# A beta-version of motion module for SDXL
Now you can generate high-resolution videos on SDXL **with/without** personalized models. Checkpoint with better quality would be available soon. Stay tuned.
## Somethings Important
- Generate videos with high-resolution (we provide recommended ones) as SDXL usually leads to worse quality for low-resolution images.
- Follow and slightly adjust the hyperparameters (e.g., #sampling steps, #guidance scale) of various personalized SDXL since these models are carefully tuned to various extent.
## Model Zoo
Motion Modules
| Name | Parameter | Storage Space |
|----------------------|-----------|---------------|
| mm_sdxl_v10_beta.ckpt | 238 M | 0.9 GB |
Recommended Resolution
| Resolution | Aspect Ratio |
|----------------------|-----------|
| 768x1344 | 9:16 |
| 832x1216 | 2:3 |
| 1024x1024 | 1:1 |
| 1216x832 | 3:2 |
| 1344x768 | 16:9 |
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Model:DynaVision
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Model:DreamShaper
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Model:DeepBlue
## Inference Example Inference at recommended resolution of 16 frames usually requires ~13GB VRAM. ### Step-1: Prepare Environment ``` git clone https://github.com/guoyww/AnimateDiff.git cd AnimateDiff git checkout sdxl conda env create -f environment.yaml conda activate animatediff_xl ``` ### Step-2: Download Base T2I & Motion Module Checkpoints We provide a beta version of motion module on SDXL. You can download the base model of SDXL 1.0 and Motion Module following instructions below. ``` git lfs install git clone https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 models/StableDiffusion/ bash download_bashscripts/0-MotionModule.sh ``` You may also directly download the motion module checkpoints from [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), then put them in `models/Motion_Module/` folder. ### Step-3: Download Personalized SDXL (you can skip this if generating videos on the original SDXL) You may run the following bash scripts to download the LoRA checkpoint from CivitAI. ``` bash download_bashscripts/1-DynaVision.sh bash download_bashscripts/2-DreamShaper.sh bash download_bashscripts/3-DeepBlue.sh ``` ### Step-4: Generate Videos Run the following commands to generate videos of **original SDXL**. ``` python -m scripts.animate --exp_config configs/prompts/1-original_sdxl.yaml --H 1024 --W 1024 --L 16 --xformers ``` Run the following commands to generate videos of **personalized SDXL**. DO NOT skip Step-3. ``` python -m scripts.animate --config configs/prompts/2-DynaVision.yaml --H 1024 --W 1024 --L 16 --xformers python -m scripts.animate --config configs/prompts/3-DreamShaper.yaml --H 1024 --W 1024 --L 16 --xformers python -m scripts.animate --config configs/prompts/4-DeepBlue.yaml --H 1024 --W 1024 --L 16 --xformers ``` The results will automatically be saved to `samples/` folder. ## Customized Inference To generate videos with a new Checkpoint/LoRA model, you may create a new config `.yaml` file in the following format: ``` motion_module_path: "models/Motion_Module/mm_sdxl_v10_beta.ckpt" # Specify the Motion Module # We support 3 types of T2I models. # 1. Checkpoint: a safetensors model contains UNet, Text_Encoders, VAE. # 2. LoRA: a safetensors model contains only the LoRA modules. # 3. You can convert the Checkpoint into a folder with the same structure as SDXL_1.0 base model. ckpt_path: "YOUR_CKPT_PATH" # path to the checkpoint type model from CivitAI. lora_path: "YOUR_LORA_PATH" # path to the LORA type model from CivitAI. base_model_path: "YOUR_BASE_MODEL_PATH" # path to the folder converted from a checkpoint steps: 50 guidance_scale: 8.5 seed: -1 # You can specify seed for each prompt. prompt: - "[positive prompt]" n_prompt: - "[negative prompt]" ``` Then run the following commands. ``` python -m scripts.animate --exp_config [path to the personalized config] --L [video frames] --H [Height of the videos] --W [Width of the videos] --xformers ```