Guiders

Guiders are components in Modular Diffusers that control how the diffusion process is guided during generation. They implement various guidance techniques to improve generation quality and control.

mindone.diffusers.guiders.guider_utils.BaseGuidance

Bases: ConfigMixin, PushToHubMixin

Base class providing the skeleton for implementing guidance techniques.

Source code in mindone/diffusers/guiders/guider_utils.py

class BaseGuidance(ConfigMixin, PushToHubMixin):
    r"""Base class providing the skeleton for implementing guidance techniques."""

    config_name = GUIDER_CONFIG_NAME
    _input_predictions = None
    _identifier_key = "__guidance_identifier__"

    def __init__(self, start: float = 0.0, stop: float = 1.0):
        self._start = start
        self._stop = stop
        self._step: int = None
        self._num_inference_steps: int = None
        self._timestep: ms.Tensor = None
        self._count_prepared = 0
        self._input_fields: Dict[str, Union[str, Tuple[str, str]]] = None
        self._enabled = True

        if not (0.0 <= start < 1.0):
            raise ValueError(f"Expected `start` to be between 0.0 and 1.0, but got {start}.")
        if not (start <= stop <= 1.0):
            raise ValueError(f"Expected `stop` to be between {start} and 1.0, but got {stop}.")

        if self._input_predictions is None or not isinstance(self._input_predictions, list):
            raise ValueError(
                "`_input_predictions` must be a list of required prediction names for the guidance technique."
            )

    def disable(self):
        self._enabled = False

    def enable(self):
        self._enabled = True

    def set_state(self, step: int, num_inference_steps: int, timestep: ms.Tensor) -> None:
        self._step = step
        self._num_inference_steps = num_inference_steps
        self._timestep = timestep
        self._count_prepared = 0

    def set_input_fields(self, **kwargs: Dict[str, Union[str, Tuple[str, str]]]) -> None:
        """
        Set the input fields for the guidance technique. The input fields are used to specify the names of the returned
        attributes containing the prepared data after `prepare_inputs` is called. The prepared data is obtained from
        the values of the provided keyword arguments to this method.

        Args:
            **kwargs (`Dict[str, Union[str, Tuple[str, str]]]`):
                A dictionary where the keys are the names of the fields that will be used to store the data once it is
                prepared with `prepare_inputs`. The values can be either a string or a tuple of length 2, which is used
                to look up the required data provided for preparation.

                If a string is provided, it will be used as the conditional data (or unconditional if used with a
                guidance method that requires it). If a tuple of length 2 is provided, the first element must be the
                conditional data identifier and the second element must be the unconditional data identifier or None.

                Example:
                ```
                data = {"prompt_embeds": <some tensor>, "negative_prompt_embeds": <some tensor>, "latents": <some tensor>}

                BaseGuidance.set_input_fields(
                    latents="latents",
                    prompt_embeds=("prompt_embeds", "negative_prompt_embeds"),
                )
                ```
        """
        for key, value in kwargs.items():
            is_string = isinstance(value, str)
            is_tuple_of_str_with_len_2 = (
                isinstance(value, tuple) and len(value) == 2 and all(isinstance(v, str) for v in value)
            )
            if not (is_string or is_tuple_of_str_with_len_2):
                raise ValueError(
                    f"Expected `set_input_fields` to be called with a string or a tuple of string with length 2, but got {type(value)} for key {key}."
                )
        self._input_fields = kwargs

    def prepare_models(self, denoiser: ms.nn.Cell) -> None:
        """
        Prepares the models for the guidance technique on a given batch of data. This method should be overridden in
        subclasses to implement specific model preparation logic.
        """
        self._count_prepared += 1

    def cleanup_models(self, denoiser: ms.nn.Cell) -> None:
        """
        Cleans up the models for the guidance technique after a given batch of data. This method should be overridden
        in subclasses to implement specific model cleanup logic. It is useful for removing any hooks or other stateful
        modifications made during `prepare_models`.
        """
        pass

    def prepare_inputs(self, data: "BlockState") -> List["BlockState"]:
        raise NotImplementedError("BaseGuidance::prepare_inputs must be implemented in subclasses.")

    def __call__(self, data: List["BlockState"]) -> Any:
        if not all(hasattr(d, "noise_pred") for d in data):
            raise ValueError("Expected all data to have `noise_pred` attribute.")
        if len(data) != self.num_conditions:
            raise ValueError(
                f"Expected {self.num_conditions} data items, but got {len(data)}. Please check the input data."
            )
        forward_inputs = {getattr(d, self._identifier_key): d.noise_pred for d in data}
        return self.construct(**forward_inputs)

    def construct(self, *args, **kwargs) -> Any:
        raise NotImplementedError("BaseGuidance::construct must be implemented in subclasses.")

    @property
    def is_conditional(self) -> bool:
        raise NotImplementedError("BaseGuidance::is_conditional must be implemented in subclasses.")

    @property
    def is_unconditional(self) -> bool:
        return not self.is_conditional

    @property
    def num_conditions(self) -> int:
        raise NotImplementedError("BaseGuidance::num_conditions must be implemented in subclasses.")

    @classmethod
    def _prepare_batch(
        cls,
        input_fields: Dict[str, Union[str, Tuple[str, str]]],
        data: "BlockState",
        tuple_index: int,
        identifier: str,
    ) -> "BlockState":
        """
        Prepares a batch of data for the guidance technique. This method is used in the `prepare_inputs` method of the
        `BaseGuidance` class. It prepares the batch based on the provided tuple index.

        Args:
            input_fields (`Dict[str, Union[str, Tuple[str, str]]]`):
                A dictionary where the keys are the names of the fields that will be used to store the data once it is
                prepared with `prepare_inputs`. The values can be either a string or a tuple of length 2, which is used
                to look up the required data provided for preparation. If a string is provided, it will be used as the
                conditional data (or unconditional if used with a guidance method that requires it). If a tuple of
                length 2 is provided, the first element must be the conditional data identifier and the second element
                must be the unconditional data identifier or None.
            data (`BlockState`):
                The input data to be prepared.
            tuple_index (`int`):
                The index to use when accessing input fields that are tuples.

        Returns:
            `BlockState`: The prepared batch of data.
        """
        from ..modular_pipelines.modular_pipeline import BlockState

        if input_fields is None:
            raise ValueError(
                "Input fields cannot be None. Please pass `input_fields` to `prepare_inputs` or call `set_input_fields` before preparing inputs."
            )
        data_batch = {}
        for key, value in input_fields.items():
            try:
                if isinstance(value, str):
                    data_batch[key] = getattr(data, value)
                elif isinstance(value, tuple):
                    data_batch[key] = getattr(data, value[tuple_index])
                else:
                    # We've already checked that value is a string or a tuple of strings with length 2
                    pass
            except AttributeError:
                logger.debug(f"`data` does not have attribute(s) {value}, skipping.")
        data_batch[cls._identifier_key] = identifier
        return BlockState(**data_batch)

    @classmethod
    @validate_hf_hub_args
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
        subfolder: Optional[str] = None,
        return_unused_kwargs=False,
        **kwargs,
    ) -> Self:
        r"""
        Instantiate a guider from a pre-defined JSON configuration file in a local directory or Hub repository.

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

                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                      the Hub.
                    - A path to a *directory* (for example `./my_model_directory`) containing the guider configuration
                      saved with [`~BaseGuidance.save_pretrained`].
            subfolder (`str`, *optional*):
                The subfolder location of a model file within a larger model repository on the Hub or locally.
            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                Whether kwargs that are not consumed by the Python class should be returned or not.
            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                is not 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.

            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
            token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                `diffusers-cli login` (stored in `~/.huggingface`) is used.
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.

        <Tip>

        To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with `hf
        auth login`. You can also activate the special
        ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a
        firewalled environment.

        </Tip>

        """
        config, kwargs, commit_hash = cls.load_config(
            pretrained_model_name_or_path=pretrained_model_name_or_path,
            subfolder=subfolder,
            return_unused_kwargs=True,
            return_commit_hash=True,
            **kwargs,
        )
        return cls.from_config(config, return_unused_kwargs=return_unused_kwargs, **kwargs)

    def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
        """
        Save a guider configuration object to a directory so that it can be reloaded using the
        [`~BaseGuidance.from_pretrained`] class method.

        Args:
            save_directory (`str` or `os.PathLike`):
                Directory where the configuration JSON file will be saved (will be created if it does not exist).
            push_to_hub (`bool`, *optional*, defaults to `False`):
                Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                namespace).
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
        """
        self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)

mindone.diffusers.guiders.guider_utils.BaseGuidance.cleanup_models(denoiser)

Cleans up the models for the guidance technique after a given batch of data. This method should be overridden in subclasses to implement specific model cleanup logic. It is useful for removing any hooks or other stateful modifications made during prepare_models.

Source code in mindone/diffusers/guiders/guider_utils.py

def cleanup_models(self, denoiser: ms.nn.Cell) -> None:
    """
    Cleans up the models for the guidance technique after a given batch of data. This method should be overridden
    in subclasses to implement specific model cleanup logic. It is useful for removing any hooks or other stateful
    modifications made during `prepare_models`.
    """
    pass

mindone.diffusers.guiders.guider_utils.BaseGuidance.from_pretrained(pretrained_model_name_or_path=None, subfolder=None, return_unused_kwargs=False, **kwargs) classmethod

Instantiate a guider from a pre-defined JSON configuration file in a local directory or Hub repository.

