lpw.py

# Copyright 2022 The HuggingFace Inc. team.
# Original code obtained from: https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion.py
# Adapted for SDA-N

# Current Status: Prompt weighting works, although recently on ptsearch.info I've seen prompts using < >, TODO: maybe we could implement them one day
# TODO: Important! Long Prompts/Prompts Extension isn't working properly. When extended, it provides a 154 tensor, and outputs an unrelated image, and
# sometimes an image only containing the last 5 - 8 words.

import re
from typing import List, Optional, Union

import torch
import oneflow

from diffusers import StableDiffusionPipeline
from diffusers.utils import logging
from transformers import CLIPTextModel, CLIPTokenizer

# ------------------------------------------------------------------------------

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

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: StableDiffusionPipeline, 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, 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] + [eos] * (max_length - 1 - len(tokens[i]))
        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 get_unweighted_text_embeddings(
    pipe: StableDiffusionPipeline,
    text_input: torch.Tensor,
    chunk_length: int,
    no_boseos_middle: Optional[bool] = True,
):
    """
    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]
            #print(f"Round: {i} - ", text_input_chunk.device)
            print(type(text_input_chunk))
            text_embedding = pipe.text_encoder(text_input_chunk)[0]
            
            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 = pipe.text_encoder(text_input)[0]
    return text_embeddings


def get_weighted_text_embeddings(
    pipe: StableDiffusionPipeline,
    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,
    **kwargs,
):
    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 (`StableDiffusionPipeline`):
            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
    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) * pipe.max_embeddings_multiples + 2

    # pad the length of tokens and weights
    bos = pipe.tokenizer.bos_token_id
    eos = pipe.tokenizer.eos_token_id
    prompt_tokens, prompt_weights = pad_tokens_and_weights(
        prompt_tokens,
        prompt_weights,
        max_length,
        bos,
        eos,
        no_boseos_middle=no_boseos_middle,
        chunk_length=pipe.tokenizer.model_max_length,
    )
    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,
            no_boseos_middle=no_boseos_middle,
            chunk_length=pipe.tokenizer.model_max_length,
        )
        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,
    )
    prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.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,
        )
        uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.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
    return text_embeddings, None

class LongPromptWeightingPipeline():
    r"""
    Slightly modified pipeline to only obtain the text embeddings. Has the same capabilities of the LPW.
    Amongst these capabilities are: weighting by using () or [], and extended token limits.

    TODO: Use the accelerated inference provided by TensorRT

    Args:
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
    """

    def __init__(
        self,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        max_embeddings_multiples: int,
    ):
        self.max_embeddings_multiples = max_embeddings_multiples
        self.text_encoder = text_encoder
        self.tokenizer = tokenizer
        self.device = torch.device("cuda")
        
    @property
    def _execution_device(self):
        return torch.device("cuda")

    def _encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt
    ):
        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.
        """
        max_embeddings_multiples = self.max_embeddings_multiples
        batch_size = len(prompt) if isinstance(prompt, list) else 1

        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`."
            )

        text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
            pipe=self,
            prompt=prompt,
            uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
            max_embeddings_multiples=max_embeddings_multiples,
        )
        bs_embed, seq_len, _ = text_embeddings.shape
        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)

        if do_classifier_free_guidance:
            bs_embed, seq_len, _ = uncond_embeddings.shape
            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
            uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
            text_embeddings = text_embeddings.to("cpu")

            text_embeddings = text_embeddings.numpy()
            text_embeddings = oneflow.from_numpy(text_embeddings)
            text_embeddings = text_embeddings.to("cuda")
            text_embeddings = text_embeddings.to(oneflow.float16)
            print(type(text_embeddings))
        return text_embeddings

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        negative_prompt: Optional[Union[str, List[str]]] = None,
        guidance_scale: float = 7.5,
        num_images_per_prompt: Optional[int] = 1,
        **kwargs,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            negative_prompt (`str` or `List[str]`, *optional*):
                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`).
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
                The max multiple length of prompt embeddings compared to the max output length of text encoder.

        Returns:
            `None` if cancelled by `is_cancelled_callback`,
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        # 2. Define call parameters
        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        ### HERE ###

        # 3. Encode input prompt
        text_embeddings = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt
        )
        dtype = text_embeddings.dtype

        return text_embeddings