Fizgig 9.2 trains a MOSS-Image LoRA in about 20 minutes instead of 49 on an RTX 5090, with far less memory

FizgigFree, open source (Apache-2.0); runs locally on Windows or Linux, or on a rented GPU

Fizgig is a free, Apache-2.0 LoRA and fine-tune studio for image, video and music models such as Krea 2, MOSS-Image, Qwen Image 2.1, FLUX Klein, MiniMax H3 and ACE-Step. Version 9.2, released on 8 November 2026, moves to PyTorch 2.15 and a faster Muon optimizer step, so a MOSS-Image LoRA on an RTX 5090 trains in about 20 minutes instead of 49 with likeness unchanged.

What it does

It trains LoRAs, LoKR, sliders and full fine-tunes from photos, video clips and songs, repairs and extracts LoRAs, and lets you compare epochs by eye in its Studio; finished LoRAs load in ComfyUI. In 9.2, memory with gradient checkpointing drops sharply (Krea 2 from 22.9 to 14.9 GB at batch 1, MOSS-Image at batch 3 from 21.7 to 7.1 GB), LoKR trains 4 to 6 times faster, and MOSS-Image at batch 3 matches OneTrainer's 1.07 s a step with less memory.

Worth knowing

The update downloads PyTorch 2.15 (about 3 GB) and needs Python 3.11 or newer and an NVIDIA driver with CUDA 13 support (580 or newer); AMD ROCm installs keep their own PyTorch. The README says it fine-tunes on consumer GPUs down to 8 GB, its RunPod deploy links carry a referral code that funds the developer, and 9.2 switches off onnxruntime and Hugging Face telemetry.

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