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Merge pull request #1310 from lllyasviel/more_schedulers

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Add Align Your Steps GITS, AYS 11 Steps and AYS 32 Steps Schedulers.
This commit is contained in:
Panchovix
2024-08-19 16:58:52 -04:00
committed by GitHub
parent d7151b4dcd 9f5a27ca4e
commit 9bc2d04ca9
1 changed files with 76 additions and 0 deletions
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76
modules/sd_schedulers.py
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@@ -134,6 +134,79 @@ def turbo_scheduler(n, sigma_min, sigma_max, inner_model, device):
sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
return sigmas.to(device)
def get_align_your_steps_sigmas_GITS(n, sigma_min, sigma_max, device):
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 shared.sd_model.is_sdxl:
sigmas = [14.615, 4.734, 2.567, 1.529, 0.987, 0.652, 0.418, 0.268, 0.179, 0.127, 0.029]
else:
sigmas = [14.615, 4.617, 2.507, 1.236, 0.702, 0.402, 0.240, 0.156, 0.104, 0.094, 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)
def ays_11_sigmas(n, sigma_min, sigma_max, device='cpu'):
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 shared.sd_model.is_sdxl:
sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
else:
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)
def ays_32_sigmas(n, sigma_min, sigma_max, device='cpu'):
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 shared.sd_model.is_sdxl:
sigmas = [14.61500000000000000, 11.14916180000000000, 8.505221270000000000, 6.488271510000000000, 5.437074020000000000, 4.603986190000000000, 3.898547040000000000, 3.274074570000000000, 2.743965270000000000, 2.299686590000000000, 1.954485140000000000, 1.671087150000000000, 1.428781520000000000, 1.231810090000000000, 1.067896490000000000, 0.925794430000000000, 0.802908860000000000, 0.696601210000000000, 0.604369030000000000, 0.528525520000000000, 0.467733440000000000, 0.413933790000000000, 0.362581860000000000, 0.310085170000000000, 0.265189250000000000, 0.223264610000000000, 0.176538770000000000, 0.139591920000000000, 0.105873810000000000, 0.055193690000000000, 0.028773340000000000, 0.015000000000000000]
else:
sigmas = [14.61500000000000000, 11.23951352000000000, 8.643630810000000000, 6.647294240000000000, 5.572508620000000000, 4.716485460000000000, 3.991960650000000000, 3.519560900000000000, 3.134904660000000000, 2.792287880000000000, 2.487736280000000000, 2.216638650000000000, 1.975083510000000000, 1.779317200000000000, 1.614753350000000000, 1.465409530000000000, 1.314849000000000000, 1.166424970000000000, 1.034755470000000000, 0.915737440000000000, 0.807481690000000000, 0.712023610000000000, 0.621739000000000000, 0.530652020000000000, 0.452909600000000000, 0.374914550000000000, 0.274618190000000000, 0.201152900000000000, 0.141058730000000000, 0.066828810000000000, 0.031661210000000000, 0.015000000000000000]
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),
@@ -149,6 +222,9 @@ schedulers = [
Scheduler('ddim', 'DDIM', ddim_scheduler, need_inner_model=True),
Scheduler('beta', 'Beta', beta_scheduler, need_inner_model=True),
Scheduler('turbo', 'Turbo', turbo_scheduler, need_inner_model=True),
Scheduler('align_your_steps_GITS', 'Align Your Steps GITS', get_align_your_steps_sigmas_GITS),
Scheduler('align_your_steps_11', 'Align Your Steps 11', ays_11_sigmas),
Scheduler('align_your_steps_32', 'Align Your Steps 32', ays_32_sigmas),
]
schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}