backend/text_processing/emphasis.py

import torch


class Emphasis:
    name: str = "Base"
    description: str = ""
    tokens: list[list[int]]
    multipliers: torch.Tensor
    z: torch.Tensor

    def after_transformers(self):
        pass


class EmphasisNone(Emphasis):
    name = "None"
    description = "disable the mechanism entirely and treat (:.1.1) as literal characters"


class EmphasisIgnore(Emphasis):
    name = "Ignore"
    description = "treat all empasised words as if they have no emphasis"


class EmphasisOriginal(Emphasis):
    name = "Original"
    description = "the original emphasis implementation"

    def after_transformers(self):
        original_mean = self.z.mean()
        self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
        new_mean = self.z.mean()
        self.z = self.z * (original_mean / new_mean)


class EmphasisOriginalNoNorm(EmphasisOriginal):
    name = "No norm"
    description = "same as original, but without normalization (seems to work better for SDXL)"

    def after_transformers(self):
        self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)


def get_current_option(emphasis_option_name):
    return next(iter([x for x in options if x.name == emphasis_option_name]), EmphasisOriginal)


def get_options_descriptions():
    return ", ".join(f"{x.name}: {x.description}" for x in options)


options = [
    EmphasisNone,
    EmphasisIgnore,
    EmphasisOriginal,
    EmphasisOriginalNoNorm,
]