Add support for Tensorboard for training embeddings

modules/shared.py

@@ -254,6 +254,10 @@ options_templates.update(options_section(('training', "Training"), {
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
     "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
+    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
+    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
+    "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
+
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {

modules/textual_inversion/textual_inversion.py

@@ -7,9 +7,11 @@ import tqdm
 import html
 import datetime
 import csv
+import numpy as np
 
+import torchvision.transforms
 from PIL import Image, PngImagePlugin
-
+from torch.utils.tensorboard import SummaryWriter
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
@@ -199,6 +201,19 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             **values,
         })
 
+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)
+
+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))
+                
+        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'
@@ -252,6 +267,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
     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)
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, entries in pbar:
         embedding.step = i + ititial_step
@@ -270,6 +291,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
+            
 
             optimizer.zero_grad()
             loss.backward()
@@ -285,6 +307,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             embedding.save(last_saved_file)
             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)
+
         write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
             "loss": f"{losses.mean():.7f}",
             "learn_rate": scheduler.learn_rate
@@ -349,6 +377,7 @@ 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)
 
             last_saved_image += f", prompt: {preview_text}"