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@@ -31,12 +31,10 @@ Our approach takes around 60 GB GPU memory to inference. NVIDIA A100 is recomman
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```
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git clone https://github.com/guoyww/animatediff.git
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-cd animatediff
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+cd AnimateDiff
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-conda create -n animatediff python=3.8
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+conda env create -f environment.yaml
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conda activate animatediff
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-
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-pip install -r requirements.txt
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```
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### Download Base T2I & Motion Module Checkpoints
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@@ -65,7 +63,7 @@ bash download_bashscripts/8-GhibliBackground.sh
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```
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### Inference
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-After downloading the above peronalized T2I checkpoints, run the following commands to generate animations.
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+After downloading the above peronalized T2I checkpoints, run the following commands to generate animations. The results will automatically be saved to `samples/` folder.
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```
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python -m scripts.animate --config configs/prompts/1-ToonYou.yaml
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python -m scripts.animate --config configs/prompts/2-Lyriel.yaml
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@@ -100,7 +98,16 @@ python -m scripts.animate --prompt configs/prompts/lora.yaml
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``` -->
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## Gallery
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-Here we demonstrate several best results we got in previous experiments.
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+Here we demonstrate several best results we found in our experiments or generated by other artists.
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+<table class="center">
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+ <tr>
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+ <td><img src="__assets__/animations/model_07/01.gif"></td>
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+ <td><img src="__assets__/animations/model_07/02.gif"></td>
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+ <td><img src="__assets__/animations/model_07/03.gif"></td>
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+ <td><img src="__assets__/animations/model_07/04.gif"></td>
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+ </tr>
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+</table>
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+<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>
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<table class="center">
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<tr>
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Yuwei Guo
