AI LinkedIn Profile Photo Generator

Gone are the days of scrambling for the perfect headshot for your LinkedIn profile. With the help of AI, you can now leverage the power of AI-powered Linkedin headshot generator. These innovative tool utilize advanced algorithms to analyze your facial features and generate professional-looking headshots based on your preferences.

The workflow discussed in this blog post is a novel, tuning-free approach for generating professional headshots specifically tailored for LinkedIn profiles. The proposed method achieves identity-preserving image generation without the need for extensive fine-tuning on large datasets. This is achieved through a multi-stage workflow that leverages the power of facial feature extraction, pose-agnostic adaptation, and controlled image synthesis.

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AI LinkedIn Profile Photo Generator Workflow on Pixelflow

The AI LinkedIn Profile Photo Generator Workflow created on Pixelflow, uses a combination of 3 AI models. Here is quick snapshot of these models:

1. InstaDepth, which is a combination of Instant ID & Controlnet Depth.
2. Codeformer, helps in enhancing the facial features in the photo
3. Background Removal V2, creates a white plain background. (This is optional)

Step 1: InstantID + ControlNet Depth

Under the hood, Insta Depth is a combination of Instant ID and ControlNet Depth models.

1. ID Embedding: This part analyzes the input image to capture the person's unique facial features, like eye color, nose shape, etc. It focuses on these defining characteristics (semantic information) rather than the exact location of each feature on the face (spatial information).
2. Lightweight Adapted Module: This module acts like an adapter, allowing the system to use the reference image itself as a visual prompt for the image generation process. The reference image can be any pose image.
3. IdentityNet: This is where the actual image generation happens. It takes the information from the ID embedding (facial characteristics) and combines it with the text prompt to create a new image.
4. ControlNet Depth enables composition transfer by understanding the depth of the input face image. It accurately preserves the person’s face in the new pose (reference image) in the output image.

Step 2: Codeformer

CodeFormer is a powerful face restoration algorithm primarily designed to enhance the quality of old or deteriorated photographs containing human faces, as well as AI-generated faces. Codeformer leverages advanced deep learning techniques to recognize and address various issues commonly found in such images. These issues may include blurriness, loss of fine details, color fading, and other forms of degradation that occur over time or during the AI generation process. By analyzing and understanding facial features and patterns, CodeFormer works to restore and improve the overall quality and appearance of faces in these images.

Step 3: Plain white Background

Lastly, Background Removal v2 model is used to effortlessly change the background colors of the respective images, offering a versatile solution for a wide range of photography scenarios. This functionality enables you to achieve a consistent and visually appealing background color with ease. In this workflow we used white background for a nice professional look. This is optional though.