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Add support for Tensorboard for training embeddings

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This commit is contained in:
Melan 2022-10-20 16:26:16 +02:00
parent 7f8ab1ee8f
commit 29e74d6e71
2 changed files with 34 additions and 1 deletions
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4
modules/shared.py
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@ -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"), {

31
modules/textual_inversion/textual_inversion.py
View File

@ -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}"