add restart sampler

# export PIP_CACHE_DIR=/scratch/dengm/cache

# export XDG_CACHE_HOME=/scratch/dengm/cache

from collections import deque

import torch

import inspect

    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),

    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),

    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),

    ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}),

]

@torch.no_grad()

def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = {0.1: [10, 2, 2]}):

    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""

    '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''

    from tqdm.auto import trange, tqdm

    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

    def heun_step(x, old_sigma, new_sigma):

        nonlocal step_id

        denoised = model(x, old_sigma * s_in, **extra_args)

        d = to_d(x, old_sigma, denoised)

        if callback is not None:

            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})

        dt = new_sigma - old_sigma

        if new_sigma == 0:

            # Euler method

            x = x + d * dt

        else:

            # Heun's method

            x_2 = x + d * dt

            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)

            d_2 = to_d(x_2, new_sigma, denoised_2)

            d_prime = (d + d_2) / 2

            x = x + d_prime * dt

        step_id += 1

        return x

    # print(sigmas)

    temp_list = dict()

    for key, value in restart_list.items():

        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value

    restart_list = temp_list

    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)

    for i in trange(len(sigmas) - 1, disable=disable):

        x = heun_step(x, sigmas[i], sigmas[i+1])

        if i + 1 in restart_list:

            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))

            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

                for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):

                    x = heun_step(x, old_sigma, new_sigma)

    return x

samplers_data_k_diffusion = [

    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)

    for label, funcname, aliases, options in samplers_k_diffusion

    if hasattr(k_diffusion.sampling, funcname)

    if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')

]

sampler_extra_params = {

        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)

        self.funcname = funcname

        self.func = getattr(k_diffusion.sampling, self.funcname)

        self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler

        self.extra_params = sampler_extra_params.get(funcname, [])

        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)

        self.sampler_noises = None