/ToonCrafter

a research paper for generative cartoon interpolation

Primary LanguagePythonApache License 2.0Apache-2.0

ToonCrafter: Generative Cartoon Interpolation

🔆 Introduction

⚠️ Please check our disclaimer first.

🤗 ToonCrafter can interpolate two cartoon images by leveraging the pre-trained image-to-video diffusion priors. Please check our project page and paper for more information.

1.1 Showcases (512x320)

Input starting frame Input ending frame Generated video

1.2 Sparse sketch guidance

Input starting frame Input ending frame Input sketch guidance Generated video

2. Applications

2.1 Cartoon Sketch Interpolation (see project page for more details)

Input starting frame Input ending frame Generated video

2.2 Reference-based Sketch Colorization

Input sketch Input reference Colorization results

📝 Changelog

  • Add sketch control and colorization function.
  • [2024.05.29]: 🔥🔥 Release code and model weights.
  • [2024.05.28]: Launch the project page and update the arXiv preprint.

🧰 Models

Model Resolution GPU Mem. & Inference Time (A100, ddim 50steps) Checkpoint
ToonCrafter_512 320x512 TBD (perframe_ae=True) Hugging Face

Currently, our ToonCrafter can support generating videos of up to 16 frames with a resolution of 512x320. The inference time can be reduced by using fewer DDIM steps.

⚙️ Setup

Install Environment via Anaconda (Recommended)

conda create -n tooncrafter python=3.8.5
conda activate tooncrafter
pip install -r requirements.txt

💫 Inference

1. Command line

Download pretrained ToonCrafter_512 and put the model.ckpt in checkpoints/tooncrafter_512_interp_v1/model.ckpt.

  sh scripts/run.sh

2. Local Gradio demo

Download the pretrained model and put it in the corresponding directory according to the previous guidelines.

  python gradio_app.py

📢 Disclaimer

Calm down. Our framework opens up the era of generative cartoon interpolation, but due to the variaity of generative video prior, the success rate is not guaranteed.

⚠️This is an open-source research exploration, instead of commercial products. It can't meet all your expectations.

This project strives to impact the domain of AI-driven video generation positively. Users are granted the freedom to create videos using this tool, but they are expected to comply with local laws and utilize it responsibly. The developers do not assume any responsibility for potential misuse by users.