PARAMETER	DESCRIPTION
pretrained_model_name_or_path	Can be either: - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on the Hub. - A path to a *directory* (for example `./my_model_directory`) containing the guider configuration saved with [`~BaseGuidance.save_pretrained`]. TYPE: `str` or `os.PathLike`, *optional* DEFAULT: None
subfolder	The subfolder location of a model file within a larger model repository on the Hub or locally. TYPE: `str`, *optional* DEFAULT: None
return_unused_kwargs	Whether kwargs that are not consumed by the Python class should be returned or not. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
cache_dir	Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. TYPE: `Union[str, os.PathLike]`, *optional*
force_download	Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. TYPE: `bool`, *optional*, defaults to `False`
proxies	A dictionary of proxy servers to use by protocol or endpoint, for example, {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. TYPE: `Dict[str, str]`, *optional*
output_loading_info	Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. TYPE: `bool`, *optional*, defaults to `False`
local_files_only	Whether to only load local model weights and configuration files or not. If set to True, the model won't be downloaded from the Hub. TYPE: `bool`, *optional*, defaults to `False`
token	The token to use as HTTP bearer authorization for remote files. If True, the token generated from diffusers-cli login (stored in ~/.huggingface) is used. TYPE: `str` or *bool*, *optional*
revision	The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git. TYPE: `str`, *optional*, defaults to `"main"`

To use private or gated models, log-in with hf auth login. You can also activate the special "offline-mode" to use this method in a firewalled environment.

Source code in mindone/diffusers/guiders/guider_utils.py

@classmethod
@validate_hf_hub_args
def from_pretrained(
    cls,
    pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
    subfolder: Optional[str] = None,
    return_unused_kwargs=False,
    **kwargs,
) -> Self:
    r"""
    Instantiate a guider from a pre-defined JSON configuration file in a local directory or Hub repository.

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

                - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                  the Hub.
                - A path to a *directory* (for example `./my_model_directory`) containing the guider configuration
                  saved with [`~BaseGuidance.save_pretrained`].
        subfolder (`str`, *optional*):
            The subfolder location of a model file within a larger model repository on the Hub or locally.
        return_unused_kwargs (`bool`, *optional*, defaults to `False`):
            Whether kwargs that are not consumed by the Python class should be returned or not.
        cache_dir (`Union[str, os.PathLike]`, *optional*):
            Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
            is not 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.

        proxies (`Dict[str, str]`, *optional*):
            A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
            'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
        output_loading_info(`bool`, *optional*, defaults to `False`):
            Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
        local_files_only(`bool`, *optional*, defaults to `False`):
            Whether to only load local model weights and configuration files or not. If set to `True`, the model
            won't be downloaded from the Hub.
        token (`str` or *bool*, *optional*):
            The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
            `diffusers-cli login` (stored in `~/.huggingface`) is used.
        revision (`str`, *optional*, defaults to `"main"`):
            The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
            allowed by Git.

    <Tip>

    To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with `hf
    auth login`. You can also activate the special
    ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a
    firewalled environment.

    </Tip>

    """
    config, kwargs, commit_hash = cls.load_config(
        pretrained_model_name_or_path=pretrained_model_name_or_path,
        subfolder=subfolder,
        return_unused_kwargs=True,
        return_commit_hash=True,
        **kwargs,
    )
    return cls.from_config(config, return_unused_kwargs=return_unused_kwargs, **kwargs)

mindone.diffusers.guiders.guider_utils.BaseGuidance.prepare_models(denoiser)

Prepares the models for the guidance technique on a given batch of data. This method should be overridden in subclasses to implement specific model preparation logic.

Source code in mindone/diffusers/guiders/guider_utils.py

def prepare_models(self, denoiser: ms.nn.Cell) -> None:
    """
    Prepares the models for the guidance technique on a given batch of data. This method should be overridden in
    subclasses to implement specific model preparation logic.
    """
    self._count_prepared += 1

mindone.diffusers.guiders.guider_utils.BaseGuidance.save_pretrained(save_directory, push_to_hub=False, **kwargs)

Save a guider configuration object to a directory so that it can be reloaded using the [~BaseGuidance.from_pretrained] class method.

PARAMETER	DESCRIPTION
save_directory	Directory where the configuration JSON file will be saved (will be created if it does not exist). TYPE: `str` or `os.PathLike`
push_to_hub	Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the repository you want to push to with repo_id (will default to the name of save_directory in your namespace). TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
kwargs	Additional keyword arguments passed along to the [~utils.PushToHubMixin.push_to_hub] method. TYPE: `Dict[str, Any]`, *optional* DEFAULT: {}

Source code in mindone/diffusers/guiders/guider_utils.py

def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
    """
    Save a guider configuration object to a directory so that it can be reloaded using the
    [`~BaseGuidance.from_pretrained`] class method.

    Args:
        save_directory (`str` or `os.PathLike`):
            Directory where the configuration JSON file will be saved (will be created if it does not exist).
        push_to_hub (`bool`, *optional*, defaults to `False`):
            Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
            repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
            namespace).
        kwargs (`Dict[str, Any]`, *optional*):
            Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
    """
    self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)

mindone.diffusers.guiders.guider_utils.BaseGuidance.set_input_fields(**kwargs)

Set the input fields for the guidance technique. The input fields are used to specify the names of the returned attributes containing the prepared data after prepare_inputs is called. The prepared data is obtained from the values of the provided keyword arguments to this method.

PARAMETER

DESCRIPTION

**kwargs

A dictionary where the keys are the names of the fields that will be used to store the data once it is prepared with prepare_inputs. The values can be either a string or a tuple of length 2, which is used to look up the required data provided for preparation. If a string is provided, it will be used as the conditional data (or unconditional if used with a guidance method that requires it). If a tuple of length 2 is provided, the first element must be the conditional data identifier and the second element must be the unconditional data identifier or None. Example: data = {"prompt_embeds": <some tensor>, "negative_prompt_embeds": <some tensor>, "latents": <some tensor>} BaseGuidance.set_input_fields( latents="latents", prompt_embeds=("prompt_embeds", "negative_prompt_embeds"), ) TYPE: `Dict[str, Union[str, Tuple[str, str]]]` DEFAULT: {}

Source code in mindone/diffusers/guiders/guider_utils.py

def set_input_fields(self, **kwargs: Dict[str, Union[str, Tuple[str, str]]]) -> None:
    """
    Set the input fields for the guidance technique. The input fields are used to specify the names of the returned
    attributes containing the prepared data after `prepare_inputs` is called. The prepared data is obtained from
    the values of the provided keyword arguments to this method.

    Args:
        **kwargs (`Dict[str, Union[str, Tuple[str, str]]]`):
            A dictionary where the keys are the names of the fields that will be used to store the data once it is
            prepared with `prepare_inputs`. The values can be either a string or a tuple of length 2, which is used
            to look up the required data provided for preparation.

            If a string is provided, it will be used as the conditional data (or unconditional if used with a
            guidance method that requires it). If a tuple of length 2 is provided, the first element must be the
            conditional data identifier and the second element must be the unconditional data identifier or None.

            Example:
            ```
            data = {"prompt_embeds": <some tensor>, "negative_prompt_embeds": <some tensor>, "latents": <some tensor>}

            BaseGuidance.set_input_fields(
                latents="latents",
                prompt_embeds=("prompt_embeds", "negative_prompt_embeds"),
            )
            ```
    """
    for key, value in kwargs.items():
        is_string = isinstance(value, str)
        is_tuple_of_str_with_len_2 = (
            isinstance(value, tuple) and len(value) == 2 and all(isinstance(v, str) for v in value)
        )
        if not (is_string or is_tuple_of_str_with_len_2):
            raise ValueError(
                f"Expected `set_input_fields` to be called with a string or a tuple of string with length 2, but got {type(value)} for key {key}."
            )
    self._input_fields = kwargs

mindone.diffusers.guiders.classifier_free_guidance.ClassifierFreeGuidance

Bases: BaseGuidance

Classifier-free guidance (CFG): https://huggingface.co/papers/2207.12598

CFG is a technique used to improve generation quality and condition-following in diffusion models. It works by jointly training a model on both conditional and unconditional data, and using a weighted sum of the two during inference. This allows the model to tradeoff between generation quality and sample diversity. The original paper proposes scaling and shifting the conditional distribution based on the difference between conditional and unconditional predictions. [x_pred = x_cond + scale * (x_cond - x_uncond)]

Diffusers implemented the scaling and shifting on the unconditional prediction instead based on the Imagen paper, which is equivalent to what the original paper proposed in theory. [x_pred = x_uncond + scale * (x_cond - x_uncond)]

The intution behind the original formulation can be thought of as moving the conditional distribution estimates further away from the unconditional distribution estimates, while the diffusers-native implementation can be thought of as moving the unconditional distribution towards the conditional distribution estimates to get rid of the unconditional predictions (usually negative features like "bad quality, bad anotomy, watermarks", etc.)

The use_original_formulation argument can be set to True to use the original CFG formulation mentioned in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time.

PARAMETER	DESCRIPTION
guidance_scale	The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and deterioration of image quality. TYPE: `float`, defaults to `7.5` DEFAULT: 7.5
guidance_rescale	The rescale factor applied to the noise predictions. This is used to improve image quality and fix overexposure. Based on Section 3.4 from Common Diffusion Noise Schedules and Sample Steps are Flawed. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
use_original_formulation	Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time. See [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details. TYPE: `bool`, defaults to `False` DEFAULT: False
start	The fraction of the total number of denoising steps after which guidance starts. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
stop	The fraction of the total number of denoising steps after which guidance stops. TYPE: `float`, defaults to `1.0` DEFAULT: 1.0

Source code in mindone/diffusers/guiders/classifier_free_guidance.py

class ClassifierFreeGuidance(BaseGuidance):
    """
    Classifier-free guidance (CFG): https://huggingface.co/papers/2207.12598

    CFG is a technique used to improve generation quality and condition-following in diffusion models. It works by
    jointly training a model on both conditional and unconditional data, and using a weighted sum of the two during
    inference. This allows the model to tradeoff between generation quality and sample diversity. The original paper
    proposes scaling and shifting the conditional distribution based on the difference between conditional and
    unconditional predictions. [x_pred = x_cond + scale * (x_cond - x_uncond)]

    Diffusers implemented the scaling and shifting on the unconditional prediction instead based on the [Imagen
    paper](https://huggingface.co/papers/2205.11487), which is equivalent to what the original paper proposed in
    theory. [x_pred = x_uncond + scale * (x_cond - x_uncond)]

    The intution behind the original formulation can be thought of as moving the conditional distribution estimates
    further away from the unconditional distribution estimates, while the diffusers-native implementation can be
    thought of as moving the unconditional distribution towards the conditional distribution estimates to get rid of
    the unconditional predictions (usually negative features like "bad quality, bad anotomy, watermarks", etc.)

