From 55c658b967af017bbe2ec88b9f487fc382711c3d Mon Sep 17 00:00:00 2001 From: Phil Wang Date: Thu, 12 May 2022 11:52:06 -0700 Subject: [PATCH] cleanup unused --- denoising_diffusion_pytorch/denoising_diffusion_pytorch.py | 6 ------ denoising_diffusion_pytorch/learned_gaussian_diffusion.py | 7 ------- 2 files changed, 13 deletions(-) diff --git a/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py b/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py index 23b67d8..904b0f5 100644 --- a/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +++ b/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py @@ -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 - diff --git a/denoising_diffusion_pytorch/learned_gaussian_diffusion.py b/denoising_diffusion_pytorch/learned_gaussian_diffusion.py index e215da8..acc5d8d 100644 --- a/denoising_diffusion_pytorch/learned_gaussian_diffusion.py +++ b/denoising_diffusion_pytorch/learned_gaussian_diffusion.py @@ -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)