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ErnieImageTransformer2DModel

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

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

ErnieImageTransformer2DModel

class diffusers.ErnieImageTransformer2DModel

< source >

( 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 )

forward

< source >

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

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.

Update on GitHub

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