| from __future__ import annotations | |
| import pathlib | |
| def find_exp_dirs(ignore_repo: bool = False) -> list[str]: | |
| repo_dir = pathlib.Path(__file__).parent | |
| exp_root_dir = repo_dir / 'experiments' | |
| if not exp_root_dir.exists(): | |
| return [] | |
| exp_dirs = sorted(exp_root_dir.glob('*')) | |
| exp_dirs = [ | |
| exp_dir for exp_dir in exp_dirs | |
| if (exp_dir / 'pytorch_lora_weights.bin').exists() | |
| ] | |
| if ignore_repo: | |
| exp_dirs = [ | |
| exp_dir for exp_dir in exp_dirs if not (exp_dir / '.git').exists() | |
| ] | |
| return [path.relative_to(repo_dir).as_posix() for path in exp_dirs] | |
| def save_model_card( | |
| save_dir: pathlib.Path, | |
| base_model: str, | |
| instance_prompt: str, | |
| test_prompt: str = '', | |
| test_image_dir: str = '', | |
| ) -> None: | |
| image_str = '' | |
| if test_prompt and test_image_dir: | |
| image_paths = sorted((save_dir / test_image_dir).glob('*')) | |
| if image_paths: | |
| image_str = f'Test prompt: {test_prompt}\n' | |
| for image_path in image_paths: | |
| rel_path = image_path.relative_to(save_dir) | |
| image_str += f'\n' | |
| model_card = f'''--- | |
| license: creativeml-openrail-m | |
| base_model: {base_model} | |
| instance_prompt: {instance_prompt} | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - lora | |
| inference: true | |
| --- | |
| # LoRA DreamBooth - {save_dir.name} | |
| These are LoRA adaption weights for [{base_model}](https://huggingface.co/{base_model}). The weights were trained on the instance prompt "{instance_prompt}" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. | |
| {image_str} | |
| ''' | |
| with open(save_dir / 'README.md', 'w') as f: | |
| f.write(model_card) | |