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wandb/
*debug*
debugs/
outputs/
samples/
__pycache__/
ossutil_output/
.ossutil_checkpoint/
scripts/*
!scripts/animate.py
*.ipynb
*.safetensors
*.ckpt
*.mp4
*.csv
models/*
!models/StableDiffusion/
models/StableDiffusion/*
!models/StableDiffusion/*.txt
!models/Motion_Module/
!models/Motion_Module/*.txt
!models/DreamBooth_LoRA/
!models/DreamBooth_LoRA/*.txt
!models/MotionLoRA/
!models/MotionLoRA/*.txt
openai/
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# AnimateDiff
## 论文
**AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning**
* https://arxiv.org/abs/2307.04725
## 模型结构
$`\mathcal{E}`$（Encoder，用于压缩原始图像），`Base T2I`（文本生成图像模型，如Stable Diffusion），`Motion Modeling Module`（运动模型模块），`Personalized T2I`（个性化图像生成模型，如使用DreamBoth训练得到的模型），$`\mathcal{D}`$（Decoder，用于恢复/生成图像）。
![Alt text](readme_imgs/image-1.png)
## 算法原理
用途：概算法可以生成高质量的动画。
原理：
1.网络膨胀
将原始图像模型中的每个2D卷积和注意力层转换为仅空间伪3D层，通过将帧轴重塑为批次轴，使网络能够独立处理每一帧。同时，新插入的运动模块在每个批次中跨帧操作，以实现动画剪辑中的运动平滑性和内容一致性。
![Alt text](readme_imgs/image-2.png)
2.注意力机制
使用原始的时间注意力机制作为运动模块的设计，实现跨帧之间的有效信息交换。
## 环境配置
### Docker（方法一）
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04.1-py39-latest
docker run --shm-size 10g --network=host --name=animatediff --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -it <your IMAGE ID> bash
pip install -r requirements.txt
### Docker（方法二）
# 需要在对应的目录下
docker build -t <IMAGE_NAME>:<TAG> .
# <your IMAGE ID>用以上拉取的docker的镜像ID替换
docker run -it --shm-size 10g --network=host --name=animatediff --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined <your IMAGE ID> bash
pip install -r requirements.txt
### Anaconda (方法三)
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
https://developer.hpccube.com/tool/
DTK驱动：dtk23.04.1
python：python3.9
torch:1.13.1
torchvision:0.14.1
torchaudio:0.13.1
deepspeed:0.9.2
apex:0.1
Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应
2、其它非特殊库参照requirements.txt安装
pip install -r requirements.txt
## 数据集
2.5M - 包含2.5M个数据（prompt-video）
http://www.robots.ox.ac.uk/~maxbain/webvid/results_2M_train.csv
http://www.robots.ox.ac.uk/~maxbain/webvid/results_2M_val.csv
10M - 包含10M个数据（prompt-video）
http://www.robots.ox.ac.uk/~maxbain/webvid/results_10M_train.csv
http://www.robots.ox.ac.uk/~maxbain/webvid/results_10M_val.csv
详情参考： https://github.com/m-bain/webvid
下载完上述`csv`文件后，需要执行`webvid`项目中的`download.py`下载相应的视频文件。
注意：该数据集为训练数据集
## 推理
### 模型下载
https://huggingface.co/guoyww/animatediff/tree/main
https://civitai.com/models/4201?modelVersionId=130072
https://civitai.com/models/30240?modelVersionId=125771
https://huggingface.co/openai/clip-vit-large-patch14/tree/main
openai/
└── clip-vit-large-patch14
├── config.json
├── merges.txt
├── preprocessor_config.json
├── pytorch_model.bin
