import os from transformers import CLIPTokenizer import comfy.ops import torch import traceback import zipfile from . import model_management import comfy.clip_model import json import logging import numbers def gen_empty_tokens(special_tokens, length): start_token = special_tokens.get("start", None) end_token = special_tokens.get("end", None) pad_token = special_tokens.get("pad") output = [] if start_token is not None: output.append(start_token) if end_token is not None: output.append(end_token) output += [pad_token] * (length - len(output)) return output class ClipTokenWeightEncoder: def encode_token_weights(self, token_weight_pairs): to_encode = list() max_token_len = 0 has_weights = False for x in token_weight_pairs: tokens = list(map(lambda a: a[0], x)) max_token_len = max(len(tokens), max_token_len) has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) to_encode.append(tokens) sections = len(to_encode) if has_weights or sections == 0: to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) o = self.encode(to_encode) out, pooled = o[:2] if pooled is not None: first_pooled = pooled[0:1].to(model_management.intermediate_device()) else: first_pooled = pooled output = [] for k in range(0, sections): z = out[k:k+1] if has_weights: z_empty = out[-1] for i in range(len(z)): for j in range(len(z[i])): weight = token_weight_pairs[k][j][1] if weight != 1.0: z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] output.append(z) if (len(output) == 0): r = (out[-1:].to(model_management.intermediate_device()), first_pooled) else: r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled) if len(o) > 2: extra = {} for k in o[2]: v = o[2][k] if k == "attention_mask": v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device()) extra[k] = v r = r + (extra,) return r class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): LAYERS = [ "last", "pooled", "hidden" ] def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32 super().__init__() assert layer in self.LAYERS if textmodel_json_config is None: textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") with open(textmodel_json_config) as f: config = json.load(f) operations = model_options.get("custom_operations", None) if operations is None: operations = comfy.ops.manual_cast self.operations = operations self.transformer = model_class(config, dtype, device, self.operations) self.num_layers = self.transformer.num_layers self.max_length = max_length if freeze: self.freeze() self.layer = layer self.layer_idx = None self.special_tokens = special_tokens self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) self.enable_attention_masks = enable_attention_masks self.zero_out_masked = zero_out_masked self.layer_norm_hidden_state = layer_norm_hidden_state self.return_projected_pooled = return_projected_pooled self.return_attention_masks = return_attention_masks if layer == "hidden": assert layer_idx is not None assert abs(layer_idx) < self.num_layers self.set_clip_options({"layer": layer_idx}) self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled) def freeze(self): self.transformer = self.transformer.eval() #self.train = disabled_train for param in self.parameters(): param.requires_grad = False def set_clip_options(self, options): layer_idx = options.get("layer", self.layer_idx) self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) if layer_idx is None or abs(layer_idx) > self.num_layers: self.layer = "last" else: self.layer = "hidden" self.layer_idx = layer_idx def reset_clip_options(self): self.layer = self.options_default[0] self.layer_idx = self.options_default[1] self.return_projected_pooled = self.options_default[2] def set_up_textual_embeddings(self, tokens, current_embeds): out_tokens = [] next_new_token = token_dict_size = current_embeds.weight.shape[0] embedding_weights = [] for x in tokens: tokens_temp = [] for y in x: if isinstance(y, numbers.Integral): tokens_temp += [int(y)] else: if y.shape[0] == current_embeds.weight.shape[1]: embedding_weights += [y] tokens_temp += [next_new_token] next_new_token += 1 else: logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1])) while len(tokens_temp) < len(x): tokens_temp += [self.special_tokens["pad"]] out_tokens += [tokens_temp] n = token_dict_size if len(embedding_weights) > 0: new_embedding = self.operations.