Video Generation from text

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

Setup

Requirements

pip install -r requirements.txt

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-skiing.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

pretrained_model_path = "./checkpoints/stable-diffusion-v1-4"
my_model_path = "./outputs/man-skiing"
unet = UNet3DConditionModel.from_pretrained(my_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()
pipe.enable_vae_slicing()

prompt = "spider man is skiing"
ddim_inv_latent = torch.load(f"{my_model_path}/inv_latents/ddim_latent-500.pt").to(torch.float16)
video = pipe(prompt, latents=ddim_inv_latent, video_length=24, height=512, width=512, num_inference_steps=50, guidance_scale=12.5).videos

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

Results

Pretrained T2I (Stable Diffusion)

Input Video	Output Video
"A man is skiing"	"Wonder Woman, is skiing"	"A little girl is skiing "
"A rabbit is eating a watermelon"	"A cat is eating a watermelon on the table"	"A puppy is eating a cheeseburger on the table, comic style"
"A jeep car is moving on the road"	"A car is moving on the road, cartoon style"	"A car is moving on the snow"