Generate samples from the model. :param model: the model module. :param shape: the shape of the samples, (N, C, H, W).

Run a single prediction on the model

Set requires_grad flag for all parameters in a model.

Scale the parameters of a module and return it.

Scale the parameters of a module and return it.

Scale the parameters of a module and return it.

Load the model into memory to make running multiple predictions efficient

Initialize distributed training environment. support both slurm and torch.distributed.launch see torch.distributed.init_process_group() for mo

Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may

Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be

Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be

Compute training losses for a single timestep. :param model: the model to evaluate loss on. :param x_start: the [N x C x .

Compute training losses for a single timestep. :param model: the model to evaluate loss on. :param x_start: the [N x C x .

Step the EMA model towards the current model.

Update the reweighting using losses from a model. Call this method from each rank with a batch of timesteps and the correspon

Update the reweighting using losses from a model. Call this method from each rank with a batch of timesteps and the correspon

write the loss information to a tensorboard file. Only for pytorch DDP mode.

Zero out the parameters of a module and return it.

Zero out the parameters of a module and return it.

Zero out the parameters of a module and return it.

Zero out the parameters of a module and return it.