Install and Run Stable Diffusion 2 on Ubuntu
Stable Diffusion 2 is an advanced AI tool that has garnered attention for its impressive capabilities in generating high-quality content. To harness its potential, it is crucial to have a properly set up environment on your Ubuntu system. This guide will walk you through the necessary prerequisites and the installation process for Stable Diffusion 2, ensuring a seamless experience.
Before starting the installation process, ensure that you have the following components installed and set up on your system:
- Python 3.10+ with Conda: The latest version of Python is essential for compatibility with the required libraries and packages. Conda serves as a package manager to streamline the installation of necessary dependencies.
- Transformers and XFormers: These libraries provide state-of-the-art models and tools for natural language processing and machine learning tasks.
- PyTorch (Torch): A popular open-source machine learning library that accelerates the development of AI applications.
- Diffusers: A package specifically designed for Stable Diffusion 2.
- Hardware Requirements: Ensure that you have a server with at least 20GB of storage and a 10GB+ GPU to handle the processing demands of Stable Diffusion 2.
conda create -n stable_diffusion python=3.10
conda activate stable_diffusion
conda install transformers xformers torch diffusers -c pytorch -c huggingface
#If you want to use pip
pip install transformers xformers torch diffusers
Run Stable Diffusion 2
from diffusers import DiffusionPipeline
from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
# Workaround for not accepting attention shape using VAE for Flash Attention
Generate Stable Diffusion Images
pipe("An image of a squirrel in Picasso style").images
Frequent Warnings or Errors
Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment.To avoid above warning do following...
pip install accelerate
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