models/samplers/edm.py

import torch
import numpy as np


def edm_sampler(
    net,
    x_N,
    conditioning=None,
    latents=None,
    randn_like=torch.randn_like,
    num_steps=18,
    sigma_min=0.002,
    sigma_max=80,
    rho=7,
    S_churn=0,
    S_min=0,
    S_max=float("inf"),
    S_noise=1,
):
    # Adjust noise levels based on what's supported by the network.
    sigma_min = max(sigma_min, net.sigma_min)
    sigma_max = min(sigma_max, net.sigma_max)

    # Time step discretization.
    step_indices = torch.arange(num_steps, dtype=torch.float64, device=x_N.device)
    t_steps = (
        sigma_max ** (1 / rho)
        + step_indices
        / (num_steps - 1)
        * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho))
    ) ** rho
    t_steps = torch.cat(
        [net.round_sigma(t_steps), torch.zeros_like(t_steps[:1])]
    )  # t_N = 0

    # Main sampling loop.
    x_next = x_N.to(torch.float64) * t_steps[0]
    for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):  # 0, ..., N-1
        x_cur = x_next

        # Increase noise temporarily.
        gamma = (
            min(S_churn / num_steps, np.sqrt(2) - 1) if S_min <= t_cur <= S_max else 0
        )
        t_hat = net.round_sigma(t_cur + gamma * t_cur)
        x_hat = x_cur + (t_hat**2 - t_cur**2).sqrt() * S_noise * randn_like(x_cur)

        # Euler step.
        denoised, latents = net(
            x_hat, t_hat.expand(x_cur.shape[0]), conditioning, previous_latents=latents
        )
        denoised = denoised.to(torch.float64)
        d_cur = (x_hat - denoised) / t_hat
        x_next = x_hat + (t_next - t_hat) * d_cur

        # Apply 2nd order correction.
        if i < num_steps - 1:
            denoised, latents = net(
                x_next,
                t_next.expand(x_cur.shape[0]),
                conditioning,
                previous_latents=latents,
            )
            denoised = denoised.to(torch.float64)
            d_prime = (x_next - denoised) / t_next
            x_next = x_hat + (t_next - t_hat) * (0.5 * d_cur + 0.5 * d_prime)

    return x_next