import os
from transformers import CLIPTokenizer
import comfy.ops
import torch
import traceback
import zipfile
from . import model_management
import comfy.clip_model
import json
import logging
import numbers
import re
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
class ClipTokenWeightEncoder:
def encode_token_weights(self, token_weight_pairs):
to_encode = list()
max_token_len = 0
has_weights = False
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:
if hasattr(self, "gen_empty_tokens"):
to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len))
else:
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)
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
class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
LAYERS = [
"last",
"pooled",
"hidden"
]
def __init__(self, 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,
return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32
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")
if isinstance(textmodel_json_config, dict):
config = textmodel_json_config
else:
with open(textmodel_json_config) as f:
config = json.load(f)
operations = model_options.get("custom_operations", None)
scaled_fp8 = None
if operations is None:
scaled_fp8 = model_options.get("scaled_fp8", None)
if scaled_fp8 is not None:
operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8)
else:
operations = comfy.ops.manual_cast
self.operations = operations
self.transformer = model_class(config, dtype, device, self.operations)
if scaled_fp8 is not None:
self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8))
self.num_layers = self.transformer.num_layers
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))
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
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)
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:
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 process_tokens(self, tokens, device):
end_token = self.special_tokens.get("end", None)
if end_token is None:
cmp_token = self.special_tokens.get("pad", -1)
else:
cmp_token = end_token
embeds_out = []
attention_masks = []
num_tokens = []
for x in tokens:
attention_mask = []
tokens_temp = []
other_embeds = []
eos = False
index = 0
for y in x:
if isinstance(y, numbers.Integral):
if eos:
attention_mask.append(0)
else:
attention_mask.append(1)
token = int(y)
tokens_temp += [token]
if not eos and token == cmp_token:
if end_token is None:
attention_mask[-1] = 0
eos = True
else:
other_embeds.append((index, y))
index += 1
tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long)
tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32)
index = 0
pad_extra = 0
for o in other_embeds:
emb = o[1]
if torch.is_tensor(emb):
emb = {"type": "embedding", "data": emb}
emb_type = emb.get("type", None)
if emb_type == "embedding":
emb = emb.get("data", None)
else:
if hasattr(self.transformer, "preprocess_embed"):
emb = self.transformer.preprocess_embed(emb, device=device)
else:
emb = None
if emb is None:
index += -1
continue
ind = index + o[0]
emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32)
emb_shape = emb.shape[1]
if emb.shape[-1] == tokens_embed.shape[-1]:
tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1)
attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:]
index += emb_shape - 1
else:
index += -1
pad_extra += emb_shape
logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(emb.shape[-1], tokens_embed.shape[-1]))
if pad_extra > 0:
padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens["pad"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32)
tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1)
attention_mask = attention_mask + [0] * pad_extra
embeds_out.append(tokens_embed)
attention_masks.append(attention_mask)
num_tokens.append(sum(attention_mask))
return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens
def forward(self, tokens):
device = self.transformer.get_input_embeddings().weight.device
embeds, attention_mask, num_tokens = self.process_tokens(tokens, device)
attention_mask_model = None
if self.enable_attention_masks:
attention_mask_model = attention_mask
outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32)
if self.layer == "last":
z = outputs[0].float()
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
def encode(self, tokens):
return self(tokens)
def load_sd(self, sd):
return self.transformer.load_state_dict(sd, strict=False)
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 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):
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)
def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format
out_list = []
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):
if isinstance(embedding_directory, str):
embedding_directory = [embedding_directory]
embedding_directory = expand_directory_list(embedding_directory)
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
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
embed_out = None
try:
if embed_path.lower().endswith(".safetensors"):
import safetensors.torch
embed = safetensors.torch.load_file(embed_path, device="cpu")
else:
try:
embed = torch.load(embed_path, weights_only=True, map_location="cpu")
except:
embed_out = safe_load_embed_zip(embed_path)
except Exception:
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:
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, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}, tokenizer_args={}):
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, **tokenizer_args)
self.max_length = max_length
self.min_length = min_length
self.end_token = None
empty = self.tokenizer('')["input_ids"]
self.tokenizer_adds_end_token = has_end_token
if has_start_token:
self.tokens_start = 1
self.start_token = empty[0]
if end_token is not None:
self.end_token = end_token
else:
if has_end_token:
self.end_token = empty[1]
else:
self.tokens_start = 0
self.start_token = None
if end_token is not None:
self.end_token = end_token
else:
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
self.pad_with_end = pad_with_end
self.pad_to_max_length = pad_to_max_length
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.
'''
split_embed = embedding_name.split()
embedding_name = split_embed[0]
leftover = ' '.join(split_embed[1:])
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, "{} {}".format(embedding_name[len(stripped):], leftover))
return (embed, leftover)
def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
'''
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
'''
text = escape_important(text)
parsed_weights = token_weights(text, 1.0)
# tokenize words
tokens = []
for weighted_segment, weight in parsed_weights:
to_tokenize = unescape_important(weighted_segment)
split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize)
to_tokenize = [split[0]]
for i in range(1, len(split)):
to_tokenize.append("{}{}".format(self.embedding_identifier, split[i]))
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
end = 999999999999
if self.tokenizer_adds_end_token:
end = -1
#parse word
tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:end]])
#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
if self.end_token is not None:
has_end_token = 1
else:
has_end_token = 0
while len(t_group) > 0:
if len(t_group) + len(batch) > self.max_length - has_end_token:
remaining_length = self.max_length - len(batch) - has_end_token
#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]])
if self.end_token is not None:
batch.append((self.end_token, 1.0, 0))
t_group = t_group[remaining_length:]
#add end token and pad
else:
if self.end_token is not None:
batch.append((self.end_token, 1.0, 0))
if self.pad_to_max_length:
batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
#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
if self.end_token is not None:
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)))
if not return_word_ids:
batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
return batched_tokens
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, name=None):
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)
tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
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 getattr(self, self.clip).state_dict()
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)
clip_model = model_options.get("{}_class".format(self.clip), clip_model)
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)