(self, x, t, cond, clip_denoised: bool)
| 248 | return posterior_mean, posterior_variance, posterior_log_variance_clipped |
| 249 | |
| 250 | def p_mean_variance(self, x, t, cond, clip_denoised: bool): |
| 251 | noise_pred = self.denoise_fn(x, t, cond=cond) |
| 252 | x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) |
| 253 | |
| 254 | if clip_denoised: |
| 255 | x_recon.clamp_(-1., 1.) |
| 256 | |
| 257 | model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) |
| 258 | return model_mean, posterior_variance, posterior_log_variance |
| 259 | |
| 260 | @torch.no_grad() |
| 261 | def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): |
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