x: [B, L, C].

x: [B, L, C].

imgs: [B, 3, H, W] of torch.float32. - mean: [0.48145466, 0.4578275, 0.40821073] - std: [0.26862954, 0.2613025

x: [B, L1, C]. context: [B, L2, C] or None. mask: [B, L2] or [B, L1, L2] or None.

Fix bfloat16 support for nearest neighbor interpolation.

x: [B, L, C].

ids: [B, L] of torch.LongTensor.

Device-aware norm implementation

Device-aware layer norm implementation

Device-balanced attention implementation

Device-balanced cross-attention implementation

Device-balanced image-to-video cross-attention implementation

Device-balanced attention block implementation

Device-consistent head implementation

Forward pass with improved device distribution

Print the memory status of all available GPUs

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Ar

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. A

Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index

Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_inde

Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`):

The index counter for current timestep. It will increase 1 after each scheduler step.

The index counter for current timestep. It will increase 1 after each scheduler step.

Convert a string to a boolean. Supported true values: 'yes', 'true', 't', 'y', '1' Supported false values: 'no', 'false', 'f', 'n', '0'

x: A list of videos each with shape [C, T, H, W]. t: [B]. context: A list of text embeddings each with s

XLMRobertaLarge adapted from Huggingface.