324 lines
13 KiB
Python
324 lines
13 KiB
Python
import math
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import os
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import sys
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import traceback
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import torch
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import numpy as np
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from torch import einsum
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from torch.nn.functional import silu
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import modules.textual_inversion.textual_inversion
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from modules import prompt_parser, devices, sd_hijack_optimizations, shared
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from modules.shared import opts, device, cmd_opts
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import ldm.modules.attention
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import ldm.modules.diffusionmodules.model
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attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
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diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
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diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
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def apply_optimizations():
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undo_optimizations()
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ldm.modules.diffusionmodules.model.nonlinearity = silu
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if cmd_opts.opt_split_attention_v1:
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
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elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
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ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
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def undo_optimizations():
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from modules.hypernetwork import hypernetwork
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ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
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ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
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ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
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class StableDiffusionModelHijack:
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fixes = None
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comments = []
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layers = None
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circular_enabled = False
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clip = None
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embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
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def hijack(self, m):
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model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
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model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
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m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
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self.clip = m.cond_stage_model
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apply_optimizations()
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def flatten(el):
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flattened = [flatten(children) for children in el.children()]
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res = [el]
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for c in flattened:
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res += c
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return res
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self.layers = flatten(m)
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def undo_hijack(self, m):
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if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords:
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m.cond_stage_model = m.cond_stage_model.wrapped
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model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
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if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
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model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
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def apply_circular(self, enable):
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if self.circular_enabled == enable:
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return
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self.circular_enabled = enable
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for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
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layer.padding_mode = 'circular' if enable else 'zeros'
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def tokenize(self, text):
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max_length = self.clip.max_length - 2
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_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
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return remade_batch_tokens[0], token_count, max_length
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class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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def __init__(self, wrapped, hijack):
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super().__init__()
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self.wrapped = wrapped
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self.hijack: StableDiffusionModelHijack = hijack
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self.tokenizer = wrapped.tokenizer
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self.max_length = wrapped.max_length
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self.token_mults = {}
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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]
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for text, ident in tokens_with_parens:
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mult = 1.0
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for c in text:
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if c == '[':
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mult /= 1.1
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if c == ']':
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mult *= 1.1
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if c == '(':
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mult *= 1.1
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if c == ')':
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mult /= 1.1
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if mult != 1.0:
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self.token_mults[ident] = mult
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def tokenize_line(self, line, used_custom_terms, hijack_comments):
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id_start = self.wrapped.tokenizer.bos_token_id
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id_end = self.wrapped.tokenizer.eos_token_id
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maxlen = self.wrapped.max_length
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if opts.enable_emphasis:
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parsed = prompt_parser.parse_prompt_attention(line)
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else:
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parsed = [[line, 1.0]]
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tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
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fixes = []
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remade_tokens = []
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multipliers = []
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for tokens, (text, weight) in zip(tokenized, parsed):
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i = 0
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while i < len(tokens):
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token = tokens[i]
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embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
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if embedding is None:
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remade_tokens.append(token)
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multipliers.append(weight)
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i += 1
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else:
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emb_len = int(embedding.vec.shape[0])
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fixes.append((len(remade_tokens), embedding))
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remade_tokens += [0] * emb_len
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multipliers += [weight] * emb_len
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used_custom_terms.append((embedding.name, embedding.checksum()))
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i += embedding_length_in_tokens
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if len(remade_tokens) > maxlen - 2:
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vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
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ovf = remade_tokens[maxlen - 2:]
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overflowing_words = [vocab.get(int(x), "") for x in ovf]
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overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
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hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
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token_count = len(remade_tokens)
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remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
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remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
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multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
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multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
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return remade_tokens, fixes, multipliers, token_count
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def process_text(self, texts):
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used_custom_terms = []
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remade_batch_tokens = []
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hijack_comments = []
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hijack_fixes = []
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token_count = 0
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cache = {}
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batch_multipliers = []
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for line in texts:
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if line in cache:
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remade_tokens, fixes, multipliers = cache[line]
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else:
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remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
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cache[line] = (remade_tokens, fixes, multipliers)
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remade_batch_tokens.append(remade_tokens)
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hijack_fixes.append(fixes)
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batch_multipliers.append(multipliers)
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return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
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def process_text_old(self, text):
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id_start = self.wrapped.tokenizer.bos_token_id
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id_end = self.wrapped.tokenizer.eos_token_id
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maxlen = self.wrapped.max_length
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used_custom_terms = []
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remade_batch_tokens = []
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overflowing_words = []
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hijack_comments = []
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hijack_fixes = []
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token_count = 0
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cache = {}
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batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
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batch_multipliers = []
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for tokens in batch_tokens:
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tuple_tokens = tuple(tokens)
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if tuple_tokens in cache:
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remade_tokens, fixes, multipliers = cache[tuple_tokens]
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else:
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fixes = []
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remade_tokens = []
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multipliers = []
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mult = 1.0
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i = 0
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while i < len(tokens):
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token = tokens[i]
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embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
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mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
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if mult_change is not None:
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mult *= mult_change
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i += 1
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elif embedding is None:
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remade_tokens.append(token)
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multipliers.append(mult)
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i += 1
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else:
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emb_len = int(embedding.vec.shape[0])
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fixes.append((len(remade_tokens), embedding))
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remade_tokens += [0] * emb_len
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multipliers += [mult] * emb_len
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used_custom_terms.append((embedding.name, embedding.checksum()))
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i += embedding_length_in_tokens
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if len(remade_tokens) > maxlen - 2:
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vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
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ovf = remade_tokens[maxlen - 2:]
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overflowing_words = [vocab.get(int(x), "") for x in ovf]
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overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
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hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
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token_count = len(remade_tokens)
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remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
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remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
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cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
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multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
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multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
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remade_batch_tokens.append(remade_tokens)
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hijack_fixes.append(fixes)
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batch_multipliers.append(multipliers)
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return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
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def forward(self, text):
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if opts.use_old_emphasis_implementation:
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batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
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else:
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batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
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self.hijack.fixes = hijack_fixes
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self.hijack.comments = hijack_comments
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if len(used_custom_terms) > 0:
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self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
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tokens = torch.asarray(remade_batch_tokens).to(device)
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outputs = self.wrapped.transformer(input_ids=tokens)
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z = outputs.last_hidden_state
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# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
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batch_multipliers = torch.asarray(batch_multipliers).to(device)
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original_mean = z.mean()
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z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
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new_mean = z.mean()
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z *= original_mean / new_mean
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return z
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class EmbeddingsWithFixes(torch.nn.Module):
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def __init__(self, wrapped, embeddings):
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super().__init__()
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self.wrapped = wrapped
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self.embeddings = embeddings
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def forward(self, input_ids):
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batch_fixes = self.embeddings.fixes
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self.embeddings.fixes = None
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inputs_embeds = self.wrapped(input_ids)
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if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
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return inputs_embeds
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vecs = []
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for fixes, tensor in zip(batch_fixes, inputs_embeds):
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for offset, embedding in fixes:
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emb = embedding.vec
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emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
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tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
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vecs.append(tensor)
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return torch.stack(vecs)
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def add_circular_option_to_conv_2d():
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conv2d_constructor = torch.nn.Conv2d.__init__
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def conv2d_constructor_circular(self, *args, **kwargs):
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return conv2d_constructor(self, *args, padding_mode='circular', **kwargs)
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torch.nn.Conv2d.__init__ = conv2d_constructor_circular
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model_hijack = StableDiffusionModelHijack()
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