cleanup unused

lucidrains · May 12, 2022 · 55c658b · 55c658b
1 parent e0f2667
commit 55c658b
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diff --git a/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py b/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
@@ -388,12 +388,6 @@ def __init__(
         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 -

diff --git a/denoising_diffusion_pytorch/learned_gaussian_diffusion.py b/denoising_diffusion_pytorch/learned_gaussian_diffusion.py
@@ -88,13 +88,6 @@ def q_posterior_mean_variance(self, x_start, x_t, t):
         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)