Update convert.py

convert.py

@@ -38,15 +38,20 @@ def convert_single(model_id: str, token:str, filename: str, model_type: str, sam
         config_url = (Path(model_id)/"resolve/main"/filename).with_suffix(".yaml")
         config_url = "https://huggingface.co/" + str(config_url)
 
-    config_file = BytesIO(requests.get(config_url).content)
-
+    #config_file = BytesIO(requests.get(config_url).content)
+    
+    response = requests.get(config_url)
+    with tempfile.NamedTemporaryFile(delete=False, mode='wb') as tmp_file:
+        tmp_file.write(response.content)
+        temp_config_file_path = tmp_file.name
+        
     if model_type == "ControlNet":
         progress(0.2, desc="Converting ControlNet Model")
-        pipeline = download_controlnet_from_original_ckpt(ckpt_file, config_file, image_size=sample_size, from_safetensors=from_safetensors, extract_ema=extract_ema)
+        pipeline = download_controlnet_from_original_ckpt(ckpt_file, temp_config_file_path, image_size=sample_size, from_safetensors=from_safetensors, extract_ema=extract_ema)
         to_args = {"dtype": torch.float16}
     else:
         progress(0.1, desc="Converting Model")
-        pipeline = download_from_original_stable_diffusion_ckpt(ckpt_file, config_file, image_size=sample_size, scheduler_type=scheduler_type, from_safetensors=from_safetensors, extract_ema=extract_ema)
+        pipeline = download_from_original_stable_diffusion_ckpt(ckpt_file, temp_config_file_path, image_size=sample_size, scheduler_type=scheduler_type, from_safetensors=from_safetensors, extract_ema=extract_ema)
         to_args = {"torch_dtype": torch.float16}
 
     pipeline.save_pretrained(folder)