├── special_tokens_map.json
├── tokenizer_config.json
├── tokenizer.json
└── vocab.json
models/
├── DreamBooth_LoRA
│   ├── lyriel_v16.safetensors
│   ├── Put personalized T2I checkpoints here.txt
│   ├── realisticVisionV51_v51VAE.safetensors
│   ├── toonyou_beta3.safetensors
│   └── toonyou_beta6.safetensors
├── MotionLoRA
│   ├── Put MotionLoRA checkpoints here.txt
│   └── v2_lora_ZoomIn.ckpt
├── Motion_Module
│   ├── mm_sd_v14.ckpt
│   ├── mm_sd_v15.ckpt
│   ├── mm_sd_v15_v2.ckpt
│   ├── Put motion module checkpoints here.txt
│   ├── v3_sd15_adapter.ckpt
│   └── v3_sd15_mm.ckpt
├── SparseCtrl
│   └── v3_sd15_sparsectrl_rgb.ckpt
└── StableDiffusion
├── Put diffusers stable-diffusion-v1-5 repo here.txt
└── stable-diffusion-v1-5
├── feature_extractor
│   └── preprocessor_config.json
├── model_index.json
├── scheduler
│   └── scheduler_config.json
├── text_encoder
│   ├── config.json
│   └── pytorch_model.bin
├── tokenizer
│   ├── merges.txt
│   ├── special_tokens_map.json
│   ├── tokenizer_config.json
│   └── vocab.json
├── unet
│   ├── config.json
│   └── diffusion_pytorch_model.bin
├── v1-5-pruned.ckpt
└── vae
├── config.json
└── diffusion_pytorch_model.bin
注意：以上模型并不是必选，仅提供文件结构，可根据需要自行选择部分或其他模型。
### 命令
python -m scripts.animate --config configs/prompts/v1/v1-1-ToonYou.yaml --without-xformers
python -m scripts.animate --config configs/prompts/v1/v1-2-Lyriel.yaml --without-xformers
python -m scripts.animate --config configs/prompts/v2/v2-1-RealisticVision.yaml --without-xformers
python -m scripts.animate --config configs/prompts/v3/v3-1-T2V.yaml --without-xformers
python -m scripts.animate --config configs/prompts/v3/v3-2-animation-RealisticVision.yaml --without-xformers
注意：以上仅是部分推理示例，可以自行修改或编写`yaml`文件。
## 训练
### 数据结构
data/
└── videos
├── xxx.mp4
└── xxx.mp4
└── xxx.csv
注意：数据准备完成后需要修改`configs/trainging`中`yaml`文件中数据路径。，如下所示。
train_data:
csv_path: "data/results_2M_val.csv"
video_folder: "data/videos"
### 微调Unet原始层（image layers）
torchrun --nnodes=1 --nproc_per_node=1 train.py --config configs/training/v1/image_finetune.yaml
### 训练motion modules
torchrun --nnodes=1 --nproc_per_node=1 train.py --config configs/training/v1/training.yaml
## result
![Alt text](readme_imgs/sample.gif)
### 精度
无
## 应用场景
### 算法类别
`AIGC`
### 热点应用行业
`媒体,科研,教育`
## 源码仓库及问题反馈
https://developer.hpccube.com/codes/modelzoo/animatediff_pytorch
## 参考资料
* https://github.com/guoyww/AnimateDiff
* https://github.com/m-bain/webvid
\ No newline at end of file
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# AnimateDiff
This repository is the official implementation of [AnimateDiff](https://arxiv.org/abs/2307.04725).
It is a plug-and-play module turning most community models into animation generators, without the need of additional training.
