load inversions from extras file

api/onnx_web/convert/__main__.py

@@ -217,9 +217,6 @@ def convert_models(ctx: ConversionContext, args, models: Models):
                         ctx, name, model["source"], model_format=model_format
                     )
 
-                    if "inversion" in model:
-                        convert_diffusion_textual_inversion(ctx, source, model["inversion"])
-
                     if model_format in model_formats_original:
                         convert_diffusion_original(
                             ctx,
@@ -232,6 +229,13 @@ def convert_models(ctx: ConversionContext, args, models: Models):
                             model,
                             source,
                         )
+
+                    for inversion in model.get("inversions", []):
+                        inversion_name = inversion["name"]
+                        inversion_source = inversion["source"]
+                        inversion_source = fetch_model(ctx, f"{name}-inversion-{inversion_name}", inversion_source)
+                        convert_diffusion_textual_inversion(ctx, inversion_name, source, inversion_source)
+
                 except Exception as e:
                     logger.error("error converting diffusion model %s: %s", name, e)
 

api/onnx_web/convert/diffusion/lora.py

@@ -39,7 +39,7 @@ def blend_loras(base: ModelProto, weights: List[ModelProto], alphas: List[float]
   return results
 
 
-def convert_loras(part: str):
+def convert_diffusion_lora(part: str):
   lora_weights = [
     f"diffusion-lora-jack/{part}/model.onnx",
     f"diffusion-lora-taters/{part}/model.onnx",
@@ -90,5 +90,5 @@ def convert_loras(part: str):
 
 
 if __name__ == "__main__":
-  convert_loras("unet")
-  convert_loras("text_encoder")
+  convert_diffusion_lora("unet")
+  convert_diffusion_lora("text_encoder")

api/onnx_web/convert/diffusion/textual_inversion.py

@@ -2,21 +2,22 @@ from os import mkdir, path
 from huggingface_hub.file_download import hf_hub_download
 from transformers import CLIPTokenizer, CLIPTextModel
 from torch.onnx import export
-from sys import argv
 from logging import getLogger
 
-from ..utils import ConversionContext, sanitize_name
+from ..utils import ConversionContext
 
 import torch
 
 logger = getLogger(__name__)
 
 
-def convert_diffusion_textual_inversion(context: ConversionContext, base_model: str, inversion: str):
-    cache_path = path.join(context.cache_path, f"inversion-{sanitize_name(inversion)}")
-    logger.info("converting textual inversion: %s -> %s", inversion, cache_path)
+def convert_diffusion_textual_inversion(context: ConversionContext, name: str, base_model: str, inversion: str):
+    cache_path = path.join(context.cache_path, f"inversion-{name}")
+    logger.info("converting Textual Inversion: %s + %s -> %s", base_model, inversion, cache_path)
+
+    if path.exists(cache_path):
+        logger.info("ONNX model already exists, skipping.")
 
-    if not path.exists(cache_path):
     mkdir(cache_path)
 
     embeds_file = hf_hub_download(repo_id=inversion, filename="learned_embeds.bin")
@@ -82,7 +83,3 @@ def convert_diffusion_textual_inversion(context: ConversionContext, base_model:
         do_constant_folding=True,
         opset_version=context.opset,
     )
-
-if __name__ == "__main__":
-    context = ConversionContext.from_environ()
-    convert_diffusion_textual_inversion(context, argv[1], argv[2])

api/schemas/extras.yaml

@@ -10,6 +10,15 @@ $defs:
         - type: number
         - type: string
 
+  textual_inversion:
+    type: object
+    required: [name, source]
+    properties:
+      name:
+        type: string
+      source:
+        type: string
+
   base_model:
     type: object
     required: [name, source]
@@ -37,6 +46,10 @@ $defs:
         properties:
           config:
             type: string
+          inversions:
+            type: array
+            items:
+              $ref: "#/$defs/textual_inversion"
           vae:
             type: string