diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index ed5e6c79b..c72d01c8f 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -35,14 +35,15 @@ samplers_k_diffusion = [ @torch.no_grad() -def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.): +def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None): """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)""" '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}''' + '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list''' from tqdm.auto import trange extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) step_id = 0 - from k_diffusion.sampling import to_d, append_zero + from k_diffusion.sampling import to_d, append_zero, get_sigmas_karras def heun_step(x, old_sigma, new_sigma, second_order = True): nonlocal step_id denoised = model(x, old_sigma * s_in, **extra_args) @@ -62,24 +63,15 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No x = x + d_prime * dt step_id += 1 return x - def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): - ramp = torch.linspace(0, 1, n).to(device) - min_inv_rho = (sigma_min ** (1 / rho)) - max_inv_rho = (sigma_max ** (1 / rho)) - if isinstance(min_inv_rho, torch.Tensor): - min_inv_rho = min_inv_rho.to(device) - if isinstance(max_inv_rho, torch.Tensor): - max_inv_rho = max_inv_rho.to(device) - sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho - return append_zero(sigmas).to(device) steps = sigmas.shape[0] - 1 - if steps >= 20: - restart_steps = 9 - restart_times = 2 if steps >= 36 else 1 - sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2], sigmas[0], device=sigmas.device) - restart_list = {0.1: [restart_steps + 1, restart_times, 2]} - else: - restart_list = dict() + if restart_list is None: + if steps >= 20: + restart_steps = 9 + restart_times = 2 if steps >= 36 else 1 + sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device) + restart_list = {0.1: [restart_steps + 1, restart_times, 2]} + else: + restart_list = dict() temp_list = dict() for key, value in restart_list.items(): temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value @@ -91,7 +83,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No 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 + sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] # remove the zero at the end while restart_times > 0: restart_times -= 1 x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5