**[AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)**
</br>
[Yuwei Guo](https://guoyww.github.io/),
[Ceyuan Yang*](https://ceyuan.me/),
[Anyi Rao](https://anyirao.com/),
[Yaohui Wang](https://wyhsirius.github.io/),
[Yu Qiao](https://scholar.google.com.hk/citations?user=gFtI-8QAAAAJ),
[Dahua Lin](http://dahua.site),
[Bo Dai](https://daibo.info)
(*Corresponding Author)
<!-- [Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) -->
[![arXiv](https://img.shields.io/badge/arXiv-2307.04725-b31b1b.svg)](https://arxiv.org/abs/2307.04725)
[![Project Page](https://img.shields.io/badge/Project-Website-green)](https://animatediff.github.io/)
[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/Masbfca/AnimateDiff)
[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-yellow)](https://huggingface.co/spaces/guoyww/AnimateDiff)
We developed four versions of AnimateDiff: `v1`, `v2` and `v3` for [Stable Diffusion V1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5); `sdxl-beta` for [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
## Next
- [ ] Update to latest diffusers version
- [ ] Update Gradio demo
- [ ] Release training scripts
- [x] Release AnimateDiff v3 and SparseCtrl
## Gallery
We show some results in the [GALLERY](./__assets__/docs/gallery.md).
Some of them are contributed by the community.
## Preparations
Note: see [ANIMATEDIFF](__assets__/docs/animatediff.md) for detailed setup.
### Setup repository and conda environment
```
git clone https://github.com/guoyww/AnimateDiff.git
cd AnimateDiff
conda env create -f environment.yaml
conda activate animatediff
```
### Download Stable Diffusion V1.5
```
git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 models/StableDiffusion/
```
### Prepare Community Models
Manually download the community `.safetensors` models from [CivitAI](https://civitai.com), and save them to `models/DreamBooth_LoRA`. We recommand [RealisticVision V5.1](https://civitai.com/models/4201?modelVersionId=130072) and [ToonYou Beta6](https://civitai.com/models/30240?modelVersionId=125771).
### Prepare AnimateDiff Modules
Manually download the AnimateDiff modules. The download links can be found in each version's model zoo, as provided in the following. Save the modules to `models/Motion_Module`.
## [2023.12] AnimateDiff v3 and SparseCtrl
In this version, we did the image model finetuning through **Domain Adapter LoRA** for more flexiblity at inference time.
Additionally, we implement two (RGB image/scribble) [SparseCtrl](https://arxiv.org/abs/2311.16933) Encoders, which can take abitary number of condition maps to control the generation process.
- **Explanation:** Domain Adapter is a LoRA module trained on static frames of the training video dataset. This process is done before training the motion module, and helps the motion module focus on motion modeling, as shown in the figure below. At inference, By adjusting the LoRA scale of the Domain Adapter, some visual attributes of the training video, e.g., the watermarks, can be removed. To utilize the SparseCtrl encoder, it's necessary to use a full Domain Adapter in the pipeline.
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<img src="__assets__/figs/adapter_explain.png" style="width:60%">
Technical details of SparseCtrl can be found in this research paper:
**[SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://arxiv.org/abs/2311.16933)**
</br>
[Yuwei Guo](https://guoyww.github.io/),
[Ceyuan Yang*](https://ceyuan.me/),
[Anyi Rao](https://anyirao.com/),
[Maneesh Agrawala](https://graphics.stanford.edu/~maneesh/),
[Dahua Lin](http://dahua.site),
[Bo Dai](https://daibo.info)
(*Corresponding Author)
[![arXiv](https://img.shields.io/badge/arXiv-2311.16933-b31b1b.svg)](https://arxiv.org/abs/2311.16933)
[![Project Page](https://img.shields.io/badge/Project-Website-green)](https://guoyww.github.io/projects/SparseCtrl/)
<details open>
<summary>AnimateDiff v3 Model Zoo</summary>
| Name | HuggingFace | Type | Storage Space | Description |
