Development of an identity-consistent generative AI personalization pipeline (Character ID) - ON-1237

Project type: Innovation
Desired discipline(s): Engineering - computer / electrical, Engineering, Engineering - other, Computer science, Mathematical Sciences
Company: Anonymous
Project Length: 4 to 6 months
Preferred start date: As soon as possible.
Language requirement: English
Location(s): ON, Canada
No. of positions: 1
Desired education level: Master'sPhDPostdoctoral fellow
Open to applicants registered at an institution outside of Canada: No

About the company: 

We are AI Innovation Development lab , forward-thinking technology company dedicated to shaping the future through advanced artificial intelligence solutions. Founded on the principle of continuous evolution, we specialize in designing, deploying, and scaling cutting-edge AI systems tailored to meet the complex demands of modern industries. Our core mission is to bridge the gap between theoretical data science and practical, real-world applications that drive efficiency, growth, and sustainable progress.We serve a diverse global audience, including healthcare providers, financial institutions, and logistics networks, empowering them with custom machine learning models, predictive analytics, and automated workflows. By integrating smart automation into daily operations, we help businesses unlock hidden value from their data, minimize operational bottlenecks, and make high-stakes decisions with confidence.our commitment extends beyond software. We prioritize ethical AI frameworks and robust data security to ensure our technological breakthroughs are responsible, fair, and transparent. Through strategic partnerships and relentless research, our dedicated team of engineers and scientists strives to create an ecosystem where technology enhances human potential. We remain focused on delivering the reliable intelligence tools necessary for navigating an increasingly automated world.

Describe the project.: 

The project aims to develop a scalable machine learning pipeline that allows users to create persistent digital identities from a small set of reference photos. By training custom adapters, the system injects this unique identity into open-weights diffusion models, ensuring a single character's facial features and proportions remain perfectly stable across various generated scenes, camera angles, and text prompts.
Research Project Goals The company's main goal is to deliver a production-grade software framework and application programming interface (API) that enables creators, studios, and developers to generate hyper-realistic, identity-consistent visual media. This eliminates the need for manual, frame-by-frame 3D modeling or video editing, expanding the boundaries of knowledge in multimodal AI persona retention.
Innovation & Incremental Improvements
The core innovation lies in developing a rapid, hybrid personalization process. Traditional fine-tuning methods require prolonged computational time. This project implements an incremental process development innovation by combining instant-embedding vision encoders (like IP-Adapter or InstantID) with lightweight, low-rank adaptation (LoRA) modules. This hybrid approach slashes identity training times from hours to under five minutes while maintaining strict facial fidelity.

Required expertise/skills: 

PyTorch & DeepSpeed: Deep learning framework proficiency and multi-GPU training optimization.Diffusion Models: Deep knowledge of Stable Diffusion, Flux, and the Hugging Face Diffusers library.Personalization Techniques: Practical experience with LoRA, Textual Inversion, ControlNet, IP-Adapter, and InstantID.Computer Vision: Expertise in facial detection, landmark alignment, and image preprocessing using OpenCV or InsightFace.
Infrastructure & Engineering SkillsDocker & Kubernetes: Containerization and scaling inference pipelines across GPU clusters.API Development: Building low-latency backend architectures using FastAPI or gRPC.CUDA Optimization: Memory management, mixed-precision training, and model quantization (FP8/INT8).