readme: ---
  license: creativeml-openrail-m
  base_model: CompVis/stable-diffusion-v1-4
  training_prompt: A man is surfing
  tags:
  - tune-a-video
  - text-to-video
  - diffusers
  inference: false
  ---
  
  # Tune-A-Video - Modern Disney
  
  ## Model Description
  - Base model: [nitrosocke/mo-di-diffusion](https://huggingface.co/nitrosocke/mo-di-diffusion)
  - Training prompt: a bear is playing guitar.
  ![sample-train](samples/train.gif)
  
  ## Samples
  
  ![sample-500](samples/sample-500.gif)
  Test prompt: a [handsome prince/magical princess/rabbit/baby] is playing guitar, modern disney style.
  
  ## Usage
  Clone the github repo
  ```bash
  git clone https://github.com/showlab/Tune-A-Video.git
  ```
  
  Run inference code
  
  ```python
  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 = "nitrosocke/mo-di-diffusion"
  unet_model_path = "Tune-A-Video-library/mo-di-bear-guitar"
  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 magical princess is playing guitar, modern disney style"
  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")
  ```
  
  ## Related Papers:
  - [Tune-A-Video](https://arxiv.org/abs/2212.11565): One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
  - [Stable Diffusion](https://arxiv.org/abs/2112.10752): High-Resolution Image Synthesis with Latent Diffusion Models
  