ComfyUI/comfy/sd1_clip.py

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import os
from transformers import CLIPTokenizer
import comfy.ops
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import torch
import traceback
import zipfile
from . import model_management
import comfy.clip_model
import json
import logging
import numbers
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def gen_empty_tokens(special_tokens, length):
start_token = special_tokens.get("start", None)
end_token = special_tokens.get("end", None)
pad_token = special_tokens.get("pad")
output = []
if start_token is not None:
output.append(start_token)
if end_token is not None:
output.append(end_token)
output += [pad_token] * (length - len(output))
return output
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class ClipTokenWeightEncoder:
def encode_token_weights(self, token_weight_pairs):
to_encode = list()
max_token_len = 0
has_weights = False
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for x in token_weight_pairs:
tokens = list(map(lambda a: a[0], x))
max_token_len = max(len(tokens), max_token_len)
has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
to_encode.append(tokens)
sections = len(to_encode)
if has_weights or sections == 0:
to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
o = self.encode(to_encode)
out, pooled = o[:2]
if pooled is not None:
first_pooled = pooled[0:1].to(model_management.intermediate_device())
else:
first_pooled = pooled
output = []
for k in range(0, sections):
z = out[k:k+1]
if has_weights:
z_empty = out[-1]
for i in range(len(z)):
for j in range(len(z[i])):
weight = token_weight_pairs[k][j][1]
if weight != 1.0:
z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
output.append(z)
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if (len(output) == 0):
r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
else:
r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
if len(o) > 2:
extra = {}
for k in o[2]:
v = o[2][k]
if k == "attention_mask":
v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device())
extra[k] = v
r = r + (extra,)
return r
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class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
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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, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
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return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32
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super().__init__()
assert layer in self.LAYERS
if textmodel_json_config is None:
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
with open(textmodel_json_config) as f:
config = json.load(f)
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operations = model_options.get("custom_operations", None)
if operations is None:
operations = comfy.ops.manual_cast
self.operations = operations
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self.transformer = model_class(config, dtype, device, self.operations)
self.num_layers = self.transformer.num_layers
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self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
self.layer_idx = None
self.special_tokens = special_tokens
self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
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self.enable_attention_masks = enable_attention_masks
self.zero_out_masked = zero_out_masked
self.layer_norm_hidden_state = layer_norm_hidden_state
self.return_projected_pooled = return_projected_pooled
self.return_attention_masks = return_attention_masks
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if layer == "hidden":
assert layer_idx is not None
assert abs(layer_idx) < self.num_layers
self.set_clip_options({"layer": layer_idx})
self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)
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def freeze(self):
self.transformer = self.transformer.eval()
#self.train = disabled_train
for param in self.parameters():
param.requires_grad = False
def set_clip_options(self, options):
layer_idx = options.get("layer", self.layer_idx)
self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
if layer_idx is None or abs(layer_idx) > self.num_layers:
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self.layer = "last"
else:
self.layer = "hidden"
self.layer_idx = layer_idx
def reset_clip_options(self):
self.layer = self.options_default[0]
self.layer_idx = self.options_default[1]
self.return_projected_pooled = self.options_default[2]
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, numbers.Integral):
tokens_temp += [int(y)]
else:
if y.shape[0] == current_embeds.weight.shape[1]:
embedding_weights += [y]
tokens_temp += [next_new_token]
next_new_token += 1
else:
logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1]))
while len(tokens_temp) < len(x):
tokens_temp += [self.special_tokens["pad"]]
out_tokens += [tokens_temp]
n = token_dict_size
if len(embedding_weights) > 0:
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new_embedding = self.operations.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
new_embedding.weight[:token_dict_size] = current_embeds.weight
for x in embedding_weights:
new_embedding.weight[n] = x
n += 1
self.transformer.set_input_embeddings(new_embedding)
processed_tokens = []
for x in out_tokens:
processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one
return processed_tokens
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def forward(self, tokens):
backup_embeds = self.transformer.get_input_embeddings()
device = backup_embeds.weight.device
tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
tokens = torch.LongTensor(tokens).to(device)
attention_mask = None
if self.enable_attention_masks or self.zero_out_masked or self.return_attention_masks:
attention_mask = torch.zeros_like(tokens)
end_token = self.special_tokens.get("end", -1)
for x in range(attention_mask.shape[0]):
for y in range(attention_mask.shape[1]):
attention_mask[x, y] = 1
if tokens[x, y] == end_token:
break
attention_mask_model = None
if self.enable_attention_masks:
attention_mask_model = attention_mask
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outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32)
self.transformer.set_input_embeddings(backup_embeds)
if self.layer == "last":
z = outputs[0].float()
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else:
z = outputs[1].float()
if self.zero_out_masked:
z *= attention_mask.unsqueeze(-1).float()
pooled_output = None
if len(outputs) >= 3:
if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
pooled_output = outputs[3].float()
elif outputs[2] is not None:
pooled_output = outputs[2].float()
extra = {}
if self.return_attention_masks:
extra["attention_mask"] = attention_mask
if len(extra) > 0:
return z, pooled_output, extra
return z, pooled_output
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def encode(self, tokens):
return self(tokens)
def load_sd(self, sd):
return self.transformer.load_state_dict(sd, strict=False)
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def parse_parentheses(string):
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"""
Split a string based off top-level nested parentheses.
