mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-29 19:05:05 +08:00
Add Align Your Steps to available schedulers
* Include both SDXL and SD 1.5 variants (https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html)
This commit is contained in:
parent
ddb28b33a3
commit
73d1caf8f2
@ -4,6 +4,7 @@ import torch
|
||||
|
||||
import k_diffusion
|
||||
|
||||
import numpy as np
|
||||
|
||||
@dataclasses.dataclass
|
||||
class Scheduler:
|
||||
@ -30,6 +31,35 @@ def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
|
||||
sigs += [0.0]
|
||||
return torch.FloatTensor(sigs).to(device)
|
||||
|
||||
def get_align_your_steps_sigmas(n, device, sigma_id):
|
||||
# https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
|
||||
def loglinear_interp(t_steps, num_steps):
|
||||
"""
|
||||
Performs log-linear interpolation of a given array of decreasing numbers.
|
||||
"""
|
||||
xs = np.linspace(0, 1, len(t_steps))
|
||||
ys = np.log(t_steps[::-1])
|
||||
|
||||
new_xs = np.linspace(0, 1, num_steps)
|
||||
new_ys = np.interp(new_xs, xs, ys)
|
||||
|
||||
interped_ys = np.exp(new_ys)[::-1].copy()
|
||||
return interped_ys
|
||||
|
||||
if sigma_id == "sdxl":
|
||||
sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
|
||||
elif sigma_id == "sd15":
|
||||
sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
|
||||
else:
|
||||
print(f'Align Your Steps sigma identifier "{sigma_id}" not recognized, defaulting to SD 1.5.')
|
||||
sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
|
||||
|
||||
if n != len(sigmas):
|
||||
sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
|
||||
else:
|
||||
sigmas.append(0.0)
|
||||
|
||||
return torch.FloatTensor(sigmas).to(device)
|
||||
|
||||
schedulers = [
|
||||
Scheduler('automatic', 'Automatic', None),
|
||||
@ -38,6 +68,8 @@ schedulers = [
|
||||
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('align_your_steps_sdxl', 'Align Your Steps (SDXL)', lambda n, sigma_min, sigma_max, device: get_align_your_steps_sigmas(n, device, "sdxl")),
|
||||
Scheduler('align_your_steps_sd15', 'Align Your Steps (SD 1.5)', lambda n, sigma_min, sigma_max, device: get_align_your_steps_sigmas(n, device, "sd15")),
|
||||
]
|
||||
|
||||
schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}
|
||||
|
Loading…
Reference in New Issue
Block a user