    The `use_original_formulation` argument can be set to `True` to use the original CFG formulation mentioned in the
    paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time.

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
            deterioration of image quality.
        guidance_rescale (`float`, defaults to `0.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
            Flawed](https://huggingface.co/papers/2305.08891).
        use_original_formulation (`bool`, defaults to `False`):
            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
            we use the diffusers-native implementation that has been in the codebase for a long time. See
            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
        start (`float`, defaults to `0.0`):
            The fraction of the total number of denoising steps after which guidance starts.
        stop (`float`, defaults to `1.0`):
            The fraction of the total number of denoising steps after which guidance stops.
    """

    _input_predictions = ["pred_cond", "pred_uncond"]

    @register_to_config
    def __init__(
        self,
        guidance_scale: float = 7.5,
        guidance_rescale: float = 0.0,
        use_original_formulation: bool = False,
        start: float = 0.0,
        stop: float = 1.0,
    ):
        super().__init__(start, stop)

        self.guidance_scale = guidance_scale
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation

    def prepare_inputs(
        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
    ) -> List["BlockState"]:
        if input_fields is None:
            input_fields = self._input_fields

        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
        data_batches = []
        for i in range(self.num_conditions):
            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
            data_batches.append(data_batch)
        return data_batches

    def construct(self, pred_cond: ms.Tensor, pred_uncond: Optional[ms.Tensor] = None) -> ms.Tensor:
        pred = None

        if not self._is_cfg_enabled():
            pred = pred_cond
        else:
            shift = pred_cond - pred_uncond
            pred = pred_cond if self.use_original_formulation else pred_uncond
            pred = pred + self.guidance_scale * shift

        if self.guidance_rescale > 0.0:
            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)

        return pred, {}

    @property
    def is_conditional(self) -> bool:
        return self._count_prepared == 1

    @property
    def num_conditions(self) -> int:
        num_conditions = 1
        if self._is_cfg_enabled():
            num_conditions += 1
        return num_conditions

    def _is_cfg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self._start * self._num_inference_steps)
            skip_stop_step = int(self._stop * self._num_inference_steps)
            is_within_range = skip_start_step <= self._step < skip_stop_step

        is_close = False
        if self.use_original_formulation:
            is_close = math.isclose(self.guidance_scale, 0.0)
        else:
            is_close = math.isclose(self.guidance_scale, 1.0)

        return is_within_range and not is_close

mindone.diffusers.guiders.classifier_free_zero_star_guidance.ClassifierFreeZeroStarGuidance

Bases: BaseGuidance

Classifier-free Zero* (CFG-Zero*): https://huggingface.co/papers/2503.18886

This is an implementation of the Classifier-Free Zero* guidance technique, which is a variant of classifier-free guidance. It proposes zero initialization of the noise predictions for the first few steps of the diffusion process, and also introduces an optimal rescaling factor for the noise predictions, which can help in improving the quality of generated images.

The authors of the paper suggest setting zero initialization in the first 4% of the inference steps.

PARAMETER	DESCRIPTION
guidance_scale	The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and deterioration of image quality. TYPE: `float`, defaults to `7.5` DEFAULT: 7.5
zero_init_steps	The number of inference steps for which the noise predictions are zeroed out (see Section 4.2). TYPE: `int`, defaults to `1` DEFAULT: 1
guidance_rescale	The rescale factor applied to the noise predictions. This is used to improve image quality and fix overexposure. Based on Section 3.4 from Common Diffusion Noise Schedules and Sample Steps are Flawed. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
use_original_formulation	Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time. See [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details. TYPE: `bool`, defaults to `False` DEFAULT: False
start	The fraction of the total number of denoising steps after which guidance starts. TYPE: `float`, defaults to `0.01` DEFAULT: 0.0
stop	The fraction of the total number of denoising steps after which guidance stops. TYPE: `float`, defaults to `0.2` DEFAULT: 1.0

Source code in mindone/diffusers/guiders/classifier_free_zero_star_guidance.py

class ClassifierFreeZeroStarGuidance(BaseGuidance):
    """
    Classifier-free Zero* (CFG-Zero*): https://huggingface.co/papers/2503.18886

    This is an implementation of the Classifier-Free Zero* guidance technique, which is a variant of classifier-free
    guidance. It proposes zero initialization of the noise predictions for the first few steps of the diffusion
    process, and also introduces an optimal rescaling factor for the noise predictions, which can help in improving the
    quality of generated images.

    The authors of the paper suggest setting zero initialization in the first 4% of the inference steps.

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
            deterioration of image quality.
        zero_init_steps (`int`, defaults to `1`):
            The number of inference steps for which the noise predictions are zeroed out (see Section 4.2).
        guidance_rescale (`float`, defaults to `0.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
            Flawed](https://huggingface.co/papers/2305.08891).
        use_original_formulation (`bool`, defaults to `False`):
            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
            we use the diffusers-native implementation that has been in the codebase for a long time. See
            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
        start (`float`, defaults to `0.01`):
            The fraction of the total number of denoising steps after which guidance starts.
        stop (`float`, defaults to `0.2`):
            The fraction of the total number of denoising steps after which guidance stops.
    """

    _input_predictions = ["pred_cond", "pred_uncond"]

    @register_to_config
    def __init__(
        self,
        guidance_scale: float = 7.5,
        zero_init_steps: int = 1,
        guidance_rescale: float = 0.0,
        use_original_formulation: bool = False,
        start: float = 0.0,
        stop: float = 1.0,
    ):
        super().__init__(start, stop)

        self.guidance_scale = guidance_scale
        self.zero_init_steps = zero_init_steps
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation

    def prepare_inputs(
        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
    ) -> List["BlockState"]:
        if input_fields is None:
            input_fields = self._input_fields

        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
        data_batches = []
        for i in range(self.num_conditions):
            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
            data_batches.append(data_batch)
        return data_batches

    def construct(self, pred_cond: ms.Tensor, pred_uncond: Optional[ms.Tensor] = None) -> ms.Tensor:
        pred = None

        if self._step < self.zero_init_steps:
            pred = mint.zeros_like(pred_cond)
        elif not self._is_cfg_enabled():
            pred = pred_cond
        else:
            pred_cond_flat = pred_cond.flatten(1)
            pred_uncond_flat = pred_uncond.flatten(1)
            alpha = cfg_zero_star_scale(pred_cond_flat, pred_uncond_flat)
            alpha = alpha.view(-1, *(1,) * (len(pred_cond.shape) - 1))
            pred_uncond = pred_uncond * alpha
            shift = pred_cond - pred_uncond
            pred = pred_cond if self.use_original_formulation else pred_uncond
            pred = pred + self.guidance_scale * shift

        if self.guidance_rescale > 0.0:
            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)

        return pred, {}

    @property
    def is_conditional(self) -> bool:
        return self._count_prepared == 1

    @property
    def num_conditions(self) -> int:
        num_conditions = 1
        if self._is_cfg_enabled():
            num_conditions += 1
        return num_conditions

    def _is_cfg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self._start * self._num_inference_steps)
            skip_stop_step = int(self._stop * self._num_inference_steps)
            is_within_range = skip_start_step <= self._step < skip_stop_step

        is_close = False
        if self.use_original_formulation:
            is_close = math.isclose(self.guidance_scale, 0.0)
        else:
            is_close = math.isclose(self.guidance_scale, 1.0)

        return is_within_range and not is_close

mindone.diffusers.guiders.skip_layer_guidance.SkipLayerGuidance

Bases: BaseGuidance

Skip Layer Guidance (SLG): https://github.com/Stability-AI/sd3.5

Spatio-Temporal Guidance (STG): https://huggingface.co/papers/2411.18664

SLG was introduced by StabilityAI for improving structure and anotomy coherence in generated images. It works by skipping the forward pass of specified transformer blocks during the denoising process on an additional conditional batch of data, apart from the conditional and unconditional batches already used in CFG ([~guiders.classifier_free_guidance.ClassifierFreeGuidance]), and then scaling and shifting the CFG predictions based on the difference between conditional without skipping and conditional with skipping predictions.

The intution behind SLG can be thought of as moving the CFG predicted distribution estimates further away from worse versions of the conditional distribution estimates (because skipping layers is equivalent to using a worse version of the model for the conditional prediction).

STG is an improvement and follow-up work combining ideas from SLG, PAG and similar techniques for improving generation quality in video diffusion models.

Additional reading: - Guiding a Diffusion Model with a Bad Version of Itself

The values for skip_layer_guidance_scale, skip_layer_guidance_start, and skip_layer_guidance_stop are defaulted to the recommendations by StabilityAI for Stable Diffusion 3.5 Medium.

