CogView4Transformer2DModel

A Diffusion Transformer model for 2D data from CogView4

The model can be loaded with the following code snippet.

from mindone.diffusers import CogView4Transformer2DModel

transformer = CogView4Transformer2DModel.from_pretrained("THUDM/CogView4-6B", subfolder="transformer", mindspore_dtype=ms.bfloat16)

mindone.diffusers.models.transformers.CogView4Transformer2DModel

Bases: ModelMixin, ConfigMixin, PeftAdapterMixin

PARAMETER	DESCRIPTION
patch_size	The size of the patches to use in the patch embedding layer. TYPE: `int`, defaults to `2` DEFAULT: 2
in_channels	The number of channels in the input. TYPE: `int`, defaults to `16` DEFAULT: 16
num_layers	The number of layers of Transformer blocks to use. TYPE: `int`, defaults to `30` DEFAULT: 30
attention_head_dim	The number of channels in each head. TYPE: `int`, defaults to `40` DEFAULT: 40
num_attention_heads	The number of heads to use for multi-head attention. TYPE: `int`, defaults to `64` DEFAULT: 64
out_channels	The number of channels in the output. TYPE: `int`, defaults to `16` DEFAULT: 16
text_embed_dim	Input dimension of text embeddings from the text encoder. TYPE: `int`, defaults to `4096` DEFAULT: 4096
time_embed_dim	Output dimension of timestep embeddings. TYPE: `int`, defaults to `512` DEFAULT: 512
condition_dim	The embedding dimension of the input SDXL-style resolution conditions (original_size, target_size, crop_coords). TYPE: `int`, defaults to `256` DEFAULT: 256
pos_embed_max_size	The maximum resolution of the positional embeddings, from which slices of shape H x W are taken and added to input patched latents, where H and W are the latent height and width respectively. A value of 128 means that the maximum supported height and width for image generation is 128 * vae_scale_factor * patch_size => 128 * 8 * 2 => 2048. TYPE: `int`, defaults to `128` DEFAULT: 128
sample_size	The base resolution of input latents. If height/width is not provided during generation, this value is used to determine the resolution as sample_size * vae_scale_factor => 128 * 8 => 1024 TYPE: `int`, defaults to `128` DEFAULT: 128

Source code in mindone/diffusers/models/transformers/transformer_cogview4.py

class CogView4Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
    r"""
    Args:
        patch_size (`int`, defaults to `2`):
            The size of the patches to use in the patch embedding layer.
        in_channels (`int`, defaults to `16`):
            The number of channels in the input.
        num_layers (`int`, defaults to `30`):
            The number of layers of Transformer blocks to use.
        attention_head_dim (`int`, defaults to `40`):
            The number of channels in each head.
        num_attention_heads (`int`, defaults to `64`):
            The number of heads to use for multi-head attention.
        out_channels (`int`, defaults to `16`):
            The number of channels in the output.
        text_embed_dim (`int`, defaults to `4096`):
            Input dimension of text embeddings from the text encoder.
        time_embed_dim (`int`, defaults to `512`):
            Output dimension of timestep embeddings.
        condition_dim (`int`, defaults to `256`):
            The embedding dimension of the input SDXL-style resolution conditions (original_size, target_size,
            crop_coords).
        pos_embed_max_size (`int`, defaults to `128`):
            The maximum resolution of the positional embeddings, from which slices of shape `H x W` are taken and added
            to input patched latents, where `H` and `W` are the latent height and width respectively. A value of 128
            means that the maximum supported height and width for image generation is `128 * vae_scale_factor *
            patch_size => 128 * 8 * 2 => 2048`.
        sample_size (`int`, defaults to `128`):
            The base resolution of input latents. If height/width is not provided during generation, this value is used
            to determine the resolution as `sample_size * vae_scale_factor => 128 * 8 => 1024`
    """

    _supports_gradient_checkpointing = True
    _no_split_modules = ["CogView4TransformerBlock", "CogView4PatchEmbed", "CogView4PatchEmbed"]
    _skip_layerwise_casting_patterns = ["patch_embed", "norm", "proj_out"]

