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)

modules/textual_inversion/textual_inversion.py

@@ -201,19 +201,30 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             **values,
         })
 
+def tensorboard_setup(log_directory):
+    os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
+    return SummaryWriter(
+            log_dir=os.path.join(log_directory, "tensorboard"),
+            flush_secs=shared.opts.training_tensorboard_flush_every)
+
+def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epoch_num):
+    tensorboard_add_scaler(tensorboard_writer, "Loss/train", loss, global_step)
+    tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", loss, step)
+    tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", learn_rate, global_step)
+    tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step)
+
 def tensorboard_add_scaler(tensorboard_writer, tag, value, step):
-    if shared.opts.training_enable_tensorboard:
-        tensorboard_writer.add_scalar(tag=tag, 
-            scalar_value=value, global_step=step)
+    tensorboard_writer.add_scalar(tag=tag, 
+        scalar_value=value, global_step=step)
 
 def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):
-    if shared.opts.training_enable_tensorboard:
-        # Convert a pil image to a torch tensor
-        img_tensor = torch.as_tensor(np.array(pil_image, copy=True))
-        img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], len(pil_image.getbands()))
-        img_tensor = img_tensor.permute((2, 0, 1))
+    # Convert a pil image to a torch tensor
+    img_tensor = torch.as_tensor(np.array(pil_image, copy=True))
+    img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], 
+        len(pil_image.getbands()))
+    img_tensor = img_tensor.permute((2, 0, 1))
                 
-        tensorboard_writer.add_image(tag, img_tensor, global_step=step)
+    tensorboard_writer.add_image(tag, img_tensor, global_step=step)
 
 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):
     assert embedding_name, 'embedding not selected'
@@ -268,10 +279,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
     if shared.opts.training_enable_tensorboard:
-        os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
-        tensorboard_writer = SummaryWriter(
-                log_dir=os.path.join(log_directory, "tensorboard"),
-                flush_secs=shared.opts.training_tensorboard_flush_every)
+        tensorboard_writer = tensorboard_setup(log_directory)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-initial_step)
     for i, entries in pbar:
@@ -308,10 +316,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             embedding_yet_to_be_embedded = True
 
         if shared.opts.training_enable_tensorboard:
-            tensorboard_add_scaler(tensorboard_writer, "Loss/train", losses.mean(), embedding.step)
-            tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", losses.mean(), epoch_step)
-            tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", scheduler.learn_rate, embedding.step)
-            tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", scheduler.learn_rate, epoch_step)
+            tensorboard_add(tensorboard_writer, loss=losses.mean(), global_step=embedding.step, 
+                step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num)
 
         write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
             "loss": f"{losses.mean():.7f}",
@@ -377,7 +383,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 embedding_yet_to_be_embedded = False
 
             image.save(last_saved_image)
-            tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)
+
+            if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+                tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", 
+                    image, embedding.step)
 
             last_saved_image += f", prompt: {preview_text}"