PARAMETER	DESCRIPTION
guidance_scale	The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and deterioration of image quality. TYPE: `float`, defaults to `7.5` DEFAULT: 7.5
skip_layer_guidance_scale	The scale parameter for skip layer guidance. Anatomy and structure coherence may improve with higher values, but it may also lead to overexposure and saturation. TYPE: `float`, defaults to `2.8` DEFAULT: 2.8
skip_layer_guidance_start	The fraction of the total number of denoising steps after which skip layer guidance starts. TYPE: `float`, defaults to `0.01` DEFAULT: 0.01
skip_layer_guidance_stop	The fraction of the total number of denoising steps after which skip layer guidance stops. TYPE: `float`, defaults to `0.2` DEFAULT: 0.2
skip_layer_guidance_layers	The layer indices to apply skip layer guidance to. Can be a single integer or a list of integers. If not provided, skip_layer_config must be provided. The recommended values are [7, 8, 9] for Stable Diffusion 3.5 Medium. TYPE: `int` or `List[int]`, *optional* DEFAULT: None
skip_layer_config	The configuration for the skip layer guidance. Can be a single LayerSkipConfig or a list of LayerSkipConfig. If not provided, skip_layer_guidance_layers must be provided. TYPE: `LayerSkipConfig` or `List[LayerSkipConfig]`, *optional* DEFAULT: None
guidance_rescale	The rescale factor applied to the noise predictions. This is used to improve image quality and fix overexposure. Based on Section 3.4 from Common Diffusion Noise Schedules and Sample Steps are Flawed. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
use_original_formulation	Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time. See [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details. TYPE: `bool`, defaults to `False` DEFAULT: False
start	The fraction of the total number of denoising steps after which guidance starts. TYPE: `float`, defaults to `0.01` DEFAULT: 0.0
stop	The fraction of the total number of denoising steps after which guidance stops. TYPE: `float`, defaults to `0.2` DEFAULT: 1.0

Source code in mindone/diffusers/guiders/skip_layer_guidance.py

class SkipLayerGuidance(BaseGuidance):
    """
    Skip Layer Guidance (SLG): https://github.com/Stability-AI/sd3.5

    Spatio-Temporal Guidance (STG): https://huggingface.co/papers/2411.18664

    SLG was introduced by StabilityAI for improving structure and anotomy coherence in generated images. It works by
    skipping the forward pass of specified transformer blocks during the denoising process on an additional conditional
    batch of data, apart from the conditional and unconditional batches already used in CFG
    ([~guiders.classifier_free_guidance.ClassifierFreeGuidance]), and then scaling and shifting the CFG predictions
    based on the difference between conditional without skipping and conditional with skipping predictions.

    The intution behind SLG can be thought of as moving the CFG predicted distribution estimates further away from
    worse versions of the conditional distribution estimates (because skipping layers is equivalent to using a worse
    version of the model for the conditional prediction).

    STG is an improvement and follow-up work combining ideas from SLG, PAG and similar techniques for improving
    generation quality in video diffusion models.

    Additional reading:
    - [Guiding a Diffusion Model with a Bad Version of Itself](https://huggingface.co/papers/2406.02507)

    The values for `skip_layer_guidance_scale`, `skip_layer_guidance_start`, and `skip_layer_guidance_stop` are
    defaulted to the recommendations by StabilityAI for Stable Diffusion 3.5 Medium.

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
            deterioration of image quality.
        skip_layer_guidance_scale (`float`, defaults to `2.8`):
            The scale parameter for skip layer guidance. Anatomy and structure coherence may improve with higher
            values, but it may also lead to overexposure and saturation.
        skip_layer_guidance_start (`float`, defaults to `0.01`):
            The fraction of the total number of denoising steps after which skip layer guidance starts.
        skip_layer_guidance_stop (`float`, defaults to `0.2`):
            The fraction of the total number of denoising steps after which skip layer guidance stops.
        skip_layer_guidance_layers (`int` or `List[int]`, *optional*):
            The layer indices to apply skip layer guidance to. Can be a single integer or a list of integers. If not
            provided, `skip_layer_config` must be provided. The recommended values are `[7, 8, 9]` for Stable Diffusion
            3.5 Medium.
        skip_layer_config (`LayerSkipConfig` or `List[LayerSkipConfig]`, *optional*):
            The configuration for the skip layer guidance. Can be a single `LayerSkipConfig` or a list of
            `LayerSkipConfig`. If not provided, `skip_layer_guidance_layers` must be provided.
        guidance_rescale (`float`, defaults to `0.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
            Flawed](https://huggingface.co/papers/2305.08891).
        use_original_formulation (`bool`, defaults to `False`):
            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
            we use the diffusers-native implementation that has been in the codebase for a long time. See
            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
        start (`float`, defaults to `0.01`):
            The fraction of the total number of denoising steps after which guidance starts.
        stop (`float`, defaults to `0.2`):
            The fraction of the total number of denoising steps after which guidance stops.
    """

    _input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]

    @register_to_config
    def __init__(
        self,
        guidance_scale: float = 7.5,
        skip_layer_guidance_scale: float = 2.8,
        skip_layer_guidance_start: float = 0.01,
        skip_layer_guidance_stop: float = 0.2,
        skip_layer_guidance_layers: Optional[Union[int, List[int]]] = None,
        skip_layer_config: Union[LayerSkipConfig, List[LayerSkipConfig], Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
        use_original_formulation: bool = False,
        start: float = 0.0,
        stop: float = 1.0,
    ):
        super().__init__(start, stop)

        self.guidance_scale = guidance_scale
        self.skip_layer_guidance_scale = skip_layer_guidance_scale
        self.skip_layer_guidance_start = skip_layer_guidance_start
        self.skip_layer_guidance_stop = skip_layer_guidance_stop
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation

        if not (0.0 <= skip_layer_guidance_start < 1.0):
            raise ValueError(
                f"Expected `skip_layer_guidance_start` to be between 0.0 and 1.0, but got {skip_layer_guidance_start}."
            )
        if not (skip_layer_guidance_start <= skip_layer_guidance_stop <= 1.0):
            raise ValueError(
                f"Expected `skip_layer_guidance_stop` to be between 0.0 and 1.0, but got {skip_layer_guidance_stop}."
            )

        if skip_layer_guidance_layers is None and skip_layer_config is None:
            raise ValueError(
                "Either `skip_layer_guidance_layers` or `skip_layer_config` must be provided to enable Skip Layer Guidance."
            )
        if skip_layer_guidance_layers is not None and skip_layer_config is not None:
            raise ValueError("Only one of `skip_layer_guidance_layers` or `skip_layer_config` can be provided.")

        if skip_layer_guidance_layers is not None:
            if isinstance(skip_layer_guidance_layers, int):
                skip_layer_guidance_layers = [skip_layer_guidance_layers]
            if not isinstance(skip_layer_guidance_layers, list):
                raise ValueError(
                    f"Expected `skip_layer_guidance_layers` to be an int or a list of ints, but got {type(skip_layer_guidance_layers)}."
                )
            skip_layer_config = [LayerSkipConfig(layer, fqn="auto") for layer in skip_layer_guidance_layers]

        if isinstance(skip_layer_config, dict):
            skip_layer_config = LayerSkipConfig.from_dict(skip_layer_config)

        if isinstance(skip_layer_config, LayerSkipConfig):
            skip_layer_config = [skip_layer_config]

        if not isinstance(skip_layer_config, list):
            raise ValueError(
                f"Expected `skip_layer_config` to be a LayerSkipConfig or a list of LayerSkipConfig, but got {type(skip_layer_config)}."
            )
        elif isinstance(next(iter(skip_layer_config), None), dict):
            skip_layer_config = [LayerSkipConfig.from_dict(config) for config in skip_layer_config]

        self.skip_layer_config = skip_layer_config
        self._skip_layer_hook_names = [f"SkipLayerGuidance_{i}" for i in range(len(self.skip_layer_config))]

    def prepare_models(self, denoiser: ms.nn.Cell) -> None:
        self._count_prepared += 1
        if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
            for name, config in zip(self._skip_layer_hook_names, self.skip_layer_config):
                _apply_layer_skip_hook(denoiser, config, name=name)

    def cleanup_models(self, denoiser: ms.nn.Cell) -> None:
        if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
            registry = HookRegistry.check_if_exists_or_initialize(denoiser)
            # Remove the hooks after inference
            for hook_name in self._skip_layer_hook_names:
                registry.remove_hook(hook_name, recurse=True)

    def prepare_inputs(
        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
    ) -> List["BlockState"]:
        if input_fields is None:
            input_fields = self._input_fields

        if self.num_conditions == 1:
            tuple_indices = [0]
            input_predictions = ["pred_cond"]
        elif self.num_conditions == 2:
            tuple_indices = [0, 1]
            input_predictions = (
                ["pred_cond", "pred_uncond"] if self._is_cfg_enabled() else ["pred_cond", "pred_cond_skip"]
            )
        else:
            tuple_indices = [0, 1, 0]
            input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]
        data_batches = []
        for i in range(self.num_conditions):
            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], input_predictions[i])
            data_batches.append(data_batch)
        return data_batches

    def construct(
        self,
        pred_cond: ms.Tensor,
        pred_uncond: Optional[ms.Tensor] = None,
        pred_cond_skip: Optional[ms.Tensor] = None,
    ) -> ms.Tensor:
        pred = None

        if not self._is_cfg_enabled() and not self._is_slg_enabled():
            pred = pred_cond
        elif not self._is_cfg_enabled():
            shift = pred_cond - pred_cond_skip
            pred = pred_cond if self.use_original_formulation else pred_cond_skip
            pred = pred + self.skip_layer_guidance_scale * shift
        elif not self._is_slg_enabled():
            shift = pred_cond - pred_uncond
            pred = pred_cond if self.use_original_formulation else pred_uncond
            pred = pred + self.guidance_scale * shift
        else:
            shift = pred_cond - pred_uncond
            shift_skip = pred_cond - pred_cond_skip
            pred = pred_cond if self.use_original_formulation else pred_uncond
            pred = pred + self.guidance_scale * shift + self.skip_layer_guidance_scale * shift_skip

        if self.guidance_rescale > 0.0:
            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)

        return pred, {}

    @property
    def is_conditional(self) -> bool:
        return self._count_prepared == 1 or self._count_prepared == 3

