/Tune-A-Video

Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation

Primary LanguagePython

Tune-A-Video

This repository is the official implementation of Tune-A-Video.

Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Wynne Hsu, Ying Shan, Xiaohu Qie, Mike Zheng Shou

Project Website arXiv Hugging Face Spaces Open In Colab


Given a video-text pair, our method, Tune-A-Video, fine-tunes a pre-trained text-to-image diffusion model for text-to-video generation.

News

Setup

Requirements

pip install -r requirements.txt

Installing xformers is highly recommended for more efficiency and speed on GPUs. To enable xformers, set enable_xformers_memory_efficient_attention=True (default).

Weights

[Stable Diffusion] Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The pre-trained Stable Diffusion models can be downloaded from Hugging Face (e.g., Stable Diffusion v1-4, v2-1). You can also use fine-tuned Stable Diffusion models trained on different styles (e.g, Modern Disney, Redshift, etc.).

[DreamBooth] DreamBooth is a method to personalize text-to-image models like Stable Diffusion given just a few images (3~5 images) of a subject. Tuning a video on DreamBooth models allows personalized text-to-video generation of a specific subject. There are some public DreamBooth models available on Hugging Face (e.g., mr-potato-head). You can also train your own DreamBooth model following this training example.

Usage

Training

To fine-tune the text-to-image diffusion models for text-to-video generation, run this command:

accelerate launch train_tuneavideo.py --config="configs/man-surfing.yaml"

Note: Tuning a video usually takes 300~500 steps, about 5~10 minutes using one A100 GPU and 10~20 minutes using one V100 GPU.

Inference

Once the training is done, run inference:

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 = "./checkpoints/stable-diffusion-v1-4"
unet_model_path = "./outputs/man-surfing/2023-XX-XXTXX-XX-XX"
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 = "a panda is surfing"
video = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=7.5).videos

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

Results

Stable Diffusion

[Training] a man is surfing. a panda is surfing. Iron Man is surfing in the desert. a raccoon is surfing, cartoon style.

Mr Potato Head

[DreamBooth] sks mr potato head. sks mr potato head, wearing a pink hat, is surfing. sks mr potato head, wearing sunglasses, is surfing. sks mr potato head is surfing in the forest.

Modern Disney

[Training] a bear is playing guitar. a handsome prince is playing guitar, modern disney style. a magical princess is playing guitar on the beach, modern disney style. a rabbit is playing guitar, modern disney style.

Redshift

[Training] a man is skiing. spider man is skiing. bat man is skiing. hulk is skiing.

Citation

If you make use of our work, please cite our paper.

@article{wu2022tuneavideo,
    title={Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation},
    author={Wu, Jay Zhangjie and Ge, Yixiao and Wang, Xintao and Lei, Stan Weixian and Gu, Yuchao and Hsu, Wynne and Shan, Ying and Qie, Xiaohu and Shou, Mike Zheng},
    journal={arXiv preprint arXiv:2212.11565},
    year={2022}
}

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