HunyuanDiT2DModel

A Diffusion Transformer model for 2D data from Hunyuan-DiT.

mindone.diffusers.HunyuanDiT2DModel

Bases: ModelMixin, ConfigMixin

Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.

PARAMETER	DESCRIPTION
num_attention_heads	The number of heads to use for multi-head attention. TYPE: `int`, *optional*, defaults to 16 DEFAULT: 16
attention_head_dim	The number of channels in each head. TYPE: `int`, *optional*, defaults to 88 DEFAULT: 88
in_channels	The number of channels in the input and output (specify if the input is continuous). TYPE: `int`, *optional* DEFAULT: None
patch_size	The size of the patch to use for the input. TYPE: `int`, *optional* DEFAULT: None
activation_fn	Activation function to use in feed-forward. TYPE: `str`, *optional*, defaults to `"geglu"` DEFAULT: 'gelu-approximate'
sample_size	The width of the latent images. This is fixed during training since it is used to learn a number of position embeddings. TYPE: `int`, *optional* DEFAULT: 32
dropout	The dropout probability to use. TYPE: `float`, *optional*, defaults to 0.0
cross_attention_dim	The number of dimension in the clip text embedding. TYPE: `int`, *optional* DEFAULT: 1024
hidden_size	The size of hidden layer in the conditioning embedding layers. TYPE: `int`, *optional* DEFAULT: 1152
num_layers	The number of layers of Transformer blocks to use. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 28
mlp_ratio	The ratio of the hidden layer size to the input size. TYPE: `float`, *optional*, defaults to 4.0 DEFAULT: 4.0
learn_sigma	Whether to predict variance. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True
cross_attention_dim_t5	The number dimensions in t5 text embedding. TYPE: `int`, *optional* DEFAULT: 2048
pooled_projection_dim	The size of the pooled projection. TYPE: `int`, *optional* DEFAULT: 1024
text_len	The length of the clip text embedding. TYPE: `int`, *optional* DEFAULT: 77
text_len_t5	The length of the T5 text embedding. TYPE: `int`, *optional* DEFAULT: 256
use_style_cond_and_image_meta_size	Whether or not to use style condition and image meta size. True for version <=1.1, False for version >= 1.2 TYPE: `bool`, *optional* DEFAULT: True

Source code in mindone/diffusers/models/transformers/hunyuan_transformer_2d.py

class HunyuanDiT2DModel(ModelMixin, ConfigMixin):
    """
    HunYuanDiT: Diffusion model with a Transformer backbone.

    Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 16):
            The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 88):
            The number of channels in each head.
        in_channels (`int`, *optional*):
            The number of channels in the input and output (specify if the input is **continuous**).
        patch_size (`int`, *optional*):
            The size of the patch to use for the input.
        activation_fn (`str`, *optional*, defaults to `"geglu"`):
            Activation function to use in feed-forward.
        sample_size (`int`, *optional*):
            The width of the latent images. This is fixed during training since it is used to learn a number of
            position embeddings.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability to use.
        cross_attention_dim (`int`, *optional*):
            The number of dimension in the clip text embedding.
        hidden_size (`int`, *optional*):
            The size of hidden layer in the conditioning embedding layers.
        num_layers (`int`, *optional*, defaults to 1):
            The number of layers of Transformer blocks to use.
        mlp_ratio (`float`, *optional*, defaults to 4.0):
            The ratio of the hidden layer size to the input size.
        learn_sigma (`bool`, *optional*, defaults to `True`):
             Whether to predict variance.
        cross_attention_dim_t5 (`int`, *optional*):
            The number dimensions in t5 text embedding.
        pooled_projection_dim (`int`, *optional*):
            The size of the pooled projection.
        text_len (`int`, *optional*):
            The length of the clip text embedding.
        text_len_t5 (`int`, *optional*):
            The length of the T5 text embedding.
        use_style_cond_and_image_meta_size (`bool`,  *optional*):
            Whether or not to use style condition and image meta size. True for version <=1.1, False for version >= 1.2
    """

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 88,
        in_channels: Optional[int] = None,
        patch_size: Optional[int] = None,
        activation_fn: str = "gelu-approximate",
        sample_size=32,
        hidden_size=1152,
        num_layers: int = 28,
        mlp_ratio: float = 4.0,
        learn_sigma: bool = True,
        cross_attention_dim: int = 1024,
        norm_type: str = "layer_norm",
        cross_attention_dim_t5: int = 2048,
        pooled_projection_dim: int = 1024,
        text_len: int = 77,
        text_len_t5: int = 256,
        use_style_cond_and_image_meta_size: bool = True,
    ):
        super().__init__()
        self.out_channels = in_channels * 2 if learn_sigma else in_channels
        self.num_heads = num_attention_heads
        self.inner_dim = num_attention_heads * attention_head_dim

