Update README.md
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README.md
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README.md
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@ -19,38 +19,26 @@ Latent blending allows you to generate smooth video transitions between two prom
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```commandline
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pip install -r requirements.txt
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```
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#### Models
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#### Download Models from Huggingface
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[Download the Stable Diffusion 2.0 Standard Model](https://huggingface.co/stabilityai/stable-diffusion-2)
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[Download the Stable Diffusion 2.0 Inpainting Model (optional)](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting)
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#### xformers efficient attention [(copied from stability)](https://github.com/Stability-AI/stablediffusion)
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For more efficiency and speed on GPUs,
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we highly recommended installing the [xformers](https://github.com/facebookresearch/xformers)
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library.
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Tested on A100 with CUDA 11.4.
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Installation needs a somewhat recent version of nvcc and gcc/g++, obtain those, e.g., via
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```commandline
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export CUDA_HOME=/usr/local/cuda-11.4
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conda install -c nvidia/label/cuda-11.4.0 cuda-nvcc
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conda install -c conda-forge gcc
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conda install -c conda-forge gxx_linux-64=9.5.0
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```
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Then, run the following (compiling takes up to 30 min).
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#### Install [Xformers](https://github.com/facebookresearch/xformers)
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With xformers, stable diffusion 2 will run much faster. The recommended way of installation is via the supplied binaries (Linux).
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```commandline
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cd ..
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git clone https://github.com/facebookresearch/xformers.git
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cd xformers
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git submodule update --init --recursive
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pip install -r requirements.txt
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pip install -e .
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cd ../stable-diffusion
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conda install xformers -c xformers/label/dev
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```
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Alternatively, you can build it from source:
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```commandline
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# (Optional) Makes the build much faster
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pip install ninja
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# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types
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pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
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# (this can take dozens of minutes)
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```
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Upon successful installation, the code will automatically default to [memory efficient attention](https://github.com/facebookresearch/xformers)
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for the self- and cross-attention layers in the U-Net and autoencoder.
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# How does it work
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![](animation.gif)
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