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EasyAnimateTransformer3DModel

A Diffusion Transformer model for 3D data from EasyAnimate was introduced by Alibaba PAI.

The model can be loaded with the following code snippet.

from diffusers import EasyAnimateTransformer3DModel

transformer = EasyAnimateTransformer3DModel.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="transformer", torch_dtype=torch.float16).to("cuda")

EasyAnimateTransformer3DModel

class diffusers.EasyAnimateTransformer3DModel

< source >

( num_attention_heads: int = 48 attention_head_dim: int = 64 in_channels: typing.Optional[int] = None out_channels: typing.Optional[int] = None patch_size: typing.Optional[int] = None sample_width: int = 90 sample_height: int = 60 activation_fn: str = 'gelu-approximate' timestep_activation_fn: str = 'silu' freq_shift: int = 0 num_layers: int = 48 mmdit_layers: int = 48 dropout: float = 0.0 time_embed_dim: int = 512 add_norm_text_encoder: bool = False text_embed_dim: int = 3584 text_embed_dim_t5: int = None norm_eps: float = 1e-05 norm_elementwise_affine: bool = True flip_sin_to_cos: bool = True time_position_encoding_type: str = '3d_rope' after_norm = False resize_inpaint_mask_directly: bool = True enable_text_attention_mask: bool = True add_noise_in_inpaint_model: bool = True )

Parameters

num_attention_heads (int, defaults to 48) — The number of heads to use for multi-head attention.
attention_head_dim (int, defaults to 64) — The number of channels in each head.
in_channels (int, defaults to 16) — The number of channels in the input.
out_channels (int, optional, defaults to 16) — The number of channels in the output.
patch_size (int, defaults to 2) — The size of the patches to use in the patch embedding layer.
sample_width (int, defaults to 90) — The width of the input latents.
sample_height (int, defaults to 60) — The height of the input latents.
activation_fn (str, defaults to "gelu-approximate") — Activation function to use in feed-forward.
timestep_activation_fn (str, defaults to "silu") — Activation function to use when generating the timestep embeddings.
num_layers (int, defaults to 30) — The number of layers of Transformer blocks to use.
mmdit_layers (int, defaults to 1000) — The number of layers of Multi Modal Transformer blocks to use.
dropout (float, defaults to 0.0) — The dropout probability to use.
time_embed_dim (int, defaults to 512) — Output dimension of timestep embeddings.
text_embed_dim (int, defaults to 4096) — Input dimension of text embeddings from the text encoder.
norm_eps (float, defaults to 1e-5) — The epsilon value to use in normalization layers.
norm_elementwise_affine (bool, defaults to True) — Whether to use elementwise affine in normalization layers.
flip_sin_to_cos (bool, defaults to True) — Whether to flip the sin to cos in the time embedding.
time_position_encoding_type (str, defaults to 3d_rope) — Type of time position encoding.
after_norm (bool, defaults to False) — Flag to apply normalization after.
resize_inpaint_mask_directly (bool, defaults to True) — Flag to resize inpaint mask directly.
enable_text_attention_mask (bool, defaults to True) — Flag to enable text attention mask.
add_noise_in_inpaint_model (bool, defaults to False) — Flag to add noise in inpaint model.

A Transformer model for video-like data in EasyAnimate.

Transformer2DModelOutput

class diffusers.models.modeling_outputs.Transformer2DModelOutput

< source >

( sample: torch.Tensor )

Parameters

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.

The output of Transformer2DModel.

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