cm/karras_diffusion.py [760:769]:
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    for i in range(len(ts) - 1):
        t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
        x0 = distiller(x, t * s_in)
        x0 = th.clamp(x0, -1.0, 1.0)
        x0 = replacement(images, x0)
        next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
        next_t = np.clip(next_t, t_min, t_max)
        x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2)

    return x, images
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