|-------------------------------|--------------------------------------------------------------------------------------------|---------------------|---------------|------------------------------------|
| `v3_adapter_sd_v15.ckpt` | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v3_sd15_adapter.ckpt) | Domain Adapter | 97.4 MB | |
| `v3_sd15_mm.ckpt.ckpt` | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v3_sd15_mm.ckpt) | Motion Module | 1.56 GB | |
| `v3_sd15_sparsectrl_scribble.ckpt` | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v3_sd15_sparsectrl_scribble.ckpt) | SparseCtrl Encoder | 1.86 GB | scribble condition |
| `v3_sd15_sparsectrl_rgb.ckpt` | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v3_sd15_sparsectrl_rgb.ckpt) | SparseCtrl Encoder | 1.85 GB | RGB image condition |
</details>
### Quick Demos
<table class="center">
<tr style="line-height: 0">
<td width=25% style="border: none; text-align: center">Input (by RealisticVision)</td>
<td width=25% style="border: none; text-align: center">Animation</td>
<td width=25% style="border: none; text-align: center">Input</td>
<td width=25% style="border: none; text-align: center">Animation</td>
</tr>
<tr>
<td width=25% style="border: none"><img src="__assets__/demos/image/RealisticVision_firework.png" style="width:100%"></td>
<td width=25% style="border: none"><img src="__assets__/animations/v3/animation_fireworks.gif" style="width:100%"></td>
<td width=25% style="border: none"><img src="__assets__/demos/image/RealisticVision_sunset.png" style="width:100%"></td>
<td width=25% style="border: none"><img src="__assets__/animations/v3/animation_sunset.gif" style="width:100%"></td>
</tr>
</table>
<table class="center">
<tr style="line-height: 0">
<td width=25% style="border: none; text-align: center">Input Scribble</td>
<td width=25% style="border: none; text-align: center">Output</td>
<td width=25% style="border: none; text-align: center">Input Scribbles</td>
<td width=25% style="border: none; text-align: center">Output</td>
</tr>
<tr>
<td width=25% style="border: none"><img src="__assets__/demos/scribble/scribble_1.png" style="width:100%"></td>
<td width=25% style="border: none"><img src="__assets__/animations/v3/sketch_boy.gif" style="width:100%"></td>
<td width=25% style="border: none"><img src="__assets__/demos/scribble/scribble_2_readme.png" style="width:100%"></td>
<td width=25% style="border: none"><img src="__assets__/animations/v3/sketch_city.gif" style="width:100%"></td>
</tr>
</table>
### Inference
Here we provide three demo inference scripts. The corresponding AnimateDiff modules and community models need to be downloaded in advance. Put motion module in `models/Motion_Module`; put SparseCtrl encoders in `models/SparseCtrl`.
```
# under general T2V setting
python -m scripts.animate --config configs/prompts/v3/v3-1-T2V.yaml
# image animation (on RealisticVision)
python -m scripts.animate --config configs/prompts/v3/v3-2-animation-RealisticVision.yaml
# sketch-to-animation and storyboarding (on RealisticVision)
python -m scripts.animate --config configs/prompts/v3/v3-3-sketch-RealisticVision.yaml
```
### Limitations
1. Small fickering is noticable. To be solved in future versions;
2. To stay compatible with comunity models, there is no specific optimizations for general T2V, leading to limited visual quality under this setting;
3. **(Style Alignment) For usage such as image animation/interpolation, it's recommanded to use images generated by the same community model.**
## [2023.11] AnimateDiff SDXL-Beta
Release the Motion Module (beta version) on SDXL, available at [Google Drive](https://drive.google.com/file/d/1EK_D9hDOPfJdK4z8YDB8JYvPracNx2SX/view?usp=share_link
) / [HuggingFace](https://huggingface.co/guoyww/animatediff/blob/main/mm_sdxl_v10_beta.ckpt
) / [CivitAI](https://civitai.com/models/108836/animatediff-motion-modules). High resolution videos (i.e., 1024x1024x16 frames with various aspect ratios) could be produced **with/without** personalized models. Inference usually requires ~13GB VRAM and tuned hyperparameters (e.g., #sampling steps), depending on the chosen personalized models.