    @property
    def num_conditions(self) -> int:
        num_conditions = 1
        if self._is_cfg_enabled():
            num_conditions += 1
        if self._is_slg_enabled():
            num_conditions += 1
        return num_conditions

    def _is_cfg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self._start * self._num_inference_steps)
            skip_stop_step = int(self._stop * self._num_inference_steps)
            is_within_range = skip_start_step <= self._step < skip_stop_step

        is_close = False
        if self.use_original_formulation:
            is_close = math.isclose(self.guidance_scale, 0.0)
        else:
            is_close = math.isclose(self.guidance_scale, 1.0)

        return is_within_range and not is_close

    def _is_slg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self.skip_layer_guidance_start * self._num_inference_steps)
            skip_stop_step = int(self.skip_layer_guidance_stop * self._num_inference_steps)
            is_within_range = skip_start_step < self._step < skip_stop_step

        is_zero = math.isclose(self.skip_layer_guidance_scale, 0.0)

        return is_within_range and not is_zero

mindone.diffusers.guiders.smoothed_energy_guidance.SmoothedEnergyGuidance

Bases: BaseGuidance

Smoothed Energy Guidance (SEG): https://huggingface.co/papers/2408.00760

SEG is only supported as an experimental prototype feature for now, so the implementation may be modified in the future without warning or guarantee of reproducibility. This implementation assumes: - Generated images are square (height == width) - The model does not combine different modalities together (e.g., text and image latent streams are not combined together such as Flux)

PARAMETER	DESCRIPTION
guidance_scale	The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and deterioration of image quality. TYPE: `float`, defaults to `7.5` DEFAULT: 7.5
seg_guidance_scale	The scale parameter for smoothed energy guidance. Anatomy and structure coherence may improve with higher values, but it may also lead to overexposure and saturation. TYPE: `float`, defaults to `3.0` DEFAULT: 2.8
seg_blur_sigma	The amount by which we blur the attention weights. Setting this value greater than 9999.0 results in infinite blur, which means uniform queries. Controlling it exponentially is empirically effective. TYPE: `float`, defaults to `9999999.0` DEFAULT: 9999999.0
seg_blur_threshold_inf	The threshold above which the blur is considered infinite. TYPE: `float`, defaults to `9999.0` DEFAULT: 9999.0
seg_guidance_start	The fraction of the total number of denoising steps after which smoothed energy guidance starts. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
seg_guidance_stop	The fraction of the total number of denoising steps after which smoothed energy guidance stops. TYPE: `float`, defaults to `1.0` DEFAULT: 1.0
seg_guidance_layers	The layer indices to apply smoothed energy guidance to. Can be a single integer or a list of integers. If not provided, seg_guidance_config must be provided. The recommended values are [7, 8, 9] for Stable Diffusion 3.5 Medium. TYPE: `int` or `List[int]`, *optional* DEFAULT: None
seg_guidance_config	The configuration for the smoothed energy layer guidance. Can be a single SmoothedEnergyGuidanceConfig or a list of SmoothedEnergyGuidanceConfig. If not provided, seg_guidance_layers must be provided. TYPE: `SmoothedEnergyGuidanceConfig` or `List[SmoothedEnergyGuidanceConfig]`, *optional* DEFAULT: None
guidance_rescale	The rescale factor applied to the noise predictions. This is used to improve image quality and fix overexposure. Based on Section 3.4 from Common Diffusion Noise Schedules and Sample Steps are Flawed. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
use_original_formulation	Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time. See [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details. TYPE: `bool`, defaults to `False` DEFAULT: False
start	The fraction of the total number of denoising steps after which guidance starts. TYPE: `float`, defaults to `0.01` DEFAULT: 0.0
stop	The fraction of the total number of denoising steps after which guidance stops. TYPE: `float`, defaults to `0.2` DEFAULT: 1.0

Source code in mindone/diffusers/guiders/smoothed_energy_guidance.py

class SmoothedEnergyGuidance(BaseGuidance):
    """
    Smoothed Energy Guidance (SEG): https://huggingface.co/papers/2408.00760

    SEG is only supported as an experimental prototype feature for now, so the implementation may be modified in the
    future without warning or guarantee of reproducibility. This implementation assumes:
    - Generated images are square (height == width)
    - The model does not combine different modalities together (e.g., text and image latent streams are not combined
      together such as Flux)

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
            deterioration of image quality.
        seg_guidance_scale (`float`, defaults to `3.0`):
            The scale parameter for smoothed energy guidance. Anatomy and structure coherence may improve with higher
            values, but it may also lead to overexposure and saturation.
        seg_blur_sigma (`float`, defaults to `9999999.0`):
            The amount by which we blur the attention weights. Setting this value greater than 9999.0 results in
            infinite blur, which means uniform queries. Controlling it exponentially is empirically effective.
        seg_blur_threshold_inf (`float`, defaults to `9999.0`):
            The threshold above which the blur is considered infinite.
        seg_guidance_start (`float`, defaults to `0.0`):
            The fraction of the total number of denoising steps after which smoothed energy guidance starts.
        seg_guidance_stop (`float`, defaults to `1.0`):
            The fraction of the total number of denoising steps after which smoothed energy guidance stops.
        seg_guidance_layers (`int` or `List[int]`, *optional*):
            The layer indices to apply smoothed energy guidance to. Can be a single integer or a list of integers. If
            not provided, `seg_guidance_config` must be provided. The recommended values are `[7, 8, 9]` for Stable
            Diffusion 3.5 Medium.
        seg_guidance_config (`SmoothedEnergyGuidanceConfig` or `List[SmoothedEnergyGuidanceConfig]`, *optional*):
            The configuration for the smoothed energy layer guidance. Can be a single `SmoothedEnergyGuidanceConfig` or
            a list of `SmoothedEnergyGuidanceConfig`. If not provided, `seg_guidance_layers` must be provided.
        guidance_rescale (`float`, defaults to `0.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
            Flawed](https://huggingface.co/papers/2305.08891).
        use_original_formulation (`bool`, defaults to `False`):
            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
            we use the diffusers-native implementation that has been in the codebase for a long time. See
            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
        start (`float`, defaults to `0.01`):
            The fraction of the total number of denoising steps after which guidance starts.
        stop (`float`, defaults to `0.2`):
            The fraction of the total number of denoising steps after which guidance stops.
    """

    _input_predictions = ["pred_cond", "pred_uncond", "pred_cond_seg"]

    @register_to_config
    def __init__(
        self,
        guidance_scale: float = 7.5,
        seg_guidance_scale: float = 2.8,
        seg_blur_sigma: float = 9999999.0,
        seg_blur_threshold_inf: float = 9999.0,
        seg_guidance_start: float = 0.0,
        seg_guidance_stop: float = 1.0,
        seg_guidance_layers: Optional[Union[int, List[int]]] = None,
        seg_guidance_config: Union[SmoothedEnergyGuidanceConfig, List[SmoothedEnergyGuidanceConfig]] = None,
        guidance_rescale: float = 0.0,
        use_original_formulation: bool = False,
        start: float = 0.0,
        stop: float = 1.0,
    ):
        super().__init__(start, stop)

        self.guidance_scale = guidance_scale
        self.seg_guidance_scale = seg_guidance_scale
        self.seg_blur_sigma = seg_blur_sigma
        self.seg_blur_threshold_inf = seg_blur_threshold_inf
        self.seg_guidance_start = seg_guidance_start
        self.seg_guidance_stop = seg_guidance_stop
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation

        if not (0.0 <= seg_guidance_start < 1.0):
            raise ValueError(f"Expected `seg_guidance_start` to be between 0.0 and 1.0, but got {seg_guidance_start}.")
        if not (seg_guidance_start <= seg_guidance_stop <= 1.0):
            raise ValueError(f"Expected `seg_guidance_stop` to be between 0.0 and 1.0, but got {seg_guidance_stop}.")

        if seg_guidance_layers is None and seg_guidance_config is None:
            raise ValueError(
                "Either `seg_guidance_layers` or `seg_guidance_config` must be provided to enable Smoothed Energy Guidance."
            )
        if seg_guidance_layers is not None and seg_guidance_config is not None:
            raise ValueError("Only one of `seg_guidance_layers` or `seg_guidance_config` can be provided.")

        if seg_guidance_layers is not None:
            if isinstance(seg_guidance_layers, int):
                seg_guidance_layers = [seg_guidance_layers]
            if not isinstance(seg_guidance_layers, list):
                raise ValueError(
                    f"Expected `seg_guidance_layers` to be an int or a list of ints, but got {type(seg_guidance_layers)}."
                )
            seg_guidance_config = [SmoothedEnergyGuidanceConfig(layer, fqn="auto") for layer in seg_guidance_layers]

        if isinstance(seg_guidance_config, dict):
            seg_guidance_config = SmoothedEnergyGuidanceConfig.from_dict(seg_guidance_config)

        if isinstance(seg_guidance_config, SmoothedEnergyGuidanceConfig):
            seg_guidance_config = [seg_guidance_config]

        if not isinstance(seg_guidance_config, list):
            raise ValueError(
                f"Expected `seg_guidance_config` to be a SmoothedEnergyGuidanceConfig or a list of SmoothedEnergyGuidanceConfig, but got {type(seg_guidance_config)}."  # noqa
            )
        elif isinstance(next(iter(seg_guidance_config), None), dict):
            seg_guidance_config = [SmoothedEnergyGuidanceConfig.from_dict(config) for config in seg_guidance_config]

        self.seg_guidance_config = seg_guidance_config
        self._seg_layer_hook_names = [f"SmoothedEnergyGuidance_{i}" for i in range(len(self.seg_guidance_config))]

    def prepare_models(self, denoiser: ms.nn.Cell) -> None:
        if self._is_seg_enabled() and self.is_conditional and self._count_prepared > 1:
            for name, config in zip(self._seg_layer_hook_names, self.seg_guidance_config):
                _apply_smoothed_energy_guidance_hook(denoiser, config, self.seg_blur_sigma, name=name)

    def cleanup_models(self, denoiser: ms.nn.Cell):
        if self._is_seg_enabled() and self.is_conditional and self._count_prepared > 1:
            registry = HookRegistry.check_if_exists_or_initialize(denoiser)
            # Remove the hooks after inference
            for hook_name in self._seg_layer_hook_names:
                registry.remove_hook(hook_name, recurse=True)

