modules/encoder_prompt_modify.py

import re
import math
import numpy as np
import torch
from diffusers import DiffusionPipeline
from typing import Any, Callable, Dict, List, Optional, Union
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.utils import (
	USE_PEFT_BACKEND,
    deprecate,
    logging,
    replace_example_docstring,
    scale_lora_layers,
    unscale_lora_layers,
)
from .prompt_parser import FrozenCLIPEmbedderWithCustomWords



re_attention = re.compile(
    r"""
\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""",
    re.X,
)


def parse_prompt_attention(text):
    """
    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
    Accepted tokens are:
      (abc) - increases attention to abc by a multiplier of 1.1
      (abc:3.12) - increases attention to abc by a multiplier of 3.12
      [abc] - decreases attention to abc by a multiplier of 1.1
      \\( - literal character '('
      \\[ - literal character '['
      \\) - literal character ')'
      \\] - literal character ']'
      \\ - literal character '\'
      anything else - just text
    >>> parse_prompt_attention('normal text')
    [['normal text', 1.0]]
    >>> parse_prompt_attention('an (important) word')
    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
    >>> parse_prompt_attention('(unbalanced')
    [['unbalanced', 1.1]]
    >>> parse_prompt_attention('\\(literal\\]')
    [['(literal]', 1.0]]
    >>> parse_prompt_attention('(unnecessary)(parens)')
    [['unnecessaryparens', 1.1]]
    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
    [['a ', 1.0],
     ['house', 1.5730000000000004],
     [' ', 1.1],
     ['on', 1.0],
     [' a ', 1.1],
     ['hill', 0.55],
     [', sun, ', 1.1],
     ['sky', 1.4641000000000006],
     ['.', 1.1]]
    """

    res = []
    round_brackets = []
    square_brackets = []

    round_bracket_multiplier = 1.1
    square_bracket_multiplier = 1 / 1.1

    def multiply_range(start_position, multiplier):
        for p in range(start_position, len(res)):
            res[p][1] *= multiplier

    for m in re_attention.finditer(text):
        text = m.group(0)
        weight = m.group(1)

        if text.startswith("\\"):
            res.append([text[1:], 1.0])
        elif text == "(":
            round_brackets.append(len(res))
        elif text == "[":
            square_brackets.append(len(res))
        elif weight is not None and len(round_brackets) > 0:
            multiply_range(round_brackets.pop(), float(weight))
        elif text == ")" and len(round_brackets) > 0:
            multiply_range(round_brackets.pop(), round_bracket_multiplier)
        elif text == "]" and len(square_brackets) > 0:
            multiply_range(square_brackets.pop(), square_bracket_multiplier)
        else:
            res.append([text, 1.0])

    for pos in round_brackets:
        multiply_range(pos, round_bracket_multiplier)

    for pos in square_brackets:
        multiply_range(pos, square_bracket_multiplier)

    if len(res) == 0:
        res = [["", 1.0]]

    # merge runs of identical weights
    i = 0
    while i + 1 < len(res):
        if res[i][1] == res[i + 1][1]:
            res[i][0] += res[i + 1][0]
            res.pop(i + 1)
        else:
            i += 1

    return res


def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str], max_length: int):
    r"""
    Tokenize a list of prompts and return its tokens with weights of each token.

    No padding, starting or ending token is included.
    """
    tokens = []
    weights = []
    truncated = False
    for text in prompt:
        texts_and_weights = parse_prompt_attention(text)
        text_token = []
        text_weight = []
        for word, weight in texts_and_weights:
            # tokenize and discard the starting and the ending token
            token = pipe.tokenizer(word).input_ids[1:-1]
            text_token += token
            # copy the weight by length of token
            text_weight += [weight] * len(token)
            # stop if the text is too long (longer than truncation limit)
            if len(text_token) > max_length:
                truncated = True
                break
        # truncate
        if len(text_token) > max_length:
            truncated = True
            text_token = text_token[:max_length]
            text_weight = text_weight[:max_length]
        tokens.append(text_token)
        weights.append(text_weight)
    if truncated:
        logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
    return tokens, weights


