# OvisImageTransformer2DModel

The model can be loaded with the following code snippet.

```python
from diffusers import OvisImageTransformer2DModel

transformer = OvisImageTransformer2DModel.from_pretrained("AIDC-AI/Ovis-Image-7B", subfolder="transformer", dtype=torch.bfloat16)
```

## OvisImageTransformer2DModel[[diffusers.OvisImageTransformer2DModel]]

#### diffusers.OvisImageTransformer2DModel[[diffusers.OvisImageTransformer2DModel]]

```python
diffusers.OvisImageTransformer2DModel(patch_size: int = 1, in_channels: int = 64, out_channels: int | None = 64, num_layers: int = 6, num_single_layers: int = 27, attention_head_dim: int = 128, num_attention_heads: int = 24, joint_attention_dim: int = 2048, axes_dims_rope: tuple = (16, 56, 56))
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_ovis_image.py#L384)

**Parameters:**

patch_size (`int`, defaults to `1`) : Patch size to turn the input data into small patches.

in_channels (`int`, defaults to `64`) : The number of channels in the input.

out_channels (`int`, *optional*, defaults to `None`) : The number of channels in the output. If not specified, it defaults to `in_channels`.

num_layers (`int`, defaults to `6`) : The number of layers of dual stream DiT blocks to use.

num_single_layers (`int`, defaults to `27`) : The number of layers of single stream DiT blocks to use.

attention_head_dim (`int`, defaults to `128`) : The number of dimensions to use for each attention head.

num_attention_heads (`int`, defaults to `24`) : The number of attention heads to use.

joint_attention_dim (`int`, defaults to `2048`) : The number of dimensions to use for the joint attention (embedding/channel dimension of `encoder_hidden_states`).

axes_dims_rope (`tuple[int]`, defaults to `(16, 56, 56)`) : The dimensions to use for the rotary positional embeddings.

The Transformer model introduced in Ovis-Image.

Reference: https://github.com/AIDC-AI/Ovis-Image

#### forward[[diffusers.OvisImageTransformer2DModel.forward]]

```python
forward(hidden_states: Tensor, encoder_hidden_states: Tensor = None, timestep: LongTensor = None, img_ids: Tensor = None, txt_ids: Tensor = None, joint_attention_kwargs: dict[str, typing.Any] | None = None, return_dict: bool = True)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_ovis_image.py#L476)

**Parameters:**

hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`) : Input `hidden_states`.

encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`) : Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.

timestep (`torch.LongTensor`) : Used to indicate denoising step.

img_ids : (`torch.Tensor`): The position ids for image tokens.

txt_ids (`torch.Tensor`) : The position ids for text tokens.

joint_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.

**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 [OvisImageTransformer2DModel](/docs/diffusers/main/en/api/models/ovisimage_transformer2d#diffusers.OvisImageTransformer2DModel) forward method.