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Running this command with the prompt 'a photorealistic vaporwave image of a lizard riding a snowboard through space' outputs the following image: Where you replace YOUR-PROMPT-HERE with the caption for which to generate an image (leaving the quotation marks).

If you run into a RuntimeError: CUDA out of memory error, try cutting down the size of the image (and make sure you are only sampling one image with -n_samples 1).

Run cat /var/log/kern.log to see useful logs.

If execution suddenly stops and killed is printed to the terminal, you are likely running into an out-of-memory error.

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If you receive an ImportError, you may have to run sudo apt-get install libsm6 libxrender1 libfontconfig1.To generate an image, run the following command: python scripts/txt2img.py -prompt 'YOUR-PROMPT-HERE' -plms -ckpt sd-v1-4.ckpt -skip_grid -n_samples 1 Make sure you are in the proper environment by executing the command conda activate ldm. To generate images with Stable Diffusion, open a terminal and navigate into the stable-diffusion directory.

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How to Generate Images with Stable Diffusion That's all of the setup we need to start using Stable Diffusion! Read on to learn how to generate images using the model.

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Download the weights with the following command: curl > sd-v1-4.ckpt Higher version numbers have been trained on more data and are, in general, better performing than lower version numbers. Several Stable Diffusion checkpoint versions have been released. If you haven't already read and accepted the Stable Diffusion license, make sure to do so now. Now that we are working in the appropriate environment to use Stable Diffusion, we need to download the weights we'll need to run it. Execute the below commands to create and activate this environment, named ldm conda env create -f environment.yamlĬonda activate ldm Step 5: Download Stable Diffusion Weights Next up we need to create the conda environment that houses all of the packages we'll need to run Stable Diffusion.

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Make sure to read an accept the Stable Diffusion license before cloning the repository. If you do not have git, you will need to install it with sudo apt install git. In the terminal, execute the following commands: git clone Now we need to clone the Stable Diffusion repository. Step 3: Clone the Stable Diffusion Repository Finally, close the terminal and then open a new one where you want to install Stable Diffusion. Next, press Enter to confirm the installation location, and then type 'yes' when asked if the installer should initialize Miniconda. Hold down Enter to get through license and then type 'yes' to continue when prompted. For Python3.8, you can download and run the installer with the following commands: wget īash Miniconda3-p圓8_4.12.0-Linux-x86_64.sh Otherwise, go to the conda website and download and run the appropriate Miniconda installer for your version of Python and operating system. If a conda version is returned, move on to the next step. Next, we need to ensure the package/environment manager conda is installed.

Yes | sudo apt-get install python3.8 Step 2: Install Miniconda

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Otherwise, install Python with sudo apt-get update If a Python version is returned, continue on to the next step. Step 1: Install Pythonįirst, check that Python is installed on your system by typing python -version into the terminal. A GPU is required to use Stable Diffusion. You will need a UNIX-based operating system to follow along with this tutorial, so if you have a Windows machine, consider using a virtual machine or WSL2. Stable Diffusion in Colab How to Install Stable Diffusion Note that you will need Colab Pro in order to generate new images given that the free version of Colab has slightly too little VRAM for sampling. Let's dive in! Use Stable Diffusion in Colabīefore we look at how to install and run Stable Diffusion locally, you can check out the below Colab notebook in order to see how to use Stable Diffusion non-locally. This article will show you how to install and run Stable Diffusion so you can get started generating your own images. Just this Monday, Stable Diffusion checkpoints were released for the first time, meaning that, right now, you can generate images like the ones below with just a few words and a few minutes time. Released earlier this month, Stable Diffusion promises to democratize text-conditional image generation by being efficient enough to run on consumer-grade GPUs. Following in the footsteps of DALL-E 2 and Imagen, the new Deep Learning model Stable Diffusionsignifies a quantum leap forward in the text-to-image domain.

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