    @register_to_config
    def __init__(
        self,
        patch_size: int = 2,
        in_channels: int = 16,
        out_channels: int = 16,
        num_layers: int = 30,
        attention_head_dim: int = 40,
        num_attention_heads: int = 64,
        text_embed_dim: int = 4096,
        time_embed_dim: int = 512,
        condition_dim: int = 256,
        pos_embed_max_size: int = 128,
        sample_size: int = 128,
        rope_axes_dim: Tuple[int, int] = (256, 256),
    ):
        super().__init__()

        # CogView4 uses 3 additional SDXL-like conditions - original_size, target_size, crop_coords
        # Each of these are sincos embeddings of shape 2 * condition_dim
        pooled_projection_dim = 3 * 2 * condition_dim
        inner_dim = num_attention_heads * attention_head_dim
        out_channels = out_channels

        # 1. RoPE
        self.rope = CogView4RotaryPosEmbed(attention_head_dim, patch_size, rope_axes_dim, theta=10000.0)

        # 2. Patch & Text-timestep embedding
        self.patch_embed = CogView4PatchEmbed(in_channels, inner_dim, patch_size, text_embed_dim)

        self.time_condition_embed = CogView3CombinedTimestepSizeEmbeddings(
            embedding_dim=time_embed_dim,
            condition_dim=condition_dim,
            pooled_projection_dim=pooled_projection_dim,
            timesteps_dim=inner_dim,
        )

        # 3. Transformer blocks
        self.transformer_blocks = nn.CellList(
            [
                CogView4TransformerBlock(inner_dim, num_attention_heads, attention_head_dim, time_embed_dim)
                for _ in range(num_layers)
            ]
        )

        # 4. Output projection
        self.norm_out = AdaLayerNormContinuous(inner_dim, time_embed_dim, elementwise_affine=False)
        self.proj_out = mint.nn.Linear(inner_dim, patch_size * patch_size * out_channels, bias=True)

        self.gradient_checkpointing = False

        self.patch_size = self.config.patch_size

    def construct(
        self,
        hidden_states: ms.Tensor,
        encoder_hidden_states: ms.Tensor,
        timestep: ms.Tensor,
        original_size: ms.Tensor,
        target_size: ms.Tensor,
        crop_coords: ms.Tensor,
        attention_kwargs: Optional[Dict[str, Any]] = None,
        return_dict: bool = True,
        attention_mask: Optional[ms.Tensor] = None,
        image_rotary_emb: Optional[Union[Tuple[ms.Tensor, ms.Tensor], List[Tuple[ms.Tensor, ms.Tensor]]]] = None,
    ) -> Union[ms.Tensor, Transformer2DModelOutput]:
        batch_size, num_channels, height, width = hidden_states.shape

        # 1. RoPE
        if image_rotary_emb is None:
            image_rotary_emb = self.rope(hidden_states)

        # 2. Patch & Timestep embeddings
        p = self.patch_size
        post_patch_height = height // p
        post_patch_width = width // p

        hidden_states, encoder_hidden_states = self.patch_embed(hidden_states, encoder_hidden_states)

        temb = self.time_condition_embed(timestep, original_size, target_size, crop_coords, hidden_states.dtype)
        temb = F.silu(temb)

        # 3. Transformer blocks
        for block in self.transformer_blocks:
            hidden_states, encoder_hidden_states = block(
                hidden_states,
                encoder_hidden_states,
                temb,
                image_rotary_emb,
                attention_mask,
                attention_kwargs,
            )

        # 4. Output norm & projection
        hidden_states = self.norm_out(hidden_states, temb)
        hidden_states = self.proj_out(hidden_states)

        # 5. Unpatchify
        hidden_states = hidden_states.reshape(batch_size, post_patch_height, post_patch_width, -1, p, p)
        output = hidden_states.permute(0, 3, 1, 4, 2, 5).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