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| import torch | |
| from scipy import integrate | |
| from ...util import append_dims | |
| class NoDynamicThresholding: | |
| def __call__(self, uncond, cond, scale): | |
| return uncond + scale * (cond - uncond) | |
| class DualThresholding: # Dual condition CFG (from instructPix2Pix) | |
| def __call__(self, uncond_1, uncond_2, cond, scale): | |
| return uncond_1 + scale[0] * (uncond_2 - uncond_1) + scale[1] * (cond - uncond_2) | |
| def linear_multistep_coeff(order, t, i, j, epsrel=1e-4): | |
| if order - 1 > i: | |
| raise ValueError(f"Order {order} too high for step {i}") | |
| def fn(tau): | |
| prod = 1.0 | |
| for k in range(order): | |
| if j == k: | |
| continue | |
| prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) | |
| return prod | |
| return integrate.quad(fn, t[i], t[i + 1], epsrel=epsrel)[0] | |
| def get_ancestral_step(sigma_from, sigma_to, eta=1.0): | |
| if not eta: | |
| return sigma_to, 0.0 | |
| sigma_up = torch.minimum( | |
| sigma_to, | |
| eta | |
| * (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5, | |
| ) | |
| sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5 | |
| return sigma_down, sigma_up | |
| def to_d(x, sigma, denoised): | |
| return (x - denoised) / append_dims(sigma, x.ndim) | |
| def to_neg_log_sigma(sigma): | |
| return sigma.log().neg() | |
| def to_sigma(neg_log_sigma): | |
| return neg_log_sigma.neg().exp() | |