diffusers/main/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", torch_dtype=torch.bfloat16)

CosmosTransformer3DModel[[diffusers.CosmosTransformer3DModel]]

diffusers.CosmosTransformer3DModel[[diffusers.CosmosTransformer3DModel]]

Source

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

forwarddiffusers.CosmosTransformer3DModel.forwardhttps://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_cosmos.py#L688[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": Tensor"}, {"name": "block_controlnet_hidden_states", "val": ": list[torch.Tensor] | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "fps", "val": ": int | None = None"}, {"name": "condition_mask", "val": ": torch.Tensor | None = None"}, {"name": "padding_mask", "val": ": torch.Tensor | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- 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.0If 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.

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.

Returns:

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

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

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

Source

The output of Transformer2DModel.

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.