diff --git a/api/onnx_web/convert/diffusion/lora.py b/api/onnx_web/convert/diffusion/lora.py
new file mode 100644
index 00000000..1e4d24c2
--- /dev/null
+++ b/api/onnx_web/convert/diffusion/lora.py
@@ -0,0 +1,94 @@
+from numpy import ndarray
+from onnx import TensorProto, helper, load, numpy_helper, ModelProto, save_model
+from typing import Dict, List, Tuple
+from logging import getLogger
+
+
+logger = getLogger(__name__)
+
+
+def load_lora(filename: str):
+  model = load(filename)
+
+  for weight in model.graph.initializer:
+    # print(weight.name, numpy_helper.to_array(weight).shape)
+    pass
+
+  return model
+
+
+def blend_loras(base: ModelProto, weights: List[ModelProto], alphas: List[float]) -> List[Tuple[TensorProto, ndarray]]:
+  total = 1 + sum(alphas)
+
+  results = []
+
+  for base_node in base.graph.initializer:
+    logger.info("blending initializer node %s", base_node.name)
+    base_weights = numpy_helper.to_array(base_node).copy()
+
+    for weight, alpha in zip(weights, alphas):
+      weight_node = next(iter([f for f in weight.graph.initializer if f.name == base_node.name]), None)
+
+      if weight_node is not None:
+        base_weights += numpy_helper.to_array(weight_node) * alpha
+      else:
+        logger.warning("missing weights: %s in %s", base_node.name, weight.doc_string)
+
+    results.append((base_node, base_weights / total))
+
+  return results
+
+
+def convert_loras(part: str):
+  lora_weights = [
+    f"diffusion-lora-jack/{part}/model.onnx",
+    f"diffusion-lora-taters/{part}/model.onnx",
+  ]
+
+  base = load_lora(f"stable-diffusion-onnx-v1-5/{part}/model.onnx")
+  weights = [load_lora(f) for f in lora_weights]
+  alphas = [1 / len(weights)] * len(weights)
+  logger.info("blending LoRAs with alphas: %s, %s", weights, alphas)
+
+  result = blend_loras(base, weights, alphas)
+  logger.info("blended result keys: %s", len(result))
+
+  del weights
+  del alphas
+
+  tensors = []
+  for node, tensor in result:
+    logger.info("remaking tensor for %s", node.name)
+    tensors.append(helper.make_tensor(node.name, node.data_type, node.dims, tensor))
+
+  del result
+
+  graph = helper.make_graph(
+    base.graph.node,
+    base.graph.name,
+    base.graph.input,
+    base.graph.output,
+    tensors,
+    base.graph.doc_string,
+    base.graph.value_info,
+    base.graph.sparse_initializer,
+  )
+  model = helper.make_model(graph)
+
+  del model.opset_import[:]
+  opset = model.opset_import.add()
+  opset.version = 14
+
+  save_model(
+    model,
+    f"/tmp/lora-{part}.onnx",
+    save_as_external_data=True,
+    all_tensors_to_one_file=True,
+    location=f"/tmp/lora-{part}.tensors",
+  )
+  logger.info("saved model to %s and tensors to %s", f"/tmp/lora-{part}.onnx", f"/tmp/lora-{part}.tensors")
+
+
+if __name__ == "__main__":
+  convert_loras("unet")
+  convert_loras("text_encoder")
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