git checkout pr/13 There are several input arguments to configure in the conversion script, but the most important ones are: checkpoint_path: the path to the .ckpt file to convert. original_config_file: a YAML file defining the configuration of the original architecture. If you can’t find this file, try searching for the YAML file in the GitHub repository where you found the .ckpt file. dump_path: the path to the converted model. For example, you can take the cldm_v15.yaml file from the ControlNet repository because the TemporalNet model is a Stable Diffusion v1.5 and ControlNet model. Now you can run the script to convert the .ckpt file:   Copied python ../diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path temporalnetv3.ckpt --original_config_file cldm_v15.yaml --dump_path ./ --controlnet Once the conversion is done, upload your converted model and test out the resulting pull request!   Copied git push origin pr/13:refs/pr/13  Keras .pb or .h5 🧪 This is an experimental feature. Only Stable Diffusion v1 checkpoints are supported by the Convert KerasCV Space at the moment. KerasCV supports training for Stable Diffusion v1 and v2. However, it offers limited support for experimenting with Stable Diffusion models for inference and deployment whereas 🤗 Diffusers has a more complete set of features for this purpose, such as different noise schedulers, flash attention, and other