def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):
    r"""
    Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
    """
    max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
    weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
    for i in range(len(tokens)):
        tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
        if no_boseos_middle:
            weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
        else:
            w = []
            if len(weights[i]) == 0:
                w = [1.0] * weights_length
            else:
                for j in range(max_embeddings_multiples):
                    w.append(1.0)  # weight for starting token in this chunk
                    w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
                    w.append(1.0)  # weight for ending token in this chunk
                w += [1.0] * (weights_length - len(w))
            weights[i] = w[:]

    return tokens, weights

def clip_skip_prompt(
	pipe,
	text_input,
	clip_skip =  None,
):
    if hasattr(pipe.text_encoder.config, "use_attention_mask") and pipe.text_encoder.config.use_attention_mask:
        attention_mask = text_inputs.attention_mask.to(device)
    else:
        attention_mask = None
    if clip_skip is not None and  clip_skip > 1:
        text_embedding = pipe.text_encoder(text_input, attention_mask=attention_mask, output_hidden_states=True)
    	# Access the `hidden_states` first, that contains a tuple of
        # all the hidden states from the encoder layers. Then index into
        # the tuple to access the hidden states from the desired layer.
        text_embedding = text_embedding[-1][-clip_skip]
        # We also need to apply the final LayerNorm here to not mess with the
        # representations. The `last_hidden_states` that we typically use for
        # obtaining the final prompt representations passes through the LayerNorm
        # layer.
        text_embedding = pipe.text_encoder.text_model.final_layer_norm(text_embedding)
    else:
        text_embedding = pipe.text_encoder(text_input, attention_mask=attention_mask)
        text_embedding = text_embedding[0]

    return text_embedding

def get_unweighted_text_embeddings(
    pipe: DiffusionPipeline,
    text_input: torch.Tensor,
    chunk_length: int,
    no_boseos_middle: Optional[bool] = True,
    clip_skip : Optional[int] = None,
):
    """
    When the length of tokens is a multiple of the capacity of the text encoder,
    it should be split into chunks and sent to the text encoder individually.
    """
    max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
    if max_embeddings_multiples > 1:
        text_embeddings = []
        for i in range(max_embeddings_multiples):
            # extract the i-th chunk
            text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()

            # cover the head and the tail by the starting and the ending tokens
            text_input_chunk[:, 0] = text_input[0, 0]
            text_input_chunk[:, -1] = text_input[0, -1]

            text_embedding = clip_skip_prompt(pipe,text_input_chunk,clip_skip)

            if no_boseos_middle:
                if i == 0:
                    # discard the ending token
                    text_embedding = text_embedding[:, :-1]
                elif i == max_embeddings_multiples - 1:
                    # discard the starting token
                    text_embedding = text_embedding[:, 1:]
                else:
                    # discard both starting and ending tokens
                    text_embedding = text_embedding[:, 1:-1]

            text_embeddings.append(text_embedding)
        text_embeddings = torch.concat(text_embeddings, axis=1)
    else:
    	text_embeddings = clip_skip_prompt(pipe,text_input,clip_skip)
    return text_embeddings


def get_weighted_text_embeddings(
    pipe: DiffusionPipeline,
    prompt: Union[str, List[str]],
    uncond_prompt: Optional[Union[str, List[str]]] = None,
    max_embeddings_multiples: Optional[int] = 3,
    no_boseos_middle: Optional[bool] = False,
    skip_parsing: Optional[bool] = False,
    skip_weighting: Optional[bool] = False,
    clip_skip : Optional[int] = None,
):
    r"""
    Prompts can be assigned with local weights using brackets. For example,
    prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.

    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.

