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019d164 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | import numpy as np
import torch
def get_beta_schedule(*, beta_start, beta_end, num_diffusion_timesteps):
betas = np.linspace(beta_start, beta_end,
num_diffusion_timesteps, dtype=np.float64)
assert betas.shape == (num_diffusion_timesteps,)
return betas
def extract(a, t, x_shape):
"""Extract coefficients from a based on t and reshape to make it
broadcastable with x_shape."""
bs, = t.shape
assert x_shape[0] == bs
out = torch.gather(torch.tensor(a, dtype=torch.float, device=t.device), 0, t.long())
assert out.shape == (bs,)
out = out.reshape((bs,) + (1,) * (len(x_shape) - 1))
return out
def denoising_step(xt, t, t_next, *,
models,
logvars,
b,
sampling_type='ddpm',
eta=0.0,
learn_sigma=False,
hybrid=False,
hybrid_config=None,
ratio=1.0,
out_x0_t=False,
edit_h=None,
):
# Compute noise and variance
if type(models) != list:
model = models
if edit_h == None:
mid_h, et = model(xt, t)
# print("check mid_h and et:", mid_h.size(), et.size())
else:
mid_h, et = model(xt, t, edit_h)
# print("Denoising for editing!")
if learn_sigma:
et, logvar_learned = torch.split(et, et.shape[1] // 2, dim=1)
logvar = logvar_learned
# print("split et:", et.size())
else:
logvar = extract(logvars, t, xt.shape)
else:
if not hybrid:
et = 0
logvar = 0
if ratio != 0.0:
et_i = ratio * models[1](xt, t)
if learn_sigma:
et_i, logvar_learned = torch.split(et_i, et_i.shape[1] // 2, dim=1)
logvar += logvar_learned
else:
logvar += ratio * extract(logvars, t, xt.shape)
et += et_i
if ratio != 1.0:
et_i = (1 - ratio) * models[0](xt, t)
if learn_sigma:
et_i, logvar_learned = torch.split(et_i, et_i.shape[1] // 2, dim=1)
logvar += logvar_learned
else:
logvar += (1 - ratio) * extract(logvars, t, xt.shape)
et += et_i
else:
for thr in list(hybrid_config.keys()):
if t.item() >= thr:
et = 0
logvar = 0
for i, ratio in enumerate(hybrid_config[thr]):
ratio /= sum(hybrid_config[thr])
et_i = models[i+1](xt, t)
if learn_sigma:
et_i, logvar_learned = torch.split(et_i, et_i.shape[1] // 2, dim=1)
logvar_i = logvar_learned
else:
logvar_i = extract(logvars, t, xt.shape)
et += ratio * et_i
logvar += ratio * logvar_i
break
# Compute the next x
bt = extract(b, t, xt.shape)
at = extract((1.0 - b).cumprod(dim=0), t, xt.shape)
if t_next.sum() == -t_next.shape[0]:
at_next = torch.ones_like(at)
else:
at_next = extract((1.0 - b).cumprod(dim=0), t_next, xt.shape)
xt_next = torch.zeros_like(xt)
if sampling_type == 'ddpm':
weight = bt / torch.sqrt(1 - at)
mean = 1 / torch.sqrt(1.0 - bt) * (xt - weight * et)
noise = torch.randn_like(xt)
mask = 1 - (t == 0).float()
mask = mask.reshape((xt.shape[0],) + (1,) * (len(xt.shape) - 1))
xt_next = mean + mask * torch.exp(0.5 * logvar) * noise
xt_next = xt_next.float()
elif sampling_type == 'ddim':
# print("check ddim incersion:", et.size())
x0_t = (xt - et * (1 - at).sqrt()) / at.sqrt()
if eta == 0:
xt_next = at_next.sqrt() * x0_t + (1 - at_next).sqrt() * et
elif at > (at_next):
print('Inversion process is only possible with eta = 0')
raise ValueError
else:
c1 = eta * ((1 - at / (at_next)) * (1 - at_next) / (1 - at)).sqrt()
c2 = ((1 - at_next) - c1 ** 2).sqrt()
xt_next = at_next.sqrt() * x0_t + c2 * et + c1 * torch.randn_like(xt)
# print("check out:", xt_next.size(), mid_h.size(), x0_t.size())
if out_x0_t == True:
# print("three output!")
return xt_next, x0_t, mid_h
else:
# print("two output!")
return xt_next, mid_h
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