diffusers/src/diffusers/loaders.py

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from collections import defaultdict
from typing import Callable, Dict, List, Optional, Union

import torch

from .models.attention_processor import LoRAAttnProcessor
from .utils import (
    DIFFUSERS_CACHE,
    HF_HUB_OFFLINE,
    _get_model_file,
    deprecate,
    is_safetensors_available,
    is_transformers_available,
    logging,
)


if is_safetensors_available():
    import safetensors

if is_transformers_available():
    from transformers import PreTrainedModel, PreTrainedTokenizer


logger = logging.get_logger(__name__)


LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"
LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"

TEXT_INVERSION_NAME = "learned_embeds.bin"
TEXT_INVERSION_NAME_SAFE = "learned_embeds.safetensors"


class AttnProcsLayers(torch.nn.Module):
    def __init__(self, state_dict: Dict[str, torch.Tensor]):
        super().__init__()
        self.layers = torch.nn.ModuleList(state_dict.values())
        self.mapping = dict(enumerate(state_dict.keys()))
        self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}

        # we add a hook to state_dict() and load_state_dict() so that the
        # naming fits with `unet.attn_processors`
        def map_to(module, state_dict, *args, **kwargs):
            new_state_dict = {}
            for key, value in state_dict.items():
                num = int(key.split(".")[1])  # 0 is always "layers"
                new_key = key.replace(f"layers.{num}", module.mapping[num])
                new_state_dict[new_key] = value

            return new_state_dict

        def map_from(module, state_dict, *args, **kwargs):
            all_keys = list(state_dict.keys())
            for key in all_keys:
                replace_key = key.split(".processor")[0] + ".processor"
                new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}")
                state_dict[new_key] = state_dict[key]
                del state_dict[key]

        self._register_state_dict_hook(map_to)
        self._register_load_state_dict_pre_hook(map_from, with_module=True)


class UNet2DConditionLoadersMixin:
    def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
        r"""
        Load pretrained attention processor layers into `UNet2DConditionModel`. Attention processor layers have to be
        defined in
        [cross_attention.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py)
        and be a `torch.nn.Module` class.

        <Tip warning={true}>

            This function is experimental and might change in the future.

        </Tip>

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                      Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
                    - A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
                      `./my_model_directory/`.
                    - A [torch state
                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether or not to only look at local files (i.e., do not try to download the model).
            use_auth_token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
                when running `diffusers-cli login` (stored in `~/.huggingface`).
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo (either remote in
                huggingface.co or downloaded locally), you can specify the folder name here.

            mirror (`str`, *optional*):
                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
                Please refer to the mirror site for more information.

        <Tip>

         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
         models](https://huggingface.co/docs/hub/models-gated#gated-models).

        </Tip>
        """

        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", None)
        weight_name = kwargs.pop("weight_name", None)
        use_safetensors = kwargs.pop("use_safetensors", None)

        if use_safetensors and not is_safetensors_available():
            raise ValueError(
                "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"
            )

        allow_pickle = False
        if use_safetensors is None:
            use_safetensors = is_safetensors_available()
            allow_pickle = True

        user_agent = {
            "file_type": "attn_procs_weights",
            "framework": "pytorch",
        }

        model_file = None
        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
            # Let's first try to load .safetensors weights
            if (use_safetensors and weight_name is None) or (
                weight_name is not None and weight_name.endswith(".safetensors")
            ):
                try:
                    model_file = _get_model_file(
                        pretrained_model_name_or_path_or_dict,
                        weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
                        cache_dir=cache_dir,
                        force_download=force_download,
                        resume_download=resume_download,
                        proxies=proxies,
                        local_files_only=local_files_only,
                        use_auth_token=use_auth_token,
                        revision=revision,
                        subfolder=subfolder,
                        user_agent=user_agent,
                    )
                    state_dict = safetensors.torch.load_file(model_file, device="cpu")
                except IOError as e:
                    if not allow_pickle:
                        raise e
                    # try loading non-safetensors weights
                    pass
            if model_file is None:
                model_file = _get_model_file(
                    pretrained_model_name_or_path_or_dict,
                    weights_name=weight_name or LORA_WEIGHT_NAME,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    use_auth_token=use_auth_token,
                    revision=revision,
                    subfolder=subfolder,
                    user_agent=user_agent,
                )
                state_dict = torch.load(model_file, map_location="cpu")
        else:
            state_dict = pretrained_model_name_or_path_or_dict

        # fill attn processors
        attn_processors = {}

        is_lora = all("lora" in k for k in state_dict.keys())

        if is_lora:
            lora_grouped_dict = defaultdict(dict)
            for key, value in state_dict.items():
                attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])
                lora_grouped_dict[attn_processor_key][sub_key] = value

            for key, value_dict in lora_grouped_dict.items():
                rank = value_dict["to_k_lora.down.weight"].shape[0]
                cross_attention_dim = value_dict["to_k_lora.down.weight"].shape[1]
                hidden_size = value_dict["to_k_lora.up.weight"].shape[0]

                attn_processors[key] = LoRAAttnProcessor(
                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank
                )
                attn_processors[key].load_state_dict(value_dict)

        else:
            raise ValueError(f"{model_file} does not seem to be in the correct format expected by LoRA training.")

