Stable Diffusion入门教程，1小时入门AI绘画

The speaker discusses their recent exploration into AI painting, focusing on two prominent tools: MidJourney and Stable Diffusion. They highlight the ease of use and cost of MidJourney, contrasting it with the open-source, free, yet more complex Stable Diffusion. The speaker shares their experience learning Stable Diffusion and introduces an hour-long tutorial aimed at getting beginners started with the tool. They proceed to explain the hardware requirements for running Stable Diffusion, emphasizing the importance of a powerful GPU from NVIDIA and a minimum of 16GB RAM. The speaker also advises on the necessary disk space and shares their personal电脑配置, highlighting the speed at which their setup can generate images.

The speaker guides the audience through the process of downloading and installing the Stable Diffusion launcher, using a Windows computer as an example. They explain how to find the installation package on Bilibili and extract it. The speaker then walks through the steps of installing the launcher's dependencies and running the application. They also demonstrate how to fix common issues, such as the browser not opening the WebUI automatically, by manually entering the URL. The speaker emphasizes the importance of downloading and installing models for Stable Diffusion, recommending specific websites and models for beginners.

The speaker delves into the concept of models in Stable Diffusion, explaining that they define the 'painting style' of the generated images. They discuss where to download models, how to install them, and how to switch between them within the WebUI. The speaker recommends specific models for different styles and provides detailed instructions on placing the downloaded models in the correct directory. They also touch on the impact of models on the generated images, using examples to illustrate the difference in styles.

The speaker explains the critical role of prompts (known as '提示词' in Chinese) in Stable Diffusion, detailing how they guide the generation of images. They cover the use of positive and negative prompts to include or exclude specific elements from the images. The speaker also discusses the syntax of prompts, including the use of parentheses and colons to adjust the weight of certain words. They introduce the concept of prompt plugins and demonstrate how to use them to enhance the image generation process.

The speaker continues the discussion on prompts, focusing on advanced techniques and additional features within the Stable Diffusion interface. They explain the use of universal prompts, the importance of prompt syntax, and the functionality of prompt plugins. The speaker also introduces the concept of 'Lora' as a tool to enhance the precision of image generation when words alone are not sufficient. They provide a brief overview of how Lora works and its significance in achieving specific styles.

The speaker elaborates on the use of Lora, a feature that allows for consistent styling in generated images. They demonstrate how to download and install Lora models and integrate them into the Stable Diffusion workflow. The speaker also explains the importance of using a compatible base model with Lora and the concept of 'trigger words' that activate specific features within a Lora model. They discuss the impact of Lora weight on the final image and the process of setting the correct cover image for Lora models.

The speaker introduces VAE (Variational Autoencoder) as a tool for colorization and filtering in Stable Diffusion. They explain the difference between using VAE and not using it, showcasing the enhanced vibrancy and color depth that VAE provides. The speaker guides the audience on how to download VAE models and integrate them into the system. They also discuss the recommendations from model authors regarding the use of VAE and how to select the appropriate VAE model based on the base model in use.

The speaker covers various features and parameters in Stable Diffusion that can influence the image generation process. They discuss the CLIP termination layer, iteration steps, and the adoption method, providing practical advice on the optimal settings for each. The speaker also explains the high-resolution repair feature and how to use it effectively to increase image quality without causing image degradation. They touch on the importance of the prompt steering coefficient and random seed number in achieving desired outcomes.

The speaker concludes the tutorial by summarizing the key points covered and offering final tips for using Stable Diffusion effectively. They demonstrate how to save and download generated images and emphasize the importance of experimenting with different settings to achieve the desired results. The speaker also mentions that more advanced topics, such as Controlnet, will be covered in future进阶课程 (advanced courses). They sign off, indicating that the hour-long入门课程 (introductory course) has come to an end.