WanTransformer3DModel

A Diffusion Transformer model for 3D video-like data was introduced in Wan 2.1 by the Alibaba Wan Team.

The model can be loaded with the following code snippet.

from mindone.diffusers import WanTransformer3DModel

transformer = WanTransformer3DModel.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="transformer", mindspore_dtype=ms.bfloat16)

mindone.diffusers.WanTransformer3DModel

Bases: ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin

A Transformer model for video-like data used in the Wan model.

PARAMETER	DESCRIPTION
patch_size	3D patch dimensions for video embedding (t_patch, h_patch, w_patch). TYPE: `Tuple[int]`, defaults to `(1, 2, 2)` DEFAULT: (1, 2, 2)
num_attention_heads	Fixed length for text embeddings. TYPE: `int`, defaults to `40` DEFAULT: 40
attention_head_dim	The number of channels in each head. TYPE: `int`, defaults to `128` DEFAULT: 128
in_channels	The number of channels in the input. TYPE: `int`, defaults to `16` DEFAULT: 16
out_channels	The number of channels in the output. TYPE: `int`, defaults to `16` DEFAULT: 16
text_dim	Input dimension for text embeddings. TYPE: `int`, defaults to `512` DEFAULT: 4096
freq_dim	Dimension for sinusoidal time embeddings. TYPE: `int`, defaults to `256` DEFAULT: 256
ffn_dim	Intermediate dimension in feed-forward network. TYPE: `int`, defaults to `13824` DEFAULT: 13824
num_layers	The number of layers of transformer blocks to use. TYPE: `int`, defaults to `40` DEFAULT: 40
window_size	Window size for local attention (-1 indicates global attention). TYPE: `Tuple[int]`, defaults to `(-1, -1)`
cross_attn_norm	Enable cross-attention normalization. TYPE: `bool`, defaults to `True` DEFAULT: True
qk_norm	Enable query/key normalization. TYPE: `bool`, defaults to `True` DEFAULT: 'rms_norm_across_heads'
eps	Epsilon value for normalization layers. TYPE: `float`, defaults to `1e-6` DEFAULT: 1e-06
add_img_emb	Whether to use img_emb. TYPE: `bool`, defaults to `False`
added_kv_proj_dim	The number of channels to use for the added key and value projections. If None, no projection is used. TYPE: `int`, *optional*, defaults to `None` DEFAULT: None

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

class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
    r"""
    A Transformer model for video-like data used in the Wan model.

    Args:
        patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`):
            3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
        num_attention_heads (`int`, defaults to `40`):
            Fixed length for text embeddings.
        attention_head_dim (`int`, defaults to `128`):
            The number of channels in each head.
        in_channels (`int`, defaults to `16`):
            The number of channels in the input.
        out_channels (`int`, defaults to `16`):
            The number of channels in the output.
        text_dim (`int`, defaults to `512`):
            Input dimension for text embeddings.
        freq_dim (`int`, defaults to `256`):
            Dimension for sinusoidal time embeddings.
        ffn_dim (`int`, defaults to `13824`):
            Intermediate dimension in feed-forward network.
        num_layers (`int`, defaults to `40`):
            The number of layers of transformer blocks to use.
        window_size (`Tuple[int]`, defaults to `(-1, -1)`):
            Window size for local attention (-1 indicates global attention).
        cross_attn_norm (`bool`, defaults to `True`):
            Enable cross-attention normalization.
        qk_norm (`bool`, defaults to `True`):
            Enable query/key normalization.
        eps (`float`, defaults to `1e-6`):
            Epsilon value for normalization layers.
        add_img_emb (`bool`, defaults to `False`):
            Whether to use img_emb.
        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
            The number of channels to use for the added key and value projections. If `None`, no projection is used.
    """

    _supports_gradient_checkpointing = True
    _skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"]
    _no_split_modules = ["WanTransformerBlock"]
    _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
    _keys_to_ignore_on_load_unexpected = ["norm_added_q"]

    @register_to_config
    def __init__(
        self,
        patch_size: Tuple[int] = (1, 2, 2),
        num_attention_heads: int = 40,
        attention_head_dim: int = 128,
        in_channels: int = 16,
        out_channels: int = 16,
        text_dim: int = 4096,
        freq_dim: int = 256,
        ffn_dim: int = 13824,
        num_layers: int = 40,
        cross_attn_norm: bool = True,
        qk_norm: Optional[str] = "rms_norm_across_heads",
        eps: float = 1e-6,
        image_dim: Optional[int] = None,
        added_kv_proj_dim: Optional[int] = None,
        rope_max_seq_len: int = 1024,
    ) -> None:
        super().__init__()

        inner_dim = num_attention_heads * attention_head_dim
        out_channels = out_channels or in_channels