        self.text_embedder = PixArtAlphaTextProjection(
            in_features=cross_attention_dim_t5,
            hidden_size=cross_attention_dim_t5 * 4,
            out_features=cross_attention_dim,
            act_fn="silu_fp32",
        )

        self.text_embedding_padding = ms.Parameter(
            ops.randn(text_len + text_len_t5, cross_attention_dim, dtype=ms.float32),
            name="text_embedding_padding",
        )

        self.pos_embed = PatchEmbed(
            height=sample_size,
            width=sample_size,
            in_channels=in_channels,
            embed_dim=hidden_size,
            patch_size=patch_size,
            pos_embed_type=None,
        )

        self.time_extra_emb = HunyuanCombinedTimestepTextSizeStyleEmbedding(
            hidden_size,
            pooled_projection_dim=pooled_projection_dim,
            seq_len=text_len_t5,
            cross_attention_dim=cross_attention_dim_t5,
            use_style_cond_and_image_meta_size=use_style_cond_and_image_meta_size,
        )

        # HunyuanDiT Blocks
        self.blocks = nn.CellList(
            [
                HunyuanDiTBlock(
                    dim=self.inner_dim,
                    num_attention_heads=self.config.num_attention_heads,
                    activation_fn=activation_fn,
                    ff_inner_dim=int(self.inner_dim * mlp_ratio),
                    cross_attention_dim=cross_attention_dim,
                    qk_norm=True,  # See http://arxiv.org/abs/2302.05442 for details.
                    skip=layer > num_layers // 2,
                )
                for layer in range(num_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)

    @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)

    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        self.set_attn_processor(HunyuanAttnProcessor2_0())

    def construct(
        self,
        hidden_states,
        timestep,
        encoder_hidden_states=None,
        text_embedding_mask=None,
        encoder_hidden_states_t5=None,
        text_embedding_mask_t5=None,
        image_meta_size=None,
        style=None,
        image_rotary_emb=None,
        controlnet_block_samples=None,
        return_dict=False,
    ):
        """
        The [`HunyuanDiT2DModel`] forward method.

        Args:
        hidden_states (`ms.Tensor` of shape `(batch size, dim, height, width)`):
            The input tensor.
        timestep ( `ms.Tensor`, *optional*):
            Used to indicate denoising step.
        encoder_hidden_states ( `ms.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
            Conditional embeddings for cross attention layer. This is the output of `BertModel`.
        text_embedding_mask: ms.Tensor
            An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
            of `BertModel`.
        encoder_hidden_states_t5 ( `ms.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
            Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder.
        text_embedding_mask_t5: ms.Tensor
            An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
            of T5 Text Encoder.
        image_meta_size (ms.Tensor):
            Conditional embedding indicate the image sizes
        style: ms.Tensor:
            Conditional embedding indicate the style
        image_rotary_emb (`ms.Tensor`):
            The image rotary embeddings to apply on query and key tensors during attention calculation.
        return_dict: bool
            Whether to return a dictionary.
        """

        height, width = hidden_states.shape[-2:]

        hidden_states = self.pos_embed(hidden_states)

        temb = self.time_extra_emb(
            timestep, encoder_hidden_states_t5, image_meta_size, style, hidden_dtype=timestep.dtype
        )  # [B, D]

        # text projection
        batch_size, sequence_length, _ = encoder_hidden_states_t5.shape
        encoder_hidden_states_t5 = self.text_embedder(
            encoder_hidden_states_t5.view(-1, encoder_hidden_states_t5.shape[-1])
        )
        encoder_hidden_states_t5 = encoder_hidden_states_t5.view(batch_size, sequence_length, -1)

        encoder_hidden_states = ops.cat([encoder_hidden_states, encoder_hidden_states_t5], axis=1)
        text_embedding_mask = ops.cat([text_embedding_mask, text_embedding_mask_t5], axis=-1)
        text_embedding_mask = text_embedding_mask.unsqueeze(2).bool()

        encoder_hidden_states = ops.where(text_embedding_mask, encoder_hidden_states, self.text_embedding_padding)

        skips = []
        for layer, block in enumerate(self.blocks):
            if layer > self.config["num_layers"] // 2:
                if controlnet_block_samples is not None:
                    skip = skips[-1] + controlnet_block_samples[-1]
                    controlnet_block_samples = controlnet_block_samples[:-1]
                else:
                    skip = skips[-1]
                skips = skips[:-1]

                hidden_states = block(
                    hidden_states,
                    temb=temb,
                    encoder_hidden_states=encoder_hidden_states,
                    image_rotary_emb=image_rotary_emb,
                    skip=skip,
                )  # (N, L, D)
            else:
                hidden_states = block(
                    hidden_states,
                    temb=temb,
                    encoder_hidden_states=encoder_hidden_states,
                    image_rotary_emb=image_rotary_emb,
                )  # (N, L, D)

            if layer < (self.config["num_layers"] // 2 - 1):
                skips.append(hidden_states)

        if controlnet_block_samples is not None and len(controlnet_block_samples) != 0:
            raise ValueError("The number of controls is not equal to the number of skip connections.")

