Merge branch 'AUTOMATIC1111:master' into img2img-api-scripts

1 files changed, 9 insertions, 2 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6a9b1398..b0cfbe71 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -13,7 +13,7 @@ import tqdm
 from einops import rearrange, repeat

 from ldm.util import default

 from modules import devices, processing, sd_models, shared, sd_samplers

-from modules.textual_inversion import textual_inversion

+from modules.textual_inversion import textual_inversion, logging

 from modules.textual_inversion.learn_schedule import LearnRateScheduler

 from torch import einsum

 from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_

@@ -457,7 +457,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
     pin_memory = shared.opts.pin_memory

 

     ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)

-    

+

+    if shared.opts.save_training_settings_to_txt:

+        saved_params = dict(

+            model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds),

+            **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}

+        )

+        logging.save_settings_to_file(log_directory, {**saved_params, **locals()})

+

     latent_sampling_method = ds.latent_sampling_method

 

     dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)