Parameters
----------
string : str
The string to be split into its top nested groups.
Returns
-------
result : list
A list of each element in string, split by top-level elements
Examples
--------
>>> string = "(foo)(bar)"
['(foo)', '(bar)']
>>> string = "(foo(bar)(test1))(test2(test3))"
['(foo(bar)(test1))', '(test2(test3))']
"""
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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):
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"""
Find the requested weight of a token, and multiply it by the current weight. For parentheses groupings with no set weight, multiply by 1.1.
Parameters
----------
string : str
The input of tokens to calculate requested weights for
current_weight : float
The current weight of all tokens
Returns
-------
out : list
A list of each token paired with the calculated weight
Examples
--------
>>> string = "(foo)"
>>> current_weight = 1.0
[('foo', 1.1)]
>>> string = "(foo)(bar)"
>>> current_weight = 1.0
[('foo', 1.1), ('bar', 1.1)]
>>> string = "(foo:2.0)"
>>> current_weight = 1.0
[('foo', 2.0)]
>>> string = "((foo))"
>>> current_weight = 1.0
[('foo', 1.21)]
>>> string = "((foo):1.1)"
>>> current_weight = 1.0
[('foo', 1.21)]
>>> string = "((foo:1.1))"
>>> current_weight = 1.0
[('foo', 1.1)]
>>> string = "(foo:0.0)"
>>> current_weight = 1.0
[('foo', 0.0)]
>>> string = "((foo:1.0):0.0)"
>>> current_weight = 1.0
[('foo', 1.0)]
>>> string = "foo ((((lol (cat:666) attack:100)))) baz"
>>> current_weight = 1.0
[('foo ', 1.0), ('lol ', 100.0), ('cat', 666.0), (' attack', 100.0), (' baz', 1.0)]
>>> string = "foo ((((lol (cat:666) attack):100))) baz"
>>> current_weight = 1.0
[('foo ', 1.0), ('lol ', 110.0), ('cat', 666.0), (' attack', 110.0), (' baz', 1.0)]
Notes
-----
See issue #4610 for more detail. One thing to note is that the default of 1.1 is multiplied
when there is no weight defined on the *interior* of the group instead of the exterior. This
behavior can be seen in the last two examples (thank you @jart for making these). In the first
example, the weight of 100 is defined inside the same parentheses grouping as both 'lol' and
'attack'. There is no parentheses between the defined weight and the tokens, so there is no
multiplication of the weight by 1.1. In the second example, the weight is outside the parentheses
grouping, so the weights inside the grouping are first given a modifier of 1.1, then given a
modifier of 100.
