# 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", dtype=torch.bfloat16).to("cuda")
```

## CogView4Transformer2DModel[[diffusers.CogView4Transformer2DModel]]

#### diffusers.CogView4Transformer2DModel[[diffusers.CogView4Transformer2DModel]]

```python
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))
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_cogview4.py#L615)

**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]]

```python
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)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_cogview4.py#L702)

**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](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.

**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](/docs/diffusers/main/en/api/models/cogview4_transformer2d#diffusers.CogView4Transformer2DModel) forward method.

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

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

```python
diffusers.models.modeling_outputs.Transformer2DModelOutput(sample: torch.Tensor)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/modeling_outputs.py#L21)

**Parameters:**

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).