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'![{image_path.stem}]({rel_path})\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 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)