Descriptions:
Fahd Mirza walks through a complete LoRA training workflow for Z-Image, a recently released 6-billion-parameter single-stream diffusion transformer developed by Tongyi at Alibaba. Z-Image is positioned as a fast, lightweight alternative to models like Flux, excelling at photorealism and prompt adherence — qualities that make it a compelling target for custom LoRA fine-tuning.
The tutorial covers the full pipeline from scratch: setting up a Conda virtual environment, cloning and installing the open-source AI Toolkit via npm, preparing a 40-image dataset of a custom subject (a Balinese mythical creature called a Barong), captioning images using an AI model, and configuring a training job inside the AI Toolkit’s browser-based UI. Key parameters covered include trigger words, training steps (3,000), and HuggingFace token setup for model access. Hardware used is an Nvidia RTX 6000 with 48GB of VRAM on an Ubuntu system.
The video is practical and beginner-accessible, showing the UI step-by-step rather than relying on command-line scripts. Mirza also explains the conceptual purpose of each stage — why image variety matters for generalization, what captioning achieves during training, and how trigger words activate the LoRA at inference time. Viewers will come away with a working local LoRA training setup for Z-Image that can be adapted to any custom subject.
📺 Source: Fahd Mirza · Published February 23, 2026
🏷️ Format: Tutorial Demo
