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)( 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
int, defaults to 1) —
Patch size to turn the input data into small patches. int, defaults to 64) —
The number of channels in the input. int, optional, defaults to None) —
The number of channels in the output. If not specified, it defaults to in_channels. int, defaults to 6) —
The number of layers of dual stream DiT blocks to use. int, defaults to 27) —
The number of layers of single stream DiT blocks to use. int, defaults to 128) —
The number of dimensions to use for each attention head. int, defaults to 24) —
The number of attention heads to use. int, defaults to 2048) —
The number of dimensions to use for the joint attention (embedding/channel dimension of
encoder_hidden_states). 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: Tensor encoder_hidden_states: Tensor = None timestep: LongTensor = None img_ids: Tensor = None txt_ids: Tensor = None return_dict: bool = True )
Parameters
torch.Tensor of shape (batch_size, image_sequence_length, in_channels)) —
Input 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. torch.LongTensor) —
Used to indicate denoising step. torch.Tensor):
The position ids for image tokens. torch.Tensor) —
The position ids for text tokens. bool, optional, defaults to True) —
Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain
tuple. The OvisImageTransformer2DModel forward method.