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author | AUTOMATIC1111 <16777216c@gmail.com> | 2022-10-21 15:36:29 +0000 |
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committer | GitHub <noreply@github.com> | 2022-10-21 15:36:29 +0000 |
commit | 5e9afa5c8a0664e06f650cccc82831c3b13e5dc6 (patch) | |
tree | cd752f22bbc5f5dd24bb7db322f53e9dd90d8b06 /modules/textual_inversion/textual_inversion.py | |
parent | 85dd62c4c7635b8e21a75f140d093036069e97a1 (diff) | |
parent | 24ce67a13bd74202d298cd8e2a306d90214980d8 (diff) | |
download | stable-diffusion-webui-gfx803-5e9afa5c8a0664e06f650cccc82831c3b13e5dc6.tar.gz stable-diffusion-webui-gfx803-5e9afa5c8a0664e06f650cccc82831c3b13e5dc6.tar.bz2 stable-diffusion-webui-gfx803-5e9afa5c8a0664e06f650cccc82831c3b13e5dc6.zip |
Merge branch 'master' into fix/train-preprocess-keep-ratio
Diffstat (limited to 'modules/textual_inversion/textual_inversion.py')
-rw-r--r-- | modules/textual_inversion/textual_inversion.py | 6 |
1 files changed, 4 insertions, 2 deletions
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 3be69562..529ed3e2 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -153,7 +153,7 @@ class EmbeddingDatabase: return None, None
-def create_embedding(name, num_vectors_per_token, init_text='*'):
+def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
cond_model = shared.sd_model.cond_stage_model
embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
@@ -165,7 +165,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
- assert not os.path.exists(fn), f"file {fn} already exists"
+ if not overwrite_old:
+ assert not os.path.exists(fn), f"file {fn} already exists"
embedding = Embedding(vec, name)
embedding.step = 0
@@ -275,6 +276,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc loss.backward()
optimizer.step()
+
epoch_num = embedding.step // len(ds)
epoch_step = embedding.step - (epoch_num * len(ds)) + 1
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