CogView3PlusTransformer2DModel  
The Transformer model introduced in [CogView3: Finer and Faster Text-to-Image Generation via Relay
Diffusion](https://huggingface.co/papers/2403.05121).  
Args:
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`
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/cogview3plus_transformer2d.md