Some changes to the tensorboard code and hypernetwork support

modules/hypernetworks/hypernetwork.py

@@ -4,6 +4,7 @@ import html
 import os
 import sys
 import traceback
+import tensorboard
 import tqdm
 import csv
 
@@ -18,7 +19,6 @@ import modules.textual_inversion.dataset
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
-
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
 
@@ -291,6 +291,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
+    if shared.opts.training_enable_tensorboard:
+        tensorboard_writer = textual_inversion.tensorboard_setup(log_directory)
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
@@ -315,6 +318,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
+
         mean_loss = losses.mean()
         if torch.isnan(mean_loss):
             raise RuntimeError("Loss diverged.")
@@ -323,6 +327,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
+        
+        if shared.opts.training_enable_tensorboard:
+            epoch_num = hypernetwork.step // len(ds)
+            epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
+            
+            textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss,
+                global_step=hypernetwork.step, step=epoch_step, 
+                learn_rate=scheduler.learn_rate, epoch_num=epoch_num)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{mean_loss:.7f}",
@@ -360,6 +372,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             processed = processing.process_images(p)
             image = processed.images[0] if len(processed.images)>0 else None
 
+            if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+                textual_inversion.tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}",
+                      image, hypernetwork.step)
+
             if unload:
                 shared.sd_model.cond_stage_model.to(devices.cpu)
                 shared.sd_model.first_stage_model.to(devices.cpu)