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GNU GENERAL PUBLIC LICENSE
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T5/loader.py
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import os
import torch
import comfy.utils
import comfy.model_patcher
from comfy import model_management
import folder_paths
from .t5v11 import T5v11Model, T5v11Tokenizer
class EXM_T5v11:
def __init__(self, textmodel_ver="xxl", embedding_directory=None, textmodel_path=None, no_init=False, device="cpu"):
if no_init:
return
if device == "auto":
size = 0
self.load_device = model_management.text_encoder_device()
self.offload_device = model_management.text_encoder_offload_device()
self.init_device = "cpu"
elif device == "bnb8bit":
# BNB doesn't support size enum
size = 12.4 * (1024**3)
# Or moving between devices
self.load_device = model_management.get_torch_device()
self.offload_device = self.load_device
self.init_device = self.load_device
elif device == "bnb4bit":
# This seems to use the same VRAM as 8bit on Pascal?
size = 6.2 * (1024**3)
self.load_device = model_management.get_torch_device()
self.offload_device = self.load_device
self.init_device = self.load_device
else:
size = 0
self.load_device = device
self.offload_device = "cpu"
self.init_device="cpu"
self.cond_stage_model = T5v11Model(
textmodel_ver = textmodel_ver,
textmodel_path = textmodel_path,
device = device,
)
self.tokenizer = T5v11Tokenizer(embedding_directory=embedding_directory)
self.patcher = comfy.model_patcher.ModelPatcher(
self.cond_stage_model,
load_device = self.load_device,
offload_device = self.offload_device,
current_device = self.load_device,
size = size,
)
def clone(self):
n = T5(no_init=True)
n.patcher = self.patcher.clone()
n.cond_stage_model = self.cond_stage_model
n.tokenizer = self.tokenizer
return n
def tokenize(self, text, return_word_ids=False):
return self.tokenizer.tokenize_with_weights(text, return_word_ids)
def encode_from_tokens(self, tokens):
self.load_model()
return self.cond_stage_model.encode_token_weights(tokens)
def encode(self, text):
tokens = self.tokenize(text)
return self.encode_from_tokens(tokens)
def load_sd(self, sd):
return self.cond_stage_model.load_sd(sd)
def get_sd(self):
return self.cond_stage_model.state_dict()
def load_model(self):
if self.load_device != "cpu":
model_management.load_model_gpu(self.patcher)
return self.patcher
def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
return self.patcher.add_patches(patches, strength_patch, strength_model)
def get_key_patches(self):
return self.patcher.get_key_patches()
def load_t5(model_type, model_ver, model_path, path_type="file", device="cpu"):
assert model_type in ["t5v11"] # Only supported model for now
model_args = {}
model_args["textmodel_ver"] = model_ver
model_args["device"] = device
if path_type == "folder":
# pass directly to transformers and initialize there
# this is to avoid having to handle multi-file state dict loading for now.
model_args["textmodel_path"] = os.path.dirname(model_path)
return EXM_T5v11(**model_args)
else:
# for some reason this returns garbage with torch.int8 weights, or just OOMs
model = EXM_T5v11(**model_args)
sd = comfy.utils.load_torch_file(model_path)
model.load_sd(sd)
return model
T5/nodes.py
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import os
import json
import torch
import folder_paths
from .loader import load_t5
# initialize custom folder path
# TODO: integrate with `extra_model_paths.yaml`
os.makedirs(
os.path.join(folder_paths.models_dir,"t5"),
exist_ok = True,
)
folder_paths.folder_names_and_paths["t5"] = (
[os.path.join(folder_paths.models_dir,"t5")],
folder_paths.supported_pt_extensions
)
class T5v11Loader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"t5v11_name": (folder_paths.get_filename_list("t5"),),
"t5v11_ver": (["xxl"],),
"path_type": (["folder", "file"],),
"device": (["auto", "cpu", "bnb8bit", "bnb4bit"],{"default":"cpu"})
}
}
RETURN_TYPES = ("T5",)
FUNCTION = "load_model"
CATEGORY = "ExtraModels/T5"
TITLE = "T5v1.1 Loader"
def load_model(self, t5v11_name, t5v11_ver, path_type, device):
return (load_t5(
model_type = "t5v11",
model_ver = t5v11_ver,
model_path = folder_paths.get_full_path("t5", t5v11_name),
path_type = path_type,
device = device,
),)
class T5TextEncode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"text": ("STRING", {"multiline": True}),
"T5": ("T5",),
}
}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "ExtraModels/T5"
TITLE = "T5 Text Encode"
def encode(self, text, T5=None):
tokens = T5.tokenize(text)
cond = T5.encode_from_tokens(tokens)
return ([[cond, {}]], )
NODE_CLASS_MAPPINGS = {
"T5v11Loader" : T5v11Loader,
"T5TextEncode" : T5TextEncode,
}
T5/t5_tokenizer/special_tokens_map.json
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{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"]}
T5/t5_tokenizer/spiece.model
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T5/t5_tokenizer/tokenizer_config.json
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{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "extra_ids": 100, "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"], "model_max_length": 512, "name_or_path": "t5-small"}
T5/t5v11-xxl_config.json
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{
"_name_or_path": "google/t5-v1_1-xxl",
"architectures": [
"T5EncoderModel"
],
"d_ff": 10240,
"d_kv": 64,
"d_model": 4096,
"decoder_start_token_id": 0,
"dense_act_fn": "gelu_new",
"dropout_rate": 0.1,
"eos_token_id": 1,
"feed_forward_proj": "gated-gelu",
"initializer_factor": 1.0,
"is_encoder_decoder": true,
"is_gated_act": true,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"num_decoder_layers": 24,
"num_heads": 64,
"num_layers": 24,
"output_past": true,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.21.1",
"use_cache": true,
"vocab_size": 32128
}
T5/t5v11.py
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"""
Adapted from comfyui CLIP code.
