38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
|
import open_clip.tokenizer
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from modules import sd_hijack_clip, devices
|
||
|
|
from modules.shared import opts
|
||
|
|
|
||
|
|
tokenizer = open_clip.tokenizer._tokenizer
|
||
|
|
|
||
|
|
|
||
|
|
class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
|
||
|
|
def __init__(self, wrapped, hijack):
|
||
|
|
super().__init__(wrapped, hijack)
|
||
|
|
|
||
|
|
self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
|
||
|
|
self.id_start = tokenizer.encoder["<start_of_text>"]
|
||
|
|
self.id_end = tokenizer.encoder["<end_of_text>"]
|
||
|
|
self.id_pad = 0
|
||
|
|
|
||
|
|
def tokenize(self, texts):
|
||
|
|
assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
|
||
|
|
|
||
|
|
tokenized = [tokenizer.encode(text) for text in texts]
|
||
|
|
|
||
|
|
return tokenized
|
||
|
|
|
||
|
|
def encode_with_transformers(self, tokens):
|
||
|
|
# set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
|
||
|
|
z = self.wrapped.encode_with_transformer(tokens)
|
||
|
|
|
||
|
|
return z
|
||
|
|
|
||
|
|
def encode_embedding_init_text(self, init_text, nvpt):
|
||
|
|
ids = tokenizer.encode(init_text)
|
||
|
|
ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
|
||
|
|
embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
|
||
|
|
|
||
|
|
return embedded
|