    Args:
        pipe (`DiffusionPipeline`):
            Pipe to provide access to the tokenizer and the text encoder.
        prompt (`str` or `List[str]`):
            The prompt or prompts to guide the image generation.
        uncond_prompt (`str` or `List[str]`):
            The unconditional prompt or prompts for guide the image generation. If unconditional prompt
            is provided, the embeddings of prompt and uncond_prompt are concatenated.
        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
            The max multiple length of prompt embeddings compared to the max output length of text encoder.
        no_boseos_middle (`bool`, *optional*, defaults to `False`):
            If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
            ending token in each of the chunk in the middle.
        skip_parsing (`bool`, *optional*, defaults to `False`):
            Skip the parsing of brackets.
        skip_weighting (`bool`, *optional*, defaults to `False`):
            Skip the weighting. When the parsing is skipped, it is forced True.
    """
    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
    prompt_tokens_id = None
    negative_prompt_tokens_id = None
    if isinstance(prompt, str):
        prompt = [prompt]

    if not skip_parsing:
        prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
        if uncond_prompt is not None:
            if isinstance(uncond_prompt, str):
                uncond_prompt = [uncond_prompt]
            uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
    else:
        prompt_tokens = [
            token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids
        ]
        prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
        if uncond_prompt is not None:
            if isinstance(uncond_prompt, str):
                uncond_prompt = [uncond_prompt]
            uncond_tokens = [
                token[1:-1]
                for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids
            ]
            uncond_weights = [[1.0] * len(token) for token in uncond_tokens]

    # round up the longest length of tokens to a multiple of (model_max_length - 2)
    max_length = max([len(token) for token in prompt_tokens])
    if uncond_prompt is not None:
        max_length = max(max_length, max([len(token) for token in uncond_tokens]))

    max_embeddings_multiples = min(
        max_embeddings_multiples,
        (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
    )
    max_embeddings_multiples = max(1, max_embeddings_multiples)
    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2

    # pad the length of tokens and weights
    bos = pipe.tokenizer.bos_token_id
    eos = pipe.tokenizer.eos_token_id
    pad = getattr(pipe.tokenizer, "pad_token_id", eos)
    prompt_tokens, prompt_weights = pad_tokens_and_weights(
        prompt_tokens,
        prompt_weights,
        max_length,
        bos,
        eos,
        pad,
        no_boseos_middle=no_boseos_middle,
        chunk_length=pipe.tokenizer.model_max_length,
    )

    prompt_tokens_id = np.array(prompt_tokens, dtype=np.int64)
    prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)
    if uncond_prompt is not None:
        uncond_tokens, uncond_weights = pad_tokens_and_weights(
            uncond_tokens,
            uncond_weights,
            max_length,
            bos,
            eos,
            pad,
            no_boseos_middle=no_boseos_middle,
            chunk_length=pipe.tokenizer.model_max_length,
        )
        negative_prompt_tokens_id = np.array(uncond_tokens, dtype=np.int64)
        uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)

    # get the embeddings
    text_embeddings = get_unweighted_text_embeddings(
        pipe,
        prompt_tokens,
        pipe.tokenizer.model_max_length,
        no_boseos_middle=no_boseos_middle,
        clip_skip = clip_skip,
    )
    prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=text_embeddings.device)
    if uncond_prompt is not None:
        uncond_embeddings = get_unweighted_text_embeddings(
            pipe,
            uncond_tokens,
            pipe.tokenizer.model_max_length,
            no_boseos_middle=no_boseos_middle,
            clip_skip = clip_skip,
        )
        uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=uncond_embeddings.device)

    # assign weights to the prompts and normalize in the sense of mean
    # TODO: should we normalize by chunk or in a whole (current implementation)?
    if (not skip_parsing) and (not skip_weighting):
        previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
        text_embeddings *= prompt_weights.unsqueeze(-1)
        current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
        text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
        if uncond_prompt is not None:
            previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
            uncond_embeddings *= uncond_weights.unsqueeze(-1)
            current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
            uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)

    if uncond_prompt is not None:
        return text_embeddings, uncond_embeddings, negative_prompt_tokens_id, prompt_tokens_id
    return text_embeddings, None, None, prompt_tokens_id


def encoder_long_prompt(
    pipe,
    prompt,
    device,
    num_images_per_prompt,
    do_classifier_free_guidance,
    negative_prompt=None,
    prompt_embeds: Optional[torch.Tensor] = None,
    negative_prompt_embeds: Optional[torch.Tensor] = None,
    lora_scale: Optional[float] = None,
    clip_skip : Optional[int] = None,
    max_embeddings_multiples: Optional[int] = 3,
):
    r"""
    Encodes the prompt into text encoder hidden states.

