feat(api): return more data for embeddings

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]