Yapay zekadan makale özeti
- Kısa
- Ayrıntılı
- Bu video, bir eğitim içeriği olup, konuşmacı Stable Diffusion kullanarak görüntü üretimi ve Deep Face AI teknolojisini kullanarak bu görüntüleri filtreleme yöntemini anlatmaktadır.
- Video, Deep Face AI'nin Stable Diffusion ile birlikte nasıl kullanılacağını adım adım göstermektedir. İçerikte önce sanal ortam oluşturma, Deep Face AI'nin kurulumu ve ardından bir script kullanarak oluşturulan görüntüleri karşılaştırma süreci detaylı olarak anlatılmaktadır. Konuşmacı, kendi eğitim verilerini kullanarak örnek bir uygulama göstermekte ve bu yöntemin manuel olarak en iyi görüntüyü bulmanın yerine daha hızlı ve etkili bir alternatif olduğunu vurgulamaktadır.
- Introduction to Deep Face AI for Image Selection
- Stable Diffusion with generative AI is necessary to obtain high quality images, but manual image selection is time-consuming.
- The video introduces a new method using Deep Face AI to streamline the image selection process.
- The tutorial will guide viewers step-by-step on how to use Deep Face AI to sort and filter images based on similarity to the original training dataset.
- 01:19Additional Resources and Virtual Environment Setup
- This approach is not restricted to training models; you can choose base images and filter any images that most closely resemble them.
- The creator has a collection of organized and easy-to-follow tutorials on GitHub for beginners and advanced image generation techniques.
- To start, create a virtual environment using Python venv command and install Deep Face AI with its dependencies.
- 03:15Training Images and Prompts
- The creator has generated images with repeated backgrounds and clouding, which is not ideal for training.
- The training process used DreamMod settings with positive and negative prompts, resolution, steps, model epoch, and realistic vision.
- The DreamBoot extension with Auto11UI has become very powerful and working well for image generation.
- 05:30Deep Face AI Script Overview
- The script imports libraries, sets paths for original images and generated images, and defines the model to use (FaceNet12).
- It sets a folder where the model will extract detected faces from the base faces.
- The script supports multi-threading to increase speed and allows setting a counter to stop processing early.
- 09:20Running the Script and Results
- The creator demonstrates processing 105 images for a demonstration, renaming all files to make it easier to control.
- The script detects faces, processes images, and displays distance scores between each image to the base images.
- The algorithm sorts images based on distance scores, with the first image having the lowest distance to all base images.
- 12:51Conclusion and Resources
- The script files are posted on the Patreon page, and viewers can download them directly.
- The GitHub repository and Patreon link are available in the video description and Pinit command.
- The best way to contact the creator is through the growing Discord channel.