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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.

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