ComfyUI/comfy/sd1_clip.py

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)