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from __future__ import annotations
import torch
from einops import repeat
from vwm.util import default, instantiate_from_config
class EDMSampling:
def __init__(self, p_mean=-1.2, p_std=1.2, num_frames=25):
self.p_mean = p_mean
self.p_std = p_std
self.num_frames = num_frames
def __call__(self, n_samples, rand=None):
bs = n_samples // self.num_frames
rand_init = torch.randn((bs,))[..., None]
rand_init = repeat(rand_init, "b 1 -> (b t)", t=self.num_frames)
rand = default(rand, rand_init)
log_sigma = self.p_mean + self.p_std * rand
return log_sigma.exp()
class DiscreteSampling:
def __init__(self, discretization_config, num_idx, do_append_zero=False, flip=True, num_frames=25):
self.num_idx = num_idx
self.sigmas = instantiate_from_config(discretization_config)(
num_idx, do_append_zero=do_append_zero, flip=flip
)
self.num_frames = num_frames
def idx_to_sigma(self, idx):
return self.sigmas[idx]
def __call__(self, n_samples, rand=None):
bs = n_samples // self.num_frames
rand_init = torch.randint(0, self.num_idx, (bs,))[..., None]
rand_init = repeat(rand_init, "b 1 -> (b t)", t=self.num_frames)
idx = default(rand, rand_init)
return self.idx_to_sigma(idx)
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