FluxTransformer2DModel

A Transformer model for image-like data from Flux.

mindone.diffusers.models.transformers.transformer_flux.FluxTransformer2DModel

Bases: ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin

The Transformer model introduced in Flux.

Reference: https://blackforestlabs.ai/announcing-black-forest-labs/

PARAMETER	DESCRIPTION
patch_size	Patch size to turn the input data into small patches. TYPE: `int` DEFAULT: 1
in_channels	The number of channels in the input. TYPE: `int`, *optional*, defaults to 16 DEFAULT: 64
num_layers	The number of layers of MMDiT blocks to use. TYPE: `int`, *optional*, defaults to 18 DEFAULT: 19
num_single_layers	The number of layers of single DiT blocks to use. TYPE: `int`, *optional*, defaults to 18 DEFAULT: 38
attention_head_dim	The number of channels in each head. TYPE: `int`, *optional*, defaults to 64 DEFAULT: 128
num_attention_heads	The number of heads to use for multi-head attention. TYPE: `int`, *optional*, defaults to 18 DEFAULT: 24
joint_attention_dim	The number of encoder_hidden_states dimensions to use. TYPE: `int`, *optional* DEFAULT: 4096
pooled_projection_dim	Number of dimensions to use when projecting the pooled_projections. TYPE: `int` DEFAULT: 768
guidance_embeds	Whether to use guidance embeddings. TYPE: `bool`, defaults to False DEFAULT: False

Source code in mindone/diffusers/models/transformers/transformer_flux.py

class FluxTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
    """
    The Transformer model introduced in Flux.

    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/

    Parameters:
        patch_size (`int`): Patch size to turn the input data into small patches.
        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
        num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
    """

    _supports_gradient_checkpointing = True
    _no_split_modules = ["FluxTransformerBlock", "FluxSingleTransformerBlock"]

    @register_to_config
    def __init__(
        self,
        patch_size: int = 1,
        in_channels: int = 64,
        num_layers: int = 19,
        num_single_layers: int = 38,
        attention_head_dim: int = 128,
        num_attention_heads: int = 24,
        joint_attention_dim: int = 4096,
        pooled_projection_dim: int = 768,
        guidance_embeds: bool = False,
        axes_dims_rope: Tuple[int] = (16, 56, 56),
    ):
        super().__init__()
        self.out_channels = in_channels
        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim

        self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)

        text_time_guidance_cls = (
            CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
        )
        self.time_text_embed = text_time_guidance_cls(
            embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
        )

        self.context_embedder = nn.Dense(self.config.joint_attention_dim, self.inner_dim)
        self.x_embedder = nn.Dense(self.config.in_channels, self.inner_dim)

        self.transformer_blocks = nn.CellList(
            [
                FluxTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=self.config.num_attention_heads,
                    attention_head_dim=self.config.attention_head_dim,
                )
                for i in range(self.config.num_layers)
            ]
        )

        self.single_transformer_blocks = nn.CellList(
            [
                FluxSingleTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=self.config.num_attention_heads,
                    attention_head_dim=self.config.attention_head_dim,
                )
                for i in range(self.config.num_single_layers)
            ]
        )

        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
        self.proj_out = nn.Dense(self.inner_dim, patch_size * patch_size * self.out_channels, has_bias=True)

        self._gradient_checkpointing = False

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: nn.Cell, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor()

            for sub_name, child in module.name_cells().items():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.name_cells().items():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: nn.Cell, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.name_cells().items():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.name_cells().items():
            fn_recursive_attn_processor(name, module, processor)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedFluxAttnProcessor2_0
    def fuse_qkv_projections(self):
        """
        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
        are fused. For cross-attention modules, key and value projection matrices are fused.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if "Added" in str(attn_processor.__class__.__name__):
                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")

        self.original_attn_processors = self.attn_processors

        for module in self.cells():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

        self.set_attn_processor(FusedFluxAttnProcessor2_0())

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>

        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    @property
    def gradient_checkpointing(self):
        return self._gradient_checkpointing

    @gradient_checkpointing.setter
    def gradient_checkpointing(self, value):
        if self._gradient_checkpointing != value:
            self._gradient_checkpointing = value
            for block in self.transformer_blocks:
                block.recompute()
            for block in self.single_transformer_blocks:
                block.recompute()

    def construct(
        self,
        hidden_states: ms.Tensor,
        encoder_hidden_states: ms.Tensor = None,
        pooled_projections: ms.Tensor = None,
        timestep: ms.Tensor = None,
        img_ids: ms.Tensor = None,
        txt_ids: ms.Tensor = None,
        guidance: ms.Tensor = None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        controlnet_block_samples=None,
        controlnet_single_block_samples=None,
        return_dict: bool = False,
        controlnet_blocks_repeat: bool = False,
    ) -> Union[ms.Tensor, Transformer2DModelOutput]:
        """
        The [`FluxTransformer2DModel`] forward method.

