diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index c5d60654..86532063 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -383,11 +383,15 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
ititial_step = hypernetwork.step or 0
if ititial_step > steps:
return hypernetwork, filename
-
+
clip_grad_mode_value = clip_grad_mode == "value"
clip_grad_mode_norm = clip_grad_mode == "norm"
+ clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm
+ if clip_grad_enabled:
+ clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
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)
@@ -407,6 +411,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
if shared.state.interrupted:
break
+ if clip_grad_enabled:
+ clip_grad_sched.step(hypernetwork.step)
+
with torch.autocast("cuda"):
c = stack_conds([entry.cond for entry in entries]).to(devices.device)
# c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
@@ -430,9 +437,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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)
+ torch.nn.utils.clip_grad_value_(weights, clip_value=clip_grad_sched.learn_rate)
elif clip_grad_mode_norm:
- torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_value)
+ torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_sched.learn_rate)
optimizer.step()
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index 2062726a..ffec3e1b 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -51,14 +51,19 @@ class LearnRateScheduler:
self.finished = False
- def apply(self, optimizer, step_number):
+ def step(self, step_number):
if step_number <= self.end_step:
- return
+ return False
try:
(self.learn_rate, self.end_step) = next(self.schedules)
- except Exception:
+ except StopIteration:
self.finished = True
+ return False
+ return True
+
+ def apply(self, optimizer, step_number):
+ if not self.step(step_number):
return
if self.verbose:
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 7bad73a6..6b00c6a1 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -255,9 +255,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
ititial_step = embedding.step or 0
if ititial_step > steps:
return embedding, filename
-
+
clip_grad_mode_value = clip_grad_mode == "value"
clip_grad_mode_norm = clip_grad_mode == "norm"
+ clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm
+ if clip_grad_enabled:
+ clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
@@ -273,6 +276,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
if shared.state.interrupted:
break
+ if clip_grad_enabled:
+ clip_grad_sched.step(embedding.step)
+
with torch.autocast("cuda"):
c = cond_model([entry.cond_text for entry in entries])
x = torch.stack([entry.latent for entry in entries]).to(devices.device)
@@ -285,9 +291,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
loss.backward()
if clip_grad_mode_value:
- torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_value)
+ torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_sched.learn_rate)
elif clip_grad_mode_norm:
- torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_value)
+ torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_sched.learn_rate)
optimizer.step()
diff --git a/modules/ui.py b/modules/ui.py
index 97de7da2..47d16429 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1305,7 +1305,9 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Row():
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
+ with gr.Row():
+ clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+ clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="1.0", show_label=False)
batch_size = gr.Number(label='Batch size', value=1, precision=0)
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
@@ -1313,9 +1315,6 @@ def create_ui(wrap_gradio_gpu_call):
training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
steps = gr.Number(label='Max steps', value=100000, precision=0)
- with gr.Row():
- clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
- clip_grad_value = gr.Number(value=1.0, show_label=False)
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)