from __future__ import annotations import copy import importlib.resources import logging import numbers import os import re import traceback import zipfile from pathlib import Path from typing import Tuple, Sequence, TypeVar, Callable, Optional, Union import torch from transformers import CLIPTokenizer, PreTrainedTokenizerBase from . import clip_model from . import model_management from . import ops from .component_model import files from .component_model.files import get_path_as_dict, get_package_as_path from .text_encoders.spiece_tokenizer import SPieceTokenizer try: from importlib.resources.abc import Traversable # pylint: disable=no-name-in-module except ImportError: from importlib.abc import Traversable # pylint: disable=no-name-in-module, deprecated-class logger = logging.getLogger(__name__) 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 and hasattr(self, "special_tokens"): if hasattr(self, "gen_empty_tokens"): to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len)) else: to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) # pylint: disable=no-member assert hasattr(self, "encode") assert isinstance(self.encode, Callable) # pylint: disable=no-member o = self.encode(to_encode) # pylint: disable=no-member 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", "all" ] def __init__(self, device="cpu", max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config: str | dict | None = None, dtype=None, model_class=clip_model.CLIPTextModel, special_tokens=None, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, return_projected_pooled=True, return_attention_masks=False, model_options=None): # clip-vit-base-patch32 super().__init__() if model_options is None: model_options = {} if special_tokens is None: special_tokens = {"start": 49406, "end": 49407, "pad": 49407} assert layer in self.LAYERS if textmodel_json_config is None and "model_name" not in model_options: model_options = {**model_options, "model_name": "clip_l"} if "model_name" in model_options and "clip" not in model_options["model_name"].lower() and textmodel_json_config is None: _model_name = model_options['model_name'] logger.warning(f"Text encoder {_model_name} provided a None textmodel_json_config, when it should have been an empty dict") textmodel_json_config = {} config = get_path_as_dict(textmodel_json_config, "sd1_clip_config.json", package=__package__) te_model_options = model_options.get("{}_model_config".format(model_options.get("model_name", "")), {}) for k, v in te_model_options.items(): config[k] = v operations = model_options.get("custom_operations", None) quant_config = model_options.get("quantization_metadata", None) if operations is None: if quant_config is not None: operations = ops.mixed_precision_ops(quant_config, dtype, full_precision_mm=True) logging.info("Using MixedPrecisionOps for text encoder") else: operations = 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 self.execution_device = None 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) self.execution_device = options.get("execution_device", self.execution_device) if isinstance(self.layer, list) or self.layer == "all": pass elif 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] self.execution_device = None def process_tokens(self, tokens, device): end_token = self.special_tokens.get("end", None) if end_token is None: cmp_token = self.special_tokens.get("pad", -1) else: cmp_token = end_token embeds_out = [] attention_masks = [] num_tokens = [] for x in tokens: attention_mask = [] tokens_temp = [] other_embeds = [] eos = False index = 0 for y in x: if isinstance(y, numbers.Integral): if eos: attention_mask.append(0) else: attention_mask.append(1) token = int(y) tokens_temp += [token] if not eos and token == cmp_token: if end_token is None: attention_mask[-1] = 0 eos = True else: other_embeds.append((index, y)) index += 1 tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long) tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32) index = 0 pad_extra = 0 embeds_info = [] for o in other_embeds: emb = o[1] if torch.is_tensor(emb): emb = {"type": "embedding", "data": emb} extra = None emb_type = emb.get("type", None) if emb_type == "embedding": emb = emb.get("data", None) else: if hasattr(self.transformer, "preprocess_embed"): emb, extra = self.transformer.preprocess_embed(emb, device=device) else: emb = None if emb is None: index += -1 continue ind = index + o[0] emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32) emb_shape = emb.shape[1] if emb.shape[-1] == tokens_embed.shape[-1]: tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1) attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:] index += emb_shape - 1 embeds_info.append({"type": emb_type, "index": ind, "size": emb_shape, "extra": extra}) else: index += -1 pad_extra += emb_shape logger.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(emb.shape[-1], tokens_embed.shape[-1])) if pad_extra > 0: padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens["pad"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32) tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1) attention_mask = attention_mask + [0] * pad_extra embeds_out.append(tokens_embed) attention_masks.append(attention_mask) num_tokens.append(sum(attention_mask)) return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info def forward(self, tokens): if self.execution_device is None: device = self.transformer.get_input_embeddings().weight.device else: device = self.execution_device embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device) attention_mask_model = None if self.enable_attention_masks: attention_mask_model = attention_mask if isinstance(self.layer, list): intermediate_output = self.layer elif self.layer == "all": intermediate_output = "all" else: intermediate_output = self.layer_idx outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32, embeds_info=embeds_info) 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 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 def load_sd(self, sd): return self.transformer.load_state_dict(sd, strict=False) def parse_parentheses(string): 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): 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(":") 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): text = text.replace("\\)", "\0\1") text = text.replace("\\(", "\0\2") return text def unescape_important(text): 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): 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 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: # todo: improve this, so that it is more compatible between linux and windows 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: try: embed = torch.load(embed_path, weights_only=True, map_location="cpu") except: embed_out = safe_load_embed_zip(embed_path) except Exception: logger.