Function simplified_euler

custom_samplers.py:249–261

(model, x, sigmas, extra_args=None, callback=None, disable=None)

# as a reference
@torch.no_grad()
def simplified_euler(model, x, sigmas, extra_args=None, callback=None, disable=None):
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1, disable=disable):
        sigma_hat = sigmas[i]
        denoised = model(x, sigma_hat * s_in, **extra_args)
        d = to_d(x, sigma_hat, denoised)
        if callback is not None:
            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
        dt = sigmas[i + 1] - sigma_hat
        # Euler method
        x = x + d * dt
    return x

class SamplerDistanceBase:
    _DISTANCE_OPTIONS = None # All options by default.