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype) new_embedding.weight[:token_dict_size] = current_embeds.weight for x in embedding_weights: new_embedding.weight[n] = x n += 1 self.transformer.set_input_embeddings(new_embedding) processed_tokens = [] for x in out_tokens: processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one return processed_tokens def forward(self, tokens): backup_embeds = self.transformer.get_input_embeddings() device = backup_embeds.weight.device tokens = self.set_up_textual_embeddings(tokens, backup_embeds) tokens = torch.LongTensor(tokens).to(device) attention_mask = None if self.enable_attention_masks or self.zero_out_masked or self.return_attention_masks: attention_mask = torch.zeros_like(tokens) end_token = self.special_tokens.get("end", -1) for x in range(attention_mask.shape[0]): for y in range(attention_mask.shape[1]): attention_mask[x, y] = 1 if tokens[x, y] == end_token: break attention_mask_model = None if self.enable_attention_masks: attention_mask_model = attention_mask outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32) self.transformer.set_input_embeddings(backup_embeds) if self.layer == "last": z = outputs[0].float() else: z = outputs[1].float() if self.zero_out_masked: z *= attention_mask.unsqueeze(-1).float() pooled_output = None if len(outputs) >= 3: if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None: pooled_output = outputs[3].float() elif outputs[2] is not None: pooled_output = outputs[2].float() extra = {} if self.return_attention_masks: extra["attention_mask"] = attention_mask if len(extra) > 0: return z, pooled_output, extra return z, pooled_output def encode(self, tokens): return self(tokens) def load_sd(self, sd): return self.transformer.load_state_dict(sd, strict=False) def parse_parentheses(string): """ Split a string based off top-level nested parentheses. Parameters ---------- string : str The string to be split into its top nested groups. Returns ------- result : list A list of each element in string, split by top-level elements Examples -------- >>> string = "(foo)(bar)" ['(foo)', '(bar)'] >>> string = "(foo(bar)(test1))(test2(test3))" ['(foo(bar)(test1))', '(test2(test3))'] """ result = [] current_item = "" nesting_level = 0 for char in string: if char == "(": if nesting_level == 0: if current_item: result.append(current_item) current_item = "(" else: current_item = "(" else: current_item += char nesting_level += 1 elif char == ")": nesting_level -= 1 if nesting_level == 0: result.append(current_item + ")") current_item = "" else: current_item += char else: current_item += char if current_item: result.append(current_item) return result def token_weights(string, current_weight): """ Find the requested weight of a token, and multiply it by the current weight. For parentheses groupings with no set weight, multiply by 1.1. Parameters ---------- string : str The input of tokens to calculate requested weights for current_weight : float The current weight of all tokens Returns ------- out : list A list of each token paired with the calculated weight Examples -------- >>> string = "(foo)" >>> current_weight = 1.0 [('foo', 1.1)] >>> string = "(foo)(bar)" >>> current_weight = 1.0 [('foo', 1.1), ('bar', 1.1)] >>> string = "(foo:2.0)" >>> current_weight = 1.0 [('foo', 2.0)] >>> string = "((foo))" >>> current_weight = 1.0 [('foo', 1.21)] >>> string = "((foo):1.1)" >>> current_weight = 1.0 [('foo', 1.21)] >>> string = "((foo:1.1))" >>> current_weight = 1.0 [('foo', 1.1)] >>> string = "(foo:0.0)" >>> current_weight = 1.0 [('foo', 0.0)] >>> string = "((foo:1.0):0.0)" >>> current_weight = 1.0 [('foo', 1.0)] >>> string = "foo ((((lol (cat:666) attack:100)))) baz" >>> current_weight = 1.0 [('foo ', 1.0), ('lol ', 100.0), ('cat', 666.0), (' attack', 100.0), (' baz', 1.0)] >>> string = "foo ((((lol (cat:666) attack):100))) baz" >>> current_weight = 1.0 [('foo ', 1.0), ('lol ', 110.0), ('cat', 666.0), (' attack', 110.0), (' baz', 1.0)] Notes ----- See issue #4610 for more detail. One thing to note is that the default of 1.1 is multiplied when there is no weight defined on the *interior* of the group instead of the exterior. This behavior can be seen in the last two examples (thank you @jart for making these). In the first example, the weight of 100 is defined inside the same parentheses grouping as both 'lol' and 'attack'. There is no parentheses between the defined weight and the tokens, so there is no multiplication of the weight by 1.1. In the second example, the weight is outside the parentheses grouping, so the weights inside the grouping are first given a modifier of 1.1, then given a modifier of 100. """ a = parse_parentheses(string) out = [] for x in a: weight = current_weight if len(x) >= 2 and x[-1] == ')' and x[0] == '(': x = x[1:-1] xx = x.rfind(":") # This line makes *all nestings* multiply the weight by 1.1 weight *= 1.1 if xx > 0: try: weight = float(x[xx+1:]) x = x[:xx] except: pass out += token_weights(x, weight) else: out += [(x, current_weight)] return out def escape_important(text): """ Replace parentheses marked via backslashes with