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Yuwei Guo
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update readme
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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
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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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pip install -r requirements.txt
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```
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### Download Base T2I & Motion Module Checkpoints
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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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``` -->
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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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environment.yaml

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name: animatediff
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channels:
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- pytorch
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- xformers
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dependencies:
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- python=3.10
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- pytorch==1.12.1
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- torchvision==0.13.1
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- torchaudio==0.12.1
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- cudatoolkit=11.3
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- xformers
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- pip
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- pip:
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- diffusers[torch]==0.11.1
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- transformers==4.25.1
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- imageio==2.27.0
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- gdown
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- einops
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- omegaconf
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- safetensors

requirements.txt

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