    def prepare_inputs(
        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
    ) -> List["BlockState"]:
        if input_fields is None:
            input_fields = self._input_fields

        if self.num_conditions == 1:
            tuple_indices = [0]
            input_predictions = ["pred_cond"]
        elif self.num_conditions == 2:
            tuple_indices = [0, 1]
            input_predictions = (
                ["pred_cond", "pred_uncond"] if self._is_cfg_enabled() else ["pred_cond", "pred_cond_seg"]
            )
        else:
            tuple_indices = [0, 1, 0]
            input_predictions = ["pred_cond", "pred_uncond", "pred_cond_seg"]
        data_batches = []
        for i in range(self.num_conditions):
            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], input_predictions[i])
            data_batches.append(data_batch)
        return data_batches

    def construct(
        self,
        pred_cond: ms.Tensor,
        pred_uncond: Optional[ms.Tensor] = None,
        pred_cond_seg: Optional[ms.Tensor] = None,
    ) -> ms.Tensor:
        pred = None

        if not self._is_cfg_enabled() and not self._is_seg_enabled():
            pred = pred_cond
        elif not self._is_cfg_enabled():
            shift = pred_cond - pred_cond_seg
            pred = pred_cond if self.use_original_formulation else pred_cond_seg
            pred = pred + self.seg_guidance_scale * shift
        elif not self._is_seg_enabled():
            shift = pred_cond - pred_uncond
            pred = pred_cond if self.use_original_formulation else pred_uncond
            pred = pred + self.guidance_scale * shift
        else:
            shift = pred_cond - pred_uncond
            shift_seg = pred_cond - pred_cond_seg
            pred = pred_cond if self.use_original_formulation else pred_uncond
            pred = pred + self.guidance_scale * shift + self.seg_guidance_scale * shift_seg

        if self.guidance_rescale > 0.0:
            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)

        return pred, {}

    @property
    def is_conditional(self) -> bool:
        return self._count_prepared == 1 or self._count_prepared == 3

    @property
    def num_conditions(self) -> int:
        num_conditions = 1
        if self._is_cfg_enabled():
            num_conditions += 1
        if self._is_seg_enabled():
            num_conditions += 1
        return num_conditions

    def _is_cfg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self._start * self._num_inference_steps)
            skip_stop_step = int(self._stop * self._num_inference_steps)
            is_within_range = skip_start_step <= self._step < skip_stop_step

        is_close = False
        if self.use_original_formulation:
            is_close = math.isclose(self.guidance_scale, 0.0)
        else:
            is_close = math.isclose(self.guidance_scale, 1.0)

        return is_within_range and not is_close

    def _is_seg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self.seg_guidance_start * self._num_inference_steps)
            skip_stop_step = int(self.seg_guidance_stop * self._num_inference_steps)
            is_within_range = skip_start_step < self._step < skip_stop_step

        is_zero = math.isclose(self.seg_guidance_scale, 0.0)

        return is_within_range and not is_zero

mindone.diffusers.guiders.perturbed_attention_guidance.PerturbedAttentionGuidance

Bases: BaseGuidance

Perturbed Attention Guidance (PAG): https://huggingface.co/papers/2403.17377

The intution behind PAG can be thought of as moving the CFG predicted distribution estimates further away from worse versions of the conditional distribution estimates. PAG was one of the first techniques to introduce the idea of using a worse version of the trained model for better guiding itself in the denoising process. It perturbs the attention scores of the latent stream by replacing the score matrix with an identity matrix for selectively chosen layers.

Additional reading: - Guiding a Diffusion Model with a Bad Version of Itself

PAG is implemented with similar implementation to SkipLayerGuidance due to overlap in the configuration parameters and implementation details.

PARAMETER	DESCRIPTION
guidance_scale	The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and deterioration of image quality. TYPE: `float`, defaults to `7.5` DEFAULT: 7.5
perturbed_guidance_scale	The scale parameter for perturbed attention guidance. TYPE: `float`, defaults to `2.8` DEFAULT: 2.8
perturbed_guidance_start	The fraction of the total number of denoising steps after which perturbed attention guidance starts. TYPE: `float`, defaults to `0.01` DEFAULT: 0.01
perturbed_guidance_stop	The fraction of the total number of denoising steps after which perturbed attention guidance stops. TYPE: `float`, defaults to `0.2` DEFAULT: 0.2
perturbed_guidance_layers	The layer indices to apply perturbed attention guidance to. Can be a single integer or a list of integers. If not provided, perturbed_guidance_config must be provided. TYPE: `int` or `List[int]`, *optional* DEFAULT: None
perturbed_guidance_config	The configuration for the perturbed attention guidance. Can be a single LayerSkipConfig or a list of LayerSkipConfig. If not provided, perturbed_guidance_layers must be provided. TYPE: `LayerSkipConfig` or `List[LayerSkipConfig]`, *optional* DEFAULT: None
guidance_rescale	The rescale factor applied to the noise predictions. This is used to improve image quality and fix overexposure. Based on Section 3.4 from Common Diffusion Noise Schedules and Sample Steps are Flawed. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
use_original_formulation	Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time. See [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details. TYPE: `bool`, defaults to `False` DEFAULT: False
start	The fraction of the total number of denoising steps after which guidance starts. TYPE: `float`, defaults to `0.01` DEFAULT: 0.0
stop	The fraction of the total number of denoising steps after which guidance stops. TYPE: `float`, defaults to `0.2` DEFAULT: 1.0

Source code in mindone/diffusers/guiders/perturbed_attention_guidance.py

class PerturbedAttentionGuidance(BaseGuidance):
    """
    Perturbed Attention Guidance (PAG): https://huggingface.co/papers/2403.17377

    The intution behind PAG can be thought of as moving the CFG predicted distribution estimates further away from
    worse versions of the conditional distribution estimates. PAG was one of the first techniques to introduce the idea
    of using a worse version of the trained model for better guiding itself in the denoising process. It perturbs the
    attention scores of the latent stream by replacing the score matrix with an identity matrix for selectively chosen
    layers.

    Additional reading:
    - [Guiding a Diffusion Model with a Bad Version of Itself](https://huggingface.co/papers/2406.02507)

    PAG is implemented with similar implementation to SkipLayerGuidance due to overlap in the configuration parameters
    and implementation details.

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
            deterioration of image quality.
        perturbed_guidance_scale (`float`, defaults to `2.8`):
            The scale parameter for perturbed attention guidance.
        perturbed_guidance_start (`float`, defaults to `0.01`):
            The fraction of the total number of denoising steps after which perturbed attention guidance starts.
        perturbed_guidance_stop (`float`, defaults to `0.2`):
            The fraction of the total number of denoising steps after which perturbed attention guidance stops.
        perturbed_guidance_layers (`int` or `List[int]`, *optional*):
            The layer indices to apply perturbed attention guidance to. Can be a single integer or a list of integers.
            If not provided, `perturbed_guidance_config` must be provided.
        perturbed_guidance_config (`LayerSkipConfig` or `List[LayerSkipConfig]`, *optional*):
            The configuration for the perturbed attention guidance. Can be a single `LayerSkipConfig` or a list of
            `LayerSkipConfig`. If not provided, `perturbed_guidance_layers` must be provided.
        guidance_rescale (`float`, defaults to `0.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
            Flawed](https://huggingface.co/papers/2305.08891).
        use_original_formulation (`bool`, defaults to `False`):
            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
            we use the diffusers-native implementation that has been in the codebase for a long time. See
            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
        start (`float`, defaults to `0.01`):
            The fraction of the total number of denoising steps after which guidance starts.
        stop (`float`, defaults to `0.2`):
            The fraction of the total number of denoising steps after which guidance stops.
    """

    # NOTE: The current implementation does not account for joint latent conditioning (text + image/video tokens in
    # the same latent stream). It assumes the entire latent is a single stream of visual tokens. It would be very
    # complex to support joint latent conditioning in a model-agnostic manner without specializing the implementation
    # for each model architecture.

    _input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]

    @register_to_config
    def __init__(
        self,
        guidance_scale: float = 7.5,
        perturbed_guidance_scale: float = 2.8,
        perturbed_guidance_start: float = 0.01,
        perturbed_guidance_stop: float = 0.2,
        perturbed_guidance_layers: Optional[Union[int, List[int]]] = None,
        perturbed_guidance_config: Union[LayerSkipConfig, List[LayerSkipConfig], Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
        use_original_formulation: bool = False,
        start: float = 0.0,
        stop: float = 1.0,
    ):
        super().__init__(start, stop)

        self.guidance_scale = guidance_scale
        self.skip_layer_guidance_scale = perturbed_guidance_scale
        self.skip_layer_guidance_start = perturbed_guidance_start
        self.skip_layer_guidance_stop = perturbed_guidance_stop
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation

        if perturbed_guidance_config is None:
            if perturbed_guidance_layers is None:
                raise ValueError(
                    "`perturbed_guidance_layers` must be provided if `perturbed_guidance_config` is not specified."
                )
            perturbed_guidance_config = LayerSkipConfig(
                indices=perturbed_guidance_layers,
                fqn="auto",
                skip_attention=False,
                skip_attention_scores=True,
                skip_ff=False,
            )
        else:
            if perturbed_guidance_layers is not None:
                raise ValueError(
                    "`perturbed_guidance_layers` should not be provided if `perturbed_guidance_config` is specified."
                )

        if isinstance(perturbed_guidance_config, dict):
            perturbed_guidance_config = LayerSkipConfig.from_dict(perturbed_guidance_config)

        if isinstance(perturbed_guidance_config, LayerSkipConfig):
            perturbed_guidance_config = [perturbed_guidance_config]

        if not isinstance(perturbed_guidance_config, list):
            raise ValueError(
                "`perturbed_guidance_config` must be a `LayerSkipConfig`, a list of `LayerSkipConfig`, or a dict that can be converted to a `LayerSkipConfig`."
            )
        elif isinstance(next(iter(perturbed_guidance_config), None), dict):
            perturbed_guidance_config = [LayerSkipConfig.from_dict(config) for config in perturbed_guidance_config]

        for config in perturbed_guidance_config:
            if config.skip_attention or not config.skip_attention_scores or config.skip_ff:
                logger.warning(
                    "Perturbed Attention Guidance is designed to perturb attention scores, "
                    "so `skip_attention` should be False, `skip_attention_scores` should be True, "
                    "and `skip_ff` should be False. "
                    "Please check your configuration. Modifying the config to match the expected values."
                )
            config.skip_attention = False
            config.skip_attention_scores = True
            config.skip_ff = False

        self.skip_layer_config = perturbed_guidance_config
        self._skip_layer_hook_names = [f"SkipLayerGuidance_{i}" for i in range(len(self.skip_layer_config))]

