[042] 小白都能看懂的 Stable Diffusion 本地部署教程 | 开源工程本地部署就这几步

The speaker, referred to as '牛哥', introduces the topic of the video, which is about deploying Stable Diffusion (SD) locally in a way that is easy for beginners to understand. The speaker mentions that they have recently updated the official code for SD and encountered compatibility issues, which they have resolved. They aim to share these solutions as they believe others might face similar problems. The video promises a step-by-step guide that is comprehensive and simplified. The speaker also references a previous video about 'stable deposition' and clarifies that today's focus is on setting up SD from scratch. They mention that the official GitHub repository will be referenced, which is a community for sharing code, and reassure viewers that they will provide organized documentation and downloads on their official website.

牛哥 provides a detailed guide on how to deploy Stable Diffusion, starting from cloning the code from GitHub to setting up the environment. They explain the process of using Git commands to clone the repository and navigate through directories using the terminal. The speaker also touches on the importance of having the right software environment, mentioning Python and Git. They provide instructions on how to install necessary software and set up the environment using a one-click package available on their website. The summary also includes instructions on how to use Git to clone the SD repository and prepare the environment for deployment.

In this section, 牛哥 delves into setting up the development environment for SD. They discuss the need for Python and pip, which is a package manager for Python. They explain how to check the Python version and pip version on one's system and provide solutions for any missing environment. The speaker also talks about creating a virtual environment using Python's venv module, which is important for managing dependencies locally. They guide viewers through the process of activating the virtual environment and installing the necessary packages using pip.

牛哥 explains the importance of models in the Stable Diffusion framework. They mention that models can be downloaded from official repositories like Hugging Face or from websites like CIVIT AI, which offers a variety of models for different styles of images. The speaker provides instructions on how to install models and set up the environment to work with SD. They also discuss potential issues with downloading models due to network restrictions and suggest using a proxy to overcome these issues.

This paragraph covers the final steps in deploying Stable Diffusion. 牛哥 talks about running the application and installing necessary dependencies from a requirements.txt file. They mention the possibility of network issues affecting the installation process and suggest using a mirror from a domestic source to speed up the process. The speaker also discusses the process of loading models into the SD framework and initiating the generation of images using the models. They demonstrate the process and show an example of a generated image.

牛哥 demonstrates how to use the Stable Diffusion model to generate an image. They show the process of selecting a model and entering keywords to generate an image. The speaker also discusses the potential for downloading additional algorithms for image processing, such as image upscaling, and mentions that these algorithms can be integrated into the SD framework. They conclude the demonstration by showing the successful generation of an image using the SD model.

In the concluding part of the script, 牛哥 summarizes the process of deploying Stable Diffusion from scratch and assures viewers that following the steps carefully will result in a successful setup. They mention that the detailed process will be available on their official website and encourage viewers to visit it for more resources. The speaker also invites viewers to support their work through likes, follows, and donations if they find the content beneficial. They end the video by inviting viewers to leave comments if they encounter any issues.