import math from collections import namedtuple import torch from modules import prompt_parser, devices from modules.shared import opts class PromptChunk: """ This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt. If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary. Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token, so just 75 tokens from prompt. """ def __init__(self): self.tokens = [] self.multipliers = [] self.fixes = [] PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding']) """This is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt chunk""" class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() self.wrapped = wrapped self.hijack = hijack self.chunk_length = 75 def empty_chunk(self): """creates an empty PromptChunk and returns it""" chunk = PromptChunk() chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1) chunk.multipliers = [1.0] * (self.chunk_length + 2) return chunk def get_target_prompt_token_count(self, token_count): """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented""" return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length def tokenize(self, texts): """Converts a batch of texts into a batch of token ids""" raise NotImplementedError def encode_with_transformers(self, tokens): """ converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens; All python lists with tokens are assumed to have same length, usually 77. if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on model - can be 768 and 1024 """ raise NotImplementedError def encode_embedding_init_text(self, init_text, nvpt): """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned.""" raise NotImplementedError def tokenize_line(self, line): """ this transforms a single prompt into a list of PromptChunk objects - as many as needed to represent the prompt. Returns the list and the total number of tokens in the prompt. """ if opts.enable_emphasis: parsed = prompt_parser.parse_prompt_attention(line) else: parsed = [[line, 1.0]] tokenized = self.tokenize([text for text, _ in parsed]) chunks = [] chunk = PromptChunk() token_count = 0 last_comma = -1 def next_chunk(): """puts current chunk into the list of results and produces the next one - empty""" nonlocal token_count nonlocal last_comma nonlocal chunk token_count += len(chunk.tokens) to_add = self.chunk_length - len(chunk.tokens) if to_add > 0: chunk.tokens += [self.id_end] * to_add chunk.multipliers += [1.0] * to_add chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end] chunk.multipliers = [1.0] + chunk.multipliers + [1.0] last_comma = -1 chunks.append(chunk) chunk = PromptChunk() for tokens, (text, weight) in zip(tokenized, parsed): position = 0 while position < len(tokens): token = tokens[position] if token == self.comma_token: last_comma = len(chunk.tokens) # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack # is a setting that specifies that is there is a comma nearby, the text after comma should be moved out of this chunk and into the next. elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack: break_location = last_comma + 1 reloc_tokens = chunk.tokens[break_location:] reloc_mults = chunk.multipliers[break_location:] chunk.tokens = chunk.tokens[:break_location] chunk.multipliers = chunk.multipliers[:break_location] next_chunk() chunk.tokens = reloc_tokens chunk.multipliers = reloc_mults if len(chunk.tokens) == self.chunk_length: next_chunk() embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position) if embedding is None: chunk.tokens.append(token) chunk.multipliers.append(weight) position += 1 continue emb_len = int(embedding.vec.shape[0]) if len(chunk.tokens) + emb_len > self.chunk_length: next_chunk() chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding)) chunk.tokens += [0] * emb_len chunk.multipliers += [weight] * emb_len position += embedding_length_in_tokens if len(chunk.tokens) > 0: next_chunk() return chunks, token_count def process_texts(self, texts): """ Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum length, in tokens, of all texts. """ token_count = 0 cache = {} batch_chunks = [] for line in texts: if line in cache: chunks = cache[line] else: chunks, current_token_count = self.tokenize_line(line) token_count = max(current_token_count, token_count) cache[line] = chunks batch_chunks.append(chunks) return batch_chunks, token_count def forward(self, texts): """ Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts. Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024. An example shape returned by this function can be: (2, 77, 768). Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream" """ if opts.use_old_emphasis_implementation: import modules.sd_hijack_clip_old return modules.sd_hijack_clip_old.forward_old(self, texts) batch_chunks, token_count = self.process_texts(texts) used_embeddings = {} chunk_count = max([len(x) for x in batch_chunks]) zs = [] for i in range(chunk_count): batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks] tokens = [x.tokens for x in batch_chunk] multipliers = [x.multipliers for x in batch_chunk] self.hijack.fixes = [x.fixes for x in batch_chunk] for fixes in self.hijack.fixes: for position, embedding in fixes: used_embeddings[embedding.name] = embedding z = self.process_tokens(tokens, multipliers) zs.append(z) if len(used_embeddings) > 0: embeddings_list = ", ".join([f'{name} [{embedding.checksum()}]' for name, embedding in used_embeddings.items()]) self.hijack.comments.append(f"Used embeddings: {embeddings_list}") return torch.hstack(zs) def process_tokens(self, remade_batch_tokens, batch_multipliers): """ sends one single prompt chunk to be encoded by transformers neural network. remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens. Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier corresponds to one token. """ tokens = torch.asarray(remade_batch_tokens).to(devices.device) # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones. if self.id_end != self.id_pad: for batch_pos in range(len(remade_batch_tokens)): index = remade_batch_tokens[batch_pos].index(self.id_end) tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad z = self.encode_with_transformers(tokens) # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise batch_multipliers = torch.asarray(batch_multipliers).to(devices.device) original_mean = z.mean() z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape) new_mean = z.mean() z *= original_mean / new_mean return z class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase): def __init__(self, wrapped, hijack): super().__init__(wrapped, hijack) self.tokenizer = wrapped.tokenizer vocab = self.tokenizer.get_vocab() self.comma_token = vocab.get(',', None) self.token_mults = {} tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k] for text, ident in tokens_with_parens: mult = 1.0 for c in text: if c == '[': mult /= 1.1 if c == ']': mult *= 1.1 if c == '(': mult *= 1.1 if c == ')': mult /= 1.1 if mult != 1.0: self.token_mults[ident] = mult self.id_start = self.wrapped.tokenizer.bos_token_id self.id_end = self.wrapped.tokenizer.eos_token_id self.id_pad = self.id_end def tokenize(self, texts): tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"] return tokenized def encode_with_transformers(self, tokens): outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) if opts.CLIP_stop_at_last_layers > 1: z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers] z = self.wrapped.transformer.text_model.final_layer_norm(z) else: z = outputs.last_hidden_state return z def encode_embedding_init_text(self, init_text, nvpt): embedding_layer = self.wrapped.transformer.text_model.embeddings ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"] embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0) return embedded