        # set correct dtype & device
        attn_processors = {k: v.to(device=self.device, dtype=self.dtype) for k, v in attn_processors.items()}

        # set layers
        self.set_attn_processor(attn_processors)

    def save_attn_procs(
        self,
        save_directory: Union[str, os.PathLike],
        is_main_process: bool = True,
        weight_name: str = None,
        save_function: Callable = None,
        safe_serialization: bool = False,
        **kwargs,
    ):
        r"""
        Save an attention processor to a directory, so that it can be re-loaded using the
        `[`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`]` method.

        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to which to save. Will be created if it doesn't exist.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful when in distributed training like
                TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
                the main process to avoid race conditions.
            save_function (`Callable`):
                The function to use to save the state dictionary. Useful on distributed training like TPUs when one
                need to replace `torch.save` by another method. Can be configured with the environment variable
                `DIFFUSERS_SAVE_MODE`.
        """
        weight_name = weight_name or deprecate(
            "weights_name",
            "0.18.0",
            "`weights_name` is deprecated, please use `weight_name` instead.",
            take_from=kwargs,
        )
        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        if save_function is None:
            if safe_serialization:

                def save_function(weights, filename):
                    return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"})

            else:
                save_function = torch.save

        os.makedirs(save_directory, exist_ok=True)

        model_to_save = AttnProcsLayers(self.attn_processors)

        # Save the model
        state_dict = model_to_save.state_dict()

        if weight_name is None:
            if safe_serialization:
                weight_name = LORA_WEIGHT_NAME_SAFE
            else:
                weight_name = LORA_WEIGHT_NAME

        # Save the model
        save_function(state_dict, os.path.join(save_directory, weight_name))
        logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}")


class TextualInversionLoaderMixin:
    r"""
    Mixin class for loading textual inversion tokens and embeddings to the tokenizer and text encoder.
    """

    def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"):
        r"""
        Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds
        to a multi-vector textual inversion embedding, this function will process the prompt so that the special token
        is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual
        inversion token or a textual inversion token that is a single vector, the input prompt is simply returned.

        Parameters:
            prompt (`str` or list of `str`):
                The prompt or prompts to guide the image generation.
            tokenizer (`PreTrainedTokenizer`):
                The tokenizer responsible for encoding the prompt into input tokens.

        Returns:
            `str` or list of `str`: The converted prompt
        """
        if not isinstance(prompt, List):
            prompts = [prompt]
        else:
            prompts = prompt

        prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]

        if not isinstance(prompt, List):
            return prompts[0]

        return prompts

    def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"):
        r"""
        Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds
        to a multi-vector textual inversion embedding, this function will process the prompt so that the special token
        is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual
        inversion token or a textual inversion token that is a single vector, the input prompt is simply returned.

        Parameters:
            prompt (`str`):
                The prompt to guide the image generation.
            tokenizer (`PreTrainedTokenizer`):
                The tokenizer responsible for encoding the prompt into input tokens.

        Returns:
            `str`: The converted prompt
        """
        tokens = tokenizer.tokenize(prompt)
        for token in tokens:
            if token in tokenizer.added_tokens_encoder:
                replacement = token
                i = 1
                while f"{token}_{i}" in tokenizer.added_tokens_encoder:
                    replacement += f"{token}_{i}"
                    i += 1

                prompt = prompt.replace(token, replacement)

        return prompt

    def load_textual_inversion(
        self, pretrained_model_name_or_path: Union[str, Dict[str, torch.Tensor]], token: Optional[str] = None, **kwargs
    ):
        r"""
        Load textual inversion embeddings into the text encoder of stable diffusion pipelines. Both `diffusers` and
        `Automatic1111` formats are supported.

        <Tip warning={true}>

            This function is experimental and might change in the future.

        </Tip>

        Parameters:
             pretrained_model_name_or_path (`str` or `os.PathLike`):
                Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                      Valid model ids should have an organization name, like
                      `"sd-concepts-library/low-poly-hd-logos-icons"`.
                    - A path to a *directory* containing textual inversion weights, e.g.
                      `./my_text_inversion_directory/`.
            weight_name (`str`, *optional*):
                Name of a custom weight file. This should be used in two cases:

                    - The saved textual inversion file is in `diffusers` format, but was saved under a specific weight
                      name, such as `text_inv.bin`.
                    - The saved textual inversion file is in the "Automatic1111" form.
            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether or not to only look at local files (i.e., do not try to download the model).
            use_auth_token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
                when running `diffusers-cli login` (stored in `~/.huggingface`).
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo (either remote in
                huggingface.co or downloaded locally), you can specify the folder name here.

            mirror (`str`, *optional*):
                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
                Please refer to the mirror site for more information.

