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from __future__ import annotations
import pathlib
def find_exp_dirs() -> 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 / 'model_index.json').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,
training_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('*.gif'))
if image_paths:
image_path = image_paths[-1]
rel_path = image_path.relative_to(save_dir)
image_str = f'''## Samples
Test prompt: {test_prompt}
'''
model_card = f'''---
license: creativeml-openrail-m
base_model: {base_model}
training_prompt: {training_prompt}
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- text-to-video
- tune-a-video
- video-p2p
inference: false
---
# Video-P2P - {save_dir.name}
## Model description
- Base model: [{base_model}](https://huggingface.co/{base_model})
- Training prompt: {training_prompt}
{image_str}
## Related papers:
- [Video-P2P](https://arxiv.org/abs/2303.04761): Video editing with cross-attention control
- [Tune-A-Video](https://arxiv.org/abs/2212.11565): One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
- [Stable-Diffusion](https://arxiv.org/abs/2112.10752): High-Resolution Image Synthesis with Latent Diffusion Models
'''
with open(save_dir / 'README.md', 'w') as f:
f.write(model_card)
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