Gradient clipping in train tab

modules/hypernetworks/hypernetwork.py

@@ -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]]):

modules/ui.py

@@ -1313,6 +1313,9 @@ 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)
@@ -1406,6 +1409,8 @@ def create_ui(wrap_gradio_gpu_call):
                 training_width,
                 training_height,
                 steps,
+                clip_grad_mode,
+                clip_grad_value,
                 create_image_every,
                 save_embedding_every,
                 template_file,
@@ -1431,6 +1436,8 @@ def create_ui(wrap_gradio_gpu_call):
                 training_width,
                 training_height,
                 steps,
+                clip_grad_mode,
+                clip_grad_value,
                 create_image_every,
                 save_embedding_every,
                 template_file,