how to train any face for any model with embeddings | automatic1111

The paragraph introduces the concept of using stable diffusion for AI image generation, specifically for creating images of celebrities like Charlize Theron from various movies. The speaker shares their process of generating images and their intention to teach viewers how to train an embedding in stable diffusion. They also mention their exploration of different models on Civitai.com and their goal to make the video informative and efficient, avoiding mispronunciations and focusing on sharing useful tricks for embedding quality improvement.

This section delves into the process of gathering images of the person whose face is to be trained. The speaker provides a detailed guide on selecting the right images, avoiding images with obstructions, other people, watermarks, or extreme brightness/darkness. They discuss using Google Images and IMDb to find suitable pictures and the importance of image resolution. The speaker also explains how to handle webp files and the process of converting them to PNG, as well as using Pinterest and Flickr for additional image sources.

The speaker continues with the process of image preparation by discussing upscaling techniques to improve image quality. They share their experiences with different upscaling tools and methods, including using Earthen View for image enhancement. The speaker emphasizes the importance of image resolution and the avoidance of graininess in upscaled images. They also discuss the process of comparing and deleting unsatisfactory images and the goal of achieving a specific image size and quality for training purposes.

In this part, the speaker explains the next step in preparing the images for training, which involves cropping the images to focus on the subject's face and body. They provide instructions on using specific tools to crop images and maintain the correct aspect ratio. The speaker also talks about organizing the images into a training set, including the importance of having a variety of poses and angles to create a comprehensive training data set.

The speaker moves on to the actual process of creating the embedding file, which is crucial for training the AI model. They explain the parameters for creating an embedding, including image size and the number of vectors per token. The speaker references a Reddit article and a GitHub page for more information on these parameters. They also discuss the creation of a hyper network and pre-processing images, providing insights on how to prepare the images for training and the importance of accurate captions for the AI to understand the content of the images.

This section focuses on reviewing the AI-generated captions for the images and adjusting them to ensure they accurately reflect the content of the images. The speaker goes through each caption, making necessary changes to remove any descriptions that are not part of the subject. They emphasize the importance of this step in preventing over-training on incorrect information and ensuring the AI model learns the correct features of the subject.

The speaker begins the training process by setting up the training parameters, including the learning rate and gradient accumulation steps. They share their experiences with different settings and the outcomes they produced. The speaker also explains how to monitor the training process, both through observing the generated images and by using a dispatch file to analyze the training data. They provide tips on how to handle potential issues during training and emphasize the importance of keeping track of the training settings for future reference.

In this part, the speaker evaluates the results of the training process by looking at the generated images and the strength of the embedding. They discuss the signs of over-training and the need to refine the training parameters to achieve better results. The speaker also talks about the process of retraining with adjusted parameters and the importance of testing the embedding with different models to find the best outcome.

The speaker demonstrates the application of the trained embedding by using it to generate images with different models on Civit AI. They explain the process of selecting a model, adjusting the prompts, and adding the trained embedding to generate realistic images of the subject. The speaker also shares their observations on the effectiveness of different models and the importance of prompt engineering in achieving the desired results.

In the concluding part, the speaker wraps up the video by showing the final results of the training process and discussing their plans for future training sessions. They reflect on the lessons learned from the current training and hint at exploring different models and techniques in upcoming videos. The speaker encourages viewers to subscribe and share their thoughts on which celebrity model they should train next.