Merge pull request #16235 from v0xie/beta-sampling

Feature: Beta scheduler

Author: AUTOMATIC1111

modules/sd_samplers_kdiffusion.py

@@ -120,6 +120,10 @@ def get_sigmas(self, p, steps):
             if scheduler.need_inner_model:
                 sigmas_kwargs['inner_model'] = self.model_wrap
 
+            if scheduler.label == 'Beta':
+                p.extra_generation_params["Beta schedule alpha"] = opts.beta_dist_alpha
+                p.extra_generation_params["Beta schedule beta"] = opts.beta_dist_beta
+
             sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=devices.cpu)
 
         if discard_next_to_last_sigma:

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 = shared.opts.beta_dist_alpha
+    beta = shared.opts.beta_dist_beta
+    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]
+    sigmas += [0.0]
+    return torch.FloatTensor(sigmas).to(device)
+
+
 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}}

modules/shared_options.py

@@ -405,6 +405,8 @@
     'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
     'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models"),
     'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling"),
+    'beta_dist_alpha': OptionInfo(0.6, "Beta scheduler - alpha", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler alpha').info('Default = 0.6; the alpha parameter of the beta distribution used in Beta sampling'),
+    'beta_dist_beta': OptionInfo(0.6, "Beta scheduler - beta", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler beta').info('Default = 0.6; the beta parameter of the beta distribution used in Beta sampling'),
 }))
 
 options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {

scripts/xyz_grid.py

@@ -259,6 +259,8 @@ def __init__(self, *args, **kwargs):
     AxisOption("Schedule min sigma", float, apply_override("sigma_min")),
     AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
     AxisOption("Schedule rho", float, apply_override("rho")),
+    AxisOption("Beta schedule alpha", float, apply_override("beta_dist_alpha")),
+    AxisOption("Beta schedule beta", float, apply_override("beta_dist_beta")),
     AxisOption("Eta", float, apply_field("eta")),
     AxisOption("Clip skip", int, apply_override('CLIP_stop_at_last_layers')),
     AxisOption("Denoising", float, apply_field("denoising_strength")),