# EasyAnimateTransformer3DModel A Diffusion Transformer model for 3D data from [EasyAnimate](https://github.com/aigc-apps/EasyAnimate) was introduced by Alibaba PAI. The model can be loaded with the following code snippet. ```python 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](https://github.com/aigc-apps/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](/docs/diffusers/main/en/api/models/easyanimate_transformer3d#diffusers.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](/docs/diffusers/main/en/api/models/transformer2d#diffusers.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](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).