optimization techniques. The Convert KerasCV Space converts .pb or .h5 files to PyTorch, and then wraps them in a StableDiffusionPipeline so it is ready for inference. The converted checkpoint is stored in a repository on the Hugging Face Hub. For this example, let’s convert the sayakpaul/textual-inversion-kerasio checkpoint which was trained with Textual Inversion. It uses the special token <my-funny-cat> to personalize images with cats. The Convert KerasCV Space allows you to input the following: Your Hugging Face token. Paths to download the UNet and text encoder weights from. Depending on how the model was trained, you don’t necessarily need to provide the paths to both the UNet and text encoder. For example, Textual Inversion only requires the embeddings from the text encoder and a text-to-image model only requires the UNet weights. Placeholder token is only applicable for textual inversion models. The output_repo_prefix is the name of the repository where the converted model is stored. Click the Submit button to automatically convert the KerasCV checkpoint! Once the checkpoint is successfully converted, you’ll see a link to the new repository containing the converted checkpoint. Follow the link to the new repository, and you’ll see the Convert KerasCV Space generated a model card with an inference widget to try out the converted model. If you prefer to run inference with code, click on the Use in Diffusers button in the upper right corner of the model card to copy and paste the code snippet:   Copied from diffusers import DiffusionPipeline