sample_dpmpp_2m_sde.py

@torch.no_grad()
def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, noise_sampler=None, solver_type='midpoint'):
    """DPM-Solver++(2M) SDE."""

    if solver_type not in {'heun', 'midpoint'}:
        raise ValueError('solver_type must be \'heun\' or \'midpoint\'')

    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
    noise_sampler = K.sampling.BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])

    old_denoised = None
    h_last = None

    for i in trange(len(sigmas) - 1, disable=disable):
        denoised = model(x, sigmas[i] * s_in, **extra_args)
        if callback is not None:
            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
        if sigmas[i + 1] == 0:
            # Denoising step
            x = denoised
        else:
            # DPM-Solver++(2M) SDE
            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
            h = s - t
            eta_h = eta * h

            x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised

            if old_denoised is not None:
                r = h_last / h
                if solver_type == 'heun':
                    x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
                elif solver_type == 'midpoint':
                    x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)

            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt()

        old_denoised = denoised
        h_last = h
    return x

Thanks for the complete explanation! And amazing, thanks for the commit!