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AImagine

Cutting-edge AI-powered product that transforms portraits into stunning artworks in a variety of artistic and hyper-realistic styles.

Project Overview

AImagine is a mobile app that allows users to create realistic avatars in various styles using Stable Diffusion. Users upload 6-10 photos to generate their avatars.

CLIENT

CodeLink

Team Model
Flutter Developer

Flutter Developer

Back-end ML Developer

Back-end ML Developer

Machine Learning Lead

Machine Learning Lead

Dev Ops Engineer

Dev Ops Engineer

Product Designer

Product Designer

PLATFORM

iOS & Android

TECHNOLOGY

Google Cloud Platform (GCP)

Low-Range Adaptation

Flutter

Prompt Engineering

Dreambooth

Stable Diffusion

Firebase Crashlytics

Python

case-study-summary

Challenge

Is it possible to use artificial intelligence to create hyper-realistic avatars based on uploaded selfies?

Request

CodeLink identified a gap in the market for creating hyper-realistic avatars, given the current quality and cost of competitor products. To bring a new level of quality and realism to the avatar creation market, CodeLink wanted to leverage the skills of its internal R&D team. Additionally, they aimed to improve processes to reduce computation and user costs.

case-study-summary

Engagement Model

The CodeLink internal team worked autonomously to design and build the Android and iOS mobile applications.

Engagement Length and Scale

The project kicked off in March of 2023 and is still ongoing.

case-study-summary

Project Outcome

To develop the app, we started by thoroughly evaluating the quality of AI-generated avatars using Stable Diffusion and Dreambooth. We used different prompt engineering practices to add styles like Samurai, Victorian, Viking, Vampire, and more. We optimized the model for training, inference, and avatar quality, and utilized various Google Cloud Platform services to host the training and inference servers. Our team also experimented with Low-Range Adaptation (LoRA) to reduce training time and decrease the size of training artifacts from 7GB to only 12MB. The final app generates realistic avatars that retain users' facial features while transforming them into different styles.

case-study-summary
Tags

Machine Learning

Artificial Intelligence

Computer Vision

Autonomous Team

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