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import math
import torch
from torch import nn
from . import sampling, utils
class VDenoiser(nn.Module):
"""A v-diffusion-pytorch model wrapper for k-diffusion."""
def __init__(self, inner_model):
super().__init__()
self.inner_model = inner_model
self.sigma_data = 1.
def get_scalings(self, sigma):
c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
return c_skip, c_out, c_in
def sigma_to_t(self, sigma):
return sigma.atan() / math.pi * 2
def t_to_sigma(self, t):
return (t * math.pi / 2).tan()
def loss(self, input, noise, sigma, **kwargs):
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
noised_input = input + noise * utils.append_dims(sigma, input.ndim)
model_output = self.inner_model(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
target = (input - c_skip * noised_input) / c_out
return (model_output - target).pow(2).flatten(1).mean(1)
def forward(self, input, sigma, **kwargs):
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
return self.inner_model(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
class DiscreteSchedule(nn.Module):
"""A mapping between continuous noise levels (sigmas) and a list of discrete noise
levels."""
def __init__(self, sigmas, quantize):
super().__init__()
self.register_buffer('sigmas', sigmas)
self.register_buffer('log_sigmas', sigmas.log())
self.quantize = quantize
@property
def sigma_min(self):
return self.sigmas[0]
@property
def sigma_max(self):
return self.sigmas[-1]
def get_sigmas(self, n=None):
if n is None:
return sampling.append_zero(self.sigmas.flip(0))
t_max = len(self.sigmas) - 1
t = torch.linspace(t_max, 0, n, device=self.sigmas.device)
return sampling.append_zero(self.t_to_sigma(t))
def sigma_to_discrete_timestep(self, sigma):
log_sigma = sigma.log()
dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
return dists.abs().argmin(dim=0).view(sigma.shape)
def sigma_to_t(self, sigma, quantize=None):
quantize = self.quantize if quantize is None else quantize
if quantize:
return self.sigma_to_discrete_timestep(sigma)
log_sigma = sigma.log()
dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
high_idx = low_idx + 1
low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx]
w = (low - log_sigma) / (low - high)
w = w.clamp(0, 1)
t = (1 - w) * low_idx + w * high_idx
return t.view(sigma.shape)
def t_to_sigma(self, t):
t = t.float()
low_idx = t.floor().long()
high_idx = t.ceil().long()
w = t-low_idx if t.device.type == 'mps' else t.frac()
log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
return log_sigma.exp()
def predict_eps_discrete_timestep(self, input, t, **kwargs):
if t.dtype != torch.int64 and t.dtype != torch.int32:
t = t.round()
sigma = self.t_to_sigma(t)
input = input * ((utils.append_dims(sigma, input.ndim) ** 2 + 1.0) ** 0.5)
return (input - self(input, sigma, **kwargs)) / utils.append_dims(sigma, input.ndim)
class DiscreteEpsDDPMDenoiser(DiscreteSchedule):
"""A wrapper for discrete schedule DDPM models that output eps (the predicted
noise)."""
def __init__(self, model, alphas_cumprod, quantize):
super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
self.inner_model = model
self.sigma_data = 1.
def get_scalings(self, sigma):
c_out = -sigma
c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
return c_out, c_in
def get_eps(self, *args, **kwargs):
return self.inner_model(*args, **kwargs)
def loss(self, input, noise, sigma, **kwargs):
c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
noised_input = input + noise * utils.append_dims(sigma, input.ndim)
eps = self.get_eps(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
return (eps - noise).pow(2).flatten(1).mean(1)
def forward(self, input, sigma, **kwargs):
c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
return input + eps * c_out
class OpenAIDenoiser(DiscreteEpsDDPMDenoiser):
"""A wrapper for OpenAI diffusion models."""
def __init__(self, model, diffusion, quantize=False, has_learned_sigmas=True, device='cpu'):
alphas_cumprod = torch.tensor(diffusion.alphas_cumprod, device=device, dtype=torch.float32)
super().__init__(model, alphas_cumprod, quantize=quantize)
self.has_learned_sigmas = has_learned_sigmas
def get_eps(self, *args, **kwargs):
model_output = self.inner_model(*args, **kwargs)
if self.has_learned_sigmas:
return model_output.chunk(2, dim=1)[0]
return model_output
class CompVisDenoiser(DiscreteEpsDDPMDenoiser):
"""A wrapper for CompVis diffusion models."""
def __init__(self, model, quantize=False, device='cpu'):
super().__init__(model, model.alphas_cumprod, quantize=quantize)
def get_eps(self, *args, **kwargs):
return self.inner_model.apply_model(*args, **kwargs)
class DiscreteVDDPMDenoiser(DiscreteSchedule):
"""A wrapper for discrete schedule DDPM models that output v."""
def __init__(self, model, alphas_cumprod, quantize):
super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
self.inner_model = model
self.sigma_data = 1.
def get_scalings(self, sigma):
c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
return c_skip, c_out, c_in
def get_v(self, *args, **kwargs):
return self.inner_model(*args, **kwargs)
def loss(self, input, noise, sigma, **kwargs):
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
noised_input = input + noise * utils.append_dims(sigma, input.ndim)
model_output = self.get_v(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
target = (input - c_skip * noised_input) / c_out
return (model_output - target).pow(2).flatten(1).mean(1)
def forward(self, input, sigma, **kwargs):
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
class CompVisVDenoiser(DiscreteVDDPMDenoiser):
"""A wrapper for CompVis diffusion models that output v."""
def __init__(self, model, quantize=False, device='cpu'):
super().__init__(model, model.alphas_cumprod, quantize=quantize)
def get_v(self, x, t, cond, **kwargs):
return self.inner_model.apply_model(x, t, cond)
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