diffusers/pr_14404/en/api/models/cogview4_transformer2d.md

CogView4Transformer2DModel

A Diffusion Transformer model for 2D data from CogView4

The model can be loaded with the following code snippet.

from diffusers import CogView4Transformer2DModel

transformer = CogView4Transformer2DModel.from_pretrained("THUDM/CogView4-6B", subfolder="transformer", dtype=torch.bfloat16).to("cuda")

CogView4Transformer2DModel[[diffusers.CogView4Transformer2DModel]]

diffusers.CogView4Transformer2DModel[[diffusers.CogView4Transformer2DModel]]

diffusers.CogView4Transformer2DModel(patch_size: int = 2, in_channels: int = 16, out_channels: int = 16, num_layers: int = 30, attention_head_dim: int = 40, num_attention_heads: int = 64, text_embed_dim: int = 4096, time_embed_dim: int = 512, condition_dim: int = 256, pos_embed_max_size: int = 128, sample_size: int = 128, rope_axes_dim: tuple = (256, 256))

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Parameters:

patch_size (int, defaults to 2) : The size of the patches to use in the patch embedding layer.

in_channels (int, defaults to 16) : The number of channels in the input.

num_layers (int, defaults to 30) : The number of layers of Transformer blocks to use.

attention_head_dim (int, defaults to 40) : The number of channels in each head.

num_attention_heads (int, defaults to 64) : The number of heads to use for multi-head attention.

out_channels (int, defaults to 16) : The number of channels in the output.

text_embed_dim (int, defaults to 4096) : Input dimension of text embeddings from the text encoder.

time_embed_dim (int, defaults to 512) : Output dimension of timestep embeddings.

condition_dim (int, defaults to 256) : The embedding dimension of the input SDXL-style resolution conditions (original_size, target_size, crop_coords).

pos_embed_max_size (int, defaults to 128) : The maximum resolution of the positional embeddings, from which slices of shape H x W are taken and added to input patched latents, where H and W are the latent height and width respectively. A value of 128 means that the maximum supported height and width for image generation is 128 * vae_scale_factor * patch_size => 128 * 8 * 2 => 2048.

sample_size (int, defaults to 128) : The base resolution of input latents. If height/width is not provided during generation, this value is used to determine the resolution as sample_size * vae_scale_factor => 128 * 8 => 1024

forward[[diffusers.CogView4Transformer2DModel.forward]]

forward(hidden_states: Tensor, encoder_hidden_states: Tensor, timestep: LongTensor, original_size: Tensor, target_size: Tensor, crop_coords: Tensor, attention_kwargs: dict[str, typing.Any] | None = None, return_dict: bool = True, attention_mask: typing.Optional[torch.Tensor] = None, image_rotary_emb: tuple[torch.Tensor, torch.Tensor] | list[tuple[torch.Tensor, torch.Tensor]] | None = None)

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Parameters:

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

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.

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

original_size (torch.Tensor) : Original image size conditioning.

target_size (torch.Tensor) : Target image size conditioning.

crop_coords (torch.Tensor) : Crop coordinates conditioning.

attention_kwargs (dict, optional) : A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.

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

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

image_rotary_emb (tuple of torch.Tensor, optional) : Pre-computed rotary positional embeddings.

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 CogView4Transformer2DModel 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.