Loading modules/sd_samplers_kdiffusion.py +6 −5 Original line number Diff line number Diff line Loading @@ -89,6 +89,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No restart_steps, restart_times, restart_max = restart_list[i + 1] min_idx = i + 1 max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) if max_idx < min_idx: sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end for times in range(restart_times): x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5 Loading Loading
modules/sd_samplers_kdiffusion.py +6 −5 Original line number Diff line number Diff line Loading @@ -89,6 +89,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No restart_steps, restart_times, restart_max = restart_list[i + 1] min_idx = i + 1 max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) if max_idx < min_idx: sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end for times in range(restart_times): x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5 Loading
