fix(api): add HF hub download to fetch logic for Inversion concepts

api/onnx_web/convert/__main__.py

@@ -144,8 +144,11 @@ def fetch_model(
     source: str,
     dest: Optional[str] = None,
     format: Optional[str] = None,
+    hf_hub_fetch: bool = False,
+    hf_hub_filename: Optional[str] = None,
 ) -> str:
-    cache_path = path.join(dest or ctx.cache_path, name)
+    cache_path = dest or ctx.cache_path
+    cache_name = path.join(cache_path, name)
 
     # add an extension if possible, some of the conversion code checks for it
     if format is None:
@@ -153,11 +156,11 @@ def fetch_model(
         ext = path.basename(url.path)
         _filename, ext = path.splitext(ext)
         if ext is not None:
-            cache_name = cache_path + ext
+            cache_name = cache_name + ext
         else:
-            cache_name = cache_path
+            cache_name = cache_name
     else:
-        cache_name = f"{cache_path}.{format}"
+        cache_name = f"{cache_name}.{format}"
 
     if path.exists(cache_name):
         logger.debug("model already exists in cache, skipping fetch")
@@ -176,6 +179,14 @@ def fetch_model(
         hub_source = remove_prefix(source, model_source_huggingface)
         logger.info("downloading model from Huggingface Hub: %s", hub_source)
         # from_pretrained has a bunch of useful logic that snapshot_download by itself down not
+        if hf_hub_fetch:
+            return hf_hub_download(
+                repo_id=source,
+                filename=hf_hub_filename,
+                cache_dir=cache_path,
+                force_filename=f"{name}.bin",
+            )
+        else:
             return hub_source
     elif source.startswith("https://"):
         logger.info("downloading model from: %s", source)
@@ -223,8 +234,14 @@ def convert_models(ctx: ConversionContext, args, models: Models):
 
                 try:
                     if network_type == "inversion" and network_model == "concept":
-                        dest = hf_hub_download(
-                            repo_id=source, filename="learned_embeds.bin"
+                        dest = fetch_model(
+                            ctx,
+                            name,
+                            source,
+                            dest=path.join(ctx.model_path, network_type),
+                            format=network_format,
+                            hf_hub_fetch=True,
+                            hf_hub_filename="learned_embeds.bin",
                         )
                     else:
                         dest = fetch_model(