        <Tip>

         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
         models](https://huggingface.co/docs/hub/models-gated#gated-models).

        </Tip>
        """
        if not hasattr(self, "tokenizer") or not isinstance(self.tokenizer, PreTrainedTokenizer):
            raise ValueError(
                f"{self.__class__.__name__} requires `self.tokenizer` of type `PreTrainedTokenizer` for calling"
                f" `{self.load_textual_inversion.__name__}`"
            )

        if not hasattr(self, "text_encoder") or not isinstance(self.text_encoder, PreTrainedModel):
            raise ValueError(
                f"{self.__class__.__name__} requires `self.text_encoder` of type `PreTrainedModel` for calling"
                f" `{self.load_textual_inversion.__name__}`"
            )

        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", None)
        weight_name = kwargs.pop("weight_name", None)
        use_safetensors = kwargs.pop("use_safetensors", None)

        if use_safetensors and not is_safetensors_available():
            raise ValueError(
                "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"
            )

        allow_pickle = False
        if use_safetensors is None:
            use_safetensors = is_safetensors_available()
            allow_pickle = True

        user_agent = {
            "file_type": "text_inversion",
            "framework": "pytorch",
        }

        # 1. Load textual inversion file
        model_file = None
        # Let's first try to load .safetensors weights
        if (use_safetensors and weight_name is None) or (
            weight_name is not None and weight_name.endswith(".safetensors")
        ):
            try:
                model_file = _get_model_file(
                    pretrained_model_name_or_path,
                    weights_name=weight_name or TEXT_INVERSION_NAME_SAFE,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    use_auth_token=use_auth_token,
                    revision=revision,
                    subfolder=subfolder,
                    user_agent=user_agent,
                )
                state_dict = safetensors.torch.load_file(model_file, device="cpu")
            except Exception as e:
                if not allow_pickle:
                    raise e

                model_file = None

        if model_file is None:
            model_file = _get_model_file(
                pretrained_model_name_or_path,
                weights_name=weight_name or TEXT_INVERSION_NAME,
                cache_dir=cache_dir,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
                revision=revision,
                subfolder=subfolder,
                user_agent=user_agent,
            )
            state_dict = torch.load(model_file, map_location="cpu")

        # 2. Load token and embedding correcly from file
        if isinstance(state_dict, torch.Tensor):
            if token is None:
                raise ValueError(
                    "You are trying to load a textual inversion embedding that has been saved as a PyTorch tensor. Make sure to pass the name of the corresponding token in this case: `token=...`."
                )
            embedding = state_dict
        elif len(state_dict) == 1:
            # diffusers
            loaded_token, embedding = next(iter(state_dict.items()))
        elif "string_to_param" in state_dict:
            # A1111
            loaded_token = state_dict["name"]
            embedding = state_dict["string_to_param"]["*"]

        if token is not None and loaded_token != token:
            logger.warn(f"The loaded token: {loaded_token} is overwritten by the passed token {token}.")
        else:
            token = loaded_token

        embedding = embedding.to(dtype=self.text_encoder.dtype, device=self.text_encoder.device)

        # 3. Make sure we don't mess up the tokenizer or text encoder
        vocab = self.tokenizer.get_vocab()
        if token in vocab:
            raise ValueError(
                f"Token {token} already in tokenizer vocabulary. Please choose a different token name or remove {token} and embedding from the tokenizer and text encoder."
            )
        elif f"{token}_1" in vocab:
            multi_vector_tokens = [token]
            i = 1
            while f"{token}_{i}" in self.tokenizer.added_tokens_encoder:
                multi_vector_tokens.append(f"{token}_{i}")
                i += 1

            raise ValueError(
                f"Multi-vector Token {multi_vector_tokens} already in tokenizer vocabulary. Please choose a different token name or remove the {multi_vector_tokens} and embedding from the tokenizer and text encoder."
            )

        is_multi_vector = len(embedding.shape) > 1 and embedding.shape[0] > 1

        if is_multi_vector:
            tokens = [token] + [f"{token}_{i}" for i in range(1, embedding.shape[0])]
            embeddings = [e for e in embedding]  # noqa: C416
        else:
            tokens = [token]
            embeddings = [embedding[0]] if len(embedding.shape) > 1 else [embedding]

        # add tokens and get ids
        self.tokenizer.add_tokens(tokens)
        token_ids = self.tokenizer.convert_tokens_to_ids(tokens)

        # resize token embeddings and set new embeddings
        self.text_encoder.resize_token_embeddings(len(self.tokenizer))
        for token_id, embedding in zip(token_ids, embeddings):
            self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding

        logger.info("Loaded textual inversion embedding for {token}.")