# CogView4Transformer2DModel

A Diffusion Transformer model for 2D data from [CogView4]()

The model can be loaded with the following code snippet.

```python
from diffusers import CogView4Transformer2DModel

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

## CogView4Transformer2DModel[[diffusers.CogView4Transformer2DModel]]

- **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`

- **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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **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.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](/docs/diffusers/main/en/api/models/cogview4_transformer2d#diffusers.CogView4Transformer2DModel) forward method.

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

- **sample** (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.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](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).