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authorAUTOMATIC <16777216c@gmail.com>2023-05-31 19:56:37 +0300
committerAUTOMATIC <16777216c@gmail.com>2023-05-31 19:56:37 +0300
commit05933840f0676dd1a90a7e2ad3f2a0672624b2cd (patch)
tree593940d16fdc678c275b2b2f21ac6df7c6aad959 /modules/textual_inversion
parentd67ef01f629d2034fb847dae6aa0143c87161b8f (diff)
rename print_error to report, use it with together with package name
Diffstat (limited to 'modules/textual_inversion')
-rw-r--r--modules/textual_inversion/textual_inversion.py7
1 files changed, 3 insertions, 4 deletions
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index b3dcb140..8da050ca 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -12,9 +12,8 @@ import numpy as np
from PIL import Image, PngImagePlugin
from torch.utils.tensorboard import SummaryWriter
-from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint
+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors
import modules.textual_inversion.dataset
-from modules.errors import print_error
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from modules.textual_inversion.image_embedding import embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay
@@ -219,7 +218,7 @@ class EmbeddingDatabase:
self.load_from_file(fullfn, fn)
except Exception:
- print_error(f"Error loading embedding {fn}", exc_info=True)
+ errors.report(f"Error loading embedding {fn}", exc_info=True)
continue
def load_textual_inversion_embeddings(self, force_reload=False):
@@ -643,7 +642,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
except Exception:
- print_error("Error training embedding", exc_info=True)
+ errors.report("Error training embedding", exc_info=True)
finally:
pbar.leave = False
pbar.close()