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Technology & Science: News, Tips, Tutorials, Tricks, Best Applications, Guides, Reviews ⤵️
Playlist of #StableDiffusion Tutorials, Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2Img ⤵️
DeepFace GitHub Repo ⤵️
https://github.com/serengil/deepface
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Scripts and the PDF Files Shown In Video ⤵️
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Midjourney Level Realism Prompts Set ⤵️
https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Midjourney-Level-Realism.md
0:00 Introduction to what DeepFace does and how we are going to utilize it
0:58 Let’s say you have generated 2000 images how to get good ones
1:17 This approach can be used for professional business purposes
1:32 If you are new to Stable Diffusion or image generation
2:17 Beginning with composing venv to install DeepFace
3:18 The training dataset images I have used for this tutorial
3:57 I have generated over 3000 images
4:06 The prompts I have used to generate images – how to use PNG info to find used prompts
5:23 How to write and use DeepFace best images finding script
9:18 How to use the script demonstration after you written and set it
11:20 Explanation of the values displayed during the script runtime
12:18 Sorted images from best to worst
Deep face is a deep learning technique that uses artificial intelligence to identify and recognize faces. It is a powerful tool that can be used for a variety of applications, including security, marketing, and entertainment.
Image similarity is the measure of how similar two images are. It can be used to find similar images in a database, to compare images for copyright infringement, or to create new images by blending together similar images.
Generative AI is a type of artificial intelligence that can create new data, such as images, text, or music. It is a powerful tool that can be used to create realistic images, to generate new content for marketing campaigns, or to create new forms of art.
Training your face for deep fake images is a process of collecting and labeling images of your face so that a deep learning model can learn to recognize your face. This process can be used to create deep fake images that are more realistic and believable.
Here are some of the benefits of using deep face, image similarity, generative AI, and training your face for deep fake images:
Security: Deep face can be used to identify and recognize faces, which can be used to improve security systems. For example, deep face can be used to authenticate users at access points or to prevent unauthorized access to buildings.
Marketing: Image similarity can be used to find similar images in a database, which can be used to improve marketing campaigns. For example, image similarity can be used to find similar images of products that are being sold online, which can help to improve the customer experience.
Entertainment: Generative AI can be used to create new images, text, or music, which can be used to create new forms of entertainment. For example, generative AI can be used to create realistic images of people or places that do not exist, which can be used to create new forms of art or to create new video games.
Education: Deep face can be used to identify and recognize faces, which can be used to improve educational experiences. For example, deep face can be used to track student attendance or to provide personalized learning experiences.
Here are some of the risks associated with using deep face, image similarity, generative AI, and training your face for deep fake images:
Privacy: Deep face can be used to identify and recognize faces, which can be a privacy concern. For example, deep face could be used to track people’s movements or to create facial recognition databases that could be used for surveillance.
Misinformation: Generative AI can be used to create fake news or propaganda, which could be used to spread misinformation or to manipulate public opinion.
Cybercrime: Deep face and generative AI could be used to create deep fake images that could be used for identity theft, fraud, or other forms of cybercrim
