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Preface This section introduces the sampling steps in sdwebui. Sampling Steps The previous section covered samplers: https://blog.vincentqiao.com/sdsampler Sampling steps refer to the number of noise addition and denoising iterations during image generation. Prompt (Chinese): A boy sitting on a chair, upper body, close to the window, blue shirt, black hair, wearing glasses, facing
Preface After installing sdwebui locally and getting familiar with the interface, you might feel lost about how to actually generate images. You can start by setting a few small goals: 1. Find an IP, anime character, or celebrity you like, and try generating images of them. 2. Take an ordinary photo of yourself and try swapping it into a cool scene.
Preface: The previous chapters covered SD installation. This chapter introduces the sdwebui interface. Introduction: From top to bottom it includes: Model selection area for choosing checkpoint models, main function tabs for switching between features like txt2img, img2img, and PNG info, prompt area, generation parameters area, and image preview area.
Preface: This section introduces the sdwebui file structure. https://github.com/AUTOMATIC1111/stablediffusionwebui. File Structure: configs - configuration files, embeddings - embedding files, extensions-builtin - built-in extensions, extensions - user extensions.
Preface: The previous section covered how to install Stable Diffusion on Mac. The Linux installation is quite similar. This section introduces how to install SD on Linux.
Preface sdwebui has many startup arguments. This article analyzes them one by one. Startup Arguments The argument parsing is located in modules/cmd_args.py. -f: Allow root user to start. --update-all-extensions: Update all extensions on startup. --skip-python-version-check: Skip Python version check.
Preface: Currently there are several common approaches for AIGC image generation: 1. SD image generation, 2. Midjourney image generation, 3. Other large model image generation. This section covers SD introduction and Mac installation.
Preface: After developing a local app with Electron, local logging is essential. It helps capture user actions and locate bugs. Local logs differ from real-time analytics — they are larger, more detailed, and not all information is suitable for reporting. Similarities and Differences with Node.js Logging: Writing local logs in Electron is similar to Node.js.
Preface: This article introduces how to register keyboard shortcuts in an Electron app. Main Process: Registering shortcuts in the main process is straightforward — just make sure to do it after app ready. Common key mappings: https://www.electronjs.org/docs/latest/api/accelerator. Also remember to unregister all shortcuts when the app quits.
Preface: After developing a desktop app with Electron, the next question is how to update subsequent versions. This article introduces several update strategies. Electron Architecture: An Electron desktop app consists of several parts: the Electron runtime itself, the main process (Node.js and non-Node.js parts), and the renderer process.
Preface: Many people have multiple WeChat accounts. On mobile, you can use two phones or Android multi-app features. This article explains how to run multiple WeChat clients on Mac by creating aliases to the WeChat executable inside the app bundle.
Preface There are many Docker images for nginx. This article packages a business nginx into a Docker image. Choosing the nginx version alpine Alpine is a minimal Linux distribution, with an image size of only 7.73M. nginx also has an alpine-based version. This article uses the alpine-based version. stable ngi