Parameters

• chunk_size (int, optional) — The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually over each tensor of dim=dim.
• dim (int, optional, defaults to 0) — The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) or dim=1 (sequence length).

Parameters

• s1 (float) — Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the “oversmoothing effect” in the enhanced denoising process.
• s2 (float) — Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the “oversmoothing effect” in the enhanced denoising process.
• b1 (float) — Scaling factor for stage 1 to amplify the contributions of backbone features.
• b2 (float) — Scaling factor for stage 2 to amplify the contributions of backbone features.

Parameters

• sample (torch.FloatTensor) — The noisy input tensor with the following shape (batch, num_frames, channel, height, width.
• timestep (torch.FloatTensor or float or int) — The number of timesteps to denoise an input.
• encoder_hidden_states (torch.FloatTensor) — The encoder hidden states with shape (batch, sequence_length, feature_dim).
• class_labels (torch.Tensor, optional, defaults to None) — Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. timestep_cond — (torch.Tensor, optional, defaults to None): Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed through the self.time_embedding layer to obtain the timestep embeddings.
• attention_mask (torch.Tensor, optional, defaults to None) — An attention mask of shape (batch, key_tokens) is applied to encoder_hidden_states. If 1 the mask is kept, otherwise if 0 it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to “discard” tokens.
• cross_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor. down_block_additional_residuals — (tuple of torch.Tensor, optional): A tuple of tensors that if specified are added to the residuals of down unet blocks. mid_block_additional_residual — (torch.Tensor, optional): A tensor that if specified is added to the residual of the middle unet block.
• return_dict (bool, optional, defaults to True) — Whether or not to return a UNet3DConditionOutput instead of a plain tuple.
• cross_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttnProcessor.

If return_dict is True, an UNet3DConditionOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

Parameters

• slice_size (str or int or list(int), optional, defaults to "auto") — When "auto", input to the attention heads is halved, so attention is computed in two steps. If "max", maximum amount of memory is saved by running only one slice at a time. If a number is provided, uses as many slices as attention_head_dim // slice_size. In this case, attention_head_dim must be a multiple of slice_size.

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.