diffusers/pr_14371/en/api/models/cosmos_transformer3d.md

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", dtype=torch.bfloat16)

CosmosTransformer3DModel[[diffusers.CosmosTransformer3DModel]]

diffusers.CosmosTransformer3DModel[[diffusers.CosmosTransformer3DModel]]

diffusers.CosmosTransformer3DModel(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: tuple = (128, 240, 240), patch_size: tuple = (1, 2, 2), rope_scale: tuple = (2.0, 1.0, 1.0), concat_padding_mask: bool = True, extra_pos_embed_type: str | None = 'learnable', use_crossattn_projection: bool = False, crossattn_proj_in_channels: int = 1024, encoder_hidden_states_channels: int = 1024, controlnet_block_every_n: int | None = None, img_context_dim_in: int | None = None, img_context_num_tokens: int = 256, img_context_dim_out: int = 2048)

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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.

controlnet_block_every_n (int, optional) : Interval between transformer blocks that should receive control residuals (for example, 7 to inject after every seventh block). Required for Cosmos Transfer2.5.

img_context_dim_in (int, optional) : The dimension of the input image context feature vector, i.e. it is the D in [B, N, D].

img_context_num_tokens (int) : The number of tokens in the image context feature vector, i.e. it is the N in [B, N, D]. If img_context_dim_in is not provided, then this parameter is ignored.

img_context_dim_out (int) : The output dimension of the image context projection layer. If img_context_dim_in is not provided, then this parameter is ignored.

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

forward[[diffusers.CosmosTransformer3DModel.forward]]

forward(hidden_states: Tensor, timestep: Tensor, encoder_hidden_states: Tensor, block_controlnet_hidden_states: list[torch.Tensor] | None = None, attention_mask: typing.Optional[torch.Tensor] = None, fps: int | None = None, condition_mask: typing.Optional[torch.Tensor] = None, padding_mask: typing.Optional[torch.Tensor] = None, return_dict: bool = True)

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Parameters:

hidden_states (torch.Tensor of shape (batch_size, num_channels, num_frames, height, width)) : Input hidden_states.

timestep (torch.LongTensor) : Used to indicate denoising step.

encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_len, embed_dims)) : Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.

block_controlnet_hidden_states (list of torch.Tensor, optional) : A list of tensors that if specified are added to the residuals of transformer blocks.

attention_mask (torch.Tensor, optional) : Mask applied to encoder_hidden_states during attention.

fps (int, optional) : Frames per second of the input video used to compute the rotary positional embeddings.

condition_mask (torch.Tensor, optional) : Mask channel concatenated to hidden_states to indicate the conditioning region.

padding_mask (torch.Tensor, optional) : Padding mask concatenated to hidden_states when concat_padding_mask is enabled.

return_dict (bool, optional, defaults to True) : Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.

Returns:

If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The CosmosTransformer3DModel forward method.

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

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

diffusers.models.modeling_outputs.Transformer2DModelOutput(sample: torch.Tensor)

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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.