#
OvisImageTransformer2DModel

The model can be loaded with the following code snippet.

    from diffusers import OvisImageTransformer2DModel

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

##
OvisImageTransformer2DModel

###
class diffusers.OvisImageTransformer2DModel

< source >

( patch_size: int = 1 in_channels: int = 64 out_channels: typing.Optional[int] = 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: typing.Tuple[int, int, int] = (16, 56, 56) )

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

< source >

( hidden_states: Tensor encoder_hidden_states: Tensor = None timestep: LongTensor = None img_ids: Tensor = None txt_ids: Tensor = None return_dict: bool = True )

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.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain tuple.

The OvisImageTransformer2DModel forward method.

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