Merge branch 'master' into gradient-clipping

1 files changed, 24 insertions, 4 deletions

diff --git a/modules/api/models.py b/modules/api/models.py
index 34dbfa16..4a632c68 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -128,7 +128,7 @@ class ExtrasBaseRequest(BaseModel):
     upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
     upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
     upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
-    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
+    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
     upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
@@ -170,14 +170,24 @@ class ProgressResponse(BaseModel):
 
 class InterrogateRequest(BaseModel):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+    model: str = Field(default="clip", title="Model", description="The interrogate model used.")
 
 class InterrogateResponse(BaseModel):
     caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
 
+class TrainResponse(BaseModel):
+    info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
+
+class CreateResponse(BaseModel):
+    info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
+
+class PreprocessResponse(BaseModel):
+    info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
+
 fields = {}
-for key, value in opts.data.items():
-    metadata = opts.data_labels.get(key)
-    optType = opts.typemap.get(type(value), type(value))
+for key, metadata in opts.data_labels.items():
+    value = opts.data.get(key)
+    optType = opts.typemap.get(type(metadata.default), type(value))
 
     if (metadata is not None):
         fields.update({key: (Optional[optType], Field(
@@ -239,3 +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: 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)")
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