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| import os | |
| # Copied from https://github.com/huggingface/diffusers/blob/31be42209ddfdb69d9640a777b32e9b5c6259bf0/examples/text_to_image/train_text_to_image_lora.py#L55 | |
| def save_model_card(base_model=str, repo_folder=None, weight_paths=None): | |
| yaml = f""" | |
| --- | |
| license: creativeml-openrail-m | |
| base_model: {base_model} | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| inference: true | |
| --- | |
| """ | |
| model_card = f""" | |
| # KerasCV Stable Diffusion in Diffusers 🧨🤗 | |
| The pipeline contained in this repository was created using [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers). The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with Diffusers. This allows users to fine-tune using KerasCV and use the fine-tuned weights in Diffusers taking advantage of its nifty features (like schedulers, fast attention, etc.).\n | |
| """ | |
| if weight_paths is not None: | |
| model_card += "Following weight paths (KerasCV) were used: {weight_paths}" | |
| with open(os.path.join(repo_folder, "README.md"), "w") as f: | |
| f.write(yaml + model_card) |