feature: beta scheduler

modules/sd_schedulers.py

@@ -2,6 +2,7 @@
 import torch
 import k_diffusion
 import numpy as np
+from scipy import stats
 
 from modules import shared
 
@@ -115,6 +116,17 @@ def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
     return torch.FloatTensor(sigs).to(device)
 
 
+def beta_scheduler(n, sigma_min, sigma_max, inner_model, device):
+    # From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024) """
+    alpha = 0.6
+    beta = 0.6
+    timesteps = 1 - np.linspace(0, 1, n)
+    timesteps = [stats.beta.ppf(x, alpha, beta) for x in timesteps]
+    sigmas = [sigma_min + ((x)*(sigma_max-sigma_min)) for x in timesteps] + [0.0]
+    sigmas = torch.FloatTensor(sigmas).to(device)
+    return sigmas
+
+
 schedulers = [
     Scheduler('automatic', 'Automatic', None),
     Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
@@ -127,6 +139,7 @@ def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
     Scheduler('simple', 'Simple', simple_scheduler, need_inner_model=True),
     Scheduler('normal', 'Normal', normal_scheduler, need_inner_model=True),
     Scheduler('ddim', 'DDIM', ddim_scheduler, need_inner_model=True),
+    Scheduler('beta', 'Beta', beta_scheduler, need_inner_model=True),
 ]
 
 schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}