soon is now

sgm/modules/diffusionmodules/sampling_utils.py

@@ -0,0 +1,48 @@
+import torch
+from scipy import integrate
+
+from ...util import append_dims
+
+
+class NoDynamicThresholding:
+    def __call__(self, uncond, cond, scale):
+        return uncond + scale * (cond - uncond)
+
+
+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()