diff --git a/api/onnx_web/convert/diffusion/textual_inversion.py b/api/onnx_web/convert/diffusion/textual_inversion.py
index 14ffa703..acd9331d 100644
--- a/api/onnx_web/convert/diffusion/textual_inversion.py
+++ b/api/onnx_web/convert/diffusion/textual_inversion.py
@@ -1,4 +1,4 @@
-from os import mkdir, path
+from os import mkdirs, path
 from huggingface_hub.file_download import hf_hub_download
 from transformers import CLIPTokenizer, CLIPTextModel
 from torch.onnx import export
@@ -12,13 +12,13 @@ logger = getLogger(__name__)
 
 
 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)
+    dest_path = path.join(context.model_path, f"inversion-{name}")
+    logger.info("converting Textual Inversion: %s + %s -> %s", base_model, inversion, dest_path)
 
-    if path.exists(cache_path):
+    if path.exists(dest_path):
         logger.info("ONNX model already exists, skipping.")
 
-    mkdir(cache_path)
+    mkdirs(path.join(dest_path, "text_encoder"))
 
     embeds_file = hf_hub_download(repo_id=inversion, filename="learned_embeds.bin")
     token_file = hf_hub_download(repo_id=inversion, filename="token_identifier.txt")
@@ -29,11 +29,11 @@ def convert_diffusion_textual_inversion(context: ConversionContext, name: str, b
     tokenizer = CLIPTokenizer.from_pretrained(
         base_model,
         subfolder="tokenizer",
-    ).to(context.training_device)
+    )
     text_encoder = CLIPTextModel.from_pretrained(
         base_model,
         subfolder="text_encoder",
-    ).to(context.training_device)
+    )
 
     loaded_embeds = torch.load(embeds_file, map_location=context.map_location)
 
@@ -72,9 +72,9 @@ def convert_diffusion_textual_inversion(context: ConversionContext, name: str, b
         text_encoder,
         # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files
         (
-            text_input.input_ids.to(device=context.training_device, dtype=torch.int32)
+            text_input.input_ids.to(dtype=torch.int32)
         ),
-        f=path.join(cache_path, "text_encoder", "model.onnx"),
+        f=path.join(dest_path, "text_encoder", "model.onnx"),
         input_names=["input_ids"],
         output_names=["last_hidden_state", "pooler_output"],
         dynamic_axes={