Gradient clipping for textual embedding

modules/textual_inversion/textual_inversion.py

@@ -206,7 +206,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
         })
 
 
-def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -256,6 +256,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     if ititial_step > steps:
         return embedding, filename
 
+    clip_grad_mode_value = clip_grad_mode == "value"
+    clip_grad_mode_norm = clip_grad_mode == "norm"
+
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
@@ -280,6 +283,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
             optimizer.zero_grad()
             loss.backward()
+
+            if clip_grad_mode_value:
+                torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_value)
+            elif clip_grad_mode_norm:
+                torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_value)
+
             optimizer.step()
 
 

modules/ui.py

@@ -1409,6 +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,