Move the extraction logic for embeddings-from-prompt, from sdkit to ED's UI

ui/easydiffusion/model_manager.py

@@ -65,9 +65,6 @@ def load_default_models(context: Context):
 
     runtime.set_vram_optimizations(context)
 
-    config = app.getConfig()
-    context.embeddings_path = os.path.join(app.MODELS_DIR, "embeddings")
-
     # init default model paths
     for model_type in MODELS_TO_LOAD_ON_START:
         context.model_paths[model_type] = resolve_model_to_use(model_type=model_type, fail_if_not_found=False)

ui/easydiffusion/types.py

@@ -73,6 +73,7 @@ class TaskData(BaseModel):
     use_hypernetwork_model: Union[str, List[str]] = None
     use_lora_model: Union[str, List[str]] = None
     use_controlnet_model: Union[str, List[str]] = None
+    use_embeddings_model: Union[str, List[str]] = None
     filters: List[str] = []
     filter_params: Dict[str, Dict[str, Any]] = {}
     control_filter_to_apply: Union[str, List[str]] = None
@@ -200,6 +201,7 @@ def convert_legacy_render_req_to_new(old_req: dict):
     model_paths["hypernetwork"] = old_req.get("use_hypernetwork_model")
     model_paths["lora"] = old_req.get("use_lora_model")
     model_paths["controlnet"] = old_req.get("use_controlnet_model")
+    model_paths["embeddings"] = old_req.get("use_embeddings_model")
 
     model_paths["gfpgan"] = old_req.get("use_face_correction", "")
     model_paths["gfpgan"] = model_paths["gfpgan"] if "gfpgan" in model_paths["gfpgan"].lower() else None

ui/media/js/main.js

@@ -845,6 +845,7 @@ function makeImage() {
             reqBody: Object.assign({ prompt: prompt }, taskTemplate.reqBody),
         })
     )
+    newTaskRequests.forEach(setEmbeddings)
     newTaskRequests.forEach(createTask)
 
     updateInitialText()
@@ -1493,6 +1494,42 @@ function getCurrentUserRequest() {
     return newTask
 }
 
+function setEmbeddings(task) {
+    let prompt = task.reqBody.prompt.toLowerCase()
+    let negativePrompt = task.reqBody.negative_prompt.toLowerCase()
+    let overallPrompt = (prompt + " " + negativePrompt).split(" ")
+
+    let embeddingsTree = modelsOptions["embeddings"]
+    let embeddings = []
+    function extract(entries, basePath = "") {
+        entries.forEach((e) => {
+            if (Array.isArray(e)) {
+                let path = basePath === "" ? basePath + e[0] : basePath + "/" + e[0]
+                extract(e[1], path)
+            } else {
+                let path = basePath === "" ? basePath + e : basePath + "/" + e
+                embeddings.push([e.toLowerCase().replace(" ", "_"), path])
+            }
+        })
+    }
+    extract(embeddingsTree)
+
+    let embeddingPaths = []
+
+    embeddings.forEach((e) => {
+        let token = e[0]
+        let path = e[1]
+
+        if (overallPrompt.includes(token)) {
+            embeddingPaths.push(path)
+        }
+    })
+
+    if (embeddingPaths.length > 0) {
+        task.reqBody.use_embeddings_model = embeddingPaths
+    }
+}
+
 function getModelInfo(models) {
     let modelInfo = models.map((e) => [e[0].value, e[1].value])
     modelInfo = modelInfo.filter((e) => e[0].trim() !== "")