wassname/denoising-diffusion-pytorch
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cleanup unused

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This commit is contained in:
Phil Wang
2022-05-12 11:52:06 -07:00
parent e0f26677d6
commit 55c658b967
2 changed files with 0 additions and 13 deletions
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denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-6
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@@ -388,12 +388,6 @@ class GaussianDiffusion(nn.Module):
register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
def q_mean_variance(self, x_start, t):
mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
denoising_diffusion_pytorch/learned_gaussian_diffusion.py
-7
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@@ -88,13 +88,6 @@ class LearnedGaussianDiffusion(GaussianDiffusion):
posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def predict_xstart_from_xprev(self, x_t, t, xprev):
# (xprev - coef2*x_t) / coef1
return (
extract(1. / self.posterior_mean_coef1, t, x_t.shape) * xprev -
extract(self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape) * x_t
)
def p_mean_variance(self, *, x, t, clip_denoised, model_output = None):
model_output = default(model_output, lambda: self.denoise_fn(x, t))
pred_noise, var_interp_frac_unnormalized = model_output.chunk(2, dim = 1)