Leading the ImagineArt 2.0 Model Programme
The research scope behind true-to-life realism, prompt understanding, precise text and cinematic control.
Lead Machine Learning Engineer
and Researcher at ImagineArt

Muhammad Ahmed Ghani is Lead Machine Learning Engineer and Researcher at ImagineArt, formerly Vyro AI, currently working in Islamabad, Pakistan. He leads the company's ML team and led research for ImagineArt 2.0 and ImagineArt 2.0 Edit, and built ImagineArt 1.5 Pro.
The work spans text-to-image generation, image-grounded editing, native 4K output, video, speech and agentic systems: model architecture, training, evaluation and the inference infrastructure that serves those models in production. As of , systems he has led are used by more than two million people.
Born on , he studied computer science at the University of Central Punjab in Lahore. Before ImagineArt came five years across Ekkel AI, Kodezi and Sigmetec — leading a team of five through more than twenty projects in speech processing, natural language processing and computer vision, and building the MLOps foundations for AI developer tooling as a founding engineer.
Generative models, end to end. Diffusion and flow-matching image models, video generation with temporal consistency, speech systems, and agentic AI — from first experiment through training, evaluation and production inference.
Muhammad Ahmed Ghani has led the ML team at ImagineArt since March 2024, a seven-person group of machine learning engineers and researchers covering model research, product delivery and production infrastructure. See the ImagineArt ML team page.
Muhammad Ahmed Ghani led research and the ML team behind ImagineArt 2.0. These are team-built systems; he separately built ImagineArt 1.5 Pro as sole developer.
Working in Islamabad, Pakistan, and originally from Lahore; working with teams globally. Open to research collaboration, consulting and speaking, at muhammad.ahmed@imagine.art or i.am.a.pakistani.programmer@gmail.com.
Research taken through
to shipped products.
Lead Research · Generative AI
Led research and an ML team across the flagship text-to-image and image-editing models: photorealism, prompt understanding, typography, multi-image composition and identity preservation.
Model Research · Native 4K
Sole developer of the realism-focused successor to 1.5, with native 4K generation, stronger anatomy and prompt adherence, and a 40% faster inference pipeline.
MoE Diffusion · Model Programme
Led model development and built the training and evaluation stack across data curation, distributed training and automated evaluation.
Agentic Creative AI
Built a personalized control agent that routes image, video and audio tools through one conversational interface with style memory and preference learning.
Model Architecture · Deployment
Delivered the first in-house realistic image model, including the MoE architecture, data pipelines, training infrastructure and scalable GPU inference.
Applied AI · Infrastructure
Shipped avatar and short-form video systems, then reduced inference latency through quantization, caching and GPU optimization.
Tools used day to day.
Where the work has happened.
Courses and certifications completed.
Undergraduate study.
First-person accounts of leading and building the ImagineArt in-house model line.
The research scope behind true-to-life realism, prompt understanding, precise text and cinematic control.
A unified image-grounded model for composition, identity preservation, product placement and style transfer.
How a realism-focused model and its inference pipeline delivered professional 4K generation at production speed.