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Gradient clipping in train tab

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This commit is contained in:
Muhammad Rizqi Nur
2022-10-28 09:44:56 +07:00
parent 737eb28fac
commit 2a25729623
2 changed files with 16 additions and 1 deletions
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10
modules/hypernetworks/hypernetwork.py
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@@ -327,7 +327,7 @@ def report_statistics(loss_info:dict):
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
from modules import images
@@ -384,6 +384,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
if ititial_step > steps:
return hypernetwork, filename
clip_grad_mode_value = clip_grad_mode == "value"
clip_grad_mode_norm = clip_grad_mode == "norm"
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
@@ -426,6 +429,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
steps_without_grad = 0
assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
if clip_grad_mode_value:
torch.nn.utils.clip_grad_value_(weights, clip_value=clip_grad_value)
elif clip_grad_mode_norm:
torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_value)
optimizer.step()
if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):