diff --git a/README.md b/README.md index 6da5ad3..fe361d2 100644 --- a/README.md +++ b/README.md @@ -17,40 +17,28 @@ Latent blending allows you to generate smooth video transitions between two prom # Installation #### Packages ```commandline - pip install -r requirements.txt +pip install -r requirements.txt ``` -#### Models +#### Download Models from Huggingface [Download the Stable Diffusion 2.0 Standard Model](https://huggingface.co/stabilityai/stable-diffusion-2) [Download the Stable Diffusion 2.0 Inpainting Model (optional)](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) -#### xformers efficient attention [(copied from stability)](https://github.com/Stability-AI/stablediffusion) -For more efficiency and speed on GPUs, -we highly recommended installing the [xformers](https://github.com/facebookresearch/xformers) -library. - -Tested on A100 with CUDA 11.4. -Installation needs a somewhat recent version of nvcc and gcc/g++, obtain those, e.g., via -```commandline -export CUDA_HOME=/usr/local/cuda-11.4 -conda install -c nvidia/label/cuda-11.4.0 cuda-nvcc -conda install -c conda-forge gcc -conda install -c conda-forge gxx_linux-64=9.5.0 -``` - -Then, run the following (compiling takes up to 30 min). +#### Install [Xformers](https://github.com/facebookresearch/xformers) +With xformers, stable diffusion 2 will run much faster. The recommended way of installation is via the supplied binaries (Linux). ```commandline -cd .. -git clone https://github.com/facebookresearch/xformers.git -cd xformers -git submodule update --init --recursive -pip install -r requirements.txt -pip install -e . -cd ../stable-diffusion +conda install xformers -c xformers/label/dev +``` + +Alternatively, you can build it from source: +```commandline +# (Optional) Makes the build much faster +pip install ninja +# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types +pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers +# (this can take dozens of minutes) ``` -Upon successful installation, the code will automatically default to [memory efficient attention](https://github.com/facebookresearch/xformers) -for the self- and cross-attention layers in the U-Net and autoencoder. # How does it work 