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feat(api): return more data for embeddings

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This commit is contained in:
Philpax 2023-01-02 12:21:22 +11:00
parent b5819d9bf1
commit c65909ad16
3 changed files with 28 additions and 8 deletions
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17
modules/api/api.py
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@ -330,9 +330,22 @@ class Api:
def get_embeddings(self):
db = sd_hijack.model_hijack.embedding_db
def convert_embedding(embedding):
return {
"step": embedding.step,
"sd_checkpoint": embedding.sd_checkpoint,
"sd_checkpoint_name": embedding.sd_checkpoint_name,
"shape": embedding.shape,
"vectors": embedding.vectors,
}
def convert_embeddings(embeddings):
return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()}
return {
"loaded": sorted(db.word_embeddings.keys()),
"skipped": sorted(db.skipped_embeddings),
"loaded": convert_embeddings(db.word_embeddings),
"skipped": convert_embeddings(db.skipped_embeddings),
}
def refresh_checkpoints(self):

11
modules/api/models.py
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@ -249,6 +249,13 @@ class ArtistItem(BaseModel):
score: float = Field(title="Score")
category: str = Field(title="Category")
class EmbeddingItem(BaseModel):
step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
class EmbeddingsResponse(BaseModel):
loaded: List[str] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: List[str] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")

8
modules/textual_inversion/textual_inversion.py
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@ -59,7 +59,7 @@ class EmbeddingDatabase:
def __init__(self, embeddings_dir):
self.ids_lookup = {}
self.word_embeddings = {}
self.skipped_embeddings = []
self.skipped_embeddings = {}
self.dir_mtime = None
self.embeddings_dir = embeddings_dir
self.expected_shape = -1
@ -91,7 +91,7 @@ class EmbeddingDatabase:
self.dir_mtime = mt
self.ids_lookup.clear()
self.word_embeddings.clear()
self.skipped_embeddings = []
self.skipped_embeddings.clear()
self.expected_shape = self.get_expected_shape()
def process_file(path, filename):
@ -136,7 +136,7 @@ class EmbeddingDatabase:
if self.expected_shape == -1 or self.expected_shape == embedding.shape:
self.register_embedding(embedding, shared.sd_model)
else:
self.skipped_embeddings.append(name)
self.skipped_embeddings[name] = embedding
for fn in os.listdir(self.embeddings_dir):
try:
@ -153,7 +153,7 @@ class EmbeddingDatabase:
print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
if len(self.skipped_embeddings) > 0:
print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings)}")
print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
def find_embedding_at_position(self, tokens, offset):
token = tokens[offset]