diffusers/pr_13966/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]]

• 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

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