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Add KL Optimal scheduler

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drhead 2024-04-23 00:09:43 -04:00 committed by GitHub
parent ddb28b33a3
commit 83266205d0
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1 changed files with 10 additions and 0 deletions
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modules/sd_schedulers.py
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@ -31,6 +31,15 @@ def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
return torch.FloatTensor(sigs).to(device) return torch.FloatTensor(sigs).to(device)
def kl_optimal(n, sigma_min, sigma_max, device):
alpha_min = torch.arctan(torch.tensor(sigma_min, device=device))
alpha_max = torch.arctan(torch.tensor(sigma_max, device=device))
sigmas = torch.empty((n+1,), device=device)
for i in range(n+1):
sigmas[i] = torch.tan((i/n) * alpha_min + (1.0-i/n) * alpha_max)
return sigmas
schedulers = [ schedulers = [
Scheduler('automatic', 'Automatic', None), Scheduler('automatic', 'Automatic', None),
Scheduler('uniform', 'Uniform', uniform, need_inner_model=True), Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
@ -38,6 +47,7 @@ schedulers = [
Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential), Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0), Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]), Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
] ]
schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}} schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}