    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.prepare_models
    def prepare_models(self, denoiser: ms.nn.Cell) -> None:
        self._count_prepared += 1
        if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
            for name, config in zip(self._skip_layer_hook_names, self.skip_layer_config):
                _apply_layer_skip_hook(denoiser, config, name=name)

    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.cleanup_models
    def cleanup_models(self, denoiser: ms.nn.Cell) -> None:
        if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
            registry = HookRegistry.check_if_exists_or_initialize(denoiser)
            # Remove the hooks after inference
            for hook_name in self._skip_layer_hook_names:
                registry.remove_hook(hook_name, recurse=True)

    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.prepare_inputs
    def prepare_inputs(
        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
    ) -> List["BlockState"]:
        if input_fields is None:
            input_fields = self._input_fields

        if self.num_conditions == 1:
            tuple_indices = [0]
            input_predictions = ["pred_cond"]
        elif self.num_conditions == 2:
            tuple_indices = [0, 1]
            input_predictions = (
                ["pred_cond", "pred_uncond"] if self._is_cfg_enabled() else ["pred_cond", "pred_cond_skip"]
            )
        else:
            tuple_indices = [0, 1, 0]
            input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]
        data_batches = []
        for i in range(self.num_conditions):
            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], input_predictions[i])
            data_batches.append(data_batch)
        return data_batches

    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.construct
    def construct(
        self,
        pred_cond: ms.Tensor,
        pred_uncond: Optional[ms.Tensor] = None,
        pred_cond_skip: Optional[ms.Tensor] = None,
    ) -> ms.Tensor:
        pred = None

        if not self._is_cfg_enabled() and not self._is_slg_enabled():
            pred = pred_cond
        elif not self._is_cfg_enabled():
            shift = pred_cond - pred_cond_skip
            pred = pred_cond if self.use_original_formulation else pred_cond_skip
            pred = pred + self.skip_layer_guidance_scale * shift
        elif not self._is_slg_enabled():
            shift = pred_cond - pred_uncond
            pred = pred_cond if self.use_original_formulation else pred_uncond
            pred = pred + self.guidance_scale * shift
        else:
            shift = pred_cond - pred_uncond
            shift_skip = pred_cond - pred_cond_skip
            pred = pred_cond if self.use_original_formulation else pred_uncond
            pred = pred + self.guidance_scale * shift + self.skip_layer_guidance_scale * shift_skip

        if self.guidance_rescale > 0.0:
            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)

        return pred, {}

    @property
    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.is_conditional
    def is_conditional(self) -> bool:
        return self._count_prepared == 1 or self._count_prepared == 3

    @property
    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.num_conditions
    def num_conditions(self) -> int:
        num_conditions = 1
        if self._is_cfg_enabled():
            num_conditions += 1
        if self._is_slg_enabled():
            num_conditions += 1
        return num_conditions

    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance._is_cfg_enabled
    def _is_cfg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self._start * self._num_inference_steps)
            skip_stop_step = int(self._stop * self._num_inference_steps)
            is_within_range = skip_start_step <= self._step < skip_stop_step

        is_close = False
        if self.use_original_formulation:
            is_close = math.isclose(self.guidance_scale, 0.0)
        else:
            is_close = math.isclose(self.guidance_scale, 1.0)

        return is_within_range and not is_close

    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance._is_slg_enabled
    def _is_slg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self.skip_layer_guidance_start * self._num_inference_steps)
            skip_stop_step = int(self.skip_layer_guidance_stop * self._num_inference_steps)
            is_within_range = skip_start_step < self._step < skip_stop_step

        is_zero = math.isclose(self.skip_layer_guidance_scale, 0.0)

        return is_within_range and not is_zero

mindone.diffusers.guiders.adaptive_projected_guidance.AdaptiveProjectedGuidance

Bases: BaseGuidance

Adaptive Projected Guidance (APG): https://huggingface.co/papers/2410.02416

PARAMETER	DESCRIPTION
guidance_scale	The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and deterioration of image quality. TYPE: `float`, defaults to `7.5` DEFAULT: 7.5
adaptive_projected_guidance_momentum	The momentum parameter for the adaptive projected guidance. Disabled if set to None. TYPE: `float`, defaults to `None` DEFAULT: None
adaptive_projected_guidance_rescale	The rescale factor applied to the noise predictions. This is used to improve image quality and fix TYPE: `float`, defaults to `15.0` DEFAULT: 15.0
guidance_rescale	The rescale factor applied to the noise predictions. This is used to improve image quality and fix overexposure. Based on Section 3.4 from Common Diffusion Noise Schedules and Sample Steps are Flawed. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
use_original_formulation	Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time. See [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details. TYPE: `bool`, defaults to `False` DEFAULT: False
start	The fraction of the total number of denoising steps after which guidance starts. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
stop	The fraction of the total number of denoising steps after which guidance stops. TYPE: `float`, defaults to `1.0` DEFAULT: 1.0

Source code in mindone/diffusers/guiders/adaptive_projected_guidance.py

class AdaptiveProjectedGuidance(BaseGuidance):
    """
    Adaptive Projected Guidance (APG): https://huggingface.co/papers/2410.02416

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
            deterioration of image quality.
        adaptive_projected_guidance_momentum (`float`, defaults to `None`):
            The momentum parameter for the adaptive projected guidance. Disabled if set to `None`.
        adaptive_projected_guidance_rescale (`float`, defaults to `15.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
        guidance_rescale (`float`, defaults to `0.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
            Flawed](https://huggingface.co/papers/2305.08891).
        use_original_formulation (`bool`, defaults to `False`):
            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
            we use the diffusers-native implementation that has been in the codebase for a long time. See
            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
        start (`float`, defaults to `0.0`):
            The fraction of the total number of denoising steps after which guidance starts.
        stop (`float`, defaults to `1.0`):
            The fraction of the total number of denoising steps after which guidance stops.
    """

    _input_predictions = ["pred_cond", "pred_uncond"]

    @register_to_config
    def __init__(
        self,
        guidance_scale: float = 7.5,
        adaptive_projected_guidance_momentum: Optional[float] = None,
        adaptive_projected_guidance_rescale: float = 15.0,
        eta: float = 1.0,
        guidance_rescale: float = 0.0,
        use_original_formulation: bool = False,
        start: float = 0.0,
        stop: float = 1.0,
    ):
        super().__init__(start, stop)

        self.guidance_scale = guidance_scale
        self.adaptive_projected_guidance_momentum = adaptive_projected_guidance_momentum
        self.adaptive_projected_guidance_rescale = adaptive_projected_guidance_rescale
        self.eta = eta
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation
        self.momentum_buffer = None

    def prepare_inputs(
        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
    ) -> List["BlockState"]:
        if input_fields is None:
            input_fields = self._input_fields

        if self._step == 0:
            if self.adaptive_projected_guidance_momentum is not None:
                self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum)
        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
        data_batches = []
        for i in range(self.num_conditions):
            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
            data_batches.append(data_batch)
        return data_batches

    def construct(self, pred_cond: ms.Tensor, pred_uncond: Optional[ms.Tensor] = None) -> ms.Tensor:
        pred = None

        if not self._is_apg_enabled():
            pred = pred_cond
        else:
            pred = normalized_guidance(
                pred_cond,
                pred_uncond,
                self.guidance_scale,
                self.momentum_buffer,
                self.eta,
                self.adaptive_projected_guidance_rescale,
                self.use_original_formulation,
            )

        if self.guidance_rescale > 0.0:
            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)

        return pred, {}

    @property
    def is_conditional(self) -> bool:
        return self._count_prepared == 1

    @property
    def num_conditions(self) -> int:
        num_conditions = 1
        if self._is_apg_enabled():
            num_conditions += 1
        return num_conditions

    def _is_apg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self._start * self._num_inference_steps)
            skip_stop_step = int(self._stop * self._num_inference_steps)
            is_within_range = skip_start_step <= self._step < skip_stop_step

        is_close = False
        if self.use_original_formulation:
            is_close = math.isclose(self.guidance_scale, 0.0)
        else:
            is_close = math.isclose(self.guidance_scale, 1.0)

        return is_within_range and not is_close

mindone.diffusers.guiders.auto_guidance.AutoGuidance

Bases: BaseGuidance

PARAMETER	DESCRIPTION
guidance_scale	The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and deterioration of image quality. TYPE: `float`, defaults to `7.5` DEFAULT: 7.5
auto_guidance_layers	The layer indices to apply skip layer guidance to. Can be a single integer or a list of integers. If not provided, skip_layer_config must be provided. TYPE: `int` or `List[int]`, *optional* DEFAULT: None
auto_guidance_config	The configuration for the skip layer guidance. Can be a single LayerSkipConfig or a list of LayerSkipConfig. If not provided, skip_layer_guidance_layers must be provided. TYPE: `LayerSkipConfig` or `List[LayerSkipConfig]`, *optional* DEFAULT: None
dropout	The dropout probability for autoguidance on the enabled skip layers (either with auto_guidance_layers or auto_guidance_config). If not provided, the dropout probability will be set to 1.0. TYPE: `float`, *optional* DEFAULT: None
guidance_rescale	The rescale factor applied to the noise predictions. This is used to improve image quality and fix overexposure. Based on Section 3.4 from Common Diffusion Noise Schedules and Sample Steps are Flawed. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
use_original_formulation	Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time. See [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details. TYPE: `bool`, defaults to `False` DEFAULT: False
start	The fraction of the total number of denoising steps after which guidance starts. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
stop	The fraction of the total number of denoising steps after which guidance stops. TYPE: `float`, defaults to `1.0` DEFAULT: 1.0

