SimDA: Simple Diffusion Adapter for Efficient Video Generation

This is the official repo of the paper SimDA: Simple Diffusion Adapter for Efficient Video Generation.

The project website is here.

Setup

Requirements

sh env.sh

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, Anything V4.0, Redshift, etc.).

Usage

Training

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

sh train.sh

Note: Tuning a 24-frame video usually takes 200~500 steps, about 5~10 minutes using one A100 GPU. Reduce n_sample_frames if your GPU memory is limited.

Inference

Once the training is done, run inference:

from simda.pipelines.pipeline_simda import SimDAPipeline
from simda.models.unet import UNet3DConditionModel
from simda.util import save_videos_grid
import torch

pretrained_model_path = "./checkpoints/stable-diffusion-v1-4"
my_model_path = "./outputs/car-turn"
unet = UNet3DConditionModel.from_pretrained(my_model_path, subfolder='unet', torch_dtype=torch.float16).to('cuda')
pipe = SimDAipeline.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")

Citation

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

@inproceedings{xing2023simda,
  title={SimDA: Simple Diffusion Adapter for Efficient Video Generation},
  author={Xing, Zhen and Dai, Qi and Hu, Han and Wu, Zuxuan and Jiang, Yu-Gang},
  booktitle={CVPR},
  year={2024}
}

Shoutouts

• This code builds on diffusers and Tune-A-Video. Thanks for open-sourcing!