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update readme

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Yuwei Guo
2023-07-12 16:57:50 +08:00
parent 05fdf470ad
commit 3742e3e524
7 changed files with 33 additions and 16 deletions
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README.md
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@@ -31,12 +31,10 @@ Our approach takes around 60 GB GPU memory to inference. NVIDIA A100 is recomman
```
git clone https://github.com/guoyww/animatediff.git
cd animatediff
cd AnimateDiff
conda create -n animatediff python=3.8
conda env create -f environment.yaml
conda activate animatediff
pip install -r requirements.txt
```
### Download Base T2I & Motion Module Checkpoints
@@ -65,7 +63,7 @@ bash download_bashscripts/8-GhibliBackground.sh
```
### Inference
After downloading the above peronalized T2I checkpoints, run the following commands to generate animations.
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
@@ -100,7 +98,16 @@ python -m scripts.animate --prompt configs/prompts/lora.yaml
``` -->
## Gallery
Here we demonstrate several best results we got in previous experiments.
Here we demonstrate several best results we found in our experiments or generated by other artists.
<table class="center">
<tr>
<td><img src="__assets__/animations/model_07/01.gif"></td>
<td><img src="__assets__/animations/model_07/02.gif"></td>
<td><img src="__assets__/animations/model_07/03.gif"></td>
<td><img src="__assets__/animations/model_07/04.gif"></td>
</tr>
</table>
<p style="margin-left: 2em; margin-top: -1em">Model：<a href="https://civitai.com/models/107295?modelVersionId=115371">holding_sign</a> (samples are contributed by CivitAI artists)</p>
<table class="center">
<tr>