"""
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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(":")
# This line makes *all nestings* multiply the weight by 1.1
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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):
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"""
Replace parentheses marked via backslashes with escape characters
Parameters
----------
text : str
The string to have its important parentheses replaced
Returns
-------
text : str
The input string, with important parentheses replaced
Examples
--------
>>> text = "\\(foo\\)(bar)"
"\0\2foo\0\1(bar)"
See Also
--------
unescape_important : Replace escape characters with parentheses
"""
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text = text.replace("\\)", "\0\1")
text = text.replace("\\(", "\0\2")
return text
def unescape_important(text):
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"""
Replace escape characters made via escape_important with parentheses
Parameters
----------
text : str
The string to have its escape characters replaced
Returns
-------
text : str
The input string, with escape characters replaced
Examples
--------
>>> text = "\0\2foo\0\1(bar)"
"(foo)(bar)"
See Also
--------
escape_important: makes strings with the escape characters this function uses
"""
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text = text.replace("\0\1", ")")
text = text.replace("\0\2", "(")
return text
def safe_load_embed_zip(embed_path):
with zipfile.ZipFile(embed_path) as myzip:
names = list(filter(lambda a: "data/" in a, myzip.namelist()))
names.reverse()
for n in names:
with myzip.open(n) as myfile:
data = myfile.read()
number = len(data) // 4
length_embed = 1024 #sd2.x
if number < 768:
continue
if number % 768 == 0:
length_embed = 768 #sd1.x
num_embeds = number // length_embed
embed = torch.frombuffer(data, dtype=torch.float)
out = embed.reshape((num_embeds, length_embed)).clone()
del embed
return out
def expand_directory_list(directories):
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"""
For all directories in a list, list all subdirectories beneath them
Parameters
----------
directories : list
The list of directories to search for subdirectories with
Returns
-------
dirs : list
A list of all subdirectories found underneath the given directories
"""
dirs = set()
for x in directories:
dirs.add(x)
for root, subdir, file in os.walk(x, followlinks=True):
dirs.add(root)
return list(dirs)
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def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format
i = 0
out_list = []
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for k in embed:
if k.startswith(prefix) and k.endswith(suffix):
out_list.append(embed[k])
if len(out_list) == 0:
return None
return torch.cat(out_list, dim=0)
def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
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if isinstance(embedding_directory, str):
embedding_directory = [embedding_directory]
embedding_directory = expand_directory_list(embedding_directory)
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valid_file = None
for embed_dir in embedding_directory:
embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name))
embed_dir = os.path.abspath(embed_dir)
try:
if os.path.commonpath((embed_dir, embed_path)) != embed_dir:
continue
except:
continue
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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:
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valid_file = embed_path
if valid_file is not None:
break
if valid_file is None:
return None
embed_path = valid_file
embed_out = None
try:
if embed_path.lower().endswith(".safetensors"):
import safetensors.torch
embed = safetensors.torch.load_file(embed_path, device="cpu")
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else:
if 'weights_only' in torch.load.__code__.co_varnames:
try:
embed = torch.load(embed_path, weights_only=True, map_location="cpu")
except:
embed_out = safe_load_embed_zip(embed_path)
else:
embed = torch.load(embed_path, map_location="cpu")
except Exception as e:
logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
return None
if embed_out is None:
if 'string_to_param' in embed:
values = embed['string_to_param'].values()
embed_out = next(iter(values))
elif isinstance(embed, list):
out_list = []
for x in range(len(embed)):
for k in embed[x]:
t = embed[x][k]
if t.shape[-1] != embedding_size:
continue
out_list.append(t.reshape(-1, t.shape[-1]))
embed_out = torch.cat(out_list, dim=0)
elif embed_key is not None and embed_key in embed:
embed_out = embed[embed_key]
else:
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embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*')
if embed_out is None:
embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key))
if embed_out is None:
values = embed.values()
embed_out = next(iter(values))
return embed_out
class SDTokenizer:
def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None, tokenizer_data={}):
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if tokenizer_path is None:
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
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self.max_length = max_length
self.min_length = min_length
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empty = self.tokenizer('')["input_ids"]
if has_start_token:
self.tokens_start = 1
self.start_token = empty[0]
self.end_token = empty[1]
else:
self.tokens_start = 0
self.start_token = None
self.end_token = empty[0]
if pad_token is not None:
self.pad_token = pad_token
elif pad_with_end:
self.pad_token = self.end_token
else:
self.pad_token = 0
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self.pad_with_end = pad_with_end
self.pad_to_max_length = pad_to_max_length
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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
self.embedding_identifier = "embedding:"
self.embedding_size = embedding_size
self.embedding_key = embedding_key
def _try_get_embedding(self, embedding_name:str):
'''
Takes a potential embedding name and tries to retrieve it.
Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
'''
embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
if embed is None:
stripped = embedding_name.strip(',')
if len(stripped) < len(embedding_name):
embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
return (embed, embedding_name[len(stripped):])
return (embed, "")
def tokenize_with_weights(self, text:str, return_word_ids=False):
'''
Takes a prompt and converts it to a list of (token, weight, word id) elements.
Tokens can both be integer tokens and pre computed CLIP tensors.
Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
Returned list has the dimensions NxM where M is the input size of CLIP
'''
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text = escape_important(text)
parsed_weights = token_weights(text, 1.0)
#tokenize words
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tokens = []
for weighted_segment, weight in parsed_weights:
to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ')
to_tokenize = [x for x in to_tokenize if x != ""]
for word in to_tokenize:
#if we find an embedding, deal with the embedding
if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
embedding_name = word[len(self.embedding_identifier):].strip('\n')
embed, leftover = self._try_get_embedding(embedding_name)
if embed is None:
logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
else:
if len(embed.shape) == 1:
tokens.append([(embed, weight)])
else:
tokens.append([(embed[x], weight) for x in range(embed.shape[0])])
#if we accidentally have leftover text, continue parsing using leftover, else move on to next word
if leftover != "":
word = leftover
else:
continue
#parse word
tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]])
#reshape token array to CLIP input size
batched_tokens = []
batch = []
if self.start_token is not None:
batch.append((self.start_token, 1.0, 0))
batched_tokens.append(batch)
for i, t_group in enumerate(tokens):
#determine if we're going to try and keep the tokens in a single batch
is_large = len(t_group) >= self.max_word_length
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while len(t_group) > 0:
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if len(t_group) + len(batch) > self.max_length - 1:
remaining_length = self.max_length - len(batch) - 1
#break word in two and add end token
if is_large:
batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
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batch.append((self.end_token, 1.0, 0))
t_group = t_group[remaining_length:]
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#add end token and pad
else:
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batch.append((self.end_token, 1.0, 0))
if self.pad_to_max_length:
batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
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#start new batch
batch = []
if self.start_token is not None:
batch.append((self.start_token, 1.0, 0))
batched_tokens.append(batch)
else:
batch.extend([(t,w,i+1) for t,w in t_group])
t_group = []
#fill last batch
batch.append((self.end_token, 1.0, 0))
if self.pad_to_max_length:
batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch)))
if self.min_length is not None and len(batch) < self.min_length:
batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch)))
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if not return_word_ids:
batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
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return batched_tokens
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def untokenize(self, token_weight_pair):
return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
def state_dict(self):
return {}
class SD1Tokenizer:
def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer):
self.clip_name = clip_name
self.clip = "clip_{}".format(self.clip_name)
setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
def tokenize_with_weights(self, text:str, return_word_ids=False):
out = {}
out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids)
return out
def untokenize(self, token_weight_pair):
return getattr(self, self.clip).untokenize(token_weight_pair)
def state_dict(self):
return {}
class SD1CheckpointClipModel(SDClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options)
class SD1ClipModel(torch.nn.Module):
def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs):
super().__init__()
if name is not None:
self.clip_name = name
self.clip = "{}".format(self.clip_name)
else:
self.clip_name = clip_name
self.clip = "clip_{}".format(self.clip_name)
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setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs))
self.dtypes = set()
if dtype is not None:
self.dtypes.add(dtype)
def set_clip_options(self, options):
getattr(self, self.clip).set_clip_options(options)
def reset_clip_options(self):
getattr(self, self.clip).reset_clip_options()
def encode_token_weights(self, token_weight_pairs):
token_weight_pairs = token_weight_pairs[self.clip_name]
out = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
return out
def load_sd(self, sd):
return getattr(self, self.clip).load_sd(sd)