FreeInit - Integrate FreeInit for Enhanced Temporal Consistency in Video Diffusion Models

Introduction to FreeInit: Enhancing Video Consistency in Diffusion Models

Overview

FreeInit is a transformative method designed to improve temporal consistency in videos generated by diffusion models. Remarkably, FreeInit achieves this without the need for additional training or learnable parameters, making it a highly adaptable solution. It can be seamlessly integrated into various video diffusion models during the inference stage.

Key Updates

January 2024: FreeInit has been successfully integrated into popular platforms like Diffusers and ComfyUI-AnimateDiff-Evolved, thanks to community contributors.
December 2023: A demo showcasing FreeInit is available on Hugging Face, along with the release of the official paper on Arxiv and the project's source code.

How It Works

FreeInit leverages existing video diffusion models, exemplified through integration with the AnimateDiff pipeline. It introduces a unique class, AnimationFreeInitPipeline, demonstrating how to modify existing structures for better results. Additionally, FreeInit incorporates a frequency filtering mechanism in its Noise Reinitialization process, enhancing the video generation process.

Quick Start Guide

Clone the Repository: Start by cloning the FreeInit repository from GitHub.

git clone https://github.com/TianxingWu/FreeInit.git
cd FreeInit
cd examples/AnimateDiff

Set Up the Environment: Use Conda to create and activate the necessary environment.
```
conda env create -f environment.yaml
conda activate animatediff
```
Download Checkpoints: Reference the AnimateDiff official repository for setup guidance.

Run Inference with FreeInit: Download the required models and execute the command to generate animations using FreeInit. Adjust parameters like num_iters for iterative refinement.

python -m scripts.animate_with_freeinit \
    --config "configs/prompts/freeinit_examples/RealisticVision_v2.yaml" \
    --num_iters 5 \
    --save_intermediate \
    --use_fp16

Gradio Demo

FreeInit offers a Gradio Demo, allowing users to interact with the model through a user-friendly interface. Launch the demo via the command line or online through Hugging Face to explore various settings and optimize video generation quality.

Community Contributions

The FreeInit project has benefited from community involvement, including integrations with ComfyUI Extentions, HuggingFace Diffusers, and Google Colab support.

Generation Results

For more detailed visual comparisons and results, visit the FreeInit project page which showcases the improvements in video consistency brought by this innovative method.

Citation

Researchers and developers utilizing FreeInit in their work are encouraged to cite the project as follows:

@article{wu2023freeinit,
     title={FreeInit: Bridging Initialization Gap in Video Diffusion Models},
     author={Wu, Tianxing and Si, Chenyang and Jiang, Yuming and Huang, Ziqi and Liu, Ziwei},
     journal={arXiv preprint arXiv:2312.07537},
     year={2023}

Acknowledgement

This initiative is made possible under the MIT License, built on the foundation of the AnimateDiff project. The FreeInit team acknowledges and appreciates the contributions of the initial AnimateDiff team.