Computes gradient of the Lovasz extension w.r.t sorted errors See Alg. 1 in paper

Multi-class Lovasz-Softmax loss probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1). Inte

Sample x_{t-1} from the model at the given timestep. :param model: the model to sample from. :param x: the current tensor at

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

Generate samples from the model and yield intermediate samples from each timestep of diffusion. Arguments are the same as p_s

Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diff

Set requires_grad flag for all parameters in a model.

Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portio

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

Update the reweighting using losses from a model. Sub-classes should override this method to update the reweighting using los

Get a numpy array of weights, one per diffusion step. The weights needn't be normalized, but must be positive.

pred: batch_size, num_classes, ... gt: batch_size, ...,

mus: [mu] * 6 logvars: [logvar] * 6

pred: -1, num_classes gt: -1,

Compute the entire variational lower-bound, measured in bits-per-dim, as well as other related quantities. :param model: the

Compute the log-likelihood of a continuous Gaussian distribution. :param x: the targets :param means: the Gaussian mean Tensor. :para

Create a ScheduleSampler from a library of pre-defined samplers. :param name: the name of the sampler. :param diffusion: the diffusion ob

Sample x_{t+1} from the model using DDIM reverse ODE.

Generate samples from the model using DDIM. Same usage as p_sample_loop().