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# AnimateDiff: training and inference setup
## Setups for Inference
### Prepare Environment
***We updated our inference code with xformers and a sequential decoding trick. Now AnimateDiff takes only ~12GB VRAM to inference, and run on a single RTX3090 !!***
```
git clone https://github.com/guoyww/AnimateDiff.git
cd AnimateDiff
conda env create -f environment.yaml
conda activate animatediff
```
### Download Base T2I & Motion Module Checkpoints
We provide two versions of our Motion Module, which are trained on stable-diffusion-v1-4 and finetuned on v1-5 seperately.
It's recommanded to try both of them for best results.
```
git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 models/StableDiffusion/
bash download_bashscripts/0-MotionModule.sh
```
You may also directly download the motion module checkpoints from [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), then put them in `models/Motion_Module/` folder.
### Prepare Personalize T2I
Here we provide inference configs for 6 demo T2I on CivitAI.
You may run the following bash scripts to download these checkpoints.
```
bash download_bashscripts/1-ToonYou.sh
bash download_bashscripts/2-Lyriel.sh
bash download_bashscripts/3-RcnzCartoon.sh
bash download_bashscripts/4-MajicMix.sh
bash download_bashscripts/5-RealisticVision.sh
bash download_bashscripts/6-Tusun.sh
bash download_bashscripts/7-FilmVelvia.sh
bash download_bashscripts/8-GhibliBackground.sh
```
### Inference
After downloading the above peronalized T2I checkpoints, run the following commands to generate animations. The results will automatically be saved to `samples/` folder.
```
python -m scripts.animate --config configs/prompts/1-ToonYou.yaml
python -m scripts.animate --config configs/prompts/2-Lyriel.yaml
python -m scripts.animate --config configs/prompts/3-RcnzCartoon.yaml
python -m scripts.animate --config configs/prompts/4-MajicMix.yaml
python -m scripts.animate --config configs/prompts/5-RealisticVision.yaml
python -m scripts.animate --config configs/prompts/6-Tusun.yaml
python -m scripts.animate --config configs/prompts/7-FilmVelvia.yaml
python -m scripts.animate --config configs/prompts/8-GhibliBackground.yaml
```
To generate animations with a new DreamBooth/LoRA model, you may create a new config `.yaml` file in the following format:
```
- inference_config: "[path to motion module config file]"
motion_module:
- "models/Motion_Module/mm_sd_v14.ckpt"
- "models/Motion_Module/mm_sd_v15.ckpt"
motion_module_lora_configs:
- path: "[path to MotionLoRA model]"
alpha: 1.0
- ...
dreambooth_path: "[path to your DreamBooth model .safetensors file]"
lora_model_path: "[path to your LoRA model .safetensors file, leave it empty string if not needed]"
steps: 25
guidance_scale: 7.5
prompt:
- "[positive prompt]"
n_prompt:
- "[negative prompt]"
```
Then run the following commands:
```
python -m scripts.animate --config [path to the config file]
```
## Steps for Training
### Dataset
@@ -92,9 +8,9 @@ Note that our examplar training script requires all the videos to be saved in a
After dataset preparations, update the below data paths in the config `.yaml` files in `configs/training/` folder:
```
train_data:
csv_path: [Replace with .csv Annotation File Path]
csv_path: [Replace with .csv Annotation File Path]
video_folder: [Replace with Video Folder Path]
sample_size: 256
sample_size: 256
```
Other training parameters (lr, epochs, validation settings, etc.) are also included in the config files.