        Args:
            hidden_states (`ms.Tensor` of shape `(batch size, channel, height, width)`):
                Input `hidden_states`.
            encoder_hidden_states (`ms.Tensor` of shape `(batch size, sequence_len, embed_dims)`):
                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
            pooled_projections (`ms.Tensor` of shape `(batch_size, projection_dim)`): Embeddings projected
                from the embeddings of input conditions.
            timestep ( `ms.Tensor`):
                Used to indicate denoising step.
            block_controlnet_hidden_states: (`list` of `ms.Tensor`):
                A list of tensors that if specified are added to the residuals of transformer blocks.
            joint_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            return_dict (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
                tuple.

        Returns:
            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
            `tuple` where the first element is the sample tensor.
        """
        if joint_attention_kwargs is not None:
            joint_attention_kwargs = joint_attention_kwargs.copy()

        if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
            logger.warning(
                "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
            )
        hidden_states = self.x_embedder(hidden_states)

        timestep = timestep.to(hidden_states.dtype) * 1000
        if guidance is not None:
            guidance = guidance.to(hidden_states.dtype) * 1000
        else:
            guidance = None
        temb = (
            self.time_text_embed(timestep, pooled_projections)
            if guidance is None
            else self.time_text_embed(timestep, guidance, pooled_projections)
        )
        encoder_hidden_states = self.context_embedder(encoder_hidden_states)

        if txt_ids.ndim == 3:
            logger.warning(
                "Passing `txt_ids` 3d ms.Tensor is deprecated."
                "Please remove the batch dimension and pass it as a 2d mindspore Tensor"
            )
            txt_ids = txt_ids[0]
        if img_ids.ndim == 3:
            logger.warning(
                "Passing `img_ids` 3d ms.Tensor is deprecated."
                "Please remove the batch dimension and pass it as a 2d mindspore Tensor"
            )
            img_ids = img_ids[0]

        ids = ops.cat((txt_ids, img_ids), axis=0)
        image_rotary_emb = self.pos_embed(ids)

        for index_block, block in enumerate(self.transformer_blocks):
            encoder_hidden_states, hidden_states = block(
                hidden_states=hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                temb=temb,
                image_rotary_emb=image_rotary_emb,
                joint_attention_kwargs=joint_attention_kwargs,
            )
            # controlnet residual
            if controlnet_block_samples is not None:
                interval_control = (len(self.transformer_blocks) + len(controlnet_block_samples) - 1) // len(
                    controlnet_block_samples
                )  # not supporting numpy
                # For Xlabs ControlNet.
                if controlnet_blocks_repeat:
                    hidden_states = (
                        hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
                    )
                else:
                    hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]

        hidden_states = ops.cat([encoder_hidden_states, hidden_states], axis=1)

        for index_block, block in enumerate(self.single_transformer_blocks):
            hidden_states = block(
                hidden_states=hidden_states,
                temb=temb,
                image_rotary_emb=image_rotary_emb,
                joint_attention_kwargs=joint_attention_kwargs,
            )

            # controlnet residual
            if controlnet_single_block_samples is not None:
                interval_control = (
                    len(self.single_transformer_blocks) + len(controlnet_single_block_samples) - 1
                ) // len(
                    controlnet_single_block_samples
                )  # not supporting numpy
                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
                    + controlnet_single_block_samples[index_block // interval_control]
                )

        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]

        hidden_states = self.norm_out(hidden_states, temb)
        output = self.proj_out(hidden_states)

        if not return_dict:
            return (output,)

        return Transformer2DModelOutput(sample=output)

mindone.diffusers.models.transformers.transformer_flux.FluxTransformer2DModel.attn_processors: Dict[str, AttentionProcessor] property

RETURNS	DESCRIPTION
Dict[str, AttentionProcessor]	dict of attention processors: A dictionary containing all attention processors used in the model with
Dict[str, AttentionProcessor]	indexed by its weight name.

mindone.diffusers.models.transformers.transformer_flux.FluxTransformer2DModel.construct(hidden_states, encoder_hidden_states=None, pooled_projections=None, timestep=None, img_ids=None, txt_ids=None, guidance=None, joint_attention_kwargs=None, controlnet_block_samples=None, controlnet_single_block_samples=None, return_dict=False, controlnet_blocks_repeat=False)

The [FluxTransformer2DModel] forward method.

