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fix floating error
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15a94d6cf7
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@ -89,11 +89,12 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
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restart_steps, restart_times, restart_max = restart_list[i + 1]
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restart_steps, restart_times, restart_max = restart_list[i + 1]
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min_idx = i + 1
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min_idx = i + 1
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max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
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max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
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sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end
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if max_idx < min_idx:
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for times in range(restart_times):
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sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end
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x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
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for times in range(restart_times):
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for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
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x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
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x = heun_step(x, old_sigma, new_sigma)
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for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
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x = heun_step(x, old_sigma, new_sigma)
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return x
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return x
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samplers_data_k_diffusion = [
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samplers_data_k_diffusion = [
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