diff options
| -rw-r--r-- | modules/hypernetworks/hypernetwork.py | 13 |
1 files changed, 7 insertions, 6 deletions
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index bbd1f673..c963fc40 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -561,6 +561,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi _loss_step = 0 #internal
# size = len(ds.indexes)
# loss_dict = defaultdict(lambda : deque(maxlen = 1024))
+ loss_logging = deque(maxlen=len(ds) * 3) # this should be configurable parameter, this is 3 * epoch(dataset size)
# losses = torch.zeros((size,))
# previous_mean_losses = [0]
# previous_mean_loss = 0
@@ -610,7 +611,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi # go back until we reach gradient accumulation steps
if (j + 1) % gradient_step != 0:
continue
-
+ loss_logging.append(_loss_step)
if clip_grad:
clip_grad(weights, clip_grad_sched.learn_rate)
@@ -644,7 +645,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi if shared.opts.training_enable_tensorboard:
epoch_num = hypernetwork.step // len(ds)
epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
- mean_loss = sum(sum(x) for x in loss_dict.values()) / sum(len(x) for x in loss_dict.values())
+ mean_loss = sum(loss_logging) / len(loss_logging)
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, steps_per_epoch, {
@@ -688,9 +689,6 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi 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)
@@ -701,7 +699,10 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi hypernetwork.train()
if image is not None:
shared.state.assign_current_image(image)
-
+ 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)
last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
last_saved_image += f", prompt: {preview_text}"
|