Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model

This is the official implementation for Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model (Accepted by NeurIPS 2024).

🔆 Overview

Diffusion models have obtained substantial progress in image-to-video (I2V) generation. However, such models are not fully understood. In this paper, we report a significant but previously overlooked issue in I2V diffusion models (I2V-DMs), namely, conditional image leakage. I2V-DMs tend to over-rely on the conditional image at large time steps, neglecting the crucial task of predicting the clean video from noisy inputs, which results in videos lacking dynamic and vivid motion. We further address this challenge from both inference and training aspects by presenting plug-and-play strategies accordingly. These strategies are validated on various I2V-DMs including DynamiCrafter, SVD and VideoCrafter1.

✅ To do list:

• [x] [2024.06.25]: Release code, models and project page.
• [x] [2024.08.11]: Add inference code for Animate-Anything and PIA

⚙️ Setup Environment

Our plug-and-play strategies can be applied to various I2V-DMs, allowing the direct use of their original environments. For example, to set up DynamiCrafter:

cd examples/DynamiCrafter
conda create -n dynamicrafter python=3.8.5
conda activate dynamicrafter
pip install -r requirements.txt

To set up VideoCrafter1:

cd examples/VideoCrafter
conda create -n videocrafter python=3.8.5
conda activate videocrafter
pip install -r requirements.txt

To set up SVD:

cd examples/SVD
conda create -n svd python=3.9.18
conda activate svd
pip install -r requirements.txt

To set up Animate-Anything:

cd examples/animate-anything
conda create -n animation python=3.10
conda activate animation
pip install -r requirements.txt

To set up PIA:

cd examples/PIA
conda env create -f pia.yml
conda activate pia

☀️ Dataset

Download the WebVid dataset from here, where we use Webvid-2M subset. Put .csv file in examples/dataset/results_2M_train.csv and video data in examples/dataset/. We use the raw data without any filters.

🧊 Inference Strategy

We clone the repo of DynamiCrafter and VideoCrafter1 and implement SVD by ourselves. We apply our plug and plug-and-play strategies on them.

🎒 Initial Noise Distribution

Model	Resolution	Initial Noise
DynamiCrafter	256x256	Initial Noise
DynamiCrafter	320x512	Initial Noise
DynamiCrafter	576x1024	Initial Noise
VideoCrafter	256x256	Initial Noise
VideoCrafter	320x512	Initial Noise
VideoCrafter	576x1024	Initial Noise
SVD	320 x 512	Initial Noise
SVD	576 x 1024	Initial Noise

😄 Example Results

Model	Conditional image	Standard inference	+ Our inference strategy
DynamiCrafter320x512
VideoCrafter320x512
SVD 576x1024
Animate-Anything
PIA

### DynamiCrafter 1. Download the original DynamiCrafter checkpoints from the [repository](https://github.com/Doubiiu/DynamiCrafter) and put it in ```examples/DynamiCrafter/ckpt/original``` ,or download our DynamiCrafter-CIL from [here](https://huggingface.co/GraceZhao/DynamiCrafter-CIL-512) and put it in ```examples/DynamiCrafter/ckpt/finetuned```. Download the initial noise in the above table and put it in ```examples/DynamiCrafter/ckpt/```. 2. Run the following commands: ``` cd examples/DynamiCrafter # for original DynamiCrafter with 320x512 resolution sh inference_512.sh # for our DynamiCrafter-CIL with 320x512 resolution sh inference_CIL_512.sh # for our DynamiCrafter-CIL with 576x1024 resolution sh inference_CIL_1024.sh ``` The relevant parameters in ```inference.sh``` for our strategy are explained as follows: * ```M```: the start timestep M. * ```whether_analytic_init```: indicates whether to use Analytic-Init; 0 means it is not applied, while 1 means it is applied * ```analytic_init_path```: the path for initializing the mean and variance of the noise if Analytic-Init is applied Note that ```M=1000, whether_analytic_init=0``` is the baseline. The effect of start time ```M ``` is as follows:

Conditional image	M=1.00T	M=0.94T	M=0.90T	M=0.86T	M=0.82T

An appropriate M can enhance performance by increasing motion without compromising other performance. A too-small M delivers poor visual quality due to the training-inference gap. ### SVD 1. Download the [pretrained SVD model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) and put it in `examples/SVD/ckpt/pretrained/stable-video-diffusion-img2vid` . Download our SVD-CIL from [here](https://huggingface.co/GraceZhao/SVD-CIL-512) and put it in ```examples/SVD/ckpt/finetuned```. Download the initial noise in the above table and put them in ```examples/SVD/ckpt/```. 2. Run the following commands: ``` cd examples/SVD # for original SVD sh inference.sh # for SVD-CIL with 320x512 resolution sh inference_CIL_512.sh ``` The relevant parameters for inference are set in `examples/SVD/config/inference.yaml`,which are explained as follows: * ```sigma_max```: the start time M. * ```analytic_init_path```: the path for initializing the mean and variance of the noise if Analytic-Init is applied ### VideoCrafter1 1. Download the original VideoCrafter checkpoints from the [repository](https://github.com/AILab-CVC/VideoCrafter) and put it in ```examples/VideoCrafter/ckpt/original```,or download our VideoCrafter-CIL from [here](https://huggingface.co/GraceZhao/VideoCrafter1-naive-finetune) and put it in ```examples/VideoCrafter/ckpt/finetuned```.Download the initial noise in the above table and put them in ```examples/VideoCrafter/ckpt```. 2. Run the following commands: ``` cd examples/VideoCrafter # for original VideoCrafter with 320x512 resolution sh inference_512.sh # for VideoCrafter-CIL with 320x512 resolution sh inference_CIL_512.sh ``` The relevant parameters in ```inference.sh``` for our strategy are explained as follows: * ```M```: the start time M * ```analytic_init_path```: the path for initializing the mean and variance of the noise if Analytic-Init is applied ### Animate-Anything 1. Download the original Animate-Anything checkpoints from the [repository](https://github.com/alibaba/animate-anything) and put it in ```cond-image-leakage/examples/animate-anything/output/latent/animate_anything_512_v1.02``` 2. Run the following commands: ``` cd cond-image-leakage/examples/animate-anything python inference.py --config example/config/concert.yaml ``` The relevant parameters in ```cond-image-leakage/examples/PIA/example/config/base.yaml``` for our strategy are explained as follows: * ```noise_scheduler_kwargs.num_train_timesteps```: the start time M ### PIA 1. Download the original Animate-Anything checkpoints from the [repository](https://github.com/open-mmlab/PIA) and put them following `cond-image-leakage/examples/PIA/README.md`. 2. Run the following commands: ``` cd cond-image-leakage/examples/PIA ``` The relevant parameters in ```inference.sh``` for our strategy are explained as follows: * ```M```: the start time M ## 🔥 Training Strategy Similar to the inference strategy, we finetune the baselines based on the repository [DynamiCrafter](https://doubiiu.github.io/projects/DynamiCrafter), [VideoCrafter1](https://github.com/AILab-CVC/VideoCrafter) and [SVD](https://github.com/Stability-AI/generative-models). ### 😄 Example Results

