画像生成AIにモデルを追加する方法【AUTOMATIC1111 Stable Diffusion web UI】

The speaker introduces the concept of models in the context of AI image generation using Stable Diffusion. They explain that models are data files of trained neural networks used to create images. The absence of a model prevents image creation. The speaker discusses the impact of different models and training iterations on the output images, highlighting that more training does not necessarily mean better results. They guide viewers on how to find and select models within the Stable Diffusion WEBUI and mention the existence of various model versions on GitHub, each with different training iterations. The speaker also explains the difference between 'checkpoints' and the two file formats for models: 'ckpt' and 'safetensors', with a preference for the latter due to safety and speed. They conclude by demonstrating how to locate and use a specific model called 'Hanging Face' and discuss the file sizes and types available for different use cases.

The speaker proceeds to demonstrate how to update the Stable Diffusion model to version 2.1, which involves downloading a larger file due to the increased image resolution capability. They walk through the process of moving the downloaded model file into the appropriate folder for the WEBUI to recognize it. Once the new model is loaded without errors, they experiment with generating an image using the updated model. The speaker also explores additional models available on the 'Hanging Face' site and another called 'Sibyl AI', discussing the benefits of trying different models and the potential licensing considerations. They mention the use of 'vae' files for image enhancement and guide viewers on how to download and apply these files for better image results. The video concludes with a tip on managing multiple models by using thumbnails for quick identification and a reminder of the large storage space required for model files, suggesting the need for SSD expansion.