mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-01-04 05:45:05 +08:00
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
import dataclasses
|
|
|
|
import torch
|
|
|
|
import k_diffusion
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class Scheduler:
|
|
name: str
|
|
label: str
|
|
function: any
|
|
|
|
default_rho: float = -1
|
|
need_inner_model: bool = False
|
|
aliases: list = None
|
|
|
|
|
|
def uniform(n, sigma_min, sigma_max, inner_model, device):
|
|
return inner_model.get_sigmas(n)
|
|
|
|
|
|
def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
|
|
start = inner_model.sigma_to_t(torch.tensor(sigma_max))
|
|
end = inner_model.sigma_to_t(torch.tensor(sigma_min))
|
|
sigs = [
|
|
inner_model.t_to_sigma(ts)
|
|
for ts in torch.linspace(start, end, n + 1)[:-1]
|
|
]
|
|
sigs += [0.0]
|
|
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))
|
|
step_indices = torch.arange(n + 1, device=device)
|
|
sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max)
|
|
return sigmas
|
|
|
|
|
|
schedulers = [
|
|
Scheduler('automatic', 'Automatic', None),
|
|
Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
|
|
Scheduler('karras', 'Karras', k_diffusion.sampling.get_sigmas_karras, default_rho=7.0),
|
|
Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
|
|
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('kl_optimal', 'KL Optimal', kl_optimal),
|
|
]
|
|
|
|
schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}
|