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changed embedding accepted shape detection to use existing code and support the new alt-diffusion model, and reformatted messages a bit #6149

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AUTOMATIC 2022-12-31 22:49:09 +03:00
parent c24a314c5e
commit bdbe09827b
1 changed files with 6 additions and 24 deletions
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30
modules/textual_inversion/textual_inversion.py
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@ -80,23 +80,8 @@ class EmbeddingDatabase:
return embedding return embedding
def get_expected_shape(self): def get_expected_shape(self):
expected_shape = -1 # initialize with unknown vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
idx = torch.tensor(0).to(shared.device) return vec.shape[1]
if expected_shape == -1:
try: # matches sd15 signature
first_embedding = shared.sd_model.cond_stage_model.wrapped.transformer.text_model.embeddings.token_embedding.wrapped(idx)
expected_shape = first_embedding.shape[0]
except:
pass
if expected_shape == -1:
try: # matches sd20 signature
first_embedding = shared.sd_model.cond_stage_model.wrapped.model.token_embedding.wrapped(idx)
expected_shape = first_embedding.shape[0]
except:
pass
if expected_shape == -1:
print('Could not determine expected embeddings shape from model')
return expected_shape
def load_textual_inversion_embeddings(self, force_reload = False): def load_textual_inversion_embeddings(self, force_reload = False):
mt = os.path.getmtime(self.embeddings_dir) mt = os.path.getmtime(self.embeddings_dir)
@ -112,8 +97,6 @@ class EmbeddingDatabase:
def process_file(path, filename): def process_file(path, filename):
name = os.path.splitext(filename)[0] name = os.path.splitext(filename)[0]
data = []
if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']: if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
embed_image = Image.open(path) embed_image = Image.open(path)
if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text: if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
@ -150,11 +133,10 @@ class EmbeddingDatabase:
embedding.vectors = vec.shape[0] embedding.vectors = vec.shape[0]
embedding.shape = vec.shape[-1] embedding.shape = vec.shape[-1]
if (self.expected_shape == -1) or (self.expected_shape == embedding.shape): if self.expected_shape == -1 or self.expected_shape == embedding.shape:
self.register_embedding(embedding, shared.sd_model) self.register_embedding(embedding, shared.sd_model)
else: else:
self.skipped_embeddings.append(name) self.skipped_embeddings.append(name)
# print('Skipping embedding {name}: shape was {shape} expected {expected}'.format(name = name, shape = embedding.shape, expected = self.expected_shape))
for fn in os.listdir(self.embeddings_dir): for fn in os.listdir(self.embeddings_dir):
try: try:
@ -169,9 +151,9 @@ class EmbeddingDatabase:
print(traceback.format_exc(), file=sys.stderr) print(traceback.format_exc(), file=sys.stderr)
continue continue
print("Textual inversion embeddings {num} loaded: {val}".format(num = len(self.word_embeddings), val = ', '.join(self.word_embeddings.keys()))) print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
if (len(self.skipped_embeddings) > 0): if len(self.skipped_embeddings) > 0:
print("Textual inversion embeddings {num} skipped: {val}".format(num = len(self.skipped_embeddings), val = ', '.join(self.skipped_embeddings))) print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings)}")
def find_embedding_at_position(self, tokens, offset): def find_embedding_at_position(self, tokens, offset):
token = tokens[offset] token = tokens[offset]