Add support Stable Diffusion 2.0
AUTOMATIC
3 years ago
parent
828438b4a1
commit
ce6911158b
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import math
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import torch
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from modules import prompt_parser, devices
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from modules.shared import opts
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def get_target_prompt_token_count(token_count):
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return math.ceil(max(token_count, 1) / 75) * 75
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class FrozenCLIPEmbedderWithCustomWordsBase(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 = hijack
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def tokenize(self, texts):
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raise NotImplementedError
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def encode_with_transformers(self, tokens):
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raise NotImplementedError
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def encode_embedding_init_text(self, init_text, nvpt):
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raise NotImplementedError
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def tokenize_line(self, line, used_custom_terms, hijack_comments):
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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.tokenize([text for text, _ in parsed])
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fixes = []
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remade_tokens = []
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multipliers = []
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last_comma = -1
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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 token == self.comma_token:
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last_comma = len(remade_tokens)
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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:
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last_comma += 1
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reloc_tokens = remade_tokens[last_comma:]
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reloc_mults = multipliers[last_comma:]
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remade_tokens = remade_tokens[:last_comma]
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length = len(remade_tokens)
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rem = int(math.ceil(length / 75)) * 75 - length
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remade_tokens += [self.id_end] * rem + reloc_tokens
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multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
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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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iteration = len(remade_tokens) // 75
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if (len(remade_tokens) + emb_len) // 75 != iteration:
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rem = (75 * (iteration + 1) - len(remade_tokens))
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remade_tokens += [self.id_end] * rem
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multipliers += [1.0] * rem
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iteration += 1
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fixes.append((iteration, (len(remade_tokens) % 75, 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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token_count = len(remade_tokens)
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prompt_target_length = get_target_prompt_token_count(token_count)
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tokens_to_add = prompt_target_length - len(remade_tokens)
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remade_tokens = remade_tokens + [self.id_end] * tokens_to_add
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multipliers = multipliers + [1.0] * tokens_to_add
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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, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
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token_count = max(current_token_count, token_count)
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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, texts):
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id_start = self.id_start
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id_end = self.id_end
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maxlen = self.wrapped.max_length # you get to stay at 77
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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_tokens = self.tokenize(texts)
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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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use_old = opts.use_old_emphasis_implementation
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if use_old:
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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.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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if use_old:
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self.hijack.fixes = hijack_fixes
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return self.process_tokens(remade_batch_tokens, batch_multipliers)
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z = None
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i = 0
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while max(map(len, remade_batch_tokens)) != 0:
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rem_tokens = [x[75:] for x in remade_batch_tokens]
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rem_multipliers = [x[75:] for x in batch_multipliers]
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self.hijack.fixes = []
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for unfiltered in hijack_fixes:
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fixes = []
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for fix in unfiltered:
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if fix[0] == i:
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fixes.append(fix[1])
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self.hijack.fixes.append(fixes)
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tokens = []
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multipliers = []
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for j in range(len(remade_batch_tokens)):
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if len(remade_batch_tokens[j]) > 0:
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tokens.append(remade_batch_tokens[j][:75])
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multipliers.append(batch_multipliers[j][:75])
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else:
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tokens.append([self.id_end] * 75)
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multipliers.append([1.0] * 75)
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z1 = self.process_tokens(tokens, multipliers)
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z = z1 if z is None else torch.cat((z, z1), axis=-2)
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remade_batch_tokens = rem_tokens
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batch_multipliers = rem_multipliers
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i += 1
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return z
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def process_tokens(self, remade_batch_tokens, batch_multipliers):
