Diffusion model with a Transformer backbone.

Diffusion model with a Transformer backbone.

A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.

A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.

The final layer of DiT.

The final layer of DiT.

Utilities for training and sampling diffusion models. Original ported from this codebase: https://github.com/hojonathanho/diffusion/blob/

Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.

Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.

A diffusion process which can skip steps in a base diffusion process. :param use_timesteps: a collection (sequence or set) of timesteps from

Embeds scalar timesteps into vector representations.

Embeds scalar timesteps into vector representations.

Which type of output the model predicts.

What is used as the model's output variance. The LEARNED_RANGE option has been added to allow the model to predict values between FIXED_S

A distribution over timesteps in the diffusion process, intended to reduce variance of the objective. By default, samplers perform unbias