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| author | AUTOMATIC1111 <16777216c@gmail.com> | 2023-01-12 22:05:10 +0300 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-01-12 22:05:10 +0300 |
| commit | d7aec59c4eb02f723b3d55c6f927a42e97acd679 (patch) | |
| tree | 981d40b68c13538aae93dc02bb8d19412aa7d266 | |
| parent | 6ffefdcc9f47b66cbc543690d97cbf8327f4ba58 (diff) | |
| parent | d48dcbd2b29eab492d53d78f482356d78e5beb19 (diff) | |
Merge pull request #6667 from Shondoit/zero-vector-ti
Allow creation of zero vectors for TI
| -rw-r--r-- | javascript/hints.js | 1 | ||||
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 9 |
2 files changed, 7 insertions, 3 deletions
diff --git a/javascript/hints.js b/javascript/hints.js index 856e1389..244bfde2 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -92,6 +92,7 @@ titles = { "Weighted sum": "Result = A * (1 - M) + B * M", "Add difference": "Result = A + (B - C) * M", + "Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors", "Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.", "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.", diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index b915b091..853246a6 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -248,11 +248,14 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'): with devices.autocast():
cond_model([""]) # will send cond model to GPU if lowvram/medvram is active
- embedded = cond_model.encode_embedding_init_text(init_text, num_vectors_per_token)
+ #cond_model expects at least some text, so we provide '*' as backup.
+ embedded = cond_model.encode_embedding_init_text(init_text or '*', num_vectors_per_token)
vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
- for i in range(num_vectors_per_token):
- vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
+ #Only copy if we provided an init_text, otherwise keep vectors as zeros
+ if init_text:
+ for i in range(num_vectors_per_token):
+ vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
# Remove illegal characters from name.
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
|