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 SDTokenizerT = TypeVar('SDTokenizerT', bound='SDTokenizer') class SDTokenizer: def __init__(self, tokenizer_path: Optional[Union[torch.Tensor, bytes, bytearray, memoryview, str, Path, Traversable]] = 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, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, tokenizer_data=None, tokenizer_args=None): if tokenizer_data is None: tokenizer_data = dict() if tokenizer_args is None: tokenizer_args = dict() if tokenizer_path is None: tokenizer_path = files.get_package_as_path("comfy.sd1_tokenizer") if isinstance(tokenizer_path, Path): tokenizer_path = str(tokenizer_path) if isinstance(tokenizer_path, Traversable): contextlib_path = importlib.resources.as_file(tokenizer_path) tokenizer_path = contextlib_path.__enter__() if issubclass(tokenizer_class, CLIPTokenizer) and not os.path.exists(os.path.join(tokenizer_path, "tokenizer_config.json")): # assumes sd1_tokenizer tokenizer_path = get_package_as_path('comfy.sd1_tokenizer') self.tokenizer_class = tokenizer_class self.tokenizer_path = tokenizer_path self.tokenizer: PreTrainedTokenizerBase | SPieceTokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args) self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length) self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length) self.end_token = None self.min_padding = min_padding self.pad_left = pad_left empty = self.tokenizer('')["input_ids"] self.tokenizer_adds_end_token = has_end_token if has_start_token: self.tokens_start = 1 self.start_token = empty[0] if end_token is not None: self.end_token = end_token else: if has_end_token: self.end_token = empty[1] else: self.tokens_start = 0 self.start_token = None if end_token is not None: self.end_token = end_token else: if has_end_token: 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 self.additional_tokens: Tuple[str, ...] = () self.add_tokens([]) 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 clone(self) -> SDTokenizerT: sd_tokenizer = copy.copy(self) # correctly copy additional vocab sd_tokenizer.tokenizer = self.tokenizer_class.from_pretrained(self.tokenizer_path, legacy=True) sd_tokenizer.add_tokens(sd_tokenizer.additional_tokens) return sd_tokenizer def add_tokens(self, tokens: Sequence[str]): self.additional_tokens += tuple(tokens) if len(tokens) > 0: self.tokenizer.add_tokens(list(tokens)) vocab = self.tokenizer.get_vocab() self.inv_vocab = {v: k for k, v in vocab.items()} 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. ''' split_embed = embedding_name.split() embedding_name = split_embed[0] leftover = ' '.join(split_embed[1:]) 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, "{} {}".format(embedding_name[len(stripped):], leftover)) return (embed, leftover) def pad_tokens(self, tokens, amount): if self.pad_left: for i in range(amount): tokens.insert(0, (self.pad_token, 1.0, 0)) else: tokens.extend([(self.pad_token, 1.0, 0)] * amount) def tokenize_with_weights(self, text: str, return_word_ids=False, tokenizer_options={}, **kwargs): ''' 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 ''' min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length) min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding) text = escape_important(text) if kwargs.get("disable_weights", False): parsed_weights = [(text, 1.0)] else: parsed_weights = token_weights(text, 1.0) vocab = self.tokenizer.get_vocab() # tokenize words tokens = [] for weighted_segment, weight in parsed_weights: to_tokenize = unescape_important(weighted_segment) split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize) to_tokenize = [split[0]] for i in range(1, len(split)): to_tokenize.append("{}{}".format(self.embedding_identifier, split[i])) 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: logger.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 end = 999999999999 if self.tokenizer_adds_end_token: end = -1 # parse word exact_word = f"{word}" if hasattr(self.tokenizer, "eos_token") and word == self.tokenizer.eos_token: tokenizer_result = [self.tokenizer.eos_token_id] elif exact_word in vocab: tokenizer_result = [vocab[exact_word]] else: tokenizer_result = self.tokenizer(word)["input_ids"][self.tokens_start:end] tokens.append([(t, weight) for t in tokenizer_result]) # 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 if self.end_token is not None: has_end_token = 1 else: has_end_token = 0 while len(t_group) > 0: if len(t_group) + len(batch) > self.max_length - has_end_token: remaining_length = self.max_length - len(batch) - has_end_token # 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]]) if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) t_group = t_group[remaining_length:] # add end token and pad else: if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: self.pad_tokens(batch, 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 if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) if min_padding is not None: self.pad_tokens(batch, min_padding) if self.pad_to_max_length and len(batch) < self.max_length: self.pad_tokens(batch, self.max_length - len(batch)) if min_length is not None and len(batch) < min_length: self.pad_tokens(batch, 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 {} SD1TokenizerT = TypeVar("SD1TokenizerT", bound="SD1Tokenizer") class SD1Tokenizer: def __init__(self, embedding_directory=None, tokenizer_data: dict = None, clip_name="l", tokenizer=SDTokenizer, name=None): if tokenizer_data is None: tokenizer_data = {} 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) tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer) self.sd_tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) def tokenize_with_weights(self, text: str, return_word_ids=False, **kwargs): out = {} out[self.clip_name] = self.sd_tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return self.sd_tokenizer.untokenize(token_weight_pair) @property def sd_tokenizer(self) -> SDTokenizer: return getattr(self, self.clip) @sd_tokenizer.setter def sd_tokenizer(self, value): setattr(self, self.clip, value) def clone(self) -> SD1TokenizerT: sd1_tokenizer = copy.copy(self) sd1_tokenizer.sd_tokenizer = self.sd_tokenizer.clone() return sd1_tokenizer def state_dict(self): return getattr(self, self.clip).state_dict() class SD1CheckpointClipModel(SDClipModel): def __init__(self, device="cpu", dtype=None, model_options=None, textmodel_json_config=None): if model_options is None: model_options = {} super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options, textmodel_json_config=textmodel_json_config) class SD1ClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None, model_options=None, clip_name="l", clip_model=SD1CheckpointClipModel, textmodel_json_config=None, name=None, **kwargs): super().__init__() if model_options is None: model_options = {} 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) clip_model = model_options.get("{}_class".format(self.clip), clip_model) model_options = {**model_options, "model_name": self.clip} setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, textmodel_json_config=textmodel_json_config, **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)