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feature: beta scheduler

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v0xie 2024-07-18 15:53:54 -07:00
parent b2453d280a
commit a5f66b5003
1 changed files with 13 additions and 0 deletions
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13
modules/sd_schedulers.py
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@ -2,6 +2,7 @@ import dataclasses
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 @@ schedulers = [
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}}