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import os
from typing import List, Union
import numpy as np
import wandb
from diffusers.utils import export_to_video
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from PIL import Image
def save_model_card(
args,
repo_id: str,
videos: Union[List[str], Union[List[Image.Image], List[np.ndarray]]],
validation_prompts: List[str],
fps: int = 30,
) -> None:
widget_dict = []
output_dir = str(args.output_dir)
if videos is not None and len(videos) > 0:
for i, (video, validation_prompt) in enumerate(zip(videos, validation_prompts)):
if not isinstance(video, str):
export_to_video(video, os.path.join(output_dir, f"final_video_{i}.mp4"), fps=fps)
widget_dict.append(
{
"text": validation_prompt if validation_prompt else " ",
"output": {"url": video if isinstance(video, str) else f"final_video_{i}.mp4"},
}
)
model_description = f"""
# LoRA Finetune
<Gallery />
## Model description
This is a lora finetune of model: `{args.pretrained_model_name_or_path}`.
The model was trained using [`finetrainers`](https://github.com/a-r-r-o-w/finetrainers).
## Download model
[Download LoRA]({repo_id}/tree/main) in the Files & Versions tab.
## Usage
Requires the [🧨 Diffusers library](https://github.com/huggingface/diffusers) installed.
```py
TODO
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) on loading LoRAs in diffusers.
"""
if wandb.run.url:
model_description += f"""
Find out the wandb run URL and training configurations [here]({wandb.run.url}).
"""
model_card = load_or_create_model_card(
repo_id_or_path=repo_id,
from_training=True,
base_model=args.pretrained_model_name_or_path,
model_description=model_description,
widget=widget_dict,
)
tags = [
"text-to-video",
"diffusers-training",
"diffusers",
"lora",
"template:sd-lora",
]
model_card = populate_model_card(model_card, tags=tags)
model_card.save(os.path.join(args.output_dir, "README.md"))
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