Learning rate sched syntax support for grad clipping (16451ca5) · Commits · github_fork / Stable Diffusion Webui

modules/hypernetworks/hypernetwork.py

+10 −3

Original line number	Diff line number	Diff line
		@@ -386,8 +386,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log

		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()

modules/textual_inversion/learn_schedule.py

+8 −3

Original line number	Diff line number	Diff line
		@@ -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:

modules/textual_inversion/textual_inversion.py

+9 −3

Original line number	Diff line number	Diff line
		@@ -258,6 +258,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc

		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()

modules/ui.py

+3 −4

Original line number	Diff line number	Diff line
		@@ -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)

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