PARAMETER	DESCRIPTION
hidden_states	Input hidden_states. TYPE: `ms.Tensor` of shape `(batch size, channel, height, width)`
encoder_hidden_states	Conditional embeddings (embeddings computed from the input conditions such as prompts) to use. TYPE: `ms.Tensor` of shape `(batch size, sequence_len, embed_dims)` DEFAULT: None
pooled_projections	Embeddings projected from the embeddings of input conditions. TYPE: `ms.Tensor` of shape `(batch_size, projection_dim)` DEFAULT: None
timestep	Used to indicate denoising step. TYPE: `ms.Tensor` DEFAULT: None
block_controlnet_hidden_states	(list of ms.Tensor): A list of tensors that if specified are added to the residuals of transformer blocks.
joint_attention_kwargs	A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor. TYPE: `dict`, *optional* DEFAULT: None
return_dict	Whether or not to return a [~models.transformer_2d.Transformer2DModelOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

RETURNS	DESCRIPTION
Union[Tensor, Transformer2DModelOutput]	If return_dict is True, an [~models.transformer_2d.Transformer2DModelOutput] is returned, otherwise a
Union[Tensor, Transformer2DModelOutput]	tuple where the first element is the sample tensor.

Source code in mindone/diffusers/models/transformers/transformer_flux.py

def construct(
    self,
    hidden_states: ms.Tensor,
    encoder_hidden_states: ms.Tensor = None,
    pooled_projections: ms.Tensor = None,
    timestep: ms.Tensor = None,
    img_ids: ms.Tensor = None,
    txt_ids: ms.Tensor = None,
    guidance: ms.Tensor = None,
    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    controlnet_block_samples=None,
    controlnet_single_block_samples=None,
    return_dict: bool = False,
    controlnet_blocks_repeat: bool = False,
) -> Union[ms.Tensor, Transformer2DModelOutput]:
    """
    The [`FluxTransformer2DModel`] forward method.

    Args:
        hidden_states (`ms.Tensor` of shape `(batch size, channel, height, width)`):
            Input `hidden_states`.
        encoder_hidden_states (`ms.Tensor` of shape `(batch size, sequence_len, embed_dims)`):
            Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
        pooled_projections (`ms.Tensor` of shape `(batch_size, projection_dim)`): Embeddings projected
            from the embeddings of input conditions.
        timestep ( `ms.Tensor`):
            Used to indicate denoising step.
        block_controlnet_hidden_states: (`list` of `ms.Tensor`):
            A list of tensors that if specified are added to the residuals of transformer blocks.
        joint_attention_kwargs (`dict`, *optional*):
            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
            `self.processor` in
            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
        return_dict (`bool`, *optional*, defaults to `False`):
            Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
            tuple.

    Returns:
        If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
        `tuple` where the first element is the sample tensor.
    """
    if joint_attention_kwargs is not None:
        joint_attention_kwargs = joint_attention_kwargs.copy()

    if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
        logger.warning(
            "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
        )
    hidden_states = self.x_embedder(hidden_states)

    timestep = timestep.to(hidden_states.dtype) * 1000
    if guidance is not None:
        guidance = guidance.to(hidden_states.dtype) * 1000
    else:
        guidance = None
    temb = (
        self.time_text_embed(timestep, pooled_projections)
        if guidance is None
        else self.time_text_embed(timestep, guidance, pooled_projections)
    )
    encoder_hidden_states = self.context_embedder(encoder_hidden_states)

    if txt_ids.ndim == 3:
        logger.warning(
            "Passing `txt_ids` 3d ms.Tensor is deprecated."
            "Please remove the batch dimension and pass it as a 2d mindspore Tensor"
        )
        txt_ids = txt_ids[0]
    if img_ids.ndim == 3:
        logger.warning(
            "Passing `img_ids` 3d ms.Tensor is deprecated."
            "Please remove the batch dimension and pass it as a 2d mindspore Tensor"
        )
        img_ids = img_ids[0]

    ids = ops.cat((txt_ids, img_ids), axis=0)
    image_rotary_emb = self.pos_embed(ids)

