#
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", torch_dtype=torch.bfloat16).to("cuda")

##
CogView4Transformer2DModel

###
class diffusers.CogView4Transformer2DModel

< source >

( 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: typing.Tuple[int, int] = (256, 256) )

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`

##
Transformer2DModelOutput

###
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.

< > Update on GitHub