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CLIP hijack rework

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AUTOMATIC 2023-01-07 01:45:28 +03:00
parent 3246a2d6b8
commit 79e39fae61
5 changed files with 246 additions and 172 deletions
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6
modules/sd_hijack.py
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@ -150,10 +150,10 @@ class StableDiffusionModelHijack:
def clear_comments(self):
self.comments = []
def tokenize(self, text):
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
def get_prompt_lengths(self, text):
_, token_count = self.clip.process_texts([text])
return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)
return token_count, self.clip.get_target_prompt_token_count(token_count)
class EmbeddingsWithFixes(torch.nn.Module):

328
modules/sd_hijack_clip.py
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@ -1,12 +1,28 @@
import math
from collections import namedtuple
import torch
from modules import prompt_parser, devices
from modules.shared import opts
def get_target_prompt_token_count(token_count):
return math.ceil(max(token_count, 1) / 75) * 75
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):
@ -14,17 +30,49 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
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, used_custom_terms, hijack_comments):
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:
@ -32,205 +80,152 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
tokenized = self.tokenize([text for text, _ in parsed])
fixes = []
remade_tokens = []
multipliers = []
chunks = []
chunk = PromptChunk()
token_count = 0
last_comma = -1
for tokens, (text, weight) in zip(tokenized, parsed):
i = 0
while i < len(tokens):
token = tokens[i]
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
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
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(remade_tokens)
elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
last_comma += 1
reloc_tokens = remade_tokens[last_comma:]
reloc_mults = multipliers[last_comma:]
last_comma = len(chunk.tokens)
remade_tokens = remade_tokens[:last_comma]
length = len(remade_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
rem = int(math.ceil(length / 75)) * 75 - length
remade_tokens += [self.id_end] * rem + reloc_tokens
multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
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:
remade_tokens.append(token)
multipliers.append(weight)
i += 1
else:
emb_len = int(embedding.vec.shape[0])
iteration = len(remade_tokens) // 75
if (len(remade_tokens) + emb_len) // 75 != iteration:
rem = (75 * (iteration + 1) - len(remade_tokens))
remade_tokens += [self.id_end] * rem
multipliers += [1.0] * rem
iteration += 1
fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
remade_tokens += [0] * emb_len
multipliers += [weight] * emb_len
used_custom_terms.append((embedding.name, embedding.checksum()))
i += embedding_length_in_tokens
chunk.tokens.append(token)
chunk.multipliers.append(weight)
position += 1
continue
token_count = len(remade_tokens)
prompt_target_length = get_target_prompt_token_count(token_count)
tokens_to_add = prompt_target_length - len(remade_tokens)
emb_len = int(embedding.vec.shape[0])
if len(chunk.tokens) + emb_len > self.chunk_length:
next_chunk()
remade_tokens = remade_tokens + [self.id_end] * tokens_to_add
multipliers = multipliers + [1.0] * tokens_to_add
chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
return remade_tokens, fixes, multipliers, token_count
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.
"""
def process_text(self, texts):
used_custom_terms = []
remade_batch_tokens = []
hijack_comments = []
hijack_fixes = []
token_count = 0
cache = {}
batch_multipliers = []
batch_chunks = []
for line in texts:
if line in cache:
remade_tokens, fixes, multipliers = cache[line]
chunks = cache[line]
else:
remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
chunks, current_token_count = self.tokenize_line(line)
token_count = max(current_token_count, token_count)
cache[line] = (remade_tokens, fixes, multipliers)
cache[line] = chunks
remade_batch_tokens.append(remade_tokens)
hijack_fixes.append(fixes)
batch_multipliers.append(multipliers)
batch_chunks.append(chunks)
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
return batch_chunks, token_count
def process_text_old(self, texts):
id_start = self.id_start
id_end = self.id_end
maxlen = self.wrapped.max_length # you get to stay at 77
used_custom_terms = []
remade_batch_tokens = []
hijack_comments = []
hijack_fixes = []
token_count = 0
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"
"""
cache = {}
batch_tokens = self.tokenize(texts)
batch_multipliers = []
for tokens in batch_tokens:
tuple_tokens = tuple(tokens)
if opts.use_old_emphasis_implementation:
import modules.sd_hijack_clip_old
return modules.sd_hijack_clip_old.forward_old(self, texts)
if tuple_tokens in cache:
remade_tokens, fixes, multipliers = cache[tuple_tokens]
else:
fixes = []
remade_tokens = []
multipliers = []
mult = 1.0
batch_chunks, token_count = self.process_texts(texts)
i = 0
while i < len(tokens):
token = tokens[i]
used_embeddings = {}
chunk_count = max([len(x) for x in batch_chunks])
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
zs = []
for i in range(chunk_count):
batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
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
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]
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")
for fixes in self.hijack.fixes:
for position, embedding in fixes:
used_embeddings[embedding.name] = embedding
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)
z = self.process_tokens(tokens, multipliers)
zs.append(z)
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
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}")
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):
use_old = opts.use_old_emphasis_implementation
if use_old:
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.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]))
if use_old:
self.hijack.fixes = hijack_fixes
return self.process_tokens(remade_batch_tokens, batch_multipliers)
z = None
i = 0
while max(map(len, remade_batch_tokens)) != 0:
rem_tokens = [x[75:] for x in remade_batch_tokens]
rem_multipliers = [x[75:] for x in batch_multipliers]
self.hijack.fixes = []
for unfiltered in hijack_fixes:
fixes = []
for fix in unfiltered:
if fix[0] == i:
fixes.append(fix[1])
self.hijack.fixes.append(fixes)
tokens = []
multipliers = []
for j in range(len(remade_batch_tokens)):
if len(remade_batch_tokens[j]) > 0:
tokens.append(remade_batch_tokens[j][:75])
multipliers.append(batch_multipliers[j][:75])
else:
tokens.append([self.id_end] * 75)
multipliers.append([1.0] * 75)
z1 = self.process_tokens(tokens, multipliers)
z = z1 if z is None else torch.cat((z, z1), axis=-2)
remade_batch_tokens = rem_tokens
batch_multipliers = rem_multipliers
i += 1
return z
return torch.hstack(zs)
def process_tokens(self, remade_batch_tokens, batch_multipliers):
if not opts.use_old_emphasis_implementation:
remade_batch_tokens = [[self.id_start] + x[:75] + [self.id_end] for x in remade_batch_tokens]
batch_multipliers = [[1.0] + x[:75] + [1.0] for x in 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)
@ -239,8 +234,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
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_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(devices.device)
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()