Checkout to the branch [sdxl](https://github.com/guoyww/AnimateDiff/tree/sdxl) for more details of the inference. More checkpoints with better-quality would be available soon. Stay tuned. Examples below are manually downsampled for fast loading.
<details open>
<summary>AnimateDiff SDXL-Beta Model Zoo</summary>
| Name | HuggingFace | Type | Storage Space |
|-------------------------------|-----------------------------------------------------------------------------------|---------------------|---------------|
| `mm_sdxl_v10_beta.ckpt` | [Link](https://huggingface.co/guoyww/animatediff/blob/main/mm_sdxl_v10_beta.ckpt) | Motion Module | 950 MB |
</details>
<table class="center">
<tr style="line-height: 0">
<td width=52% style="border: none; text-align: center">Original SDXL</td>
<td width=30% style="border: none; text-align: center">Community SDXL</td>
<td width=18% style="border: none; text-align: center">Community SDXL</td>
</tr>
<tr>
<td width=52% style="border: none"><img src="__assets__/animations/motion_xl/01.gif" style="width:100%"></td>
<td width=30% style="border: none"><img src="__assets__/animations/motion_xl/02.gif" style="width:100%"></td>
<td width=18% style="border: none"><img src="__assets__/animations/motion_xl/03.gif" style="width:100%"></td>
</tr>
</table>
## [2023.09] AnimateDiff v2
In this version, the motion module is trained upon larger resolution and batch size.
We observe this significantly helps improve the sample quality.
Moreover, we support **MotionLoRA** for eight basic camera movements.
<details open>
<summary>AnimateDiff v2 Model Zoo</summary>
| Name | HuggingFace | Type | Parameter | Storage Space |
|--------------------------------------|--------------------------------------------------------------------------------------------------|---------------|-----------|---------------|
| mm_sd_v15_v2.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt) | Motion Module | 453 M | 1.7 GB |
| v2_lora_ZoomIn.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v2_lora_ZoomIn.ckpt) | MotionLoRA | 19 M | 74 MB |
| v2_lora_ZoomOut.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v2_lora_ZoomOut.ckpt) | MotionLoRA | 19 M | 74 MB |
| v2_lora_PanLeft.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v2_lora_PanLeft.ckpt) | MotionLoRA | 19 M | 74 MB |
| v2_lora_PanRight.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v2_lora_PanRight.ckpt) | MotionLoRA | 19 M | 74 MB |
| v2_lora_TiltUp.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v2_lora_TiltUp.ckpt) | MotionLoRA | 19 M | 74 MB |
| v2_lora_TiltDown.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v2_lora_TiltDown.ckpt) | MotionLoRA | 19 M | 74 MB |
| v2_lora_RollingClockwise.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v2_lora_RollingClockwise.ckpt) | MotionLoRA | 19 M | 74 MB |
| v2_lora_RollingAnticlockwise.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/v2_lora_RollingAnticlockwise.ckpt) | MotionLoRA | 19 M | 74 MB |
</details>
- Release **MotionLoRA** and its model zoo, **enabling camera movement controls**! Please download the MotionLoRA models (**74 MB per model**, available at [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI?usp=sharing) / [HuggingFace](https://huggingface.co/guoyww/animatediff) / [CivitAI](https://civitai.com/models/108836/animatediff-motion-modules) ) and save them to the `models/MotionLoRA` folder. Example:
```
python -m scripts.animate --config configs/prompts/v2/5-RealisticVision-MotionLoRA.yaml
```
<table class="center">
<tr style="line-height: 0">
<td colspan="2" style="border: none; text-align: center">Zoom In</td>
<td colspan="2" style="border: none; text-align: center">Zoom Out</td>
<td colspan="2" style="border: none; text-align: center">Zoom Pan Left</td>
<td colspan="2" style="border: none; text-align: center">Zoom Pan Right</td>
</tr>
<tr>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_01/01.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_02/02.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_01/02.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_02/01.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_01/03.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_02/04.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_01/04.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_02/03.gif"></td>
</tr>
<tr style="line-height: 0">