https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/sd1_clip.py
"""
import os
from transformers import T5Tokenizer, T5EncoderModel, T5Config, modeling_utils
import comfy.ops
import torch
import traceback
import zipfile
from comfy import model_management
from comfy.sd1_clip import parse_parentheses, token_weights, escape_important, unescape_important, safe_load_embed_zip, expand_directory_list, load_embed
class T5v11Model(torch.nn.Module):
def __init__(self, textmodel_ver="xxl", textmodel_json_config=None, textmodel_path=None, device="cpu", max_length=120, freeze=True, dtype=None):
super().__init__()
self.num_layers = 24
self.max_length = max_length
if textmodel_path is not None:
model_args = {}
model_args["low_cpu_mem_usage"] = True # Don't take 2x system ram on cpu
if device == "bnb8bit":
self.bnb = True
model_args["load_in_8bit"] = True
elif device == "bnb4bit":
self.bnb = True
model_args["load_in_4bit"] = True
else:
self.bnb = False
# TODO: custom device map?
print(f"Loading T5 from '{textmodel_path}'")
self.transformer = T5EncoderModel.from_pretrained(textmodel_path, **model_args)
else:
if textmodel_json_config is None:
textmodel_json_config = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
f"t5v11-{textmodel_ver}_config.json"
)
config = T5Config.from_json_file(textmodel_json_config)
self.num_layers = config.num_hidden_layers
with comfy.ops.use_comfy_ops(device, dtype):
with modeling_utils.no_init_weights():
self.transformer = T5EncoderModel(config)
if dtype is not None and not self.bnb:
self.transformer.to(dtype)
self.transformer.encoder.embeddings.embed_tokens.to(torch.float32)
if freeze:
self.freeze()
self.empty_tokens = [[0] * self.max_length] # <pad> token
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, tokens):
device = self.transformer.get_input_embeddings().weight.device
tokens = torch.LongTensor(tokens).to(device)
attention_mask = torch.zeros_like(tokens)
max_token = 1 # </s> token
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] == max_token:
break
outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask)
z = outputs['last_hidden_state']
z.detach().cpu().float()
return z
def encode(self, tokens):
return self(tokens)
def load_sd(self, sd):
return self.transformer.load_state_dict(sd, strict=False)
def to(self, *args, **kwargs):
"""BNB complains if you try to change the device or dtype"""
if self.bnb:
print("Thanks to BitsAndBytes, T5 becomes an immovable rock.", args, kwargs)
else:
self.transformer.to(*args, **kwargs)
def encode_token_weights(self, token_weight_pairs, return_padded=False):
to_encode = list(self.empty_tokens)
for x in token_weight_pairs:
tokens = list(map(lambda a: a[0], x))
to_encode.append(tokens)
out = self.encode(to_encode)
z_empty = out[0:1]
output = []
for k in range(1, out.shape[0]):
z = out[k:k+1]
for i in range(len(z)):
for j in range(len(z[i])):
weight = token_weight_pairs[k - 1][j][1]
z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j]
output.append(z)
if (len(output) == 0):
return z_empty.cpu()
out = torch.cat(output, dim=-2)
if not return_padded:
# Count number of tokens that aren't <pad>, then use that number as an index.
keep_index = sum([sum([1 for y in x if y[0] != 0]) for x in token_weight_pairs])
out = out[:, :keep_index, :]
return out
class T5v11Tokenizer:
"""
This is largely just based on the ComfyUI CLIP code.
"""
def __init__(self, tokenizer_path=None, max_length=120, embedding_directory=None, embedding_size=4096, embedding_key='t5'):
if tokenizer_path is None:
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
self.tokenizer = T5Tokenizer.from_pretrained(tokenizer_path)
self.max_length = max_length
self.max_tokens_per_section = self.max_length - 1 # </s> but no <BOS>
self.pad_token = self.tokenizer("<pad>", add_special_tokens=False)["input_ids"][0]
self.end_token = self.tokenizer("</s>", add_special_tokens=False)["input_ids"][0]
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 # haven't verified this
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 T5 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 T5
'''
pad_token = self.pad_token
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).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:
print(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, add_special_tokens=False)["input_ids"]])
#reshape token array to T5 input size
batched_tokens = []
batch = []
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
while len(t_group) > 0:
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]])
batch.append((self.end_token, 1.0, 0))
t_group = t_group[remaining_length:]
#add end token and pad
else:
batch.append((self.end_token, 1.0, 0))
batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
#start new batch
batch = []
batched_tokens.append(batch)
else:
batch.extend([(t,w,i+1) for t,w in t_group])
t_group = []
# fill last batch
batch.extend([(self.end_token, 1.0, 0)] + [(self.pad_token, 1.0, 0)] * (self.max_length - len(batch) - 1))
# instead of filling, just add EOS (DEBUG)
# batch.extend([(self.end_token, 1.0, 0)])
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))