README.md

---
license: creativeml-openrail-m
base_model: Bingsu/my-korean-stable-diffusion-v1-5
training_prompt: A man is surfing
tags:
- tune-a-video
- text-to-video
- diffusers
- korean
inference: false
---

# Tune-A-VideKO - Korean Stable Diffusion v1-5
Github: [Kyujinpy/Tune-A-VideKO](https://github.com/KyujinHan/Tune-A-VideKO)

## Model Description
- Base model: [Bingsu/my-korean-stable-diffusion-v1-5](https://huggingface.co/Bingsu/my-korean-stable-diffusion-v1-5)
- Training prompt: A man is surfing
![sample-train](sample/surfing.gif)

## Samples

![sample-500](sample/video10.gif)
Test prompt: 미키마우스가 서핑을 타고 있습니다

![sample-500](sample/video11.gif)
Test prompt: 한 여자가 서핑을 타고 있습니다

![sample-500](sample/video12.gif)
Test prompt: 흰색 옷을 입은 남자가 바다를 걷고 있습니다

## Usage
Clone the github repo
```bash
git clone https://github.com/showlab/Tune-A-Video.git
```

Run inference code

```python
from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
from tuneavideo.models.unet import UNet3DConditionModel
from tuneavideo.util import save_videos_grid
import torch

pretrained_model_path = "Bingsu/my-korean-stable-diffusion-v1-5"
unet_model_path = "kyujinpy/Tune-A-VideKO-v1-5"
unet = UNet3DConditionModel.from_pretrained(unet_model_path, subfolder='unet', torch_dtype=torch.float16).to('cuda')
pipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to("cuda")
pipe.enable_xformers_memory_efficient_attention()

prompt = "흰색 옷을 입은 남자가 바다를 걷고 있습니다"
video = pipe(prompt, video_length=24, height=512, width=512, num_inference_steps=50, guidance_scale=12.5).videos

save_videos_grid(video, f"./{prompt}.gif")
```

## Related Papers:
- [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