diffusers/pr_13751/en/api/models/ovisimage_transformer2d.md

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[[diffusers.OvisImageTransformer2DModel]]

diffusers.OvisImageTransformer2DModel[[diffusers.OvisImageTransformer2DModel]]

Source

The Transformer model introduced in Ovis-Image.

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

forwarddiffusers.OvisImageTransformer2DModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_13751/src/diffusers/models/transformers/transformer_ovis_image.py#L476[{"name": "hidden_states", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": Tensor = None"}, {"name": "timestep", "val": ": LongTensor = None"}, {"name": "img_ids", "val": ": Tensor = None"}, {"name": "txt_ids", "val": ": Tensor = None"}, {"name": "return_dict", "val": ": bool = True"}]- 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.0If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The OvisImageTransformer2DModel forward method.

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.

Returns:

If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.