diffusers/pr_13881/en/api/models/easyanimate_transformer3d.md

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[[diffusers.EasyAnimateTransformer3DModel]]

• 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.

• hidden_states (torch.Tensor of shape (batch_size, channels, num_frames, height, width)) -- Input hidden_states.
• timestep (torch.LongTensor) -- Used to indicate denoising step.
• timestep_cond (torch.Tensor, optional) -- Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed through the self.time_embedding layer to obtain the final timestep embeddings.
• encoder_hidden_states (torch.Tensor, optional) -- Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
• encoder_hidden_states_t5 (torch.Tensor, optional) -- Additional conditional embeddings computed from a T5 text encoder.
• inpaint_latents (torch.Tensor, optional) -- Latents concatenated to hidden_states for inpainting variants of the model.
• control_latents (torch.Tensor, optional) -- Latents concatenated to hidden_states for control variants of the model.
• return_dict (bool, optional, defaults to True) -- Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The EasyAnimateTransformer3DModel forward method.

Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

• 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.