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This isn't used anywhere.
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@ -33,7 +33,6 @@ class DDIMSampler(object):
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assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
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to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.device)
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self.register_buffer('betas', to_torch(self.model.betas))
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self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
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self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
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@ -195,7 +194,7 @@ class DDIMSampler(object):
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temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
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unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
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ucg_schedule=None, denoise_function=None, extra_args=None, to_zero=True, end_step=None, disable_pbar=False):
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device = self.model.betas.device
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device = self.model.alphas_cumprod.device
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b = shape[0]
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if x_T is None:
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img = torch.randn(shape, device=device)
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