import math import os import sys import traceback import torch import numpy as np from torch import einsum from torch.nn.functional import silu import modules.textual_inversion.textual_inversion from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork from modules.shared import opts, device, cmd_opts import ldm.modules.attention import ldm.modules.diffusionmodules.model attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward def apply_optimizations(): undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu if cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward def undo_optimizations(): ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward def get_target_prompt_token_count(token_count): if token_count < 75: return 75 return math.ceil(token_count / 10) * 10 class StableDiffusionModelHijack: fixes = None comments = [] layers = None circular_enabled = False clip = None embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir) def hijack(self, m): model_embeddings = m.cond_stage_model.transformer.text_model.embeddings model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self) m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self) self.clip = m.cond_stage_model apply_optimizations() def flatten(el): flattened = [flatten(children) for children in el.children()] res = [el] for c in flattened: res += c return res self.layers = flatten(m) def undo_hijack(self, m): if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords: m.cond_stage_model = m.cond_stage_model.wrapped model_embeddings = m.cond_stage_model.transformer.text_model.embeddings if type(model_embeddings.token_embedding) == EmbeddingsWithFixes: model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped def apply_circular(self, enable): if self.circular_enabled == enable: return self.circular_enabled = enable for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]: layer.padding_mode = 'circular' if enable else 'zeros' def clear_comments(self): self.comments = [] def tokenize(self, text): _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() self.wrapped = wrapped self.hijack: StableDiffusionModelHijack = hijack self.tokenizer = wrapped.tokenizer self.token_mults = {} tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_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 def tokenize_line(self, line, used_custom_terms, hijack_comments): id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id if opts.enable_emphasis: parsed = prompt_parser.parse_prompt_attention(line) else: parsed = [[line, 1.0]] tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"] fixes = [] remade_tokens = [] multipliers = [] for tokens, (text, weight) in zip(tokenized, parsed): i = 0 while i < len(tokens): token = tokens[i] embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) if embedding is None: remade_tokens.append(token) multipliers.append(weight) i += 1 else: emb_len = int(embedding.vec.shape[0]) fixes.append((len(remade_tokens), embedding)) remade_tokens += [0] * emb_len multipliers += [weight] * emb_len used_custom_terms.append((embedding.name, embedding.checksum())) i += embedding_length_in_tokens token_count = len(remade_tokens) prompt_target_length = get_target_prompt_token_count(token_count) tokens_to_add = prompt_target_length - len(remade_tokens) + 1 remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add multipliers = [1.0] + multipliers + [1.0] * tokens_to_add return remade_tokens, fixes, multipliers, token_count def process_text(self, texts): used_custom_terms = [] remade_batch_tokens = [] hijack_comments = [] hijack_fixes = [] token_count = 0 cache = {} batch_multipliers = [] for line in texts: if line in cache: remade_tokens, fixes, multipliers = cache[line] else: remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments) token_count = max(current_token_count, token_count) cache[line] = (remade_tokens, fixes, multipliers) remade_batch_tokens.append(remade_tokens) hijack_fixes.append(fixes) batch_multipliers.append(multipliers) return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count def process_text_old(self, text): id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id maxlen = self.wrapped.max_length # you get to stay at 77 used_custom_terms = [] remade_batch_tokens = [] overflowing_words = [] hijack_comments = [] hijack_fixes = [] token_count = 0 cache = {} batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"] batch_multipliers = [] for tokens in batch_tokens: tuple_tokens = tuple(tokens) if tuple_tokens in cache: remade_tokens, fixes, multipliers = cache[tuple_tokens] else: fixes = [] remade_tokens = [] multipliers = [] mult = 1.0 i = 0 while i < len(tokens): token = tokens[i] embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) mult_change = self.token_mults.get(token) if opts.enable_emphasis else None if mult_change is not None: mult *= mult_change i += 1 elif embedding is None: remade_tokens.append(token) multipliers.append(mult) i += 1 else: emb_len = int(embedding.vec.shape[0]) fixes.append((len(remade_tokens), embedding)) remade_tokens += [0] * emb_len multipliers += [mult] * emb_len used_custom_terms.append((embedding.name, embedding.checksum())) i += embedding_length_in_tokens if len(remade_tokens) > maxlen - 2: vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} ovf = remade_tokens[maxlen - 2:] overflowing_words = [vocab.get(int(x), "") for x in ovf] overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words)) hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") token_count = len(remade_tokens) remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens)) remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end] cache[tuple_tokens] = (remade_tokens, fixes, multipliers) multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers)) multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0] remade_batch_tokens.append(remade_tokens) hijack_fixes.append(fixes) batch_multipliers.append(multipliers) return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count def forward(self, text): if opts.use_old_emphasis_implementation: batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text) else: batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text) self.hijack.fixes = hijack_fixes self.hijack.comments += hijack_comments if len(used_custom_terms) > 0: self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) target_token_count = get_target_prompt_token_count(token_count) + 2 position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76] position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1)) remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens] tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device) outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids) z = outputs.last_hidden_state # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers] batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(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 EmbeddingsWithFixes(torch.nn.Module): def __init__(self, wrapped, embeddings): super().__init__() self.wrapped = wrapped self.embeddings = embeddings def forward(self, input_ids): batch_fixes = self.embeddings.fixes self.embeddings.fixes = None inputs_embeds = self.wrapped(input_ids) if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0: return inputs_embeds vecs = [] for fixes, tensor in zip(batch_fixes, inputs_embeds): for offset, embedding in fixes: emb = embedding.vec emb_len = min(tensor.shape[0]-offset-1, emb.shape[0]) tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]]) vecs.append(tensor) return torch.stack(vecs) def add_circular_option_to_conv_2d(): conv2d_constructor = torch.nn.Conv2d.__init__ def conv2d_constructor_circular(self, *args, **kwargs): return conv2d_constructor(self, *args, padding_mode='circular', **kwargs) torch.nn.Conv2d.__init__ = conv2d_constructor_circular model_hijack = StableDiffusionModelHijack()