71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
from __future__ import annotations
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import torch
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class Emphasis:
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"""Emphasis class decides how to death with (emphasized:1.1) text in prompts"""
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name: str = "Base"
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description: str = ""
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tokens: list[list[int]]
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"""tokens from the chunk of the prompt"""
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multipliers: torch.Tensor
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"""tensor with multipliers, once for each token"""
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z: torch.Tensor
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"""output of cond transformers network (CLIP)"""
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def after_transformers(self):
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"""Called after cond transformers network has processed the chunk of the prompt; this function should modify self.z to apply the emphasis"""
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pass
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class EmphasisNone(Emphasis):
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name = "None"
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description = "disable the mechanism entirely and treat (:.1.1) as literal characters"
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class EmphasisIgnore(Emphasis):
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name = "Ignore"
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description = "treat all empasised words as if they have no emphasis"
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class EmphasisOriginal(Emphasis):
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name = "Original"
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description = "the original emphasis implementation"
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def after_transformers(self):
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original_mean = self.z.mean()
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self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
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# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
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new_mean = self.z.mean()
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self.z = self.z * (original_mean / new_mean)
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class EmphasisOriginalNoNorm(EmphasisOriginal):
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name = "No norm"
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description = "same as original, but without normalization (seems to work better for SDXL)"
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def after_transformers(self):
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self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
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def get_current_option(emphasis_option_name):
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return next(iter([x for x in options if x.name == emphasis_option_name]), EmphasisOriginal)
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def get_options_descriptions():
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return ", ".join(f"{x.name}: {x.description}" for x in options)
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options = [
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EmphasisNone,
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EmphasisIgnore,
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EmphasisOriginal,
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EmphasisOriginalNoNorm,
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]
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