        # final layer
        hidden_states = self.norm_out(hidden_states, temb.to(ms.float32))
        hidden_states = self.proj_out(hidden_states)
        # (N, L, patch_size ** 2 * out_channels)

        # unpatchify: (N, out_channels, H, W)
        patch_size = self.pos_embed.patch_size
        height = height // patch_size
        width = width // patch_size

        hidden_states = hidden_states.reshape(
            hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels
        )
        # hidden_states = ops.einsum("nhwpqc->nchpwq", hidden_states)
        hidden_states = hidden_states.transpose(0, 5, 1, 3, 2, 4)
        output = hidden_states.reshape(
            hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size
        )
        if not return_dict:
            return (output,)
        return Transformer2DModelOutput(sample=output)

    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
    def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
        """
        Sets the attention processor to use [feed forward
        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).

        Parameters:
            chunk_size (`int`, *optional*):
                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
                over each tensor of dim=`dim`.
            dim (`int`, *optional*, defaults to `0`):
                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
                or dim=1 (sequence length).
        """
        if dim not in [0, 1]:
            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")

        # By default chunk size is 1
        chunk_size = chunk_size or 1

        def fn_recursive_feed_forward(module: nn.Cell, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.name_cells().values():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.name_cells().values():
            fn_recursive_feed_forward(module, chunk_size, dim)

    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
    def disable_forward_chunking(self):
        def fn_recursive_feed_forward(module: nn.Cell, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.name_cells().values():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.name_cells().values():
            fn_recursive_feed_forward(module, None, 0)

mindone.diffusers.HunyuanDiT2DModel.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.HunyuanDiT2DModel.construct(hidden_states, timestep, encoder_hidden_states=None, text_embedding_mask=None, encoder_hidden_states_t5=None, text_embedding_mask_t5=None, image_meta_size=None, style=None, image_rotary_emb=None, controlnet_block_samples=None, return_dict=False)

The [HunyuanDiT2DModel] forward method.

hidden_states (ms.Tensor of shape (batch size, dim, height, width)): The input tensor. timestep ( ms.Tensor, optional): Used to indicate denoising step. encoder_hidden_states ( ms.Tensor of shape (batch size, sequence len, embed dims), optional): Conditional embeddings for cross attention layer. This is the output of BertModel. text_embedding_mask: ms.Tensor An attention mask of shape (batch, key_tokens) is applied to encoder_hidden_states. This is the output of BertModel. encoder_hidden_states_t5 ( ms.Tensor of shape (batch size, sequence len, embed dims), optional): Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder. text_embedding_mask_t5: ms.Tensor An attention mask of shape (batch, key_tokens) is applied to encoder_hidden_states. This is the output of T5 Text Encoder. image_meta_size (ms.Tensor): Conditional embedding indicate the image sizes style: ms.Tensor: Conditional embedding indicate the style image_rotary_emb (ms.Tensor): The image rotary embeddings to apply on query and key tensors during attention calculation. return_dict: bool Whether to return a dictionary.

Source code in mindone/diffusers/models/transformers/hunyuan_transformer_2d.py

def construct(
    self,
    hidden_states,
    timestep,
    encoder_hidden_states=None,
    text_embedding_mask=None,
    encoder_hidden_states_t5=None,
    text_embedding_mask_t5=None,
    image_meta_size=None,
    style=None,
    image_rotary_emb=None,
    controlnet_block_samples=None,
    return_dict=False,
):
    """
    The [`HunyuanDiT2DModel`] forward method.

    Args:
    hidden_states (`ms.Tensor` of shape `(batch size, dim, height, width)`):
        The input tensor.
    timestep ( `ms.Tensor`, *optional*):
        Used to indicate denoising step.
    encoder_hidden_states ( `ms.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
        Conditional embeddings for cross attention layer. This is the output of `BertModel`.
    text_embedding_mask: ms.Tensor
        An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
        of `BertModel`.
    encoder_hidden_states_t5 ( `ms.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
        Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder.
    text_embedding_mask_t5: ms.Tensor
        An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
        of T5 Text Encoder.
    image_meta_size (ms.Tensor):
        Conditional embedding indicate the image sizes
    style: ms.Tensor:
        Conditional embedding indicate the style
    image_rotary_emb (`ms.Tensor`):
        The image rotary embeddings to apply on query and key tensors during attention calculation.
    return_dict: bool
        Whether to return a dictionary.
    """

    height, width = hidden_states.shape[-2:]

    hidden_states = self.pos_embed(hidden_states)

    temb = self.time_extra_emb(
        timestep, encoder_hidden_states_t5, image_meta_size, style, hidden_dtype=timestep.dtype
    )  # [B, D]