    Args:
        prompt (`str` or `list(int)`):
            prompt to be encoded
        device: (`torch.device`):
            torch device
        num_images_per_prompt (`int`):
            number of images that should be generated per prompt
        do_classifier_free_guidance (`bool`):
            whether to use classifier free guidance or not
        negative_prompt (`str` or `List[str]`):
            The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
            if `guidance_scale` is less than `1`).
        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
            The max multiple length of prompt embeddings compared to the max output length of text encoder.
    """

    # set lora scale so that monkey patched LoRA
    # function of text encoder can correctly access it
    if lora_scale is not None and isinstance(pipe, LoraLoaderMixin):
        pipe._lora_scale = lora_scale
        # dynamically adjust the LoRA scale
        if not USE_PEFT_BACKEND:
            adjust_lora_scale_text_encoder(pipe.text_encoder, lora_scale)
        else:
            scale_lora_layers(pipe.text_encoder, lora_scale)
    if prompt is not None and isinstance(prompt, str):
        batch_size = 1
    elif prompt is not None and isinstance(prompt, list):
        batch_size = len(prompt)
    else:
        batch_size = prompt_embeds.shape[0]

    negative_prompt_tokens_id, prompt_tokens_id = None, None
    if negative_prompt_embeds is None:
        if negative_prompt is None:
            negative_prompt = [""] * batch_size
        elif isinstance(negative_prompt, str):
            negative_prompt = [negative_prompt] * batch_size
        if batch_size != len(negative_prompt):
            raise ValueError(
                f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                " the batch size of `prompt`."
            )
    if prompt_embeds is None or negative_prompt_embeds is None:
        if isinstance(pipe, TextualInversionLoaderMixin):
            prompt = pipe.maybe_convert_prompt(prompt, pipe.tokenizer)
            if do_classifier_free_guidance and negative_prompt_embeds is None:
                negative_prompt = pipe.maybe_convert_prompt(negative_prompt, pipe.tokenizer)

        prompt_embeds1, negative_prompt_embeds1, negative_prompt_tokens_id, prompt_tokens_id = get_weighted_text_embeddings(
            pipe=pipe,
            prompt=prompt,
            uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
            max_embeddings_multiples=int(max_embeddings_multiples),
            clip_skip = clip_skip,
        )
        if prompt_embeds is None:
            prompt_embeds = prompt_embeds1
        if negative_prompt_embeds is None:
            negative_prompt_embeds = negative_prompt_embeds1

    bs_embed, seq_len, _ = prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
    prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

    if do_classifier_free_guidance:
        bs_embed, seq_len, _ = negative_prompt_embeds.shape
        negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
        negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

    if isinstance(pipe, LoraLoaderMixin) and USE_PEFT_BACKEND:
        # Retrieve the original scale by scaling back the LoRA layers
        unscale_lora_layers(pipe.text_encoder, lora_scale)

    return  prompt_embeds, negative_prompt_embeds, [negative_prompt_tokens_id, prompt_tokens_id]




def encode_short_prompt(
    pipe,
    prompt,
    device,
    num_images_per_prompt,
    do_classifier_free_guidance,
    negative_prompt=None,
    prompt_embeds: Optional[torch.Tensor] = None,
    negative_prompt_embeds: Optional[torch.Tensor] = None,
    lora_scale: Optional[float] = None,
    clip_skip: Optional[int] = None,
):
    r"""
    Encodes the prompt into text encoder hidden states.

