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import torch.nn as nn
from ...util import append_dims, instantiate_from_config
class Denoiser(nn.Module):
def __init__(self, weighting_config, scaling_config):
super().__init__()
self.weighting = instantiate_from_config(weighting_config)
self.scaling = instantiate_from_config(scaling_config)
def possibly_quantize_sigma(self, sigma):
return sigma
def possibly_quantize_c_noise(self, c_noise):
return c_noise
def w(self, sigma):
return self.weighting(sigma)
def __call__(self, network, input, sigma, cond):
sigma = self.possibly_quantize_sigma(sigma)
sigma_shape = sigma.shape
sigma = append_dims(sigma, input.ndim)
c_skip, c_out, c_in, c_noise = self.scaling(sigma)
c_noise = self.possibly_quantize_c_noise(c_noise.reshape(sigma_shape))
# import pdb; pdb.set_trace()
# import torchvision, einops
# tmp = einops.rearrange(input, 'b c t h w -> (b t) c h w')
# torchvision.utils.save_image(tmp[:,:3], 'tmp.png', normalize=True)
'''
input * c_in: noised_input multiplied by the coefficient of the corresponding t. (not sure)
c_in: torch.Size([2, 1, 1, 1, 1]); 0.0683, 0.0683
c_noise: the step t. e.g., tensor([451], device='cuda:0')
cond: the condition. e.g., cond['crossattn']: [1, 77, 1024]
c_out: e.g., -1.3762. Don't know why multiply this and why it's negative.
c_skip: e.g., 1.0. Don't know why multiply this.
'''
return network(input * c_in, c_noise, cond) * c_out + input * c_skip
class DiscreteDenoiser(Denoiser):
def __init__(
self,
weighting_config,
scaling_config,
num_idx,
discretization_config,
do_append_zero=False,
quantize_c_noise=True,
flip=True,
):
super().__init__(weighting_config, scaling_config)
sigmas = instantiate_from_config(discretization_config)(
num_idx, do_append_zero=do_append_zero, flip=flip
)
self.register_buffer("sigmas", sigmas)
self.quantize_c_noise = quantize_c_noise
def sigma_to_idx(self, sigma):
dists = sigma - self.sigmas[:, None]
return dists.abs().argmin(dim=0).view(sigma.shape)
def idx_to_sigma(self, idx):
return self.sigmas[idx]
def possibly_quantize_sigma(self, sigma):
return self.idx_to_sigma(self.sigma_to_idx(sigma))
def possibly_quantize_c_noise(self, c_noise):
if self.quantize_c_noise:
return self.sigma_to_idx(c_noise)
else:
return c_noise |