Parameters

• hidden_states (torch.FloatTensor of shape (batch size, in_channels, sequence_len)) — Input hidden_states.
• timestep ( torch.LongTensor) — Used to indicate denoising step.
• encoder_hidden_states (torch.FloatTensor of shape (batch size, encoder_sequence_len, cross_attention_input_dim)) — Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
• global_hidden_states (torch.FloatTensor of shape (batch size, global_sequence_len, global_states_input_dim)) — Global embeddings that will be prepended to the hidden states.
• rotary_embedding (torch.Tensor) — The rotary embeddings to apply on query and key tensors during attention calculation.
• return_dict (bool, optional, defaults to True) — Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.
• attention_mask (torch.Tensor of shape (batch_size, sequence_len), optional) — Mask to avoid performing attention on padding token indices, formed by concatenating the attention masks for the two text encoders together. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
• encoder_attention_mask (torch.Tensor of shape (batch_size, sequence_len), optional) — Mask to avoid performing attention on padding token cross-attention indices, formed by concatenating the attention masks for the two text encoders together. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.

Parameters

• processor (dict of AttentionProcessor or only AttentionProcessor) — The instantiated processor class or a dictionary of processor classes that will be set as the processor for all Attention layers.

  If processor is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.