    Args:
        prompt (`str` or `List[str]`, *optional*):
            prompt to be encoded
        device: (`torch.device`):
            torch device
        num_images_per_prompt (`int`):
            number of images that should be generated per prompt
        do_classifier_free_guidance (`bool`):
            whether to use classifier free guidance or not
        negative_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts not to guide the image generation. If not defined, one has to pass
            `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
            less than `1`).
        prompt_embeds (`torch.Tensor`, *optional*):
            Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
            provided, text embeddings will be generated from `prompt` input argument.
        negative_prompt_embeds (`torch.Tensor`, *optional*):
            Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
            weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
            argument.
        lora_scale (`float`, *optional*):
            A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
        clip_skip (`int`, *optional*):
            Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
            the output of the pre-final layer will be used for computing the prompt embeddings.
    """
    # set lora scale so that monkey patched LoRA
    # function of text encoder can correctly access it
    if lora_scale is not None and isinstance(pipe, LoraLoaderMixin):
        pipe._lora_scale = lora_scale

        # dynamically adjust the LoRA scale
        if not USE_PEFT_BACKEND:
            adjust_lora_scale_text_encoder(pipe.text_encoder, lora_scale)
        else:
            scale_lora_layers(pipe.text_encoder, lora_scale)

    if prompt is not None and isinstance(prompt, str):
        batch_size = 1
    elif prompt is not None and isinstance(prompt, list):
        batch_size = len(prompt)
    else:
        batch_size = prompt_embeds.shape[0]

    prompt_tokens_id = None
    negative_prompt_tokens_id = None

    if prompt_embeds is None:
        # textual inversion: process multi-vector tokens if necessary
        if isinstance(pipe, TextualInversionLoaderMixin):
            prompt = pipe.maybe_convert_prompt(prompt, pipe.tokenizer)

        text_inputs = pipe.tokenizer(
            prompt,
            padding="max_length",
            max_length=pipe.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids
        prompt_tokens_id = text_inputs.input_ids.detach().cpu().numpy()
        untruncated_ids = pipe.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
            text_input_ids, untruncated_ids
        ):
            removed_text = pipe.tokenizer.batch_decode(
                untruncated_ids[:, pipe.tokenizer.model_max_length - 1 : -1]
            )
            logger.warning(
                "The following part of your input was truncated because CLIP can only handle sequences up to"
                f" {pipe.tokenizer.model_max_length} tokens: {removed_text}"
            )

        if hasattr(pipe.text_encoder.config, "use_attention_mask") and pipe.text_encoder.config.use_attention_mask:
            attention_mask = text_inputs.attention_mask.to(device)
        else:
            attention_mask = None

        if clip_skip is not None and  clip_skip > 1:
            prompt_embeds = pipe.text_encoder(
                text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
            )
            # Access the `hidden_states` first, that contains a tuple of
            # all the hidden states from the encoder layers. Then index into
            # the tuple to access the hidden states from the desired layer.
            prompt_embeds = prompt_embeds[-1][-clip_skip]
            # We also need to apply the final LayerNorm here to not mess with the
            # representations. The `last_hidden_states` that we typically use for
            # obtaining the final prompt representations passes through the LayerNorm
            # layer.
            prompt_embeds = pipe.text_encoder.text_model.final_layer_norm(prompt_embeds)
        else:
            prompt_embeds = pipe.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
            prompt_embeds = prompt_embeds[0]              

    if pipe.text_encoder is not None:
        prompt_embeds_dtype = pipe.text_encoder.dtype
    elif pipe.unet is not None:
        prompt_embeds_dtype = pipe.unet.dtype
    else:
        prompt_embeds_dtype = prompt_embeds.dtype

    prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

    bs_embed, seq_len, _ = prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
    prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance and negative_prompt_embeds is None:
        uncond_tokens: List[str]
        if negative_prompt is None:
            uncond_tokens = [""] * batch_size
        elif prompt is not None and type(prompt) is not type(negative_prompt):
            raise TypeError(
                f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                f" {type(prompt)}."
            )
        elif isinstance(negative_prompt, str):
            uncond_tokens = [negative_prompt]
        elif batch_size != len(negative_prompt):
            raise ValueError(
                f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                " the batch size of `prompt`."
            )
        else:
            uncond_tokens = negative_prompt

