# What is latent blending? Latent blending allows you to generate smooth video transitions between two prompts. It is based on (stable diffusion 2.0)[https://stability.ai/blog/stable-diffusion-v2-release] and remixes the latent reprensetation using spherical linear interpolations. This results in imperceptible transitions, where one image slowly turns into another one. # Example 1: simple transition (mp4), code # Example 2: inpainting transition (mp4), code # Example 3: concatenated transition (mp4), code # Relevant parameters # Installation #### Packages ```commandline pip install -r requirements.txt ``` #### Models [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). ```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 ``` 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 ![](animation.gif) what makes a transition a good transition? * absence of movement * every frame looks like a credible photo