soaska/my-sd
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AUTOMATIC 2023-01-07 07:48:44 +03:00
parent 08066676a4
commit 1740c33547
1 changed files with 16 additions and 5 deletions
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21
modules/sd_hijack_clip.py
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@ -3,7 +3,7 @@ from collections import namedtuple
import torch import torch
from modules import prompt_parser, devices from modules import prompt_parser, devices, sd_hijack
from modules.shared import opts from modules.shared import opts
@ -22,14 +22,24 @@ class PromptChunk:
PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding']) PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
"""This is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt chunk""" """An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
"""A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
have unlimited prompt length and assign weights to tokens in prompt.
"""
def __init__(self, wrapped, hijack): def __init__(self, wrapped, hijack):
super().__init__() super().__init__()
self.wrapped = wrapped self.wrapped = wrapped
self.hijack = hijack """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
depending on model."""
self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
self.chunk_length = 75 self.chunk_length = 75
def empty_chunk(self): def empty_chunk(self):
@ -55,7 +65,8 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens; converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;
All python lists with tokens are assumed to have same length, usually 77. All python lists with tokens are assumed to have same length, usually 77.
if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
model - can be 768 and 1024 model - can be 768 and 1024.
Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
""" """
raise NotImplementedError raise NotImplementedError
@ -113,7 +124,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
last_comma = len(chunk.tokens) last_comma = len(chunk.tokens)
# this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
# is a setting that specifies that is there is a comma nearby, the text after comma should be moved out of this chunk and into the next. # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack: elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
break_location = last_comma + 1 break_location = last_comma + 1