diffusers/pr_13751/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]]

Source

forwarddiffusers.ErnieImageTransformer2DModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_13751/src/diffusers/models/transformers/transformer_ernie_image.py#L348[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": Tensor"}, {"name": "text_bth", "val": ": Tensor"}, {"name": "text_lens", "val": ": Tensor"}, {"name": "return_dict", "val": ": bool = True"}]- 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.0

The ErnieImageTransformer2DModel forward method.

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.