README.md

metadata

license: creativeml-openrail-m
base_model: Bingsu/my-korean-stable-diffusion-v1-5
training_prompt: A rabbit is eating a watermelon on the table
tags:
  - tune-a-video
  - text-to-video
  - diffusers
  - korean
inference: false

Tune-A-VideKO - Korean Stable Diffusion v1-5

Github: Kyujinpy/Tune-A-VideKO

Model Description

• Base model: Bingsu/my-korean-stable-diffusion-v1-5
• Training prompt: A rabbit is eating a watermelon on the table

Samples

Test prompt: 고양이가 해변에서 수박을 먹고 있습니다

Test prompt: 강아지가 오렌지를 먹고 있습니다

Usage

Clone the github repo

git clone https://github.com/showlab/Tune-A-Video.git

Run inference code

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 = "강아지가 만화 스타일로 상자를 먹고 있습니다"
video = pipe(prompt, video_length=8, 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: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
• Stable Diffusion: High-Resolution Image Synthesis with Latent Diffusion Models