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(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware

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In our previous post, Exploring Quantization Backends in Diffusers, we dived into how various quantization techniques can shrink diffusion models like FLUX.1-dev, making them significantly more accessible for inference without drastically compromising performance. We saw how bitsandbytes, torchao, and others reduce memory footprints for generating images. Performing inference is cool, but to make these models truly our own, we also need to be able to fine-tune them. Therefore, in this post, we tackle efficient fine-tuning of these models with peak memory use under ~10 GB of VRAM on a single GPU. This post will guide you through fine-tuning FLUX.1-dev using QLoRA with the diffusers library. We'll showcase results from an NVIDIA RTX 4090. We'll also highlight how FP8 training with torchao can further optimize speed on compatible hardware. We aim to fine-tune black-forest-labs/FLUX.1-dev to adopt the artistic style of Alphonse Mucha, using a small dataset. The model consists of three main

In our previous post, Exploring Quantization Backends in Diffusers, we dived into how various quantization techniques can shrink diffusion models like FLUX.1-dev, making them significantly more accessible for inference without drastically compromising performance. We saw how bitsandbytes, torchao, and others reduce memory footprints for generating images. Performing inference is cool, but to make these models truly our own, we also need to be able to fine-tune them. Therefore, in this post, we tackle efficient fine-tuning of these models with peak memory use under ~10 GB of VRAM on a single…

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