diff --git a/T5/LICENSE-ComfyUI b/T5/LICENSE-ComfyUI new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/T5/LICENSE-ComfyUI @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/T5/loader.py b/T5/loader.py new file mode 100644 index 0000000..fee4472 --- /dev/null +++ b/T5/loader.py @@ -0,0 +1,105 @@ +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 diff --git a/T5/nodes.py b/T5/nodes.py new file mode 100644 index 0000000..a7ef50c --- /dev/null +++ b/T5/nodes.py @@ -0,0 +1,67 @@ +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, +} diff --git a/T5/t5_tokenizer/special_tokens_map.json b/T5/t5_tokenizer/special_tokens_map.json new file mode 100644 index 0000000..881bdbf --- /dev/null +++ b/T5/t5_tokenizer/special_tokens_map.json @@ -0,0 +1 @@ +{"eos_token": "", "unk_token": "", "pad_token": "", "additional_special_tokens": ["", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""]} \ No newline at end of file diff --git a/T5/t5_tokenizer/spiece.model b/T5/t5_tokenizer/spiece.model new file mode 100644 index 0000000..4e28ff6 Binary files /dev/null and b/T5/t5_tokenizer/spiece.model differ diff --git a/T5/t5_tokenizer/tokenizer_config.json b/T5/t5_tokenizer/tokenizer_config.json new file mode 100644 index 0000000..b114c31 --- /dev/null +++ b/T5/t5_tokenizer/tokenizer_config.json @@ -0,0 +1 @@ +{"eos_token": "", "unk_token": "", "pad_token": "", "extra_ids": 100, "additional_special_tokens": ["", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], "model_max_length": 512, "name_or_path": "t5-small"} \ No newline at end of file diff --git a/T5/t5v11-xxl_config.json b/T5/t5v11-xxl_config.json new file mode 100644 index 0000000..d133daa --- /dev/null +++ b/T5/t5v11-xxl_config.json @@ -0,0 +1,31 @@ +{ + "_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 +} diff --git a/T5/t5v11.py b/T5/t5v11.py new file mode 100644 index 0000000..8105a4f --- /dev/null +++ b/T5/t5v11.py @@ -0,0 +1,228 @@ +""" +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] # 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 # 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 , 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 # but no + + self.pad_token = self.tokenizer("", add_special_tokens=False)["input_ids"][0] + self.end_token = self.tokenizer("", 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)) diff --git a/__init__.py b/__init__.py index 6cac34e..92ae2e5 100644 --- a/__init__.py +++ b/__init__.py @@ -14,6 +14,10 @@ else: from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes NODE_CLASS_MAPPINGS.update(DiT_Nodes) + # T5 + from .T5.nodes import NODE_CLASS_MAPPINGS as T5_Nodes + NODE_CLASS_MAPPINGS.update(T5_Nodes) + # VAE from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes NODE_CLASS_MAPPINGS.update(VAE_Nodes)