<td colspan="2" style="border: none; text-align: center">Tilt Up</td>
<td colspan="2" style="border: none; text-align: center">Tilt Down</td>
<td colspan="2" style="border: none; text-align: center">Rolling Anti-Clockwise</td>
<td colspan="2" style="border: none; text-align: center">Rolling Clockwise</td>
</tr>
<tr>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_01/05.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_02/05.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_01/06.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_02/06.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_01/07.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_02/07.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_01/08.gif"></td>
<td style="border: none"><img src="__assets__/animations/motion_lora/model_02/08.gif"></td>
</tr>
</table>
- New Motion Module release! `mm_sd_v15_v2.ckpt` was trained on larger resolution & batch size, and gains noticeable quality improvements. Check it out at [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI?usp=sharing) / [HuggingFace](https://huggingface.co/guoyww/animatediff) / [CivitAI](https://civitai.com/models/108836/animatediff-motion-modules) and use it with `configs/inference/inference-v2.yaml`. Example:
```
python -m scripts.animate --config configs/prompts/v2/5-RealisticVision.yaml
```
Here is a qualitative comparison between `mm_sd_v15.ckpt` (left) and `mm_sd_v15_v2.ckpt` (right):
<table class="center">
<tr>
<td><img src="__assets__/animations/compare/old_0.gif"></td>
<td><img src="__assets__/animations/compare/new_0.gif"></td>
<td><img src="__assets__/animations/compare/old_1.gif"></td>
<td><img src="__assets__/animations/compare/new_1.gif"></td>
<td><img src="__assets__/animations/compare/old_2.gif"></td>
<td><img src="__assets__/animations/compare/new_2.gif"></td>
<td><img src="__assets__/animations/compare/old_3.gif"></td>
<td><img src="__assets__/animations/compare/new_3.gif"></td>
</tr>
</table>
## [2023.07] AnimateDiff v1
<details open>
<summary>AnimateDiff v1 Model Zoo</summary>
| Name | HuggingFace | Parameter | Storage Space |
|-----------------|------------------------------------------------------------------------------|-----------|---------------|
| mm_sd_v14.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v14.ckpt) | 417 M | 1.6 GB |
| mm_sd_v15.ckpt | [Link](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15.ckpt) | 417 M | 1.6 GB |
</details>
### Quick Demos
<table class="center">
<tr>
<td><img src="__assets__/animations/model_01/01.gif"></td>
<td><img src="__assets__/animations/model_01/02.gif"></td>
<td><img src="__assets__/animations/model_01/03.gif"></td>
<td><img src="__assets__/animations/model_01/04.gif"></td>
</tr>
</table>
<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/30240/toonyou">ToonYou</a></p>
<table>
<tr>
<td><img src="__assets__/animations/model_03/01.gif"></td>
<td><img src="__assets__/animations/model_03/02.gif"></td>
<td><img src="__assets__/animations/model_03/03.gif"></td>
<td><img src="__assets__/animations/model_03/04.gif"></td>
</tr>
</table>
<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/4201/realistic-vision-v20">Realistic Vision V2.0</a></p>
### Inference
Here we provide several demo inference scripts. The corresponding AnimateDiff modules and community models need to be downloaded in advance. See [ANIMATEDIFF](__assets__/docs/animatediff.md) for detailed setup.
```
python -m scripts.animate --config configs/prompts/1-ToonYou.yaml
python -m scripts.animate --config configs/prompts/3-RcnzCartoon.yaml
```
## Community Contributions
User Interface developed by community:
- A1111 Extension [sd-webui-animatediff](https://github.com/continue-revolution/sd-webui-animatediff) (by [@continue-revolution](https://github.com/continue-revolution))
- ComfyUI Extension [ComfyUI-AnimateDiff-Evolved](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) (by [@Kosinkadink](https://github.com/Kosinkadink))
- Google Colab: [Colab](https://colab.research.google.com/github/camenduru/AnimateDiff-colab/blob/main/AnimateDiff_colab.ipynb) (by [@camenduru](https://github.com/camenduru))
## Gradio Demo
We created a Gradio demo to make AnimateDiff easier to use. To launch the demo, please run the following commands:
```
conda activate animatediff
python app.py
```
By default, the demo will run at `localhost:7860`.
