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feat(api): return more data for embeddings

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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)")