diffusers/pr_14282/en/api/models/ernie_image_transformer2d.md

ErnieImageTransformer2DModel

A Transformer model for image-like data from ERNIE-Image.

A Transformer model for image-like data from ERNIE-Image-Turbo.

ErnieImageTransformer2DModel[[diffusers.ErnieImageTransformer2DModel]]

diffusers.ErnieImageTransformer2DModel[[diffusers.ErnieImageTransformer2DModel]]

diffusers.ErnieImageTransformer2DModel(hidden_size: int = 3072, num_attention_heads: int = 24, num_layers: int = 24, ffn_hidden_size: int = 8192, in_channels: int = 128, out_channels: int = 128, patch_size: int = 1, text_in_dim: int = 2560, rope_theta: int = 256, rope_axes_dim: typing.Tuple[int, int, int] = (32, 48, 48), eps: float = 1e-06, qk_layernorm: bool = True)

Source

forward[[diffusers.ErnieImageTransformer2DModel.forward]]

forward(hidden_states: Tensor, timestep: Tensor, text_bth: Tensor, text_lens: Tensor, return_dict: bool = True)

Source

Parameters:

hidden_states (torch.Tensor of shape (batch_size, in_channels, height, width)) : Input hidden_states.

timestep (torch.LongTensor) : Used to indicate denoising step.

text_bth (torch.Tensor) : Conditional text embeddings (embeddings computed from the input conditions such as prompts) to use, shaped (batch_size, text_length, embed_dims).

text_lens (torch.Tensor) : Per-sample text sequence lengths used to build the attention mask.

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

The ErnieImageTransformer2DModel forward method.