mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-09-11 20:17:30 +00:00
4977f203fa
* P2 of qwen edit model. * Typo. * Fix normal qwen. * Fix. * Make the TextEncodeQwenImageEdit also set the ref latent. If you don't want it to set the ref latent and want to use the ReferenceLatent node with your custom latent instead just disconnect the VAE.
691 lines
27 KiB
Python
691 lines
27 KiB
Python
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",
|
|
"all"
|
|
]
|
|
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 "model_name" not in model_options:
|
|
model_options = {**model_options, "model_name": "clip_l"}
|
|
|
|
if isinstance(textmodel_json_config, dict):
|
|
config = textmodel_json_config
|
|
else:
|
|
with open(textmodel_json_config) as f:
|
|
config = json.load(f)
|
|
|
|
te_model_options = model_options.get("{}_model_config".format(model_options.get("model_name", "")), {})
|
|
for k, v in te_model_options.items():
|
|
config[k] = v
|
|
|
|
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 self.layer == "all":
|
|
pass
|
|
elif 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
|
|
embeds_info = []
|
|
for o in other_embeds:
|
|
emb = o[1]
|
|
if torch.is_tensor(emb):
|
|
emb = {"type": "embedding", "data": emb}
|
|
|
|
extra = None
|
|
emb_type = emb.get("type", None)
|
|
if emb_type == "embedding":
|
|
emb = emb.get("data", None)
|
|
else:
|
|
if hasattr(self.transformer, "preprocess_embed"):
|
|
emb, extra = 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
|
|
embeds_info.append({"type": emb_type, "index": ind, "size": emb_shape, "extra": extra})
|
|
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, embeds_info
|
|
|
|
def forward(self, tokens):
|
|
device = self.transformer.get_input_embeddings().weight.device
|
|
embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device)
|
|
|
|
attention_mask_model = None
|
|
if self.enable_attention_masks:
|
|
attention_mask_model = attention_mask
|
|
|
|
if self.layer == "all":
|
|
intermediate_output = "all"
|
|
else:
|
|
intermediate_output = self.layer_idx
|
|
|
|
outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32, embeds_info=embeds_info)
|
|
|
|
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, min_padding=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 = tokenizer_data.get("{}_max_length".format(embedding_key), max_length)
|
|
self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length)
|
|
self.end_token = None
|
|
self.min_padding = min_padding
|
|
|
|
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:
|
|
if has_end_token:
|
|
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, tokenizer_options={}, **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
|
|
'''
|
|
min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length)
|
|
min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
|
|
|
|
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 min_padding is not None:
|
|
batch.extend([(self.pad_token, 1.0, 0)] * min_padding)
|
|
if self.pad_to_max_length and len(batch) < self.max_length:
|
|
batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch)))
|
|
if min_length is not None and len(batch) < min_length:
|
|
batch.extend([(self.pad_token, 1.0, 0)] * (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, **kwargs)
|
|
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)
|
|
model_options = {**model_options, "model_name": self.clip}
|
|
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)
|