<br><img src="__assets__/figs/gradio.jpg" style="width: 50em; margin-top: 1em">
## Common Issues
<details>
<summary>Installation</summary>
Please ensure the installation of [xformer](https://github.com/facebookresearch/xformers) that is applied to reduce the inference memory.
</details>
<details>
<summary>Various resolution or number of frames</summary>
Currently, we recommend users to generate animation with 16 frames and 512 resolution that are aligned with our training settings. Notably, various resolution/frames may affect the quality more or less.
</details>
<details>
<summary>How to use it without any coding</summary>
1) Get lora models: train lora model with [A1111](https://github.com/continue-revolution/sd-webui-animatediff) based on a collection of your own favorite images (e.g., tutorials [English](https://www.youtube.com/watch?v=mfaqqL5yOO4), [Japanese](https://www.youtube.com/watch?v=N1tXVR9lplM), [Chinese](https://www.bilibili.com/video/BV1fs4y1x7p2/))
or download Lora models from [Civitai](https://civitai.com/).
2) Animate lora models: using gradio interface or A1111
(e.g., tutorials [English](https://github.com/continue-revolution/sd-webui-animatediff), [Japanese](https://www.youtube.com/watch?v=zss3xbtvOWw), [Chinese](https://941ai.com/sd-animatediff-webui-1203.html))
3) Be creative togther with other techniques, such as, super resolution, frame interpolation, music generation, etc.
</details>
<details>
<summary>Animating a given image</summary>
We totally agree that animating a given image is an appealing feature, which we would try to support officially in future. For now, you may enjoy other efforts from the [talesofai](https://github.com/talesofai/AnimateDiff).
</details>
<details>
<summary>Contributions from community</summary>
Contributions are always welcome!! The <code>dev</code> branch is for community contributions. As for the main branch, we would like to align it with the original technical report :)
</details>
## Training and inference
Please refer to [ANIMATEDIFF](./__assets__/docs/animatediff.md) for the detailed setup.
<!-- ## Gallery -->
<!-- We collect several generated results in [GALLERY](./__assets__/docs/gallery.md). -->
## BibTeX
```
@article{guo2023animatediff,
title={AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning},
author={Guo, Yuwei and Yang, Ceyuan and Rao, Anyi and Wang, Yaohui and Qiao, Yu and Lin, Dahua and Dai, Bo},
journal={arXiv preprint arXiv:2307.04725},
year={2023}
}
@article{guo2023sparsectrl,
title={SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models},
author={Guo, Yuwei and Yang, Ceyuan and Rao, Anyi and Agrawala, Maneesh and Lin, Dahua and Dai, Bo},
journal={arXiv preprint arXiv:2311.16933},
year={2023}
}
```
## Disclaimer
This project is released for academic use. We disclaim responsibility for user-generated content. Users are solely liable for their actions. The project contributors are not legally affiliated with, nor accountable for, users' behaviors. Use the generative model responsibly, adhering to ethical and legal standards.
## Contact Us
**Yuwei Guo**: [guoyuwei@pjlab.org.cn](mailto:guoyuwei@pjlab.org.cn)
**Ceyuan Yang**: [yangceyuan@pjlab.org.cn](mailto:yangceyuan@pjlab.org.cn)
**Bo Dai**: [daibo@pjlab.org.cn](mailto:daibo@pjlab.org.cn)
## Acknowledgements
Codebase built upon [Tune-a-Video](https://github.com/showlab/Tune-A-Video).
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