Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add Embedding Parsing (#1973)

Browse Source 
* Add Embedding Parsing

* Add Embedding Parsing

* Return token_dim in embedding_info

* fixes to handle other variants

1. Handle the case of a .bin file being mislabeled .pt (seen in the
wild at https://cyberes.github.io/stable-diffusion-textual-inversion-models/)

2. Handle the "broken" .pt files reported by https://github.com/invoke-ai/InvokeAI/issues/1829

3. When token name is not available, use the basename of the pt or bin file rather than the
   whole path.

fixes #1829

* remove whitespace

Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
This commit is contained in:
blessedcoolant 2022-12-16 11:26:36 +13:00 committed by GitHub
parent 1050f2726a
commit 69cc0993f8
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 99 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

114
ldm/modules/embedding_manager.py
View File

@ -256,32 +256,22 @@ class EmbeddingManager(nn.Module):
return [x for x in expanded_paths if os.path.splitext(x)[1] in ('.pt','.bin')]
def _load(self, ckpt_path, full=True):
try:
scan_result = scan_file_path(ckpt_path)
if scan_result.infected_files == 1:
print(f'\n### Security Issues Found in Model: {scan_result.issues_count}')
print('### For your safety, InvokeAI will not load this embed.')
return
except Exception:
print(f"### WARNING::: Invalid or corrupt embeddings found. Ignoring: {ckpt_path}")
return
ckpt = torch.load(ckpt_path, map_location='cpu')
# Handle .pt textual inversion files
if 'string_to_token' in ckpt and 'string_to_param' in ckpt:
filename = os.path.basename(ckpt_path)
token_str = '.'.join(filename.split('.')[:-1]) # filename excluding extension
if len(ckpt["string_to_token"]) > 1:
print(f">> {ckpt_path} has >1 embedding, only the first will be used")
string_to_param_dict = ckpt['string_to_param']
embedding = list(string_to_param_dict.values())[0]
self.add_embedding(token_str, embedding, full)
# Handle .bin textual inversion files from Huggingface Concepts
# https://huggingface.co/sd-concepts-library
embedding_info = self.parse_embedding(ckpt_path)
if embedding_info:
self.max_vectors_per_token = embedding_info['num_vectors_per_token']
self.add_embedding(embedding_info['name'], embedding_info['embedding'], full)
else:
for token_str in list(ckpt.keys()):
embedding = ckpt[token_str]
self.add_embedding(token_str, embedding, full)
print(f'>> Failed to load embedding located at {ckpt_path}. Unsupported file.')
def add_embedding(self, token_str, embedding, full):
if token_str in self.string_to_param_dict:
@ -302,6 +292,92 @@ class EmbeddingManager(nn.Module):
self.string_to_token_dict[token_str] = token
self.string_to_param_dict[token_str] = torch.nn.Parameter(embedding)
def parse_embedding(self, embedding_file: str):
file_type = embedding_file.split('.')[-1]
if file_type == 'pt':
return self.parse_embedding_pt(embedding_file)
elif file_type == 'bin':
return self.parse_embedding_bin(embedding_file)
else:
print(f'>> Not a recognized embedding file: {embedding_file}')
def parse_embedding_pt(self, embedding_file):
embedding_ckpt = torch.load(embedding_file, map_location='cpu')
embedding_info = {}
# Check if valid embedding file
if 'string_to_token' and 'string_to_param' in embedding_ckpt:
# Catch variants that do not have the expected keys or values.
try:
embedding_info['name'] = embedding_ckpt['name'] or os.path.basename(os.path.splitext(embedding_file)[0])
# Check num of embeddings and warn user only the first will be used
embedding_info['num_of_embeddings'] = len(embedding_ckpt["string_to_token"])
if embedding_info['num_of_embeddings'] > 1:
print('>> More than 1 embedding found. Will use the first one')
embedding = list(embedding_ckpt['string_to_param'].values())[0]
except (AttributeError,KeyError):
return self.handle_broken_pt_variants(embedding_ckpt, embedding_file)
embedding_info['embedding'] = embedding
embedding_info['num_vectors_per_token'] = embedding.size()[0]
embedding_info['token_dim'] = embedding.size()[1]
try:
embedding_info['trained_steps'] = embedding_ckpt['step']
embedding_info['trained_model_name'] = embedding_ckpt['sd_checkpoint_name']
embedding_info['trained_model_checksum'] = embedding_ckpt['sd_checkpoint']
except AttributeError:
print(">> No Training Details Found. Passing ...")
# .pt files found at https://cyberes.github.io/stable-diffusion-textual-inversion-models/
# They are actually .bin files
elif len(embedding_ckpt.keys())==1:
print('>> Detected .bin file masquerading as .pt file')
embedding_info = self.parse_embedding_bin(embedding_file)
else:
print('>> Invalid embedding format')
embedding_info = None
return embedding_info
def parse_embedding_bin(self, embedding_file):
embedding_ckpt = torch.load(embedding_file, map_location='cpu')
embedding_info = {}
if list(embedding_ckpt.keys()) == 0:
print(">> Invalid concepts file")
embedding_info = None
else:
for token in list(embedding_ckpt.keys()):
embedding_info['name'] = token or os.path.basename(os.path.splitext(embedding_file)[0])
embedding_info['embedding'] = embedding_ckpt[token]
embedding_info['num_vectors_per_token'] = 1 # All Concepts seem to default to 1
embedding_info['token_dim'] = embedding_info['embedding'].size()[0]
return embedding_info
def handle_broken_pt_variants(self, embedding_ckpt:dict, embedding_file:str)->dict:
'''
This handles the broken .pt file variants. We only know of one at present.
'''
embedding_info = {}
if isinstance(list(embedding_ckpt['string_to_token'].values())[0],torch.Tensor):
print('>> Detected .pt file variant 1') # example at https://github.com/invoke-ai/InvokeAI/issues/1829
for token in list(embedding_ckpt['string_to_token'].keys()):
embedding_info['name'] = token if token != '*' else os.path.basename(os.path.splitext(embedding_file)[0])
embedding_info['embedding'] = embedding_ckpt['string_to_param'].state_dict()[token]
embedding_info['num_vectors_per_token'] = embedding_info['embedding'].shape[0]
embedding_info['token_dim'] = embedding_info['embedding'].size()[0]
else:
print('>> Invalid embedding format')
embedding_info = None
return embedding_info
def has_embedding_for_token(self, token_str):
return token_str in self.string_to_token_dict