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Add Align Your Steps to available schedulers

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* Include both SDXL and SD 1.5 variants (https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html)
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Logan 2024-05-10 12:38:10 +10:00
parent ddb28b33a3
commit 73d1caf8f2
1 changed files with 32 additions and 0 deletions
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32
modules/sd_schedulers.py
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@ -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}}