diff --git a/api/onnx_web/convert/diffusion/diffusers.py b/api/onnx_web/convert/diffusion/diffusers.py
index fb331334..84bb034d 100644
--- a/api/onnx_web/convert/diffusion/diffusers.py
+++ b/api/onnx_web/convert/diffusion/diffusers.py
@@ -65,7 +65,7 @@ def onnx_export(
     )
 
     if half:
-        logger.info("converting model to FP16 internally: %s", output_file)
+        logger.info("converting model to fp16 internally: %s", output_file)
         infer_shapes_path(output_file)
         base_model = load_model(output_file)
         opt_model = convert_float_to_float16(
@@ -99,6 +99,7 @@ def convert_diffusion_diffusers(
 
     dtype = torch.float32  # torch.float16 if ctx.half else torch.float32
     dest_path = path.join(ctx.model_path, name)
+    model_index = path.join(dest_path, "model_index.json")
 
     # diffusers go into a directory rather than .onnx file
     logger.info(
@@ -108,7 +109,7 @@ def convert_diffusion_diffusers(
     if single_vae:
         logger.info("converting model with single VAE")
 
-    if path.exists(dest_path):
+    if path.exists(dest_path) and path.exists(model_index):
         logger.info("ONNX model already exists, skipping")
         return
 
diff --git a/api/onnx_web/convert/diffusion/original.py b/api/onnx_web/convert/diffusion/original.py
index 8833806e..a05a67e5 100644
--- a/api/onnx_web/convert/diffusion/original.py
+++ b/api/onnx_web/convert/diffusion/original.py
@@ -1667,20 +1667,22 @@ def convert_diffusion_original(
     name = model["name"]
     source = source or model["source"]
 
-    dest = os.path.join(ctx.model_path, name)
+    dest_path = os.path.join(ctx.model_path, name)
+    dest_index = os.path.join(dest_path, "model_index.json")
     logger.info(
-        "converting original Diffusers checkpoint %s: %s -> %s", name, source, dest
+        "converting original Diffusers checkpoint %s: %s -> %s", name, source, dest_path
     )
 
-    if os.path.exists(dest):
+    if os.path.exists(dest_path) and os.path.exists(dest_index):
         logger.info("ONNX pipeline already exists, skipping")
         return
 
     torch_name = name + "-torch"
     torch_path = os.path.join(ctx.cache_path, torch_name)
     working_name = os.path.join(ctx.cache_path, torch_name, "working")
+    model_index = os.path.join(working_name, "model_index.json")
 
-    if os.path.exists(torch_path):
+    if os.path.exists(torch_path) and os.path.exists(model_index):
         logger.info("torch pipeline already exists, reusing: %s", torch_path)
     else:
         logger.info(
diff --git a/api/onnx_web/convert/diffusion/textual_inversion.py b/api/onnx_web/convert/diffusion/textual_inversion.py
index 90ec28bc..df91181e 100644
--- a/api/onnx_web/convert/diffusion/textual_inversion.py
+++ b/api/onnx_web/convert/diffusion/textual_inversion.py
@@ -26,11 +26,15 @@ def convert_diffusion_textual_inversion(
         "converting Textual Inversion: %s + %s -> %s", base_model, inversion, dest_path
     )
 
-    if path.exists(dest_path):
+    encoder_path = path.join(dest_path, "text_encoder")
+    encoder_model = path.join(encoder_path, "model.onnx")
+    tokenizer_path = path.join(dest_path, "tokenizer")
+
+    if path.exists(dest_path) and path.exists(encoder_model) and path.exists(tokenizer_path):
         logger.info("ONNX model already exists, skipping.")
         return
 
-    makedirs(path.join(dest_path, "text_encoder"), exist_ok=True)
+    makedirs(encoder_path, exist_ok=True)
 
     if format == "concept":
         embeds_file = hf_hub_download(repo_id=inversion, filename="learned_embeds.bin")
@@ -112,14 +116,14 @@ def convert_diffusion_textual_inversion(
     )
 
     logger.info("saving tokenizer for textual inversion")
-    tokenizer.save_pretrained(path.join(dest_path, "tokenizer"))
+    tokenizer.save_pretrained(tokenizer_path)
 
     logger.info("saving text encoder for textual inversion")
     export(
         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(dtype=torch.int32)),
-        f=path.join(dest_path, "text_encoder", "model.onnx"),
+        f=encoder_model,
         input_names=["input_ids"],
         output_names=["last_hidden_state", "pooler_output"],
         dynamic_axes={