Source code in mindone/diffusers/guiders/auto_guidance.py

class AutoGuidance(BaseGuidance):
    """
    AutoGuidance: https://huggingface.co/papers/2406.02507

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
            deterioration of image quality.
        auto_guidance_layers (`int` or `List[int]`, *optional*):
            The layer indices to apply skip layer guidance to. Can be a single integer or a list of integers. If not
            provided, `skip_layer_config` must be provided.
        auto_guidance_config (`LayerSkipConfig` or `List[LayerSkipConfig]`, *optional*):
            The configuration for the skip layer guidance. Can be a single `LayerSkipConfig` or a list of
            `LayerSkipConfig`. If not provided, `skip_layer_guidance_layers` must be provided.
        dropout (`float`, *optional*):
            The dropout probability for autoguidance on the enabled skip layers (either with `auto_guidance_layers` or
            `auto_guidance_config`). If not provided, the dropout probability will be set to 1.0.
        guidance_rescale (`float`, defaults to `0.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
            Flawed](https://huggingface.co/papers/2305.08891).
        use_original_formulation (`bool`, defaults to `False`):
            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
            we use the diffusers-native implementation that has been in the codebase for a long time. See
            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
        start (`float`, defaults to `0.0`):
            The fraction of the total number of denoising steps after which guidance starts.
        stop (`float`, defaults to `1.0`):
            The fraction of the total number of denoising steps after which guidance stops.
    """

    _input_predictions = ["pred_cond", "pred_uncond"]

    @register_to_config
    def __init__(
        self,
        guidance_scale: float = 7.5,
        auto_guidance_layers: Optional[Union[int, List[int]]] = None,
        auto_guidance_config: Union[LayerSkipConfig, List[LayerSkipConfig], Dict[str, Any]] = None,
        dropout: Optional[float] = None,
        guidance_rescale: float = 0.0,
        use_original_formulation: bool = False,
        start: float = 0.0,
        stop: float = 1.0,
    ):
        super().__init__(start, stop)

        self.guidance_scale = guidance_scale
        self.auto_guidance_layers = auto_guidance_layers
        self.auto_guidance_config = auto_guidance_config
        self.dropout = dropout
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation

        if auto_guidance_layers is None and auto_guidance_config is None:
            raise ValueError(
                "Either `auto_guidance_layers` or `auto_guidance_config` must be provided to enable Skip Layer Guidance."
            )
        if auto_guidance_layers is not None and auto_guidance_config is not None:
            raise ValueError("Only one of `auto_guidance_layers` or `auto_guidance_config` can be provided.")
        if (dropout is None and auto_guidance_layers is not None) or (
            dropout is not None and auto_guidance_layers is None
        ):
            raise ValueError("`dropout` must be provided if `auto_guidance_layers` is provided.")

        if auto_guidance_layers is not None:
            if isinstance(auto_guidance_layers, int):
                auto_guidance_layers = [auto_guidance_layers]
            if not isinstance(auto_guidance_layers, list):
                raise ValueError(
                    f"Expected `auto_guidance_layers` to be an int or a list of ints, but got {type(auto_guidance_layers)}."
                )
            auto_guidance_config = [
                LayerSkipConfig(layer, fqn="auto", dropout=dropout) for layer in auto_guidance_layers
            ]

        if isinstance(auto_guidance_config, dict):
            auto_guidance_config = LayerSkipConfig.from_dict(auto_guidance_config)

        if isinstance(auto_guidance_config, LayerSkipConfig):
            auto_guidance_config = [auto_guidance_config]

        if not isinstance(auto_guidance_config, list):
            raise ValueError(
                f"Expected `auto_guidance_config` to be a LayerSkipConfig or a list of LayerSkipConfig, but got {type(auto_guidance_config)}."
            )
        elif isinstance(next(iter(auto_guidance_config), None), dict):
            auto_guidance_config = [LayerSkipConfig.from_dict(config) for config in auto_guidance_config]

        self.auto_guidance_config = auto_guidance_config
        self._auto_guidance_hook_names = [f"AutoGuidance_{i}" for i in range(len(self.auto_guidance_config))]

    def prepare_models(self, denoiser: ms.nn.Cell) -> None:
        self._count_prepared += 1
        if self._is_ag_enabled() and self.is_unconditional:
            for name, config in zip(self._auto_guidance_hook_names, self.auto_guidance_config):
                _apply_layer_skip_hook(denoiser, config, name=name)

    def cleanup_models(self, denoiser: ms.nn.Cell) -> None:
        if self._is_ag_enabled() and self.is_unconditional:
            for name in self._auto_guidance_hook_names:
                registry = HookRegistry.check_if_exists_or_initialize(denoiser)
                registry.remove_hook(name, recurse=True)

    def prepare_inputs(
        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
    ) -> List["BlockState"]:
        if input_fields is None:
            input_fields = self._input_fields

        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
        data_batches = []
        for i in range(self.num_conditions):
            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
            data_batches.append(data_batch)
        return data_batches

    def construct(self, pred_cond: ms.Tensor, pred_uncond: Optional[ms.Tensor] = None) -> ms.Tensor:
        pred = None

        if not self._is_ag_enabled():
            pred = pred_cond
        else:
            shift = pred_cond - pred_uncond
            pred = pred_cond if self.use_original_formulation else pred_uncond
            pred = pred + self.guidance_scale * shift

        if self.guidance_rescale > 0.0:
            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)

        return pred, {}

    @property
    def is_conditional(self) -> bool:
        return self._count_prepared == 1

    @property
    def num_conditions(self) -> int:
        num_conditions = 1
        if self._is_ag_enabled():
            num_conditions += 1
        return num_conditions

    def _is_ag_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self._start * self._num_inference_steps)
            skip_stop_step = int(self._stop * self._num_inference_steps)
            is_within_range = skip_start_step <= self._step < skip_stop_step

        is_close = False
        if self.use_original_formulation:
            is_close = math.isclose(self.guidance_scale, 0.0)
        else:
            is_close = math.isclose(self.guidance_scale, 1.0)

        return is_within_range and not is_close

mindone.diffusers.guiders.tangential_classifier_free_guidance.TangentialClassifierFreeGuidance

Bases: BaseGuidance

Tangential Classifier Free Guidance (TCFG): https://huggingface.co/papers/2503.18137

PARAMETER	DESCRIPTION
guidance_scale	The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and deterioration of image quality. TYPE: `float`, defaults to `7.5` DEFAULT: 7.5
guidance_rescale	The rescale factor applied to the noise predictions. This is used to improve image quality and fix overexposure. Based on Section 3.4 from Common Diffusion Noise Schedules and Sample Steps are Flawed. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
use_original_formulation	Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time. See [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details. TYPE: `bool`, defaults to `False` DEFAULT: False
start	The fraction of the total number of denoising steps after which guidance starts. TYPE: `float`, defaults to `0.0` DEFAULT: 0.0
stop	The fraction of the total number of denoising steps after which guidance stops. TYPE: `float`, defaults to `1.0` DEFAULT: 1.0

Source code in mindone/diffusers/guiders/tangential_classifier_free_guidance.py

class TangentialClassifierFreeGuidance(BaseGuidance):
    """
    Tangential Classifier Free Guidance (TCFG): https://huggingface.co/papers/2503.18137

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
            deterioration of image quality.
        guidance_rescale (`float`, defaults to `0.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
            Flawed](https://huggingface.co/papers/2305.08891).
        use_original_formulation (`bool`, defaults to `False`):
            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
            we use the diffusers-native implementation that has been in the codebase for a long time. See
            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
        start (`float`, defaults to `0.0`):
            The fraction of the total number of denoising steps after which guidance starts.
        stop (`float`, defaults to `1.0`):
            The fraction of the total number of denoising steps after which guidance stops.
    """

    _input_predictions = ["pred_cond", "pred_uncond"]

    @register_to_config
    def __init__(
        self,
        guidance_scale: float = 7.5,
        guidance_rescale: float = 0.0,
        use_original_formulation: bool = False,
        start: float = 0.0,
        stop: float = 1.0,
    ):
        super().__init__(start, stop)

        self.guidance_scale = guidance_scale
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation

    def prepare_inputs(
        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
    ) -> List["BlockState"]:
        if input_fields is None:
            input_fields = self._input_fields

        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
        data_batches = []
        for i in range(self.num_conditions):
            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
            data_batches.append(data_batch)
        return data_batches

    def construct(self, pred_cond: ms.Tensor, pred_uncond: Optional[ms.Tensor] = None) -> ms.Tensor:
        pred = None

        if not self._is_tcfg_enabled():
            pred = pred_cond
        else:
            pred = normalized_guidance(pred_cond, pred_uncond, self.guidance_scale, self.use_original_formulation)

        if self.guidance_rescale > 0.0:
            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)

        return pred, {}

    @property
    def is_conditional(self) -> bool:
        return self._num_outputs_prepared == 1

    @property
    def num_conditions(self) -> int:
        num_conditions = 1
        if self._is_tcfg_enabled():
            num_conditions += 1
        return num_conditions

    def _is_tcfg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self._start * self._num_inference_steps)
            skip_stop_step = int(self._stop * self._num_inference_steps)
            is_within_range = skip_start_step <= self._step < skip_stop_step

        is_close = False
        if self.use_original_formulation:
            is_close = math.isclose(self.guidance_scale, 0.0)
        else:
            is_close = math.isclose(self.guidance_scale, 1.0)

        return is_within_range and not is_close