I tried Convert_Civitai_OpenVINO.py and got many warnings and an error.

D:\Program Files\Python310\lib\site-packages\diffusers\models\unet_2d_condition.py:915: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if dim % default_overall_up_factor != 0:
D:\Program Files\Python310\lib\site-packages\diffusers\models\downsampling.py:135: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert hidden_states.shape[1] == self.channels
D:\Program Files\Python310\lib\site-packages\diffusers\models\downsampling.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert hidden_states.shape[1] == self.channels
D:\Program Files\Python310\lib\site-packages\diffusers\models\upsampling.py:149: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert hidden_states.shape[1] == self.channels
D:\Program Files\Python310\lib\site-packages\diffusers\models\upsampling.py:165: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if hidden_states.shape[0] >= 64:
D:\Program Files\Python310\lib\site-packages\diffusers\models\unet_2d_condition.py:1206: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if not return_dict:
Unet successfully converted to ONNX
Traceback (most recent call last):
File "D:\astable-diffusion\openvino_notebooks\notebooks\225-stable-diffusion-text-to-image\t5\civitai.py", line 87, in
model = mo.convert_model(UNET_ONNX_PATH, compress_to_fp16=True)
File "D:\Program Files\Python310\lib\site-packages\openvino\tools\mo\convert.py", line 364, in convert_model
ov_model, _ = _convert(cli_parser, framework, params, True)
File "D:\Program Files\Python310\lib\site-packages\openvino\tools\mo\convert_impl.py", line 957, in _convert
raise e.with_traceback(None)
RuntimeError: Check 'false' failed at src\frontends\common\src\frontend.cpp:54:
Converting input model
bad allocation

torch: 2.1.2+cpu
onnx: 1.15.0
onnxruntime: 1.16.3
tensorflow-intel: 2.14.0
Intel Core i5-6500 CPU

I found openvinotoolkit/openvino#12747 that tensorflow-intel >=2.8.0 will cause unexpected error.
I tried to downgrade tensorflow-intel to a version tensorflow-intel==2.8.0, but it asked me to downgrade other packages like
Other possible workarounds are:

1. Downgrade the protobuf package to 3.20.x or lower.
2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

Since you made it working, could you create virtualenv with needed packages?

error: torch.onnx.errors.UnsupportedOperatorError: Exporting the operator 'aten::scaled_dot_product_attention' to ONNX opset version 16 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues.

torch:2.0.0
onnx:1.13.1
onnxruntime:1.14.1