escape characters Parameters ---------- text : str The string to have its important parentheses replaced Returns ------- text : str The input string, with important parentheses replaced Examples -------- >>> text = "\\(foo\\)(bar)" "\0\2foo\0\1(bar)" See Also -------- unescape_important : Replace escape characters with parentheses """ text = text.replace("\\)", "\0\1") text = text.replace("\\(", "\0\2") return text def unescape_important(text): """ Replace escape characters made via escape_important with parentheses Parameters ---------- text : str The string to have its escape characters replaced Returns ------- text : str The input string, with escape characters replaced Examples -------- >>> text = "\0\2foo\0\1(bar)" "(foo)(bar)" See Also -------- escape_important: makes strings with the escape characters this function uses """ text = text.replace("\0\1", ")") text = text.replace("\0\2", "(") return text def safe_load_embed_zip(embed_path): with zipfile.ZipFile(embed_path) as myzip: names = list(filter(lambda a: "data/" in a, myzip.namelist())) names.reverse() for n in names: with myzip.open(n) as myfile: data = myfile.read() number = len(data) // 4 length_embed = 1024 #sd2.x if number < 768: continue if number % 768 == 0: length_embed = 768 #sd1.x num_embeds = number // length_embed embed = torch.frombuffer(data, dtype=torch.float) out = embed.reshape((num_embeds, length_embed)).clone() del embed return out def expand_directory_list(directories): """ For all directories in a list, list all subdirectories beneath them Parameters ---------- directories : list The list of directories to search for subdirectories with Returns ------- dirs : list A list of all subdirectories found underneath the given directories """ dirs = set() for x in directories: dirs.add(x) for root, subdir, file in os.walk(x, followlinks=True): dirs.add(root) return list(dirs) def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format i = 0 out_list = [] for k in embed: if k.startswith(prefix) and k.endswith(suffix): out_list.append(embed[k]) if len(out_list) == 0: return None return torch.cat(out_list, dim=0) def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None): if isinstance(embedding_directory, str): embedding_directory = [embedding_directory] embedding_directory = expand_directory_list(embedding_directory) valid_file = None for embed_dir in embedding_directory: embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) embed_dir = os.path.abspath(embed_dir) try: if os.path.commonpath((embed_dir, embed_path)) != embed_dir: continue except: continue if not os.path.isfile(embed_path): extensions = ['.safetensors', '.pt', '.bin'] for x in extensions: t = embed_path + x if os.path.isfile(t): valid_file = t break else: valid_file = embed_path if valid_file is not None: break if valid_file is None: return None embed_path = valid_file embed_out = None try: if embed_path.lower().endswith(".safetensors"): import safetensors.torch embed = safetensors.torch.load_file(embed_path, device="cpu") else: if 'weights_only' in torch.load.__code__.co_varnames: try: embed = torch.load(embed_path, weights_only=True, map_location="cpu") except: embed_out = safe_load_embed_zip(embed_path) else: embed = torch.load(embed_path, map_location="cpu") except Exception as e: logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name)) return None if embed_out is None: if 'string_to_param' in embed: values = embed['string_to_param'].values() embed_out = next(iter(values)) elif isinstance(embed, list): out_list = [] for x in range(len(embed)): for k in embed[x]: t = embed[x][k] if t.shape[-1] != embedding_size: continue out_list.append(t.reshape(-1, t.shape[-1])) embed_out = torch.cat(out_list, dim=0) elif embed_key is not None and embed_key in embed: embed_out = embed[embed_key] else: embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*') if embed_out is None: embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key)) if embed_out is None: values = embed.values() embed_out = next(iter(values)) return embed_out class SDTokenizer: def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None, tokenizer_data={}): if tokenizer_path is None: tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) self.max_length = max_length self.min_length = min_length empty = self.tokenizer('')["input_ids"] if has_start_token: self.tokens_start = 1 self.start_token = empty[0] self.end_token = empty[1] else: self.tokens_start = 0 self.start_token = None self.end_token = empty[0] if pad_token is not None: self.pad_token = pad_token elif pad_with_end: self.pad_token = self.end_token else: self.pad_token = 0 self.pad_with_end = pad_with_end self.pad_to_max_length = pad_to_max_length vocab = self.tokenizer.get_vocab() self.inv_vocab = {v: k for k, v in vocab.items()} self.embedding_directory = embedding_directory self.max_word_length = 8 self.embedding_identifier = "embedding:" self.embedding_size = embedding_size self.embedding_key = embedding_key def _try_get_embedding(self, embedding_name:str): ''' Takes a potential embedding name and tries to retrieve it. Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. ''' embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) if embed is None: stripped = embedding_name.strip(',') if len(stripped) < len(embedding_name): embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) return (embed, embedding_name[len(stripped):]) return (embed, "") def tokenize_with_weights(self, text:str, return_word_ids=False): ''' Takes a prompt and converts it to a list of (token, weight, word id) elements. Tokens can both be integer tokens and pre computed CLIP tensors. Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. Returned list has the dimensions NxM where M is the input size of CLIP ''' text = escape_important(text) parsed_weights = token_weights(text, 1.0) #tokenize words tokens = [] for weighted_segment, weight in parsed_weights: to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ') to_tokenize = [x for x in to_tokenize if x != ""] for word in to_tokenize: #if we find an embedding, deal with the embedding if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: embedding_name = word[len(self.embedding_identifier):].strip('\n') embed, leftover = self._try_get_embedding(embedding_name) if embed is None: logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring") else: if len(embed.shape) == 1: tokens.append([(embed, weight)]) else: tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) #if we accidentally have leftover text, continue parsing using leftover, else move on to next word if leftover != "": word = leftover else: continue #parse word tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]]) #reshape token array to CLIP input size batched_tokens = [] batch = [] if self.start_token is not None: batch.append((self.start_token, 1.0, 0)) batched_tokens.append(batch) for i, t_group in enumerate(tokens): #determine if we're going to try and keep the tokens in a single batch is_large = len(t_group) >= self.max_word_length while len(t_group) > 0: if len(t_group) + len(batch) > self.max_length - 1: remaining_length = self.max_length - len(batch) - 1 #break word in two and add end token if is_large: batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) batch.append((self.end_token, 1.0, 0)) t_group = t_group[remaining_length:] #add end token and pad else: batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length)) #start new batch batch = [] if self.start_token is not None: batch.append((self.start_token, 1.0, 0)) batched_tokens.append(batch) else: batch.extend([(t,w,i+1) for t,w in t_group]) t_group = [] #fill last batch batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch))) if self.min_length is not None and len(batch) < self.min_length: batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch))) if not return_word_ids: batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] return batched_tokens def untokenize(self, token_weight_pair): return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) def state_dict(self): return {} class SD1Tokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer): self.clip_name = clip_name self.clip = "clip_{}".format(self.clip_name) setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)) def tokenize_with_weights(self, text:str, return_word_ids=False): out = {} out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids) return out def untokenize(self, token_weight_pair): return getattr(self, self.clip).untokenize(token_weight_pair) def state_dict(self): return {} class SD1CheckpointClipModel(SDClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options) class SD1ClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs): super().__init__() if name is not None: self.clip_name = name self.clip = "{}".format(self.clip_name) else: self.clip_name = clip_name self.clip = "clip_{}".format(self.clip_name) setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs)) self.dtypes = set() if dtype is not None: self.dtypes.add(dtype) def set_clip_options(self, options): getattr(self, self.clip).set_clip_options(options) def reset_clip_options(self): getattr(self, self.clip).reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs = token_weight_pairs[self.clip_name] out = getattr(self, self.clip).encode_token_weights(token_weight_pairs) return out def load_sd(self, sd): return getattr(self, self.clip).load_sd(sd)