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import os
from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextConfig
import torch
class ClipTokenWeightEncoder:
def encode_token_weights(self, token_weight_pairs):
z_empty = self.encode(self.empty_tokens)
output = []
for x in token_weight_pairs:
tokens = [list(map(lambda a: a[0], x))]
z = self.encode(tokens)
for i in range(len(z)):
for j in range(len(z[i])):
weight = x[j][1]
z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j]
output += [z]
if (len(output) == 0):
return self.encode(self.empty_tokens)
return torch.cat(output, dim=-2)
class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
LAYERS = [
"last",
"pooled",
"hidden"
]
def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, textmodel_path=None): # clip-vit-base-patch32
super().__init__()
assert layer in self.LAYERS
if textmodel_path is not None:
self.transformer = CLIPTextModel.from_pretrained(textmodel_path)
else:
if textmodel_json_config is None:
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
config = CLIPTextConfig.from_json_file(textmodel_json_config)
self.transformer = CLIPTextModel(config)
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
self.layer_idx = None
self.empty_tokens = [[49406] + [49407] * 76]
if layer == "hidden":
assert layer_idx is not None
assert abs(layer_idx) <= 12
self.clip_layer(layer_idx)
def freeze(self):
self.transformer = self.transformer.eval()
#self.train = disabled_train
for param in self.parameters():
param.requires_grad = False
def clip_layer(self, layer_idx):
if abs(layer_idx) >= 12:
self.layer = "last"
else:
self.layer = "hidden"
self.layer_idx = layer_idx
def set_up_textual_embeddings(self, tokens, current_embeds):
out_tokens = []
next_new_token = token_dict_size = current_embeds.weight.shape[0]
embedding_weights = []
for x in tokens:
tokens_temp = []
for y in x:
if isinstance(y, int):
tokens_temp += [y]
else:
embedding_weights += [y]
tokens_temp += [next_new_token]
next_new_token += 1
out_tokens += [tokens_temp]
if len(embedding_weights) > 0:
new_embedding = torch.nn.Embedding(next_new_token, current_embeds.weight.shape[1])
new_embedding.weight[:token_dict_size] = current_embeds.weight[:]
n = token_dict_size
for x in embedding_weights:
new_embedding.weight[n] = x
n += 1
self.transformer.set_input_embeddings(new_embedding)
return out_tokens
def forward(self, tokens):
backup_embeds = self.transformer.get_input_embeddings()
tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
tokens = torch.LongTensor(tokens).to(self.device)
outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
self.transformer.set_input_embeddings(backup_embeds)
if self.layer == "last":
z = outputs.last_hidden_state
elif self.layer == "pooled":
z = outputs.pooler_output[:, None, :]
else:
z = outputs.hidden_states[self.layer_idx]
z = self.transformer.text_model.final_layer_norm(z)
return z
def encode(self, tokens):
return self(tokens)
def parse_parentheses(string):
result = []
current_item = ""
nesting_level = 0
for char in string:
if char == "(":
if nesting_level == 0:
if current_item:
result.append(current_item)
current_item = "("
else:
current_item = "("
else:
current_item += char
nesting_level += 1
elif char == ")":
nesting_level -= 1
if nesting_level == 0:
result.append(current_item + ")")
current_item = ""
else:
current_item += char
else:
current_item += char
if current_item:
result.append(current_item)
return result
def token_weights(string, current_weight):
a = parse_parentheses(string)
out = []
for x in a:
weight = current_weight
if len(x) >= 2 and x[-1] == ')' and x[0] == '(':
x = x[1:-1]
xx = x.rfind(":")
weight *= 1.1
if xx > 0:
try:
weight = float(x[xx+1:])
x = x[:xx]
except:
pass
out += token_weights(x, weight)
else:
out += [(x, current_weight)]
return out
def escape_important(text):
text = text.replace("\\)", "\0\1")
text = text.replace("\\(", "\0\2")
return text
def unescape_important(text):
text = text.replace("\0\1", ")")
text = text.replace("\0\2", "(")
return text
def load_embed(embedding_name, embedding_directory):
if isinstance(embedding_directory, str):
embedding_directory = [embedding_directory]
valid_file = None
for embed_dir in embedding_directory:
embed_path = os.path.join(embed_dir, embedding_name)
if not os.path.isfile(embed_path):
extensions = ['.safetensors', '.pt', '.bin']
for x in extensions:
t = embed_path + x
if os.path.isfile(t):
valid_file = t
break
else:
valid_file = embed_path
if valid_file is not None:
break
if valid_file is None:
return None
embed_path = valid_file
if embed_path.lower().endswith(".safetensors"):
import safetensors.torch
embed = safetensors.torch.load_file(embed_path, device="cpu")
else:
if 'weights_only' in torch.load.__code__.co_varnames:
embed = torch.load(embed_path, weights_only=True, map_location="cpu")
else:
embed = torch.load(embed_path, map_location="cpu")
if 'string_to_param' in embed:
values = embed['string_to_param'].values()
else:
values = embed.values()
return next(iter(values))
class SD1Tokenizer:
def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None):
if tokenizer_path is None:
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
self.max_length = max_length
self.max_tokens_per_section = self.max_length - 2
empty = self.tokenizer('')["input_ids"]
self.start_token = empty[0]
self.end_token = empty[1]
self.pad_with_end = pad_with_end
vocab = self.tokenizer.get_vocab()
self.inv_vocab = {v: k for k, v in vocab.items()}
self.embedding_directory = embedding_directory
self.max_word_length = 8
def tokenize_with_weights(self, text):
text = escape_important(text)
parsed_weights = token_weights(text, 1.0)
tokens = []
for t in parsed_weights:
to_tokenize = unescape_important(t[0]).replace("\n", " ").split(' ')
while len(to_tokenize) > 0:
word = to_tokenize.pop(0)
temp_tokens = []
embedding_identifier = "embedding:"
if word.startswith(embedding_identifier) and self.embedding_directory is not None:
embedding_name = word[len(embedding_identifier):].strip('\n')
embed = load_embed(embedding_name, self.embedding_directory)
if embed is None:
stripped = embedding_name.strip(',')
if len(stripped) < len(embedding_name):
embed = load_embed(stripped, self.embedding_directory)
if embed is not None:
to_tokenize.insert(0, embedding_name[len(stripped):])
if embed is not None:
if len(embed.shape) == 1:
temp_tokens += [(embed, t[1])]
else:
for x in range(embed.shape[0]):
temp_tokens += [(embed[x], t[1])]
else:
print("warning, embedding:{} does not exist, ignoring".format(embedding_name))
elif len(word) > 0:
tt = self.tokenizer(word)["input_ids"][1:-1]
for x in tt:
temp_tokens += [(x, t[1])]
tokens_left = self.max_tokens_per_section - (len(tokens) % self.max_tokens_per_section)
#try not to split words in different sections
if tokens_left < len(temp_tokens) and len(temp_tokens) < (self.max_word_length):
for x in range(tokens_left):
tokens += [(self.end_token, 1.0)]
tokens += temp_tokens
out_tokens = []
for x in range(0, len(tokens), self.max_tokens_per_section):
o_token = [(self.start_token, 1.0)] + tokens[x:min(self.max_tokens_per_section + x, len(tokens))]
o_token += [(self.end_token, 1.0)]
if self.pad_with_end:
o_token +=[(self.end_token, 1.0)] * (self.max_length - len(o_token))
else:
o_token +=[(0, 1.0)] * (self.max_length - len(o_token))
out_tokens += [o_token]
return out_tokens
def untokenize(self, token_weight_pair):
return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
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