    for index_block, block in enumerate(self.transformer_blocks):
        encoder_hidden_states, hidden_states = block(
            hidden_states=hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            temb=temb,
            image_rotary_emb=image_rotary_emb,
            joint_attention_kwargs=joint_attention_kwargs,
        )
        # controlnet residual
        if controlnet_block_samples is not None:
            interval_control = (len(self.transformer_blocks) + len(controlnet_block_samples) - 1) // len(
                controlnet_block_samples
            )  # not supporting numpy
            # For Xlabs ControlNet.
            if controlnet_blocks_repeat:
                hidden_states = (
                    hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
                )
            else:
                hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]

    hidden_states = ops.cat([encoder_hidden_states, hidden_states], axis=1)

    for index_block, block in enumerate(self.single_transformer_blocks):
        hidden_states = block(
            hidden_states=hidden_states,
            temb=temb,
            image_rotary_emb=image_rotary_emb,
            joint_attention_kwargs=joint_attention_kwargs,
        )

        # controlnet residual
        if controlnet_single_block_samples is not None:
            interval_control = (
                len(self.single_transformer_blocks) + len(controlnet_single_block_samples) - 1
            ) // len(
                controlnet_single_block_samples
            )  # not supporting numpy
            hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
                hidden_states[:, encoder_hidden_states.shape[1] :, ...]
                + controlnet_single_block_samples[index_block // interval_control]
            )

    hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]

    hidden_states = self.norm_out(hidden_states, temb)
    output = self.proj_out(hidden_states)

    if not return_dict:
        return (output,)

    return Transformer2DModelOutput(sample=output)

mindone.diffusers.models.transformers.transformer_flux.FluxTransformer2DModel.fuse_qkv_projections()

Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) are fused. For cross-attention modules, key and value projection matrices are fused.

This API is 🧪 experimental.

Source code in mindone/diffusers/models/transformers/transformer_flux.py

def fuse_qkv_projections(self):
    """
    Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
    are fused. For cross-attention modules, key and value projection matrices are fused.

    <Tip warning={true}>

    This API is 🧪 experimental.

    </Tip>
    """
    self.original_attn_processors = None

    for _, attn_processor in self.attn_processors.items():
        if "Added" in str(attn_processor.__class__.__name__):
            raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")

    self.original_attn_processors = self.attn_processors

    for module in self.cells():
        if isinstance(module, Attention):
            module.fuse_projections(fuse=True)

    self.set_attn_processor(FusedFluxAttnProcessor2_0())

mindone.diffusers.models.transformers.transformer_flux.FluxTransformer2DModel.set_attn_processor(processor)

Sets the attention processor to use to compute attention.

PARAMETER	DESCRIPTION
processor	The instantiated processor class or a dictionary of processor classes that will be set as the processor for all Attention layers. If processor is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors. TYPE: `dict` of `AttentionProcessor` or only `AttentionProcessor`

Source code in mindone/diffusers/models/transformers/transformer_flux.py

def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
    r"""
    Sets the attention processor to use to compute attention.

    Parameters:
        processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
            The instantiated processor class or a dictionary of processor classes that will be set as the processor
            for **all** `Attention` layers.

            If `processor` is a dict, the key needs to define the path to the corresponding cross attention
            processor. This is strongly recommended when setting trainable attention processors.

    """
    count = len(self.attn_processors.keys())

    if isinstance(processor, dict) and len(processor) != count:
        raise ValueError(
            f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
            f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
        )

    def fn_recursive_attn_processor(name: str, module: nn.Cell, processor):
        if hasattr(module, "set_processor"):
            if not isinstance(processor, dict):
                module.set_processor(processor)
            else:
                module.set_processor(processor.pop(f"{name}.processor"))

        for sub_name, child in module.name_cells().items():
            fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

    for name, module in self.name_cells().items():
        fn_recursive_attn_processor(name, module, processor)

mindone.diffusers.models.transformers.transformer_flux.FluxTransformer2DModel.unfuse_qkv_projections()

Disables the fused QKV projection if enabled.

This API is 🧪 experimental.

Source code in mindone/diffusers/models/transformers/transformer_flux.py

def unfuse_qkv_projections(self):
    """Disables the fused QKV projection if enabled.

    <Tip warning={true}>

    This API is 🧪 experimental.

    </Tip>

    """
    if self.original_attn_processors is not None:
        self.set_attn_processor(self.original_attn_processors)