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if not opts.use_old_emphasis_implementation:
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remade_batch_tokens = [[self.id_start] + x[:75] + [self.id_end] for x in remade_batch_tokens]
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batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
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tokens = torch.asarray(remade_batch_tokens).to(devices.device)
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if self.id_end != self.id_pad:
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for batch_pos in range(len(remade_batch_tokens)):
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index = remade_batch_tokens[batch_pos].index(self.id_end)
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tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad
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z = self.encode_with_transformers(tokens)
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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_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
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batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(devices.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 FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
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def __init__(self, wrapped, hijack):
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super().__init__(wrapped, hijack)
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self.tokenizer = wrapped.tokenizer
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self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
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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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self.id_start = self.wrapped.tokenizer.bos_token_id
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self.id_end = self.wrapped.tokenizer.eos_token_id
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self.id_pad = self.id_end
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def tokenize(self, texts):
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tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
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return tokenized
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def encode_with_transformers(self, tokens):
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outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
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if opts.CLIP_stop_at_last_layers > 1:
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z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
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z = self.wrapped.transformer.text_model.final_layer_norm(z)
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else:
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z = outputs.last_hidden_state
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return z
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def encode_embedding_init_text(self, init_text, nvpt):
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embedding_layer = self.wrapped.transformer.text_model.embeddings
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ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
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embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
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return embedded
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@ -0,0 +1,37 @@
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import open_clip.tokenizer
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import torch
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from modules import sd_hijack_clip, devices
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from modules.shared import opts
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tokenizer = open_clip.tokenizer._tokenizer
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class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
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def __init__(self, wrapped, hijack):
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super().__init__(wrapped, hijack)
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self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
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self.id_start = tokenizer.encoder["<start_of_text>"]
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self.id_end = tokenizer.encoder["<end_of_text>"]
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self.id_pad = 0
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def tokenize(self, texts):
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assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
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tokenized = [tokenizer.encode(text) for text in texts]
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return tokenized
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def encode_with_transformers(self, tokens):
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# set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
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z = self.wrapped.encode_with_transformer(tokens)
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return z
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def encode_embedding_init_text(self, init_text, nvpt):
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ids = tokenizer.encode(init_text)
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ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
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embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
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return embedded
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@ -0,0 +1,70 @@
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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 64
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channels: 4
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
|
||||||
|
scale_factor: 0.18215
|
||||||
|
use_ema: False
|
||||||
|
|
||||||
|
scheduler_config: # 10000 warmup steps
|
||||||
|
target: ldm.lr_scheduler.LambdaLinearScheduler
|
||||||
|
params:
|
||||||
|
warm_up_steps: [ 10000 ]
|
||||||
|
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
|
||||||
|
f_start: [ 1.e-6 ]
|
||||||
|
f_max: [ 1. ]
|
||||||
|
f_min: [ 1. ]
|
||||||
|
|
||||||
|
unet_config:
|
||||||
|
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||||
|
params:
|
||||||
|
image_size: 32 # unused
|
||||||
|
in_channels: 4
|
||||||
|
out_channels: 4
|
||||||
|
model_channels: 320
|
||||||
|
attention_resolutions: [ 4, 2, 1 ]
|
||||||
|
num_res_blocks: 2
|
||||||
|
channel_mult: [ 1, 2, 4, 4 ]
|
||||||
|
num_heads: 8
|
||||||
|
use_spatial_transformer: True
|
||||||
|
transformer_depth: 1
|
||||||
|
context_dim: 768
|
||||||
|
use_checkpoint: True
|
||||||
|
legacy: False
|
||||||
|
|
||||||
|
first_stage_config:
|
||||||
|
target: ldm.models.autoencoder.AutoencoderKL
|
||||||
|
params:
|
||||||
|
embed_dim: 4
|
||||||
|
monitor: val/rec_loss
|
||||||
|
ddconfig:
|
||||||
|
double_z: true
|
||||||
|
z_channels: 4
|
||||||
|
resolution: 256
|
||||||
|
in_channels: 3
|
||||||
|
out_ch: 3
|
||||||
|
ch: 128
|
||||||
|
ch_mult:
|
||||||
|
- 1
|
||||||
|
- 2
|
||||||
|
- 4
|
||||||
|
- 4
|
||||||
|
num_res_blocks: 2
|
||||||
|
attn_resolutions: []
|
||||||
|
dropout: 0.0
|
||||||
|
lossconfig:
|
||||||
|
target: torch.nn.Identity
|
||||||
|
|
||||||
|
cond_stage_config:
|
||||||
|
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
|
||||||
Loading…
Reference in New Issue