        # textual inversion: process multi-vector tokens if necessary
        if isinstance(pipe, TextualInversionLoaderMixin):
            uncond_tokens = pipe.maybe_convert_prompt(uncond_tokens, pipe.tokenizer)

        max_length = prompt_embeds.shape[1]
        uncond_input = pipe.tokenizer(
            uncond_tokens,
            padding="max_length",
            max_length=max_length,
            truncation=True,
            return_tensors="pt",
        )
        negative_prompt_tokens_id = uncond_input.input_ids.detach().cpu().numpy()

        if hasattr(pipe.text_encoder.config, "use_attention_mask") and pipe.text_encoder.config.use_attention_mask:
            attention_mask = uncond_input.attention_mask.to(device)
        else:
            attention_mask = None

        if clip_skip is not None and  clip_skip > 1:
            negative_prompt_embeds = pipe.text_encoder(
                uncond_input.input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
            )
            # Access the `hidden_states` first, that contains a tuple of
            # all the hidden states from the encoder layers. Then index into
            # the tuple to access the hidden states from the desired layer.
            negative_prompt_embeds = negative_prompt_embeds[-1][-clip_skip ]
            # We also need to apply the final LayerNorm here to not mess with the
            # representations. The `last_hidden_states` that we typically use for
            # obtaining the final prompt representations passes through the LayerNorm
            # layer.
            negative_prompt_embeds = pipe.text_encoder.text_model.final_layer_norm(negative_prompt_embeds)
        else:
            negative_prompt_embeds = pipe.text_encoder(uncond_input.input_ids.to(device), attention_mask=attention_mask)
            negative_prompt_embeds = negative_prompt_embeds[0]

    if do_classifier_free_guidance:
        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        seq_len = negative_prompt_embeds.shape[1]

        negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

        negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
        negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

    if isinstance(pipe, LoraLoaderMixin) and USE_PEFT_BACKEND:
        # Retrieve the original scale by scaling back the LoRA layers
        unscale_lora_layers(pipe.text_encoder, lora_scale)

    return prompt_embeds, negative_prompt_embeds, [negative_prompt_tokens_id, prompt_tokens_id]



def encode_prompt_automatic1111(
    pipe,
    prompt,
    device,
    num_images_per_prompt,
    do_classifier_free_guidance,
    negative_prompt=None,
    prompt_embeds: Optional[torch.Tensor] = None,
    negative_prompt_embeds: Optional[torch.Tensor] = None,
    lora_scale: Optional[float] = None,
    clip_skip: Optional[int] = None,
):
    if lora_scale is not None and isinstance(pipe, LoraLoaderMixin):
        pipe._lora_scale = lora_scale

        # dynamically adjust the LoRA scale
        if not USE_PEFT_BACKEND:
            adjust_lora_scale_text_encoder(pipe.text_encoder, lora_scale)
        else:
            scale_lora_layers(pipe.text_encoder, lora_scale)

    if prompt is not None and isinstance(prompt, str):
        batch_size = 1
    elif prompt is not None and isinstance(prompt, list):
        batch_size = len(prompt)
    else:
        batch_size = prompt_embeds.shape[0]

    prompt_tokens_id = None
    negative_prompt_tokens_id = None

            
    # get unconditional embeddings for classifier free guidance
    uncond_tokens = []
    if do_classifier_free_guidance and negative_prompt_embeds is None:
        if negative_prompt is None:
            uncond_tokens = [""] * batch_size
        elif prompt is not None and type(prompt) is not type(negative_prompt):
            raise TypeError(
                f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                f" {type(prompt)}."
            )
        elif isinstance(negative_prompt, str):
            uncond_tokens = [negative_prompt] + [""] * (batch_size - 1)
        elif batch_size != len(negative_prompt):
            raise ValueError(
                f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                " the batch size of `prompt`."
            )
        else:
            uncond_tokens = negative_prompt

