mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-11-04 12:03:36 +00:00
c3c8eef9fd
train: make it possible to make text files with prompts train: rework scheduler so that there's less repeating code in textual inversion and hypernets train: move epochs setting to options
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
import tqdm
|
|
|
|
|
|
class LearnScheduleIterator:
|
|
def __init__(self, learn_rate, max_steps, cur_step=0):
|
|
"""
|
|
specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000
|
|
"""
|
|
|
|
pairs = learn_rate.split(',')
|
|
self.rates = []
|
|
self.it = 0
|
|
self.maxit = 0
|
|
for i, pair in enumerate(pairs):
|
|
tmp = pair.split(':')
|
|
if len(tmp) == 2:
|
|
step = int(tmp[1])
|
|
if step > cur_step:
|
|
self.rates.append((float(tmp[0]), min(step, max_steps)))
|
|
self.maxit += 1
|
|
if step > max_steps:
|
|
return
|
|
elif step == -1:
|
|
self.rates.append((float(tmp[0]), max_steps))
|
|
self.maxit += 1
|
|
return
|
|
else:
|
|
self.rates.append((float(tmp[0]), max_steps))
|
|
self.maxit += 1
|
|
return
|
|
|
|
def __iter__(self):
|
|
return self
|
|
|
|
def __next__(self):
|
|
if self.it < self.maxit:
|
|
self.it += 1
|
|
return self.rates[self.it - 1]
|
|
else:
|
|
raise StopIteration
|
|
|
|
|
|
class LearnRateScheduler:
|
|
def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):
|
|
self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)
|
|
(self.learn_rate, self.end_step) = next(self.schedules)
|
|
self.verbose = verbose
|
|
|
|
if self.verbose:
|
|
print(f'Training at rate of {self.learn_rate} until step {self.end_step}')
|
|
|
|
self.finished = False
|
|
|
|
def apply(self, optimizer, step_number):
|
|
if step_number <= self.end_step:
|
|
return
|
|
|
|
try:
|
|
(self.learn_rate, self.end_step) = next(self.schedules)
|
|
except Exception:
|
|
self.finished = True
|
|
return
|
|
|
|
if self.verbose:
|
|
tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')
|
|
|
|
for pg in optimizer.param_groups:
|
|
pg['lr'] = self.learn_rate
|
|
|