diff options
| -rw-r--r-- | modules/api/api.py | 17 | ||||
| -rw-r--r-- | modules/api/models.py | 11 | ||||
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 8 |
3 files changed, 28 insertions, 8 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index 30bf3dac..9c670f00 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -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): diff --git a/modules/api/models.py b/modules/api/models.py index a8472dc9..4a632c68 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -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)")
\ No newline at end of file + 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)")
\ No newline at end of file diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 1e5722e7..fd253477 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -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]
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