Diffusers

You are viewing main version, which requires installation from source. If you'd like regular pip install, checkout the latest stable version (v0.35.1).

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

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.

Update on GitHub

←OmniGenTransformer2DModel PixArtTransformer2DModel→