pollinations / tune-a-video

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

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Input

Output

Run time and cost

This model costs approximately $0.71 to run on Replicate, or 1 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 9 minutes. The predict time for this model varies significantly based on the inputs.

Readme

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 Page | arXiv

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

You can download the pre-trained Stable Diffusion models (e.g., Stable Diffusion v1-4):

git lfs install
git clone https://huggingface.co/CompVis/stable-diffusion-v1-4

Alternatively, you can use a personalized DreamBooth model (e.g., mr-potato-head):

git lfs install
git clone https://huggingface.co/sd-dreambooth-library/mr-potato-head

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"

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

model_id = "path-to-your-trained-model"
unet = UNet3DConditionModel.from_pretrained(model_id, subfolder='unet', torch_dtype=torch.float16).to('cuda')
pipe = TuneAVideoPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", unet=unet, torch_dtype=torch.float16).to("cuda")

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

Fine-tuning on Stable Diffusion

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

Fine-tuning on 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.

BibTeX

@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}
}