diff options
Diffstat (limited to 'modules/textual_inversion/textual_inversion.py')
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 31 |
1 files changed, 19 insertions, 12 deletions
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 24b43045..45882ed6 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -1,6 +1,7 @@ import os
import sys
import traceback
+import inspect
import torch
import tqdm
@@ -17,6 +18,8 @@ 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)
+from modules.textual_inversion.logging import save_settings_to_file
+
class Embedding:
def __init__(self, vec, name, step=None):
@@ -76,7 +79,6 @@ class EmbeddingDatabase: self.word_embeddings[embedding.name] = embedding
- # TODO changing between clip and open clip changes tokenization, which will cause embeddings to stop working
ids = model.cond_stage_model.tokenize([embedding.name])[0]
first_id = ids[0]
@@ -149,18 +151,19 @@ class EmbeddingDatabase: else:
self.skipped_embeddings[name] = embedding
- for fn in os.listdir(self.embeddings_dir):
- try:
- fullfn = os.path.join(self.embeddings_dir, fn)
+ for root, dirs, fns in os.walk(self.embeddings_dir):
+ for fn in fns:
+ try:
+ fullfn = os.path.join(root, fn)
- if os.stat(fullfn).st_size == 0:
- continue
+ if os.stat(fullfn).st_size == 0:
+ continue
- process_file(fullfn, fn)
- except Exception:
- print(f"Error loading embedding {fn}:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
- continue
+ process_file(fullfn, fn)
+ except Exception:
+ print(f"Error loading embedding {fn}:", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+ continue
print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
if len(self.skipped_embeddings) > 0:
@@ -229,6 +232,7 @@ def write_loss(log_directory, filename, step, epoch_len, values): **values,
})
+
def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
assert model_name, f"{name} not selected"
assert learn_rate, "Learning rate is empty or 0"
@@ -292,8 +296,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ if initial_step >= steps:
shared.state.textinfo = "Model has already been trained beyond specified max steps"
return embedding, filename
+
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
-
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \
torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \
None
@@ -307,6 +311,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ 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=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, 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:
+ save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **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)
|