    # text projection
    batch_size, sequence_length, _ = encoder_hidden_states_t5.shape
    encoder_hidden_states_t5 = self.text_embedder(
        encoder_hidden_states_t5.view(-1, encoder_hidden_states_t5.shape[-1])
    )
    encoder_hidden_states_t5 = encoder_hidden_states_t5.view(batch_size, sequence_length, -1)

    encoder_hidden_states = ops.cat([encoder_hidden_states, encoder_hidden_states_t5], axis=1)
    text_embedding_mask = ops.cat([text_embedding_mask, text_embedding_mask_t5], axis=-1)
    text_embedding_mask = text_embedding_mask.unsqueeze(2).bool()

    encoder_hidden_states = ops.where(text_embedding_mask, encoder_hidden_states, self.text_embedding_padding)

    skips = []
    for layer, block in enumerate(self.blocks):
        if layer > self.config["num_layers"] // 2:
            if controlnet_block_samples is not None:
                skip = skips[-1] + controlnet_block_samples[-1]
                controlnet_block_samples = controlnet_block_samples[:-1]
            else:
                skip = skips[-1]
            skips = skips[:-1]

            hidden_states = block(
                hidden_states,
                temb=temb,
                encoder_hidden_states=encoder_hidden_states,
                image_rotary_emb=image_rotary_emb,
                skip=skip,
            )  # (N, L, D)
        else:
            hidden_states = block(
                hidden_states,
                temb=temb,
                encoder_hidden_states=encoder_hidden_states,
                image_rotary_emb=image_rotary_emb,
            )  # (N, L, D)

        if layer < (self.config["num_layers"] // 2 - 1):
            skips.append(hidden_states)

    if controlnet_block_samples is not None and len(controlnet_block_samples) != 0:
        raise ValueError("The number of controls is not equal to the number of skip connections.")

    # final layer
    hidden_states = self.norm_out(hidden_states, temb.to(ms.float32))
    hidden_states = self.proj_out(hidden_states)
    # (N, L, patch_size ** 2 * out_channels)

    # unpatchify: (N, out_channels, H, W)
    patch_size = self.pos_embed.patch_size
    height = height // patch_size
    width = width // patch_size

    hidden_states = hidden_states.reshape(
        hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels
    )
    # hidden_states = ops.einsum("nhwpqc->nchpwq", hidden_states)
    hidden_states = hidden_states.transpose(0, 5, 1, 3, 2, 4)
    output = hidden_states.reshape(
        hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size
    )
    if not return_dict:
        return (output,)
    return Transformer2DModelOutput(sample=output)

mindone.diffusers.HunyuanDiT2DModel.enable_forward_chunking(chunk_size=None, dim=0)

Sets the attention processor to use feed forward chunking.

PARAMETER	DESCRIPTION
chunk_size	The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually over each tensor of dim=dim. TYPE: `int`, *optional* DEFAULT: None
dim	The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) or dim=1 (sequence length). TYPE: `int`, *optional*, defaults to `0` DEFAULT: 0

Source code in mindone/diffusers/models/transformers/hunyuan_transformer_2d.py

def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
    """
    Sets the attention processor to use [feed forward
    chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).

    Parameters:
        chunk_size (`int`, *optional*):
            The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
            over each tensor of dim=`dim`.
        dim (`int`, *optional*, defaults to `0`):
            The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
            or dim=1 (sequence length).
    """
    if dim not in [0, 1]:
        raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")

    # By default chunk size is 1
    chunk_size = chunk_size or 1

    def fn_recursive_feed_forward(module: nn.Cell, chunk_size: int, dim: int):
        if hasattr(module, "set_chunk_feed_forward"):
            module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

        for child in module.name_cells().values():
            fn_recursive_feed_forward(child, chunk_size, dim)

    for module in self.name_cells().values():
        fn_recursive_feed_forward(module, chunk_size, dim)

mindone.diffusers.HunyuanDiT2DModel.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/hunyuan_transformer_2d.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.HunyuanDiT2DModel.set_default_attn_processor()

Disables custom attention processors and sets the default attention implementation.

Source code in mindone/diffusers/models/transformers/hunyuan_transformer_2d.py

def set_default_attn_processor(self):
    """
    Disables custom attention processors and sets the default attention implementation.
    """
    self.set_attn_processor(HunyuanAttnProcessor2_0())