#
CosmosTransformer3DModel

A Diffusion Transformer model for 3D video-like data was introduced in Cosmos World Foundation Model Platform for Physical AI by NVIDIA.

The model can be loaded with the following code snippet.

    from diffusers import CosmosTransformer3DModel

    transformer = CosmosTransformer3DModel.from_pretrained("nvidia/Cosmos-1.0-Diffusion-7B-Text2World", subfolder="transformer", torch_dtype=torch.bfloat16)

##
CosmosTransformer3DModel

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class diffusers.CosmosTransformer3DModel

< source >

( in_channels: int = 16 out_channels: int = 16 num_attention_heads: int = 32 attention_head_dim: int = 128 num_layers: int = 28 mlp_ratio: float = 4.0 text_embed_dim: int = 1024 adaln_lora_dim: int = 256 max_size: typing.Tuple[int, int, int] = (128, 240, 240) patch_size: typing.Tuple[int, int, int] = (1, 2, 2) rope_scale: typing.Tuple[float, float, float] = (2.0, 1.0, 1.0) concat_padding_mask: bool = True extra_pos_embed_type: typing.Optional[str] = 'learnable' )

Parameters

- 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.
- num_attention_heads (`int`, defaults to `32`) — The number of heads to use for multi-head attention.
- attention_head_dim (`int`, defaults to `128`) — The number of channels in each attention head.
- num_layers (`int`, defaults to `28`) — The number of layers of transformer blocks to use.
- mlp_ratio (`float`, defaults to `4.0`) — The ratio of the hidden layer size to the input size in the feedforward network.
- text_embed_dim (`int`, defaults to `4096`) — Input dimension of text embeddings from the text encoder.
- adaln_lora_dim (`int`, defaults to `256`) — The hidden dimension of the Adaptive LayerNorm LoRA layer.
- max_size (`Tuple[int, int, int]`, defaults to `(128, 240, 240)`) — The maximum size of the input latent tensors in the temporal, height, and width dimensions.
- patch_size (`Tuple[int, int, int]`, defaults to `(1, 2, 2)`) — The patch size to use for patchifying the input latent tensors in the temporal, height, and width dimensions.
- rope_scale (`Tuple[float, float, float]`, defaults to `(2.0, 1.0, 1.0)`) — The scaling factor to use for RoPE in the temporal, height, and width dimensions.
- concat_padding_mask (`bool`, defaults to `True`) — Whether to concatenate the padding mask to the input latent tensors.
- extra_pos_embed_type (`str`, optional, defaults to `learnable`) — The type of extra positional embeddings to use. Can be one of `None` or `learnable`.

A Transformer model for video-like data used in Cosmos.

##
Transformer2DModelOutput

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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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