        # 1. Patch & position embedding
        self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)
        self.patch_embedding = mint.nn.Conv3d(
            in_channels, inner_dim, kernel_size=tuple(patch_size), stride=tuple(patch_size)
        )

        # 2. Condition embeddings
        # image_embedding_dim=1280 for I2V model
        self.condition_embedder = WanTimeTextImageEmbedding(
            dim=inner_dim,
            time_freq_dim=freq_dim,
            time_proj_dim=inner_dim * 6,
            text_embed_dim=text_dim,
            image_embed_dim=image_dim,
        )

        # 3. Transformer blocks
        self.blocks = nn.CellList(
            [
                WanTransformerBlock(
                    inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim
                )
                for _ in range(num_layers)
            ]
        )

        # 4. Output norm & projection
        self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False)
        self.proj_out = mint.nn.Linear(inner_dim, out_channels * math.prod(patch_size))
        self.scale_shift_table = ms.Parameter(mint.randn(1, 2, inner_dim) / inner_dim**0.5)

        self.gradient_checkpointing = False

        self.config_patch_size = self.config.patch_size

    def construct(
        self,
        hidden_states: ms.Tensor,
        timestep: ms.Tensor,
        encoder_hidden_states: ms.Tensor,
        encoder_hidden_states_image: Optional[ms.Tensor] = None,
        return_dict: bool = False,
        attention_kwargs: Optional[Dict[str, Any]] = None,
    ) -> Union[ms.Tensor, Dict[str, ms.Tensor]]:
        if attention_kwargs is not None and "scale" in attention_kwargs:
            # weight the lora layers by setting `lora_scale` for each PEFT layer here
            # and remove `lora_scale` from each PEFT layer at the end.
            # scale_lora_layers & unscale_lora_layers maybe contains some operation forbidden in graph mode
            raise RuntimeError(
                f"You are trying to set scaling of lora layer by passing {attention_kwargs['scale']=}. "
                f"However it's not allowed in on-the-fly model forwarding. "
                f"Please manually call `scale_lora_layers(model, lora_scale)` before model forwarding and "
                f"`unscale_lora_layers(model, lora_scale)` after model forwarding. "
                f"For example, it can be done in a pipeline call like `StableDiffusionPipeline.__call__`."
            )

        batch_size, num_channels, num_frames, height, width = hidden_states.shape
        p_t, p_h, p_w = self.config_patch_size
        post_patch_num_frames = num_frames // p_t
        post_patch_height = height // p_h
        post_patch_width = width // p_w

        rotary_emb = self.rope(hidden_states)

        hidden_states = self.patch_embedding(hidden_states)
        hidden_states = hidden_states.flatten(2).swapaxes(1, 2)

        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
            timestep, encoder_hidden_states, encoder_hidden_states_image
        )
        timestep_proj = unflatten(timestep_proj, 1, (6, -1))

        if encoder_hidden_states_image is not None:
            encoder_hidden_states = mint.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)

        # 4. Transformer blocks
        for block in self.blocks:
            hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb)

        # 5. Output norm, projection & unpatchify
        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)

        hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
        hidden_states = self.proj_out(hidden_states)

        hidden_states = hidden_states.reshape(
            batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1
        )
        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

        if not return_dict:
            return (output,)

        return Transformer2DModelOutput(sample=output)

mindone.diffusers.models.modeling_outputs.Transformer2DModelOutput dataclass

Bases: BaseOutput

The output of [Transformer2DModel].

PARAMETER	DESCRIPTION
`(batch	The hidden states output conditioned on the encoder_hidden_states input. If discrete, returns probability distributions for the unnoised latent pixels. TYPE: size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete

Source code in mindone/diffusers/models/modeling_outputs.py

@dataclass
class Transformer2DModelOutput(BaseOutput):
    """
    The output of [`Transformer2DModel`].

    Args:
        sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or
        `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
            distributions for the unnoised latent pixels.
    """

    sample: "ms.Tensor"  # noqa: F821