Model	Conditional image	Finetuned-Baseline	+ Our training strategy
DynamiCrafter
VideoCrafter
SVD

### DynamiCrafter 1. Download the DynamiCrafter checkpoints from the [repository](https://github.com/Doubiiu/DynamiCrafter) and put them in ```examples/DynamiCrafter/ckpt/original```. 2. Run the following commands: ``` cd examples/DynamiCrafter sh train.sh ``` The relevant parameters in ```train.sh``` for our strategy are explained as follows: * ```beta_m```: the maximum noise level. * ```a```: the exponent of center of the distribution: $\mu(t)=2t^a-1$, where $a > 0$. The effect of ```beta_m```is as follows:

Conditional Image	beta_m=25	beta_m=100	beta_m=700

Higher beta_m correspond to more dynamic motion and lower temporal consistency and image alignment. The effect of ```a``` is as follows:

Conditional Image	a = 5.0	a = 1.0	a = 0.1

Lower a correspond to more dynamic motion and lower temporal consistency and image alignment. ### SVD 1. Download the SVD checkpoints from the [repository](https://github.com/Stability-AI/generative-models) and put them in ```examples/SVD/ckpt/pretrained/stable-video-diffusion-img2vid```; 2. Run the following commands: ``` cd examples/SVD sh train.sh ``` The relevant parameters in `examples/SVD/config/train.yaml`for our strategy are explained as follows: * ```beta_m```: the maximum noise level. Higher beta_m correspond to more dynamic motion and lower temporal consistency and image alignment. * ```a```: the exponent of center of the distribution: $\mu(t)=2t^a-1$, where $a > 0$. Lower $a$ correspond to more dynamic motion and lower temporal consistency and image alignment. Note that original SVD first add noise on conditional image and then feed it into VAE. Here we first feed the conditional image into VAE and then add noise on the conditional latents. ### VideoCrafter1 1. Download the VideoCrafter checkpoints from the [repository](https://github.com/AILab-CVC/VideoCrafter) and put them in ```examples/VideoCrafter/original/ckpt/```. 2. Run the following commands: ``` cd examples/VideoCrafter sh train.sh ``` The relevant parameters in ```train.sh``` for our strategy are explained as follows: * ```beta_m```: the maximum noise level. Higher beta_m correspond to more dynamic motion and lower temporal consistency and image alignment. * ```a```: the exponent of center of the distribution: $\mu(t)=2t^a-1$, where $a > 0$. Lower $a$ correspond to more dynamic motion and lower temporal consistency and image alignment. ### 🎒 Checkpoints Naive fine-tuning and ours were trained under the same settings for fair comparison. In the future, we will release the model without watermark. | Model | Naive Fine-Tuning | Ours on Webvid |Watermark-free |:------------------------|:---------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | DynamiCrafter | [320x512](https://huggingface.co/GraceZhao/DynamiCrafter-naive-finetune) | [320x512](https://huggingface.co/GraceZhao/DynamiCrafter-CIL-512) |[320x512](https://huggingface.co/GraceZhao/DynamiCrafter-CIL-512-no-watermark) [576x1024](https://huggingface.co/GraceZhao/DynamiCrafter-CIL-1024)| | SVD | [320x512](https://huggingface.co/GraceZhao/SVD-naive-finetune) | [320x512](https://huggingface.co/GraceZhao/SVD-CIL-512) |[320x512](https://huggingface.co/zhuhz22/SVD-CIL-watermark-free)| | VideoCrafter1 | [320x512](https://huggingface.co/GraceZhao/VideoCrafter1-naive-finetune) | [320x512](https://huggingface.co/GraceZhao/VideoCrafter1-CIL-512) |-| ## 😄 Citation If you find this repository helpful, please cite as: ```bib @article{zhao2024identifying, title={Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model}, author={Zhao, Min and Zhu, Hongzhou and Xiang, Chendong and Zheng, Kaiwen and Li, Chongxuan and Zhu, Jun}, journal={arXiv preprint arXiv:2406.15735}, year={2024} } ``` ## ❤️ Acknowledgements This implementation is based on the following work: * [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://doubiiu.github.io/projects/DynamiCrafter) * [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://github.com/Stability-AI/generative-models) * [VideoCrafter1: Open Diffusion Models for High-Quality Video Generation](https://github.com/AILab-CVC/VideoCrafter/) Thanks to the authors for sharing their code and models.