81
modules/sd_hijack_clip_old.py Normal file
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@ -0,0 +1,81 @@
from modules import sd_hijack_clip
from modules import shared
def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
id_start = self.id_start
id_end = self.id_end
maxlen = self.wrapped.max_length # you get to stay at 77
used_custom_terms = []
remade_batch_tokens = []
hijack_comments = []
hijack_fixes = []
token_count = 0
cache = {}
batch_tokens = self.tokenize(texts)
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 shared.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_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = process_text_old(self, texts)
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]))
self.hijack.fixes = hijack_fixes
return self.process_tokens(remade_batch_tokens, batch_multipliers)

1
modules/textual_inversion/textual_inversion.py
View File

@ -79,7 +79,6 @@ class EmbeddingDatabase:
self.word_embeddings[embedding.name] = embedding
# TODO changing between clip and open clip changes tokenization, which will cause embeddings to stop working
ids = model.cond_stage_model.tokenize([embedding.name])[0]
first_id = ids[0]

2
modules/ui.py
View File

@ -368,7 +368,7 @@ def update_token_counter(text, steps):
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
prompts = [prompt_text for step, prompt_text in flat_prompts]
tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
style_class = ' class="red"' if (token_count > max_length) else ""
return f"<span {style_class}>{token_count}/{max_length}</span>"