        # textual inversion: process multi-vector tokens if necessary
        if isinstance(pipe, TextualInversionLoaderMixin):
            uncond_tokens = pipe.maybe_convert_prompt(uncond_tokens, pipe.tokenizer)
    if len(uncond_tokens) == 0:
        uncond_tokens = [""]* batch_size
        # textual inversion: process multi-vector tokens if necessary
        if isinstance(pipe, TextualInversionLoaderMixin):
            uncond_tokens = pipe.maybe_convert_prompt(uncond_tokens, pipe.tokenizer)

    if prompt_embeds is None:
        if not isinstance(prompt,list):
            prompt = [prompt]
        # textual inversion: process multi-vector tokens if necessary
        if isinstance(pipe, TextualInversionLoaderMixin):
            prompt = pipe.maybe_convert_prompt(prompt, pipe.tokenizer)

    prompt_parser = FrozenCLIPEmbedderWithCustomWords(pipe.tokenizer, pipe.text_encoder,clip_skip)
    prompt_embeds_lst = []
    negative_prompt_embeds_lst =[]
    negative_prompt_tokens_id_lst =[]
    prompt_tokens_id_lst =[]
    for i in range(0,batch_size):
        text_ids, text_embeddings = prompt_parser([uncond_tokens[i], prompt[i]])
        negative_prompt_embeddings, prompt_embeddings = torch.chunk(text_embeddings, 2, dim=0)
        text_ids = np.split(text_ids,text_ids.shape[0])
        negative_prompt_embeddings_id, prompt_embeddings_id = text_ids[0], text_ids[1]
        prompt_embeds_lst.append(prompt_embeddings)
        negative_prompt_embeds_lst.append(negative_prompt_embeddings)
        negative_prompt_tokens_id_lst.append(negative_prompt_embeddings_id)
        prompt_tokens_id_lst.append(prompt_embeddings_id)

    if prompt_embeds is None:
        prompt_embeds = torch.cat(prompt_embeds_lst)
        prompt_tokens_id = np.concatenate(prompt_tokens_id_lst)
    if do_classifier_free_guidance and negative_prompt_embeds is None: 
        negative_prompt_embeds = torch.cat(negative_prompt_embeds_lst)
        negative_prompt_tokens_id = np.concatenate(negative_prompt_tokens_id_lst)
    
    if pipe.text_encoder is not None:
        prompt_embeds_dtype = pipe.text_encoder.dtype
    elif pipe.unet is not None:
        prompt_embeds_dtype = pipe.unet.dtype
    else:
        prompt_embeds_dtype = prompt_embeds.dtype

    prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

    bs_embed, seq_len, _ = prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
    prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

    if do_classifier_free_guidance:
        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        seq_len = negative_prompt_embeds.shape[1]

        negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

        negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
        negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

    if isinstance(pipe, LoraLoaderMixin) and USE_PEFT_BACKEND:
        # Retrieve the original scale by scaling back the LoRA layers
        unscale_lora_layers(pipe.text_encoder, lora_scale)

    return prompt_embeds, negative_prompt_embeds, [negative_prompt_tokens_id, prompt_tokens_id]




def encode_prompt_function(
    pipe,
    prompt,
    device,
    num_images_per_prompt,
    do_classifier_free_guidance,
    negative_prompt=None,
    prompt_embeds: Optional[torch.Tensor] = None,
    negative_prompt_embeds: Optional[torch.Tensor] = None,
    lora_scale: Optional[float] = None,
    clip_skip: Optional[int] = None,
    long_encode: Optional[bool] = False,
):
    if long_encode == 0:
        return encode_prompt_automatic1111(pipe, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds, negative_prompt_embeds, lora_scale, clip_skip)
    elif long_encode == 1:
    	return encoder_long_prompt(pipe, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds, negative_prompt_embeds, lora_scale, clip_skip)
    return encode_short_prompt(pipe, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds, negative_prompt_embeds, lora_scale, clip_skip)