINGS = {
|
| "Mahiro": "Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)",
|
| }
|
|
|
