support sdxl

__assets__/docs/animatediff.md

@@ -1,112 +0,0 @@
-# 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:
-```
-NewModel:
-  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
-Before training, download the videos files and the `.csv` annotations of [WebVid10M](https://maxbain.com/webvid-dataset/) to the local mechine.
-Note that our examplar training script requires all the videos to be saved in a single folder. You may change this by modifying `animatediff/data/dataset.py`.
-
-### Configuration
-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]
-  video_folder: [Replace with Video Folder Path]
-  sample_size:  256
-```
-Other training parameters (lr, epochs, validation settings, etc.) are also included in the config files.
-
-### Training
-To train motion modules
-```
-torchrun --nnodes=1 --nproc_per_node=1 train.py --config configs/training/training.yaml
-```
-
-To finetune the unet's image layers
-```
-torchrun --nnodes=1 --nproc_per_node=1 train.py --config configs/training/image_finetune.yaml
-```
-

__assets__/docs/gallery.md

@@ -1,93 +0,0 @@
-# Gallery
-Here we demonstrate several best results we found in our experiments.
-
-<table class="center">
-    <tr>
-    <td><img src="../animations/model_01/01.gif"></td>
-    <td><img src="../animations/model_01/02.gif"></td>
-    <td><img src="../animations/model_01/03.gif"></td>
-    <td><img src="../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="../animations/model_02/01.gif"></td>
-    <td><img src="../animations/model_02/02.gif"></td>
-    <td><img src="../animations/model_02/03.gif"></td>
-    <td><img src="../animations/model_02/04.gif"></td>
-    </tr>
-</table>
-<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/4468/counterfeit-v30">Counterfeit V3.0</a></p>
-
-<table>
-    <tr>
-    <td><img src="../animations/model_03/01.gif"></td>
-    <td><img src="../animations/model_03/02.gif"></td>
-    <td><img src="../animations/model_03/03.gif"></td>
-    <td><img src="../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>
-
-<table>
-    <tr>
-    <td><img src="../animations/model_04/01.gif"></td>
-    <td><img src="../animations/model_04/02.gif"></td>
-    <td><img src="../animations/model_04/03.gif"></td>
-    <td><img src="../animations/model_04/04.gif"></td>
-    </tr>
-</table>
-<p style="margin-left: 2em; margin-top: -1em">Model： <a href="https://civitai.com/models/43331/majicmix-realistic">majicMIX Realistic</a></p>
-
-<table>
-    <tr>
-    <td><img src="../animations/model_05/01.gif"></td>
-    <td><img src="../animations/model_05/02.gif"></td>
-    <td><img src="../animations/model_05/03.gif"></td>
-    <td><img src="../animations/model_05/04.gif"></td>
-    </tr>
-</table>
-<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/66347/rcnz-cartoon-3d">RCNZ Cartoon</a></p>
-
-<table>
-    <tr>
-    <td><img src="../animations/model_06/01.gif"></td>
-    <td><img src="../animations/model_06/02.gif"></td>
-    <td><img src="../animations/model_06/03.gif"></td>
-    <td><img src="../animations/model_06/04.gif"></td>
-    </tr>
-</table>
-<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/33208/filmgirl-film-grain-lora-and-loha">FilmVelvia</a></p>
-
-#### Community Cases
-Here are some samples contributed by the community artists. Create a Pull Request if you would like to show your results here😚.
-
-<table>
-    <tr>
-    <td><img src="../animations/model_07/init.jpg"></td>
-    <td><img src="../animations/model_07/01.gif"></td>
-    <td><img src="../animations/model_07/02.gif"></td>
-    <td><img src="../animations/model_07/03.gif"></td>
-    <td><img src="../animations/model_07/04.gif"></td>
-    </tr>
-</table>
-<p style="margin-left: 2em; margin-top: -1em">
-Character Model：<a href="https://civitai.com/models/13237/genshen-impact-yoimiya">Yoimiya</a> 
-(with an initial reference image, see <a href="https://github.com/talesofai/AnimateDiff">WIP fork</a> for the extended implementation.)
-
-
-<table>
-    <tr>
-    <td><img src="../animations/model_08/01.gif"></td>
-    <td><img src="../animations/model_08/02.gif"></td>
-    <td><img src="../animations/model_08/03.gif"></td>
-    <td><img src="../animations/model_08/04.gif"></td>
-    </tr>
-</table>
-<p style="margin-left: 2em; margin-top: -1em">
-Character Model：<a href="https://civitai.com/models/9850/paimon-genshin-impact">Paimon</a>;
-Pose Model：<a href="https://civitai.com/models/107295/or-holdingsign">Hold Sign</a></p>
-
-